Psychophysiology Methods (Neuromethods, 206) [1st ed. 2024] 1071635441, 9781071635445

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Neuromethods   206

Massimiliano Valeriani Marina de Tommaso  Editors

Psychophysiology Methods

NEUROMETHODS

Series Editor Wolfgang Walz University of Saskatchewan Saskatoon, SK, Canada

For further volumes: http://www.springer.com/series/7657

Neuromethods publishes cutting-edge methods and protocols in all areas of neuroscience as well as translational neurological and mental research. Each volume in the series offers tested laboratory protocols, step-by-step methods for reproducible lab experiments and addresses methodological controversies and pitfalls in order to aid neuroscientists in experimentation. Neuromethods focuses on traditional and emerging topics with wide-ranging implications to brain function, such as electrophysiology, neuroimaging, behavioral analysis, genomics, neurodegeneration, translational research and clinical trials. Neuromethods provides investigators and trainees with highly useful compendiums of key strategies and approaches for successful research in animal and human brain function including translational “bench to bedside” approaches to mental and neurological diseases.

Psychophysiology Methods Edited by

Massimiliano Valeriani Developmental Neurology Unit, Ospedale Pediatrico Bambino Gesú, ROMA, Roma, Italy

Marina de Tommaso Neurophysiopathology Unit, DiBrain Department, Università degli Studi di Bari, Bari, Italy

Editors Massimiliano Valeriani Developmental Neurology Unit Ospedale Pediatrico Bambino Gesu´ ROMA, Roma, Italy

Marina de Tommaso Neurophysiopathology Unit DiBrain Department Universita` degli Studi di Bari Bari, Italy

ISSN 0893-2336 ISSN 1940-6045 (electronic) Neuromethods ISBN 978-1-0716-3544-5 ISBN 978-1-0716-3545-2 (eBook) https://doi.org/10.1007/978-1-0716-3545-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A. Paper in this product is recyclable.

Preface to the Series Experimental life sciences have two basic foundations: concepts and tools. The Neuromethods series focuses on the tools and techniques unique to the investigation of the nervous system and excitable cells. It will not, however, shortchange the concept side of things as care has been taken to integrate these tools within the context of the concepts and questions under investigation. In this way, the series is unique in that it not only collects protocols but also includes theoretical background information and critiques which led to the methods and their development. Thus it gives the reader a better understanding of the origin of the techniques and their potential future development. The Neuromethods publishing program strikes a balance between recent and exciting developments like those concerning new animal models of disease, imaging, in vivo methods, and more established techniques, including, for example, immunocytochemistry and electrophysiological technologies. New trainees in neurosciences still need a sound footing in these older methods in order to apply a critical approach to their results. Under the guidance of its founders, Alan Boulton and Glen Baker, the Neuromethods series has been a success since its first volume published through Humana Press in 1985. The series continues to flourish through many changes over the years. It is now published under the umbrella of Springer Protocols. While methods involving brain research have changed a lot since the series started, the publishing environment and technology have changed even more radically. Neuromethods has the distinct layout and style of the Springer Protocols program, designed specifically for readability and ease of reference in a laboratory setting. The careful application of methods is potentially the most important step in the process of scientific inquiry. In the past, new methodologies led the way in developing new disciplines in the biological and medical sciences. For example, Physiology emerged out of Anatomy in the nineteenth century by harnessing new methods based on the newly discovered phenomenon of electricity. Nowadays, the relationships between disciplines and methods are more complex. Methods are now widely shared between disciplines and research areas. New developments in electronic publishing make it possible for scientists that encounter new methods to quickly find sources of information electronically. The design of individual volumes and chapters in this series takes this new access technology into account. Springer Protocols makes it possible to download single protocols separately. In addition, Springer makes its print-on-demand technology available globally. A print copy can therefore be acquired quickly and for a competitive price anywhere in the world. Saskatoon, SK, Canada

Wolfgang Walz

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Preface Investigation on cognitive neurosciences has taken advantage from the modern technology, allowing us to better understand the cerebral mechanisms underlying cognition. Due to their huge temporal resolution (in the order of milliseconds), the electrophysiological methods (electroencephalography – EEG and event-related potentials – ERPs) proved able to disentangle the time courses of neurological events occurring very close in time. On the other hand, neuroimaging techniques, such as functional magnetic resonance imaging – fMRI and positron emission tomography – PET, show a notable spatial resolution (in the orders of micrometers) which allow them to identify even small cluster of neurons and/or synapses involved in tasks or activated/inhibited by an external input. While the term of psychophysiology has been presently enlarged also including most neuroimaging techniques, it was originally used to refer only to the electrophysiological methods addressed at investigating the cerebral activity during cognitive processes. Currently, there is growing interest in precision medicine, focusing on specific cognitive and emotional profiles characterizing single clinical pictures within large disease categories. The methods here reported, while standardized, are rarely implemented in clinical practice, but their knowledge and diffusion could improve the clinical approach to neurological and psychiatric disorders. This book aims to present the most recent updates in methods used for research in psychophysiology and cognitive neurosciences. It is endorsed by the Italian Society of Psychophysiology and Cognitive Neurosciences (SIPF). Although most authors are SIPF members, the book aims to play a primary role in the current literature, also thanks to the invaluable contribution of international prominent researchers. The classical event-related brain responses (P300, mismatch negativity – MMN, and contingent negative variation – CNV) are reprised to show how they keep having a role in cognitive investigation (Chaps. 1 and 2). The chapters offer a summer of relevant literature, with the expert opinion of outstanding researchers in the field of psychophysiological techniques. Chapters 3 and 4 explain different techniques of EEG analysis, based on either amplitude or frequency. They are addressed at unveiling the cerebral activity preparing to and preceding upcoming events and action, and finding biomarkers of human degenerative diseases. Such methods could implement the standard ERP analysis, allowing the detection of subtle brain dysfunction, useful for early detection of neurodegeneration and prompt application of possibly disease modifying therapies. Pain and smell represent basic sensory abilities shared by animals and human beings. Noteworthy, their persistence in the evolutionary scale supports their importance for the survival of the species. Chapters 5 and 6 show how the anatomical structures and physiological mechanisms underlying the capability of feeling pain and smelling can be explored by special psychophysiological techniques, which can be used not only for research but also for clinical purposes. In Chap. 7, the authors encompass the electrophysiological correlates of vision in the normal and pathological human visual system. They detail all the up-to-date techniques used to facilitate the understanding and assessment of the visual system. Chapter 8 deals with the technique which can be considered as an EEG evolution, with the scope to resolve the link between time and space resolution. Namely, magnetoencephalography (MEG) is based on the capture of the

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magnetic fields generated by the cerebral electrical activity, thus increasing the capability of localizing the generators of the biological signal recorded on scalp surface. EEG source localization can also take advantage by the combined use of the EEG with a neuroimaging method, such as the functional near-infrared spectroscopy (Chap. 9). In Chap. 10, the authors deal with brain-computer interface techniques, using the electrical activity generated by the brain to accomplish simple tasks, prevented in pathological situations. It is evident that human cognition is not based on an individual input, but on the complex interactions between different senses. Chapter 11 shows how several psychophysiological methods are needed to investigate these relationships. In the last Chap. 12, the authors describe in detail our methodological toolbox to investigate what can still be considered a mystery, namely the neural mechanisms working during physiological or pathological sleep. We truly hope that this book will promote the use of the psychophysiological techniques in the investigation of human cognition, as well as increase the interest of clinicians toward their implementation in the clinical puzzle of neurological and psychiatric disorders. We expect the readers interested in this field of research to get inspired and perhaps contribute to a possible future edition. Roma, Italy Bari, Italy

Massimiliano Valeriani Marina de Tommaso

Acknowledgment

We want to thank the Italian Society of Psychophysiology and Cognitive Neurosciences for the endorsement of this book.

Massimiliano Valeriani Marina de Tommaso

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Contents Preface to the Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1 Recent Advances in Clinical Applications of P300 and MMN. . . . . . . . . . . . . . . . . Michael Falkenstein 2 Contingent Negative Variation (CNV) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Francesco Fattapposta, Caterina Pauletti, Daniela Mannarelli, Vilfredo De Pascalis, Joseph Ciorciari, David Crewther, David White, and Gennady Knyazev 3 The Study of Anticipatory Brain Activity in Cognitive Tasks by Means of Event-Related Potential, Frequency, and Time-Frequency Methods . . . . . . . . Valentina Bianco, Esteban Sarrias-Arrabal, Manuel Va´zquez-Marrufo, and Francesco Di Russo 4 qEEG Methods to Probe Abnormal Brain Rhythms Related to Quiet Vigilance in Patients with Dementia Due to Alzheimer’s, Parkinson’s, and Lewy Body Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ¨ ntekin, Go¨rsev Yener, and Claudio Del Percio Claudio Babiloni, Bahar Gu 5 Pain-Related Evoked Potentials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elena Ammendola, Massimiliano Valeriani, and Marina de Tommaso 6 Chemosensory Neuro-olfactometry, Pheromones Perceptions, and EEG Signal Processing Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sara Invitto and Soheil Keshmiri 7 Psychophysiology and Electrophysiology of the Visual System . . . . . . . . . . . . . . . . Ferdinando Sartucci and Vittorio Porciatti 8 MEG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Giorgio Arcara, Giovanni Pellegrino, Annalisa Pascarella, Dante Mantini, Eliane Kobayashi, and Karim Jerbi 9 EEG/fNIRS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eleonora Gentile and Antonio Casas Barraga´n 10 Brain–Computer Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elena Sibilano, Vladimiro Suglia, Antonio Brunetti, and Domenico Buongiorno, Nicholas Caporusso, Christoph Guger, and Vitoantonio Bevilacqua

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Methods for the Assessment of Multisensory Processing: Behavioral and Neuropsychological Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Damian M. Manzone, Elena Nava, and Nadia Bolognini Psychophysiology of Sleep . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Maria P. Mogavero, Giuseppe Lanza, Lourdes M. DelRosso, and Raffaele Ferri

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors ELENA AMMENDOLA • AnpLab, Applied Neurophysiology and Pain, Bari Aldo Moro University, Policlinico General Hospital, Bari, Italy GIORGIO ARCARA • IRCCS San Camillo Hospital, Venice, Italy CLAUDIO BABILONI • Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino, Cassino, Frosinone, Italy ANTONIO CASAS BARRAGA´N • Department of Physical Therapy, Faculty of Health Sciences, University of Granada, Granada, Spain VITOANTONIO BEVILACQUA • Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, BA, Italy; Apulian Bioengineering s.r.l., Modugno, Italy VALENTINA BIANCO • Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Rome, Italy; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy NADIA BOLOGNINI • Department of Psychology, University of Milano-Bicocca, Milan, Italy; Neuropsychological Laboratory, IRCCS Istituto Auxologico Italiano, Milan, Italy ANTONIO BRUNETTI • Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, BA, Italy; Apulian Bioengineering s.r.l., Modugno, Italy DOMENICO BUONGIORNO • Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, BA, Italy; Apulian Bioengineering s.r.l., Modugno, Italy NICHOLAS CAPORUSSO • Department of Computer Science, Northern Kentucky University, Highland Heights, KY, USA JOSEPH CIORCIARI • Brain & Psychological Sciences Research Centre Swinburne University of Technology, Swinburne, Australia DAVID CREWTHER • Brain & Psychological Sciences Research Centre Swinburne University of Technology, Swinburne, Australia VILFREDO DE PASCALIS • Department of Psychology, Sapienza University of Rome, Rome, Italy MARINA DE TOMMASO • AnpLab, Applied Neurophysiology and Pain, Bari Aldo Moro University, Policlinico General Hospital, Bari, Italy; Neurophysiopathology Unit, DiBrain Department, Bari Aldo Moro University, Policlinico General Hospital, Bari, Italy CLAUDIO DEL PERCIO • Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy LOURDES M. DELROSSO • University of California San Francisco, Fresno, CA, USA FRANCESCO DI RUSSO • Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Rome, Italy; Santa Lucia Foundation IRCCS, Rome, Italy MICHAEL FALKENSTEIN • ALA Institute, Bochum, Germany FRANCESCO FATTAPPOSTA • Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy RAFFAELE FERRI • Oasi Research Institute – IRCCS, Troina, Italy

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ELEONORA GENTILE • Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy CHRISTOPH GUGER • g.tec Medical Engineering GmbH, Schiedlberg, Austria BAHAR GU¨NTEKIN • Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey; Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey SARA INVITTO • INSPIRE LAB-Laboratory Laboratory of Cognitive and Psychophysiological Olfactory Processes, DiSTeBA, University of Salento, Lecce, Italy KARIM JERBI • Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, Montreal, QC, Canada SOHEIL KESHMIRI • Optical Neuroimaging Unit Okinawa Institute of Science and Technology, Okinawa, Japan GENNADY KNYAZEV • Institute of Physiology and Basic Medicine, Novosibirsk, Russia ELIANE KOBAYASHI • Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, QC, Canada GIUSEPPE LANZA • Oasi Research Institute – IRCCS, Troina, Italy; Department of Surgery and Medical-Surgical Specialties, University of Catania, Catania, Italy DANIELA MANNARELLI • Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy DANTE MANTINI • Movement Control and Neuroplasticity Research Group, Leuven, Belgium DAMIAN M. MANZONE • Faculty of Kinesiology and Physical Education, University of Toronto, Toronto, ON, Canada MARIA P. MOGAVERO • Vita-Salute San Raffaele University, Milan, Italy; Sleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy ELENA NAVA • Department of Psychology, University of Milano-Bicocca, Milan, Italy ANNALISA PASCARELLA • Institute for Applied Mathematics Mauro Picone, National Research Council, Rome, Italy CATERINA PAULETTI • Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy GIOVANNI PELLEGRINO • Epilepsy program, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada VITTORIO PORCIATTI • Bascom Palmer Eye Institute, Miami, FL, USA ESTEBAN SARRIAS-ARRABAL • Department of Experimental Psychology, Faculty of Psychology, University of Seville, Seville, Spain FERDINANDO SARTUCCI • Neurophysiopathology Unit, Department of Clinical and Experimental Medicine, Pisa University Medical School, Pisa, Italy; CNR Neuroscience Institute, Pisa, Italy; Don Carlo Gnocchi Foundation, Massa, Italy; San Rossore Nursing Home, Pisa, Italy ELENA SIBILANO • Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, BA, Italy VLADIMIRO SUGLIA • Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, BA, Italy MASSIMILIANO VALERIANI • Developmental Neurology Unit, Ospedale Pediatrico Bambino Gesu`, Rome, Italy; Center for Sensory-Motor Interaction, Aalborg University, Aalborg, Denmark

Contributors

MANUEL VA´ZQUEZ-MARRUFO • Department of Experimental Psychology, Faculty of Psychology, University of Seville, Seville, Spain DAVID WHITE • Brain & Psychological Sciences Research Centre Swinburne University of Technology, Swinburne, Australia GO¨RSEV YENER • Faculty of Medicine, Izmir University of Economics, Izmir, Turkey

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Chapter 1 Recent Advances in Clinical Applications of P300 and MMN Michael Falkenstein Abstract Event-related potentials (ERP), in particular P300 and MMN, have been used for decades in clinical research, but hardly in clinical practice. This chapter provides an overview of recent clinical ERP studies with P300 and MMN as primary components. Due to the (non-)availability of recent studies, this review is restricted to traumatic brain injury, Parkinson’s disease, attention deficit/hyperactivity disorder, borderline personality disorder, schizophrenia, depression, alcohol use disorder, and, in particular, dementia/mild cognitive impairment. The main findings are summarized at the end of each chapter. In the general discussion, possibilities for the clinical application of ERPs as derived from the current research are summarized, and strategies to promote the use of ERPs in clinical practice are suggested. Key words ERP, P300, MMN, Clinical, Neurology, Psychiatry, Advances

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Introduction Event-related potentials (ERPs) consist of various components characterized by latency, amplitude, and scalp topography. ERP components are the neural correlates of various sensory, cognitive, and motor processes. Anomalies in amplitude and/or latency of ERP components have been reported in various neurological and in particular psychiatric disorders, hence reflecting changes of those processes. Therefore, ERPs could characterize biological markers of pathophysiological mechanisms in those disorders which can serve to aid diagnosis, assist in choosing the most appropriate treatment, and help with detecting illness at an early phase [1].

1.1 P300 and MMN: Psychological Correlates, Neural Generators, and Methodology

P300 [2, 4] and mismatch negativity (MMN) [3] are the most frequently investigated ERP components. The P300 is a large positive potential which can be derived from the scalp midline, usually with a maximum over centro-parietal electrodes. A P300 can be elicited tasks that include conscious and attentive cognitive processing. In the earlier studies, a rather easy task, the “oddball paradigm” has been frequently used. In this task, the P300 to rare

Massimiliano Valeriani and Marina de Tommaso (eds.), Psychophysiology Methods, Neuromethods, vol. 206, https://doi.org/10.1007/978-1-0716-3545-2_1, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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unpredictable stimuli is measured on a background of frequent predictable stimuli [2]. Later the P300 has been found for more complex tasks, such as 4-choice [5] or working memory tasks [6]. It is generally assumed that the P300 is sensitive to attention and task difficulty. The amplitude of the P300 is related to the amount of attentional resources engaged during the task with higher task difficulty leading to reduced P300 amplitudes, while the peak latency of the P300 is longer with higher demands on attention and with increasing task complexity in general [5, 6]. Hence P300 latency has been related to the speed of cognitive processing, with shorter latencies associated with higher processing speed. As found later, the P300 consists of (at least) two subcomponents. The first one (P3a or P-SR) has a fronto-central topography and has been linked to stimulus-driven attention mechanisms, and the second one (P3b) has a parietal maximum which likely reflects controlled processing such as response selection or memory [5, 6]. By using time pressure, the latency of the P3b as well as the overt response can be speeded up [7]. Both P3a and P3b are attenuated and delayed in older vs. younger subjects [6]. However, as found in an auditory distraction task, the P3b is larger in highperforming than low-performing older adults [8]. Prior to the P300 component, a negative component, the N2, is seen that most probably reflects controlled processing (e.g., [9]). In paradigms that focus on inhibition, such as the Go/Nogo task or the flanker task, a frontal P300-like wave (the Nogo-P3) is elicited after Nogo or incongruent trials, which has been linked to some aspect of motor inhibition (e.g., [10]). The P300 and certainly its subcomponents stem from distinct and probably multiple brain sources. In an early study of Anderer and coworkers [11] with 172 normal subjects aged between 20 and 88 years in an auditory oddball paradigm, electrical activity in the brain corresponding to the P300 was localized by means of low-resolution electromagnetic tomography (LORETA). The P300 LORETA generators were located predominantly in the frontal neocortex and less pronounced in the posterior parietal cortex. In order to identify distracter- and target-elicited P300 components in a visual oddball paradigm, Bocquillon and coworkers [12] recorded 128 channel EEG from 15 healthy subjects. Their results suggest that distracter- and target-elicited P300 are both generated by the dorsal fronto-parietal network, while target processing recruits a specific ventral network in addition. Other more recent studies suggest widely distributed multiple sources of the P300. In a more recent study of Sabeti and coworkers [13] with 30 healthy young subjects, a novel method (timereduction region-suppression LCMV with multi-resolution approach) was used to localize neural sources. The authors could show that frontal, temporal, parietal, and cingulate gyrus are the most prominent sources of P300.

Recent Advances in Clinical Applications of P300 and MMN

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The Nogo-P3 is most probably driven by activity in the lateral orbitofrontal cortex, as shown in various studies. For example, in an early study by Bokura and coworkers [14], its source was located in the left OFC, while the source of the Go-P3 was located mainly in the medial part of the parietal cortex. Huster and coworkers [15] could show different sources of the Nogo-P3 precentral, middle frontal and mid-cingulate cortices, as well as the insulae. This also shows a pattern of multiple sources for the Nogo-P3. P3a, P3b, and Nogo-P3 can be measured by standard EEG methods which have to include the midline electrodes. To disentangle P3a and P3b, more complex tasks than the oddball are recommended, such as a 4-choice task [5]. Of course, for measuring the Nogo-P3, tasks with Nogo or incongruent trials or the stop-task paradigm are needed which all activate motor inhibition [10, 16]. The mismatch negativity (MMN) is an early negative ERP component that is elicited by infrequent, physically deviant auditory stimuli in a sequence of frequent homogeneous stimuli (standards) [3, 17]. It is elicited by any discernible change in a repetitive sound even in the absence of attention, hence reflecting an involuntary change detection in the auditory environment [3, 18]. More specifically, it reflects a process comparing the deviant sensory input with the neural memory trace encoding the physical features of the repetitive sound. Deviant aspects not only can be physical stimulus features (e.g., frequency, intensity, spatial location) but also can be more complex, “abstract,” regularities based, for example, on relationships between various physical features of the stimuli or in patterns present in the auditory stream (cf. [19] for a review). The MMN is highly correlated with behavior discrimination performance [20, 21]. For example, with an increasing physical difference between standard and deviant stimuli reaction time and MMN latency decrease and MMN amplitude increase. In later studies, an MMN has been also found after visual stimuli, which suggests an unintentional prediction about the next state of a visual object in the immediate future on the basis of its temporal context (cf [22, 23] for reviews). As to brain sources of the MMN, Kropotov and colleagues [18] recorded MMNs to tonal frequency changes directly from the human temporal cortex of patients with electrodes implanted in the brain for diagnosis and therapy. The intracranially recorded MMN was found to be attention independent and modality specific, and confined to a rather small area in the temporal cortex. Later, also a frontal contribution to the MMN has been found. Using scalp radial current density analysis, Giard and colleagues [24] showed that the potential distribution could be attributed to the sum of activities of two sets of neural generators: one temporal, located in the vicinity of the primary auditory cortex,

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predominantly activated in the hemisphere contralateral to the ear of stimulation, and the other frontal, involving mainly the right hemisphere. Giard and colleagues related the temporal activities to a sensory memory mechanism and the frontal ones to an automatic attention-switching process. Rinne and colleagues [25] could show that the frontal MMN activity is elicited slightly later than the temporal one, which confirms the functional model that the frontal aspect of the MMN reflects an involuntary switching of attention to the sound change which is reflected in the temporal activity. As to the visual MMN, Susac and coworkers [26] used magnetoencephalography and spatio-temporal source localization to determine its generators. The authors could find a source in the occipital cortex to nonattended deviants which differed from the sources of standards. In his early review, Schro¨ger [20] provided valuable methodological issues. The paradigms for recording the auditory MMN depend on the stimulus feature to be studied. Simple features such as frequency elicit larger MMNs than higher-order features. For studying simple features, the train of stimuli consists of a series of standards with occasional deviants. To record the auditory MMN, at least electrodes over the scalp midline (Fz, Cz, Pz), over the left and right frontal hemispheres (e.g., F3 and F4), and over the mastoids should be used. The MMN is measured by subtracting the standard ERP from that of the deviant. Since a reduced MMN may be elicited by the standard immediately after the deviant, those trials should be excluded. The MMN amplitude is usually defined as the mean amplitude of the difference wave in a particular time window, which should be centered around the MMN peak in the grand averages. MMN latency is more difficult to measure because the individual MMN peak is often not clearly seen. Some advanced methods to measure MMN latency can be found in Schro¨ger’s paper as well as in the review of Pazo-Alvarez and colleagues [22]. The methods for measuring the visual MMN do not principally differ from those for the auditory MMN [22]. The MMN is often followed by a P3a (or novelty P3) and a further negativity, called RON (reorienting negativity), which appears to reflect the reorienting of attention to the task [6]. To measure P3a and RON, deviant minus standard difference ERPs are used as for measuring the MMN [20, 8]. Due to their optimal time resolution and the possibility to observe different sensory and cognitive processes between stimulus and response, ERPs make it possible to specify the neurocognitive origin of a deficit related to a disease [1, 27]. Even more important is that ERPs could be used to predict the occurrence or track the development of a disease, as will be shown later in detail. The former is of utmost importance for early treatment, such as in dementias.

Recent Advances in Clinical Applications of P300 and MMN

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Despite those obvious advantages and the easy methodology of ERPs it took a while before ERPs, and in particular P300 and MMN, were applied for clinical purposes. Usually simple tasks such as the oddball paradigm for the P300 are used in clinical studies, e.g., [28]. The classical areas in the early years of the clinical use of the P300 were dementia, depression, and schizophrenia, which remained more or less the same in later years. For the MMN, which is usually elicited after auditory stimuli, it took more time before it was used for clinical purposes. Since P300 and MMN are related to sensory and cognitive processes, the main areas of clinical application are psychiatry, neurology, and less so ophthalmology and otolaryngology. Based on the availability of new studies, the present narrative review on recent advances in clinical applications of P300 and MMN is focused on the following neurological and psychiatric diseases: traumatic brain injury, Parkinson’s disease, attentiondeficit hyperactivity disorder, borderline personality disorder, schizophrenia, depression, alcohol use disorder, and dementias including mild cognitive impairment. As in the beginning, the majority of the recent clinical articles using P300 and MMN concerns dementias and, in particular, mild cognitive impairment (MCI). The focus of the review is hence on psychiatric disorders, particularly on AD and MCI (Subheading 2.8). A literature search was conducted using PubMed, PMC, and Google Scholar. Studies from 2016 to November 2021 are included, with a focus on more recent articles from 2020 and 2021.

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Diseases

2.1 Traumatic Brain Disorder (TBI)

ERPs have been frequently used as indicator of cognitive dysfunction after traumatic brain disorder (TBI). Brush and coworkers [29] reviewed all available ERP studies to document cognitive control processes in individuals with a history of head concussion. Most of the studies show reductions in P3 amplitude and enhancements in P3 latency in previously concussed individuals. As shown in a recent study [30], such P3 reductions are related to impaired daily living and social function. In particular, sports-related concussions lead to ERP alterations. Ruiter and coworkers [31] studied whether 19 retired Canadian Football League (CFL) athletes with a history of concussions exhibit alterations in ERPs compared to 18 healthy age-matched controls with no history of concussion. Both MMN and P300 in an auditory oddball task were assessed. Relative to controls, CFL players exhibited reduced MMN and P300 amplitudes as well as response delays. The authors conclude that multiple concussions sustained over several years can lead to altered cognitive function and that ERPs provide an objective assessment for evaluating longterm cognitive consequences of concussion.

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Clayton and coworkers [32] measured the P300 in a 2-tone auditory task in 364 student athletes participating in contact sports. Concussed players experienced significant reaction time prolongations and/or P300 amplitude reductions compared to pre-concussion baseline. P300 changes persisted in 38% of the players after standard measures, including reaction times, had cleared. The authors conclude P300 amplitude changes after concussions that are quantifiable and consistent, often normalizing more slowly than other standard assessments. P300 methods may also reveal compensatory changes to maintain a high performance after TBI, as shown by Clayton and colleagues [33]. In that study, participants with poorer performance on neuropsychological tests exhibited the usual reduced P300 but showed the typical P300 parietal scalp distribution. In contrast, better performing participants demonstrated no amplitude change but a substantially altered scalp distribution of P300 with more frontal activity. This likely reflects a compensatory recruitment of anterior brain regions to keep performance on a high level. In summary, recent studies confirm earlier ones, showing smaller amplitudes of P300, and also MMN, as well as latency delays in P300, in patients with TBI. P300 changes sometimes persist longer than behavioral impairments, showing residual cognitive dysfunctions. Moreover, the P300 pattern can differentiate subjects who show compensatory activity to maintain performance. 2.2 Parkinson’s Disease

Many results of ERP studies with patients with Parkinson’s disease (PD) reflect the cognitive deficits that are linked with PD. In 2016, Seer and coworkers [34] provided a comprehensive review of ERP changes in PD. Concerning P300 and MMN, they revealed evidence for delayed P3b and attenuated MMN amplitude in PD with dementia, but not in non-demented PD. In contrast, ERP correlates of executive functions, such as N2 and error(-related) negativity (Ne/ERN), appear to be attenuated in non-demented PD patients in a dopamine-dependent manner. A more recent review by Jafari and coworkers [35] focused on the auditory P3a. The authors found the P3a to be attenuated and its latency prolonged in PD and that these alterations are related to duration and severity of the disease. Moreover, they found evidence for a prolonged P3a latency in PD patients with MCI, stating P3a to be a sensitive marker to identify MCI in patients with PD. The P3a findings were confirmed in a further review that focused on auditory ERPs in early- and late-stage PD [36]. P3a reductions were described already in early state PD, while MMN reductions were mostly found in advanced stages of PD. The authors stated that MMN reductions are only evident when PD is associated with dementia. In 2019, Pauletti and coworkers [37] investigated P3a as well as P3b in PD patients with and without fatigue. P3b latency was

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delayed in both patient groups compared to healthy controls. In contrast, P3a latency was longer and P3a amplitude smaller only in PD patients with fatigue. In summary, in recent reviews and studies, MMN was found to be reduced and P3b delayed in Parkinson’s disease with dementia. In contrast, the P3a might be reduced already in early state Parkinson’s disease. Hence, a reduced P3a amplitude might be suitable for assessing disease progression. Apart from P300 and MMN changes, ERP correlates of executive functions are also diminished in non-demented patients with Parkinson’s disease. 2.3 Attention-Deficit Hyperactivity Disorder (ADHD)

Attention-deficit hyperactivity disorder (ADHD) is the most common psychiatric disorder of childhood and often persists in adulthood. In 2020, Kaiser and coworkers [38] provided a meta-analysis on earlier and later cognitive ERP differences between children, adolescents, and adults with ADHD and without ADHD (non-ADHD), which also included several variants of the P300 usually recorded in Go/Nogo tasks. Fifty-two relevant articles were identified including n = 1576 ADHD and n = 1794 non-ADHD. Individuals with ADHD showed smaller amplitudes of Cue-P300 and NoGo-P300 and longer latencies of Go- and Nogo-P3. Also in 2020, Zhang and coworkers [39] compared the MMN and P3a between preschool ADHD and normal children using a three-stimulus oddball paradigm. MMN elicited by deviants and P3a elicited by novelty were significantly reduced in ADHD patients than in controls. In addition, the P3a amplitude negatively correlated with ADHD symptoms. These data suggest a dysfunction of pre-attentive change detection and attentional shift in ADHD children. In 2020, Breitling-Ziegler and coworkers [40] proposed a task combination including a nogo task and an n-back task to assess inhibition and working memory in ADHD. They found the expected behavioral deficits as well as diminished n-back P3 and nogo-P3 amplitudes. The authors claim that the combined n-back/nogo task is an effective paradigm for the economical assessment of working memory and response inhibition deficits in ADHD on a behavioral and neurophysiological level. More recently, Peisch and coworkers [41] reviewed the literature on ERP correlates of stimulant response in children with ADHD. Forty-three articles were examined and results suggest that stimulant medications normalize the amplitude of the P300 component, which is also associated with behavioral improvement. The authors conclude that ERP methods represent a promising approach for precision medicine care of patients with ADHD. Brunner and coworkers [42] investigated if changes in eventrelated potentials (ERPs) induced by a single dose of stimulant medication were different in responders and non-responders.

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Indeed, a single dose of stimulant medication induced a significant increase in P3 no-go in responders only, probably reflecting improvements in aspects of executive function. More recent studies often focus on adult ADHD. In 2018, Marquardt and coworkers [43] investigated ERPs in 27 adults with ADHD and 28 controls, using a modified flanker task. Adults with ADHD showed higher error rates in incompatible trials, which correlated positively with the ASRS scores. Also, they observed lower P3 amplitudes in incompatible trials, which were inversely correlated with symptom load in the ADHD group. Meachon and coworkers [44] used a stop-signal task and ERPs including the P300 to differentiate ADHD from developmental coordination disorder (DCD). The Nogo-P3 was smaller for the ADHD than for the DCD group at several fronto-temporal electrodes, while the groups did not differ in behavioral measures. Hsieh and coworkers [45] recorded ERPs in 52 drug-naive adults with ADHD and 62 controls using a passive durationdeviant auditory oddball paradigm. MMN and reorienting negativity (RON) were measured at Cz. MMN amplitude was reduced in the patients and correlated with inattention symptoms, executive dysfunctions, attentional vigilance, and decision-making scores. No group effects were seen for the RON. Kim and coworkers [46] measured the MMN in 34 adults with ADHD and 45 controls using a passive auditory oddball paradigm. The patients showed lower MMN amplitudes at the fronto-central electrodes and reduced MMN source activation in the frontal, temporal, and limbic lobes, which were closely associated with MMN generators and ADHD pathophysiology. Source activities were significantly correlated with ADHD symptoms. The best classification performance for adult ADHD patients and HCs showed more than 80% accuracy, sensitivity, and specificity based on MMN source activity features. The authors claim that the MMN might serve as a neuromarker of adult ADHD. In summary, the recent literature shows amplitude reductions in MMN and various late positive components, as well as latency delays of P300 in patients with ADHD. Some of those ERP alterations correlate with ADHD symptoms. Changes of late positive waves and MMN were also observed in adults with ADHD. Moreover, the effects of stimulant medication can be tracked by the normalization of P300 and Nogo-P3 amplitudes. ERPs including the P300 have also been used in a recent study to differentiate ADHD from a similar disorder. 2.4 Borderline Personality Disorder (BPD)

Borderline personality disorder (BPD) is characterized by emotional dysregulation and difficulties in cognitive control. In 2021, Flasbeck and coworkers [47] published a comprehensive review on ERPs in BPD patients which also covered the P300. It was found that the majority of studies reported decreased amplitudes and

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prolonged latencies of the P300 in BPD. The authors tentatively interpreted this pattern as a delay of the P3b and hence delayed controlled/cognitive processing, while the P3a might be unaffected or even enhanced. Indeed BPD studies that measured selectively the P3a showed higher amplitudes and a deficient habituation of the P3a in patients with BPD, which suggests increased stimulusrelated attention in BPD. More recently, Penengo and coworkers [48] conducted a meta-analysis on auditory ERP components associated with BPD. Among other results, amplitude and latency of the P300 were altered in BPD patients compared to controls. Ramos-Loyo and coworkers [49] investigated the effects of implicit emotional contexts on response inhibition in BPD patients, using a response inhibition task (Go-NoGo). Among other results, higher NoGo-P3 amplitudes were observed in the patients compared to controls. Also, higher NoGo-P3 amplitudes were correlated with more pronounced psychopathological symptoms. In summary, amplitude and latency of the P300 are altered in BPD patients compared to controls. Different subcomponents/ variants of the P300 show different effects in BPD: while the more fronto-central components P3a and/or Nogo-P3 were enhanced, suggesting increased attention/inhibition, the parietal P3b is rather attenuated or delayed, suggesting delayed and/or attenuated controlled processing. 2.5

Schizophrenia

Mismatch negativity (MMN) and P300 event-related potential (ERP) were often found to be reduced in schizophrenia (SZ) in earlier studies, e.g., [50]. More recently, Hamilton and coworkers [51] assessed the MMN to auditory deviants and P300 to infrequent auditory target and nontarget novel stimuli in highfunctioning (HF-SZ) and low-functioning (LF-SZ) patients and in healthy controls (HC). MMN was significantly diminished in LF-SZ compared to HF-SZ and HC, the two latter groups having comparable MMN. In contrast, P300 was significantly reduced in both LF-SZ and HF-SZ compared to HC. Logistic regression suggested independent sensitivity of MMN to functioning in SZ over and above P300 measures. Those results replicate MMN and P300 abnormalities in SZ and also suggest that the neural mechanisms associated with the preattentive detection of auditory deviance (MMN) are most compromised in patients with functional disability. Most of the earlier MMN studies with SZ patients used deviants that vary in a single acoustic dimension. In a meta-analysis, MMN deficits in SZ using simple and more complex deviants were compared [52]. The authors found that studies using simple deviants demonstrate larger deficits than those using complex deviant. P3b deficits, while large, were only modestly greater than MMN deficits. The authors claim that MMN to simple deviants may still be optimal as a biomarker for SCZ.

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However, there is no simple association between symptom severity and MMN impairment in SZ, as found in a meta-analysis from 2017 [53]. Instead, the authors found associations between MMN impairment and lower education as well as higher age in the SZ group. These results suggest that MMN impairment may be more closely associated with premorbid functioning than with the expression of psychotic symptoms. Recent ERP research on SZ has often focused on the identification of EEG and ERP alterations in the prodromal and early phases of the disorder, focusing on the prediction of clinical and functional outcome. In a recent review of 133 studies [54], a reduction of MMN and P300 amplitude in subjects with at-risk mental state and early stages of schizophrenia was found. In summary, the recent studies confirm the older ones, showing a reduction of P300 and MMN in SZ. The MMN changes appear to be independent of P300 changes. For MMN studies, simple deviants appear to yield larger effects than complex deviant. The reduction of MMN and P300 amplitude can be also observed in subjects with at-risk mental state and early stages of SZ. 2.6

Depression

Earlier research usually showed a smaller P300 amplitude in unipolar depression (UD), e.g., [55], while some studies could not find such a reduction [56]. Most of the earlier studies have used simple oddball tasks; more recent studies [57] confirmed a P300 reduction also in the flanker task, along with increased error negativity (Ne/ERN) and decreased error positivity (Pe). Santopetro and coworkers [58] investigated the prospective utility of the P300 on depression in female adolescents. They found that a reduction of P300 amplitude predicts an increase in depression 2 years later. The authors claim that a reduction of P300 amplitude can be utilized as a risk marker for adolescents at risk of developing increases in depressive symptoms. Later the group confirmed the predictive utility also for adults with a current UD: a reduction of P300 amplitude at the initial visit was associated with higher total depressive symptoms at 9 month follow-up, even after controlling for initial depressive symptoms [59]. These data show the clinical utility for the P300 as a neural marker of the course of UD. In the more recent studies the P300 was also measured in patients with bipolar disorder (BD). In a comprehensive metaanalysis with different paradigms, a reduced P3a/P3b amplitude and delayed P3b latency could be observed with any paradigm [60]. Psychosis, phase of the disease, and diagnostic subcategories did not affect P300 abnormalities. The authors claimed that P300 abnormalities may be a trait marker of BD. In a study by Fu and coworkers [61], P300 amplitude was found attenuated and P300 latency delayed in BD compared to healthy controls. In convalescent patients, P300 amplitude was still

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reduced, but there was no difference in P300 latency compared with the controls. The authors concluded that BD patients had P300 abnormalities and deficits in cognitive processing which may be present even when emotional symptoms were relieved. In a meta-analysis from 2019, P300 differences between UD and BD were examined [62]. The authors found significantly longer P300 latencies in BD than in UD patients, both during the depressive episode and remission. Also, P300 latency was still longer in remitted BD patients than in healthy controls. In contrast, in UD patients, P300 latency decreased to normal values during remission. Also the MMN appears to be altered in depression. In a recent meta-analysis [63], 13 studies consisting of 339 patients with UD and 343 healthy controls were included. MMN amplitudes and latencies to duration, but not frequency deviants, were significantly impaired in patients with UD compared to HC. Acute patients showed much stronger MMN alterations than chronic patients. As shown in a further meta-analysis, the MMN is also attenuated in BD, even though to a lesser extent than in schizophrenia [64]. This was confirmed in a more recent meta-analysis, who showed an impairment of the MMN mainly in BD with psychotic features [65]. Kim and colleagues measured the MMN in patients with MD or BD without psychotic symptoms [66]. The MMN at frontocentral sites was smaller for BD, but not for MD, than for healthy controls. The source activity of the MMN from several frontal brain areas was increased in the BD compared to the MD group. Significant correlations were detected between the functional outcomes and MMN amplitudes for both types of depression. The authors claim that the MMN might be used as an evaluation tool for functional outcomes in mood disorders. In summary, P3a and P3b are generally reduced in unipolar depression and also in adolescents at risk for developing depressive symptoms. Moreover, the P300 reduction appears to predict the course of depression, and in bipolar depression, the amplitude of P3a and P3b is reduced and P3b latency delayed. Also, the MMN is attenuated in unipolar and bipolar depression, with the largest effects being seen for duration of MMN. MMN alterations appear to be larger in acute than chronic patients. Finally, MMN alterations appear to be related to functional changes in both types of depression. 2.7 Alcohol Use Disorder (AUD)

Alcohol dependence is currently one of the most serious public health problems. Indeed, 3–8% of all deaths worldwide are attributable to effects of alcohol consumption [27]. A meta-analysis from 2019 found that AUD was related to an amplitude reduction of the visual P3b, while the difference in auditory P3b amplitude was only marginally different between

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AUD patients and healthy controls [67]. Moreover, auditory P3b latency was found to be increased in AUD. Already in the early 1980s and the subsequent years, Begleiter and coworkers have shown that the P300 is not only reduced in patients with AUD who are abstinent but also in relatives and children who are (still) healthy. This genetic predisposition for AUD as seen in the reduced P300 was proposed to reflect CNS disinhibition/hyperexcitability [68, 69]. In a recent longitudinal study of Harper and colleagues [70] with a large (N = 594) sample of adolescents, reduced targetrelated P3 and theta activity at age 14 prospectively predicted drinking at age 17. Importantly, the endophenotypes provided significant incremental predictive power of future non-clinical alcohol use beyond relevant risk factors. Liu and coworkers [71] studied 30 AUD patients and 30 controls with a three stimulus oddball task. AUD patients showed reductions of P3a and P3b amplitude, even after 4-week alcohol abstinence. For P3a and P3b latencies, no significant differences were observed. P3 amplitudes can be also larger in AUD than in controls depending on the task. For example, Campanella and colleagues [27] showed that P300 amplitudes to alcohol-related pictures and words were larger in patients with AUD than in controls. This suggests an attentional bias toward alcohol-related stimuli in AUD that increase the urge to drink. Moreover, the authors presented evidence for impaired inhibitory control (as reflected in the Nogo-P3) in AUD. Even more important, an enhanced amplitude of the Nogo-P3 (reflecting more activation for inhibition) after detoxification predicted a relapse of the patients. This suggests that P300 (P3b) and Nogo-P3 can be used to assess different aspects of AUD pathology and predict the development of those processes. In summary, in recent reviews, the amplitude of the standard visual P3b was found reduced and the latency of the auditory P3b prolonged in AUD. A reduced P3 in adolescence might be able to predict drinking some years later. In contrast, the P3 to alcoholrelated stimuli appears to be enhanced in AUD suggesting an attentional bias toward those stimuli. Different P3-waves may be able to assess different aspects of AUD pathology. 2.8 Dementias and Mild Cognitive Dysfunction

Goodin and coworkers [28] were the first to show an increase of P300 latency in dementia. Since that finding, dementias, mainly Alzheimer’s dementia (AD), have been regularly investigated with ERPs, as well as P300 and MMN. More recently, many ERP studies on mild cognitive dysfunction (MCI) have been conducted. In a review of EEG and ERP biomarkers of AD, Horvath and coworkers [72] collected evidence that most previous studies found a prolonged latency and decreased amplitude of the P300 in AD

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compared to age-matched healthy controls. Furthermore, P300 latency was often correlated with the severity of cognitive deficits in AD. MCI patients also show a P300 latency delay compared to healthy controls. In their review from 2020, Babiloni and colleagues confirmed that P300 amplitude was consistently attenuated and its latency prolonged in AD and MCI compared to normal controls [73]. A more recent review by Babiloni et al. focused on vascular dementia [74]. As for Alzheimer’s disease, patients with vascular dementia showed a prolonged latency of the P300 (and of the N2). Fruehwirt and coworkers [75] studied the associations of ERPs and the severity of Alzheimer’s disease in 63 subjects with possible and probable AD using an oddball paradigm. They found that not only P300 latency exhibited the strongest association with AD severity in probable AD patients but also N200 latency correlated (but less than P300) with disease severity in those patients. Many of the recent studies focus on patients with (probable) MCI and its sub-domains. A meta-analysis by Gu and colleagues showed P3 latency to be increased also in MCI [76]. The authors claim that the P300 (as well as some other ERP components) might be potential electrophysiological biomarkers for MCI diagnosis. In the same vein, Horvath and coworkers [72] stated that P300 latency can reliably differentiate between patients with MCI and controls. In a more recent study, it was found that patients with probable MCI had a smaller P3b amplitudes for deviant stimuli than normal controls [77]. Correa-Jaraba and coworkers [78] examined the effects of different subtypes of amnestic MCI (aMCI), namely single-domain aMCI (sdaMCI) or multi-domain aMCI (mdaMCI) on the P3a to novel auditory stimuli. The early part of the P3a was larger in mdaMCI than in sdaMCI participants, and the late P3a was larger in mdaMCI than in sdaMCI and control participants. This was interpreted as a greater capture of attention to unattended novel auditory stimuli and allocation of more attentional resources for the subsequent evaluation of these stimuli in mdaMCI participants. The authors suggest that the P3a subcomponents may represent optimal neurocognitive markers for differentiating aMCI subtypes. Gao and coworkers [79] investigated the relationship between P300 latency and gray matter volume in aMCI. They found a negative correlation between P300 delay and reduced left hippocampal volume only in the aMCI group. The authors concluded that a reduced left hippocampal volume may be the potential structural basis of the delayed P300 in aMCI. Gu and coworkers [80] measured the P300 in a working memory task (N-back) in 39 aMCI patients (27 APOE ε4 non-carriers and 12 APOE ε4 carriers) and their 43 matched controls (25 APOE ε4 non-carriers and 18 APOE ε4 carriers). In both HC and aMCI

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patients, APOE ε4 carriers showed reduced P300 amplitude with respect to non-carriers, whereas no significant differences in accuracy or RT were detected between APOE ε4 carriers and non-carriers. The authors concluded that P300 amplitude could predict VSWM deficits in aMCI patients and contribute to early detection of working memory deficits in APOE ε4 carriers. A recent comprehensive review on about 100 studies on ERPs and AD or MCI by Paitel and coworkers [81] reported that most of the studies focused on N200, P300, and N400, and the majority used the oddball paradigm. Only few of the cited P300 studies were published after 2016 and dealt almost exclusively with MCI patients [78, 82–94]. The results of these more recent studies largely confirm the results of earlier ones, e.g., a smaller amplitude and/or longer latency of the P300 in AD/MCI compared to healthy controls, if effects were found at all. However, when all studies are taken into account, this pattern is only found in about 65% of the AD studies. Hence, as stated by the authors, conclusions of the sensitivity of P300 to early AD pathology should be made with caution. Paitel and coworkers [81] also included some studies with healthy subjects that carried the apolipoprotein-E eps4 allele (vs. healthy non-carriers). In three of five studies, a pattern of delayed P300 latency was seen when using an olfactory identification task, but not when using simple oddball tasks in the same subjects. Interestingly, P300 may be also enhanced in the carriers, which suggests compensatory neural activation [95]. Similarly, in the study of Cid-Fernandez and coworkers [83] which used a Go/Nogo task, subjects with single-domain amnestic MCI showed an enhanced late positive wave, while the Go-P3 was unchanged. The multiple-domain MCI group did not show this late compensatory activity. In their review, Paitel and colleagues [81] stated that the tasks used are crucial. P300 amplitude effects are best seen when using executive function tasks. Notably, Ramos-Goicoa and coworkers [91], using a Stroop task, found a delay of the P300 latency in a middle-aged MCI group vs. middle-aged healthy controls, with a sensitivity and specificity of 0.9 for distinguishing the groups. Paitel and coworkers hence state that complex attention and executive tasks may be best able to characterize neural changes in the course of neuropathology. They also noted that only few P300 studies considered the subcomponents, so any effects are hard to interpret in terms of neuronal processes. More optimistically, Horvath and coworkers state in their review [72] that P300 changes appear to be objective and sensitive measures for discriminating subjects with MCI and AD. Further, they argue that P300 measures might be useful in the detection of the transition of MCI to AD. They also mention pharmacological studies that suggest P300 latency to be useful for quantifying the effects of AD medication.

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Regarding MMN, Horvath and coworkers [72] mentioned several studies that show a reduction of MMN amplitude in AD patients compared to healthy controls. On the other hand, mild AD, and more so MCI, does not always show up in reduced MMN amplitudes. In two more recent studies, MMN latency changes were observed in MCI, which were, however, contradictory. Gao and coworkers [96] found the latency of the MMN to be shorter in aMCI than in healthy controls. Also, in the aMCI group, MMN latency was negatively correlated with episodic memory. A study by Papadaniil and colleagues [90] found a delayed MMN latency in AD compared to HC. The MMN was smaller in AD than in MCI group, but only for left ear stimulation. Ruzzoli and colleagues [97] measured the MMN to duration deviants in AD, MCI, and healthy controls. At a short intertrial interval (ITI), the MCI group showed only the temporal component of MMN and the AD group the frontal component, compared to healthy elderly who presented both. At a longer ITI, the MMN was elicited only in normal aging subjects at the temporal locations. However, as mentioned above, Horvath and coworkers stated that MMN changes in AD and MCI vary greatly, i.e., different studies showed different MMN alterations. As to the authors, this might be due to small samples, different tasks, and differences in data analysis. Laptinskaya and colleagues [98] assessed the amplitude of the MMN for short and long stimulus-onset asynchrony (SOA) in a neuropsychologically well-characterized cohort of older adults at risk of dementia. Their results suggest that the MMN might be suitable as an indicator for the decline in episodic memory in older adults at risk of dementia. In light of the new literature, the P300 appears to be suitable to classify a subject to AD, MCI, or a healthy control group. However, the P300 results are not entirely robust due to suboptimal tasks and methods used. As claimed by Paitel and coworkers, more complex paradigms taxing attention and executive functions should be used in future studies [81]. Also, the P300 subcomponents should be considered and measured by using appropriate tasks. Moreover, further components such as the N2 [99, 100] or the error (related) negativity (Ne or ERN) [101, 102] should be investigated to improve the diagnostic power of ERPs. N2 and Ne clearly showed differences between AD and/or MCI and healthy controls in older and more recent studies. Even more important, the N2 appears to be more sensitive than the P300 in case of early AD, MCI, and even in healthy elders being at risk for AD [103, 104, 105]. Moreover, to improve classification of subjects into AD vs. healthy, more sophisticated methods should be used to improve the accuracy of ERP measures, rather than measuring just amplitudes and latencies. For example, Chapman and coworkers [106] could improve the classification by calculating a weighted combination of temporo-spatial ERP markers. Jervis and colleagues [107] could differentiate

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subjects with new-onset AD from healthy controls with 100% accuracy, based on the back-projected independent components of the P300 peak in an auditory oddball task. In summary, the recent P300 studies largely confirm the older ones, showing a reduced amplitude and prolonged latency of the P300 in AD and less so in MCI. However, this pattern is not found in all studies. MMN changes in AD and MCI vary even more, i.e., different studies showed different MMN alterations. This heterogeneity is likely due to variable and often suboptimal tasks and scoring methods. Moreover, since some other ERP components show more robust effects than P300 and MMN, they should be also measured with appropriate tasks. Hence, a combination of suitable tasks, multicomponent ERP analysis, and optimized classification methods should be pursued in future clinical studies of AD and MCI.

3

General Discussion and Conclusion This chapter shows how ERPs, in particular P300 and MMN, can be useful in clinical practice for prediction, diagnosis, and timecourse of diseases, as well as the effect of treatments. P300 and MMN measures can support an early diagnosis of a disease because their changes might occur before open symptoms are evident. The ERP measures can track the development of cognitive changes during the course of a disease and its treatment. They can uncover residual cognitive deficits in the convalescence phase of a disease, which are not seen in behavioral measures. Also, they can be used to assess the vulnerability of a person for developing a disease such as depression. They cannot only reveal cognitive deficits but also compensatory changes in order to maintain performance during a disease. However, even though ERP methods are cheap and easy to use, and several ERP components have proved to be valuable biomarkers of the pathophysiological mechanisms of various mental illnesses, and despite decades of research, their clinical utility is still a matter of debate, and ERPs are rarely used in clinical practice [1]. There are several reasons for this. First, the ERP results are not always consistent, which is partly due to different tasks used. Also, subtypes of diseases are not always considered and medication effects not taken into account [1]. Moreover, single components such as the P300 or the MMN, which are used in most of the clinical studies, are usually not specific for a disease. In particular, decrements of P3b amplitude and enhancements of P3b latency are seen in most of the CNS diseases described. They indicate impairment and slowing of controlled processing, which is linked to those diseases. Patterns of ERP changes appear to be more specific for a certain disease, in particular enhancements of

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components such as the enhanced P3b after alcohol-specific stimuli in alcohol use disorder. Hence, despite the importance of P300 and MMN, clinicians should include further ERP components such as other P3-like components (P3a, Nogo-P3, cue-P3), as well as N200 and Ne/ERN to improve the utility of ERPs. Moreover, complex attention and executive tasks, rather than simple tasks such as the oddball task, could be used for improving the diagnostic power and for characterizing changes in the course of neuropathology. Campanella [1, 27] suggested some research strategies to better implement ERPs in clinical ERP studies in psychiatry. Among these are the combination of ERPs with behavioral data, serial ERP recordings, and a multi-component ERP approach, which was already mentioned in the dementia section. The combination of ERPs with the task-related behavioral data allows the proper interpretation of ERP changes. Serial recordings, which are well possible because ERPs are highly stable within an individual, reflect the development of the cognitive changes during the disease and its treatment. Regarding the multi-component issue, it is important to measure not only P300 and MMN but also as many relevant components as possible in a reasonable time by using optimal tasks. Campanella proposed a cognitive ERP battery consisting of four tasks that could be used across disorders. Those tasks cover the cognitive processes disturbed in most psychiatric disorders. A problem is certainly to reach an agreement on the tasks. To conclude, ERPs, in particular P300 and MMN, are promising tools in clinical practice. However, several research strategies and agreements are necessary to foster the clinical use of ERPs in future. References 1. Campanella S (2021) Use of cognitive eventrelated potentials in the management of psychiatric disorders: towards an individual follow-up and multi-component clinical approach. World J Psychiatry 11:153–168 2. Sutton S, Braren M, Zubin J et al (1965) Evoked-potential correlates of stimulus uncertainty. Science 150:1187–1188 3. N€a€at€anen R, Gaillard AWK, M€antysalo S (1978) Early selective-attention effect on evoked potential reinterpreted. Acta Psychol 42:313–329 4. Polich J (2007) Updating P300: an integrative theory of P3a and P3b. Clin Neurophysiol 118:2128–2148 5. Falkenstein M, Hohnsbein J, Hoormann J (1994) Effects of choice complexity on different subcomponents of the late positive complex of the event-related potential.

Electroencephalogr Clin Neurophysiol 92: 148–160 6. Gajewski PD, Falkenstein M (2014) Age-related effects on ERP and oscillatory EEG-dynamics in a 2-back task. J Psychophysiol 28:162–177 7. Falkenstein M, Hohnsbein J, Hoormann J (1994) Time pressure effects on late components of the event-related potential (ERP). J Psychophysiol 8:22–30 8. Getzmann S, Gajewski PD, Falkenstein M (2013) Does age increase auditory distraction? Electrophysiological correlates of high and low performance in seniors. Neurobiol Aging 34:1952–1962 9. Folstein JR, Van Petten C (2008) Influence of cognitive control and mismatch on the N2 component of the ERP: a review. Psychophysiology 45:152–170

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10. Falkenstein M, Hoormann J, Hohnsbein J (2002) Inhibition-related ERP components: variation with age and time-on-task. J Psychophysiol 16:167–175 11. Anderer P, Pascual-Marqui RD, Semlitsch HV et al (1998) Electrical sources of P300 eventrelated brain potentials revealed by low resolution electromagnetic tomography. Neuropsychobiology 37:20–27 12. Bocquillon P, Bourrie JL, Palmero-Soler E et al (2011) Use of swLORETA to localize the cortical sources of target- and distracterelicited P300 components. Clin Neurophysiol 122:1991–2002 13. Sabeti M, Katebi SD, Rastgar K et al (2016) A multi-resolution approach to localize neural sources of P300 event-related brain potential. Comput Methods Progr Biomed 133:155– 168 14. Bokura H, Yamaguchi S, Kobayashi S (2001) Electrophysiological correlates for response inhibition in a Go/NoGo task. Clin Neurophysiol 112:2224–2232 15. Huster RJ, Enriquez-Geppert S, Lavallee CF et al (2012) Electroencephalography of response inhibition tasks: functional networks and cognitive contributions. Int J Psychophysiol 87:217–233 16. Ramautar JR, Kok A, Ridderinkhof KR (2006) Effects of stop-signal modality on the N2/P3 complex elicited in the stop-signal paradigm. Biol Psychol 72:96–109 17. N€a€at€anen R, Paavilainen P, Titinen H et al (1993) Attention and mismatch negativity. Psychophysiology 30:436–450 18. Kropotov J, N€a€at€anen R, Sevostianov V et al (1995) Mismatch negativity to auditory stimulus change recorded directly from the human temporal cortex. Psychophysiology 32:418– 422 19. Paavilainen P (2013) The mismatchnegativity (MMN) component of the auditory event-related potential to violations of abstract regularities: a review. Int J Psychophysiol 88:109–123 20. Schro¨ger E (1998) Measurement and interpretation of the mismatch negativity. Behav Res Methods Instr Comput 30:131–145 21. Schro¨ger E, Giard MH, Wolff C (2000) Auditory distraction: event-related potential and behavioral indices. Clin Neurophysiol 111: 1450–1460 22. Pazo-Alvarez P, Cadaveira F, Amenedo E (2003) MMN in the visual modality: a review. Biol Psychol 63:199–236 23. Kimura M, Schro¨ger E, Czigler I (2011) Visual mismatch negativity and its importance

in visual cognitive sciences. Neuroreport 22: 669–673 24. Giard MH, Perrin F, Pernier J et al (1990) Brain generators implicated in the processing of auditory stimulus deviance: a topographic event-related potential study. Psychophysiology 27(6):627–640 25. Rinne T, Alho K, Ilmoniemi RJ et al (2000) Separate time behaviors of the temporal and frontal mismatch negativity sources. NeuroImage 12:14–19 26. Susac A, Heslenfeld DJ, Huonker R et al (2014) Magnetic source localization of early visual mismatch response. Brain Topogr 27: 648–651 27. Campanella S, Schroder E, Kajosch H et al (2019) Why cognitive event-related potentials (ERPs) should have a role in the management of alcohol disorders. Neurosci Biobehav Rev 106:234–244 28. Goodin DS, Squires KC, Starr A (1978) Long latency event-related components of the auditory evoked potential in dementia. Brain 101: 635–648 29. Brush CJ, Ehmann PJ, Olson RL et al (2018) Do sport-related concussions result in longterm cognitive impairment? A review of eventrelated potential research. Int J Psychophysiol 132:124–134 30. Li H, Li N, Xing Y et al (2021) P300 as a potential indicator in the evaluation of neurocognitive disorders after traumatic brain injury. Front Neurol 12:690792 31. Ruiter KI, Boshra R, Doughty M et al (2019) Disruption of function: neurophysiological markers of cognitive deficits in retired football players. Clin Neurophysiol 130:111–121 32. Clayton G, Davis N, Holliday A et al (2020) In-clinic event related potentials after sports concussion: a 4-year study. J Pediat Rehab Med 13:81–92 33. Davis TM, Hill BD, Evans KJ et al (2017) P300 event-related potentials differentiate better performing individuals with traumatic brain injury: a preliminary study of semantic processing. J Head Trauma Rehab 32:E27– E36 34. Seer C, Lange F, Georgiev D et al (2016) Event-related potentials and cognition in Parkinson’s disease: an integrative review. Neurosci Biobehav Rev 71:691–714 35. Jafari Z, Kolb BE, Mohajerani MH (2020) Auditory dysfunction in Parkinson’s disease. Mov Disord 35:537–550 36. De Groote E, De Keyser K, Santens P et al (2020) Future perspectives on the relevance

Recent Advances in Clinical Applications of P300 and MMN of auditory markers in prodromal Parkinson’s disease. Front Neurol 11:1–17 37. Pauletti C, Mannarellia D, Locuratolo N et al (2019) Central fatigue and attentional processing in Parkinson’s disease: an event-related potentials study. Clin Neurophysiol 130: 692–700 38. Kaiser A, Aggensteiner PM, Baumeister S et al (2020) Earlier versus later cognitive eventrelated potentials (ERPs) in attention-deficit/hyperactivity disorder (ADHD): a metaanalysis. Neurosci Biobehav Rev 112:117– 134 39. Zhang J, Qiu M, Pan J et al (2020) The preattentive change detection in preschool children with attention deficit hyperactivity disorder: a mismatch negativity study. Neuroreport 31:776–779 40. Breitling-Ziegler C, Tegelbeckers J, Flechtner HH et al (2020) Economical assessment of working memory and response inhibition in ADHD using a combined n-back/Nogo paradigm: an ERP study. Front Hum Neurosci 14:322 41. Peisch V, Rutter T, Wilkinson CL et al (2021) Sensory processing and P300 event-related potential correlates of stimulant response in children with attention-deficit/hyperactivity disorder: a critical review. Clin Neurophysiol 132:953–966 42. Brunner JF (2016) Predicting clinical outcome of stimulant medication in pediatric attention, deficit/hyperactivity disorder (ADHD): single-dose changes in eventrelated potentials (ERPs). Eur Psychiatry 33: S144 43. Marquardt L, Eichele H, Lundervold AJ et al (2018) Event-related-potential (ERP) correlates of performance monitoring in adults with attention-deficit hyperactivity disorder (ADHD). Front Psychol 9:485 44. Meachon EJ, Meyer M, Wilmut K (2021) Evoked potentials differentiate developmental coordination disorder from attention-deficit/ hyperactivity disorder in a stop-signal task: a pilot study. Front Hum Neurosci 15:629479 45. Hsieh MH, Chien YL, Shur-Fen Gau S (2021) Mismatch negativity and P3a in drug-naive adults with attention-deficit hyperactivity disorder. Psychol Med 52(15): 1–11 46. Kim S, Baek JH, Kwon JY (2021) Machinelearning-based diagnosis of drug-naive adult patients with attention-deficit hyperactivity disorder using mismatch negativity. Transl Psychiatry 11:484. https://doi.org/10. 1038/s41398-021-01604-3

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47. Flasbeck V, Juckel G, Bru¨ne M (2020) Evidence for altered neural processing in patients with borderline personality disorder: a review of event-related potential studies. J Psychophysiol 35:163–185 48. Penengo C, Colli C, Bonivento C et al (2022) Auditory event-related electroencephalographic potentials in borderline personality disorder. J Affect Dis 296:454–464 49. Ramos-Loyo J, Jua´rez-Garcı´a C, LlamasAlonso LA et al (2021) Inhibitory control under emotional contexts in women with borderline personality disorder: an electrophysiological study. J Psychiat Res 132:182–190 50. Pfefferbaum A, Wenegrat BG, Ford JM et al (1984) Clinical application of the P3 component of event-related potentials. II. Dementia, depression and schizophrenia. Electroenceph Clin Neurophysiol/Evoked Pot Section 59: 104–124 51. Hamilton HK, Perez VB, Ford JM et al (2018) Mismatch negativity but not P300 is associated with functional disability in schizophrenia. Schizophr Bull 44:492–504 52. Avissar M, Xie S, Vail B et al (2018) Metaanalysis of mismatch negativity to simple versus complex deviants in schizophrenia. Schizophrenia Res 191:25–34 53. Erickson MA, Albrecht M, Ruffle A et al (2017) No association between symptom severity and MMN impairment in schizophrenia: a meta-analytic approach. Schizophr Res Cogn 9:13–17 54. Perrottelli A, Giordano GM, Brando F et al (2021) EEG-based measures in at-risk mental state and early stages of schizophrenia: a systematic review. FrontPsychiatry 12:653642 55. Diner BC, Holcomb PJ, Dykman RA (1985) P300 in major depressive disorder. Psychiatry Res 15:175–184 56. Bruder GE, Kroppmann CJ, Kayser J et al (2009) Reduced brain responses to novel sounds in depression: P3 findings in a novelty oddball task. Psychiatry Res 170:218–223 57. Klawohn J, Santopetro NJ, Meyer A et al (2019) Reduced P300 in depression: evidence from a flanker task and impact on ERN, CRN, and Pe. Psychophysiology 57:e13520 58. Santopetro NJ, Kallen AM, Threadgill H et al (2020) Reduced flanker P300 prospectively predicts increases in depression in female adolescents. Biol Psychol 156:107967 59. Santopetro NJ, Brush CJ, Bruchnak A et al (2021) A reduced P300 prospectively predicts increased depressive severity in adults with clinical depression. Psychophysiology 58: e13767

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60. Wada M, Kurose S, Miyazaki T et al (2019) The P300 event-related potential in bipolar disorder: a systematic review and metaanalysis. J Affect Dis 256:234–249 61. Fu L, Xiang D, Subodh D et al (2018) Auditory P300 study in patients with convalescent bipolar depression and bipolar depression. Neuroreport 29:968–973 62. Zhong BL, Xu YM, Xie WX et al (2019) Can P300 aid in the differential diagnosis of unipolar disorder versus bipolar disorder depression? A meta-analysis of comparative studies. J Affect Dis 245:219–227 63. Tseng YJ, Nouchi R, Cheng CH (2021) Mismatch negativity in patients with major depressive disorder: a meta-analysis. Clin Neurophysiol 132:2654–2665 64. Hermens DF, Chitty KM, Kaur M (2018) Mismatch negativity in bipolar disorder: a neurophysiological biomarker of intermediate effect? Schizophr Res 191:132–139 65. Raggi A, Lanza G, Ferri R (2021) Auditory mismatch negativity in bipolar disorder: a focused review. Rev Neurosci 33(1):17–30 66. Kim S, Baek JH, Shim S et al (2020) Mismatch negativity indices and functional outcomes in unipolar and bipolar depression. Sci Rep 10:12831 67. Hamidovic A, Wang Y (2019) The P300 in alcohol use disorder: a meta-analysis and meta-regression. Progr NeuroPsychopharmacol Biol Psychiatry 95:109716 68. Begleiter H, Porjesz B, Bihari B et al (1984) Event-related brain potentials in boys at risk for alcoholism. Science 225:1493–1496 69. Begleiter H, Porjesz B (1999) What is inherited in the predisposition toward alcoholism? A proposed model. Alcoholism Clin Exp Res 23:1125–1135 70. Harper J, Malone SM, Iacono WG (2021) Parietal P3 and midfrontal theta prospectively predict the development of adolescent alcohol use. Psychol Med 51:416–425 71. Liu X, Zhou H, Jiang C et al (2020) Cognitive control deficits in alcohol dependence are a trait- and state-dependent biomarker: an ERP study. Front Psych 11:606891 72. Horvath A, Szucs A, Csukly G et al (2018) EEG and ERP biomarkers of Alzheimer’s disease: a critical review. Front Biosci 23:183– 220 73. Babiloni C, Blinowska K, Bonanni L et al (2020) What electrophysiology tells us about Alzheimer’s disease: a window into the synchronization and connectivity of brain neurons. Neurobiol Aging 85:58–73

74. Babiloni C, Arakaki X, Bonanni L et al (2021) EEG measures for clinical research in major vascular cognitive impairment: recommendations by an expert panel. Neurobiol Aging 103:78–97 75. Fruehwirt W, Dorffner G, Roberts S et al (2019) Associations of event-related brain potentials and Alzheimer’s disease severity: a longitudinal study. Progr NeuroPsychopharmacol Biol Psychiatry 92:31–38 76. Gu L, Zhang Z (2017) Exploring potential electrophysiological biomarkers in mild cognitive impairment: a systematic review and meta-analysis of event-related potential studies. J Alzh Dis 58:1283–1292 77. Bell KL, Lister JJ, Conter R et al (2021) Cognitive event-related potential responses differentiate older adults with and without probable mild cognitive impairment. Exp Aging Res 47:145–164 78. Correa-Jaraba KS, Lindı´n M, Dı´az F (2018) Increased amplitude of the P3a ERP component as a neurocognitive marker for differentiating amnestic subtypes of mild cognitive impairment. Front Aging Neurosci 10:19 79. Gao L, Gu L, Shu H et al (2020) The reduced left hippocampal volume related to the delayed P300 latency in amnestic mild cognitive impairment. Psychol Med 20:1–9 80. Gu L, Chen J, Gao L et al (2017) The effect of apolipoprotein E ε4 (APOE ε4) on visuospatial working memory in healthy elderly and amnestic mild cognitive impairment patients: an event-related potentials study. Front Aging Neurosci 9:145 81. Paitel ER, Samii MR, Nielson KA (2021) A systematic review of cognitive event-related potentials in mild cognitive impairment and Alzheimer’s disease. Behav Brain Res 396: 112904 82. Chiang HS, Spence JS, Kraut MA et al (2018) Age effects on event-related potentials in individuals with amnestic mild cognitive impairment during semantic categorization Go/NoGo tasks. Neurosci Lett 670:19–21 83. Cid-Ferna´ndez S, Lindı´n M, Dı´az F (2017) Neurocognitive and behavioral indexes for identifying the amnestic subtypes of mild cognitive impairment. J Alzheimers Dis 60:633– 649 84. Fraga FJ, Quispe Mamani G, Johns E et al (2018) Early diagnosis of mild cognitive impairment and Alzheimer’s with eventrelated potentials and event-related desynchronization in N-back working memory tasks. Comput Methods Prog Biomed 164: 1–13

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96. Gao L, Chen J, Gu L et al (2018) Effects of gender and apolipoprotein E on novelty MMN and P3a in healthy elderly and amnestic mild cognitive impairment. Front Aging Neurosci 21:10 97. Ruzzoli M, Pirulli C, Mazza V et al (2016) The mismatch negativity as an index of cognitive decline for the early detection of Alzheimer’s disease. Sci Rep 6:33167 98. Laptinskaya D, Thurm F, Ku¨ster OC et al (2018) Auditory memory decay as reflected by a new mismatch negativity score is associated with episodic memory in older adults at risk of dementia. Front Aging Neurosci 10:5 99. Ritter W, Simson R, Vaughan HG et al (1979) A brain event related to the making of a sensory discrimination. Science 203:1358–1361 100. Gajewski PD, Stoerig P, Falkenstein M (2008) ERP-correlates of response selection in a response conflict paradigm. Brain Res 1189:127–134 101. Falkenstein M, Hohnsbein J, Hoormann J et al (1991) Effects of crossmodal divided attention on late ERP components. II. Error processing in choice reaction tasks. Electroencephalogr Clin Neurophysiol 78:447–455 102. Gehring WJ, Coles MGH, Meyer DE et al (1993) A neural system for error detection and compensation. Psychol Sci 4:385–390 103. Verleger R, Ko¨mpf D, Neuk€ater W (1992) Event-related EEG potentials in mild dementia of the Alzheimer type. Electroencephalogr Clin Neurophysiol/Evoked Pot Sect 84:332– 343 104. Thurm F, Antonenko D, Schlee W et al (2013) Effects of aging and mild cognitive impairment on electrophysiological correlates of performance monitoring. J Alzheimers Dis 35:575–587 105. Elverman KH, Paitel ER, Figueroa CM et al (2021) Event-related potentials, inhibition, and risk for Alzheimer’s disease among cognitively intact elders. J Alzheimers Dis 80: 1413–1428 106. Chapman RM, Gardner MN, Klorman R et al (2018) Temporospatial components of brain ERPs as biomarkers for Alzheimer’s disease. Alzheimers Dement (Amst) 10:604–614 107. Jervis BW, Bigan C, Jervis MW et al (2019) New-onset Alzheimer’s disease and normal subjects 100% differentiated by P300. Am J Alzheimers Dis Other Dement 34:308–313

Chapter 2 Contingent Negative Variation (CNV) Francesco Fattapposta, Caterina Pauletti, Daniela Mannarelli, Vilfredo De Pascalis, Joseph Ciorciari, David Crewther, David White, and Gennady Knyazev Abstract The contingent negative variation (CNV), first described by Gray Walter in 1964 as “expectancy wave,” is a slow cortical endogenous potential widely recognized as the electrophysiological signature of a task-specific preparatory state that facilitates the stimulus perception and the required response. Here, we describe the techniques needed to elicit, record, and analyze this event-related potential, extensively used in healthy subjects and many pathological conditions as a valuable tool in describing and understanding the impacts of diseases on cognition. Many functions are sequentially engaged during a typical CNV task, such as anticipatory attention, stimulus discrimination, and motor preparation, and CNV, therefore, represents a trustworthy index of the sensorimotor association linked to these cognitive operations. Key words Contingent negative variation, Anticipatory attention, Expectancy, Slow cortical potentials, PINV, Warning stimulus, Imperative stimulus, Motor preparation, ERPs, Readiness potential

1

Introduction Anticipation plays a crucial role in everyday life: the ability to predict future events allows us to select our responses more efficiently and more appropriately and, ultimately, better adapt our behavior to the environment [1]. Anticipation is active even in event uncertainty or temporal uncertainty, and it implies prior knowledge about the need for a response, relying on memory of recent events and learning processes from past experiences. It requires learning what to pay attention to (similar to classical conditioning learning), where to pay attention to, when to pay attention, and all these characteristics are influenced by the context (or task) in which the behavior takes place [2].

Massimiliano Valeriani and Marina de Tommaso (eds.), Psychophysiology Methods, Neuromethods, vol. 206, https://doi.org/10.1007/978-1-0716-3545-2_2, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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This function is crucial to prepare brain areas that the subsequent cognitive operations will activate before they occur, to speed up processes, and to obtain more efficient processing [3]. In order to select and execute an appropriate response at the proper time, it is necessary to activate many sensory and motor areas that are under top-down control of the prefrontal cortex via cortico-cortical and thalamo-cortical networks [1]. Goal-directed behavior relies on a top-down control related to the ability to predict where and when an important event will occur to direct attention to it before it happens (anticipatory attention). Moreover, it involves motor preparation, which dramatically enhances an action performance. Both aspects, perceptual and motor, are involved in anticipatory behavior. Perceptual and motor processes are strictly related to each other, mutually influence each other, and are no longer considered separate and subsequent functions [4]. The first evidence that the human brain was able to generate an anticipatory activity prior to a relevant event was obtained by Grey Walter and his colleagues in 1964, in a pivotal study for psychophysiology and cognitive neuroscience [5]. They described a slow surface negative potential occurring between two stimuli, the first warning stimulus (S1) being an acoustic signal (click) and the second being an imperative stimulus (S2), a train of repetitive flashes (or vice versa) that the subject should stop by pressing a button. The electric response to the warning stimulus was composed of three main components: the first two (a brief positive and a brief negative waves) appeared to be dependent on the sensory modality of the stimulation. The third one, much more prolonged negativity, seemed to be related to the contingency between stimuli and the presence of a prompt response performed by an attentive subject and was therefore labeled contingent negative variation (CNV) or “expectancy wave” [5]. During the following years, many studies aimed to describe the nature of this endogenous phenomenon in terms of stimulus characteristics (modalities, probability, interstimulus intervals) and behavioral characteristics (arousal, attention, preparation, motivation, estimation, motor control) [6, 7], and CNV is nowadays widely recognized as the electrophysiological signature of a taskspecific preparatory state that facilitates the stimulus perception and the required response. It reflects the subsequent activation of multiple brain areas, which compose a specific sensorimotor neural set attentionally controlled by frontoparietal networks [8–10]. Several studies have demonstrated that the dorsolateral prefrontal cortex plays a crucial role in the genesis of CNV and that additional areas are involved, such as the supplementary motor cortex, primary motor cortex, anterior cingulated cortex, basal ganglia, thalamus, orbitofrontal cortex, and even parietal areas and cerebellum [11– 13].

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When the interval between S1 and S2 is sufficiently long (1.5 s up to several seconds) [14], two components have been identified: the early and the late CNV, which differ in topography and that have been therefore associated with different aspects of anticipatory attention [15]. In particular, the early wave, which was strictly related to the characteristics of S1, was interpreted as a marker of the orienting attentional shift, while the psychophysiological significance of the late wave was a matter of debate since it was discovered [3, 16]. In 1983, in their famous review [17], Rohrbaugh and Gaillard stated that the late CNV could be identified with the readiness potential (or Bereitschaftspotential (BP)), a negative potential preceding voluntary motor response discovered by Kornhuber and Deecke in 1965 [18], and that was purely motor-related. On the other hand, many studies indicated that a motor response was sufficient but not necessary to elicit a CNV, and even though it was widely recognized that the late CNV also reflected motor preparation, it was hypothesized that it was not simply coincident with the BP. However, it also comprises aspects related to perceptual expectancy [19, 20]. This became evident when a negative slow wave preceding visual feedback about the correctness of the movement performed during an S1-S2 task (Knowledge-of-Results KR) and exclusively reflecting stimulus anticipation was described by Brunia and Damen in 1987 and was called stimulus preceding negativity (SPN) [21]. Here, we describe the techniques needed to elicit, record, and analyze CNV, which has been extensively used in healthy subjects and many pathological conditions as a useful tool in describing and understanding the impacts of diseases on cognition.

2

Methods

2.1

CNV Recording

2.1.1

EEG Technique

There are a few technical requirements that researchers should take into account when recording and analyzing slow cortical potentials such as CNV. EEG recordings must be acquired in a faradized and light-attenuated room and possibly with an anatomic chair. Silver chloride electrodes are a prerequisite to better reproducing this brain electrical signal. An appropriate montage should include at least the Fz, Cz, and Pz midline scalp locations, with more extensive electrode arrays at the F3, Fz, F4, C3, Cz, C4, P3, Pz, and P4 sites, according to the International 10–20 System, recommended to obtain information on scalp distribution. The EEG signals have to be grounded generally at the forehead or nose. The most common references are the two earlobes or the mastoids. Physically linking the two earlobes (or mastoids) is not recommended because the current shunting between electrode sites may distort the distribution of voltage over the scalp. Instead, it is recommended that electrical signals from the two references electrodes be recorded

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separately using one electrode as a reference and mathematically combined offline to yield an average references derivation. Additional channels are necessary to monitor the horizontal electrooculogram (EOG) at the two external canthi and vertical EOG for eye movements and blinks above and below the target eye. All inter-electrode impedances have been possibly kept below 3 KOhm. Because CNV is a slow cortical potential, a high-pass setting of 0.01 Hz is optimal, although up to 0.1 is acceptable. The low-pass setting depends on the other components of interest and the analog-to-digital (A/D) conversion rate (approximately 1 = 4 A/D rate). A setting of 30 Hz is recommended. If necessary, additional filtering can be done offline using digital filtering techniques (e.g., 0.01–20 Hz). A digitization rate between 200 and 1024 Hz is recommended. Specific filters and artifact removal procedures must be applied during an EEG-NIBS simultaneous registration (see Note 1). 2.1.2

CNV Task

A typical CNV paradigm consists of a sequence of stimuli (trial) in which an S1, warning stimulus, is followed by an S2, imperative stimulus. At S2 arrival, the subject is invited to respond to the stimulus as quickly as possible, usually by pressing a button to interrupt a train of stimuli. When S2 requires discrimination (double choice reaction time task), the subject can only press the button when S2 is the target. The task could also require a mental response such as counting [22]; the CNV amplitude is less pronounced in the latter case. Anyhow, the presence of an operant response on S2 is necessary to elicit the “expectancy wave” [5]. The used S1-S2 inter-stimulus intervals (ISI) are 1.0 and 1.5 s. However, CNV has been measured over a wide range of ISI from 0.5 to 20 s [5, 6, 23] and up to 30 s. When the interval exceeds 10–15 s, several slow cortical waves appear, suggesting an attempt by the subjects to form several time clusters, even though the typical CNV waveform is not detectable [6, 23]. For intervals of 0.5 s, CNV is fully developed, whereas for 0.25 and 0.125 s intervals, CNV is partially suppressed [5]. Many studies suggest that for ISI of 0.8 and 1.6 s, the CNV amplitude is stable and significantly represented. The inter-trial interval (ITI) could vary between 3 and 10 s [5]. For longer ITI, the contingency between stimuli is lost, and CNV genesis is compromised. CNV can be evoked by combining visual and auditory stimuli or using stimuli consisting of a single sensory modality [5, 6]. Auditory stimuli are more commonly used, and usually, they are single tones. Duration, frequency, and intensity parameters can be varied. Generally, the duration is fixed up to 200 ms. The intensity varies from 60 to 80 dB. The frequency range can be highly variable but generally is between 500 and 2000 Hz. Visual stimuli can be simple

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(as a flash sent by a stroboscope lamp) or complex (for example, geometric figure, a letter, a word, a picture sent by a PC monitor). For the visual stimuli, some parameters should be fixed, such as the duration (range 100–1000 ms), the size (central or fitted to the screen), and the location of the image relative to the screen (central, lateral, above or below the central point). To form the contingency between stimuli and obtain the conditioning effect necessary to evoke the CNV, a training period of several minutes is required. Therefore, maximum CNV amplitude occurs after about 30 trials [24]. Moreover, increasing the number of trials improves the stability of the CNV waveform. During task recording, subjects are instructed to minimize eyes’ movements. Generally, they have to keep their eyes closed. If visual stimuli as images sent via a PC monitor are used in the task, a fixation point is also present on the screen during the inter-trial interval. 2.2

CNV Analysis

The first step to the CNV offline analysis is the definition of a correct EEG analysis epoch for each CNV trial. A recommended analysis epoch is 5 s with a 500 ms pre-stimulus baseline before S1 stimulus. The analysis epoch must be long enough to allow CNV activity to return to baseline. A first automatic procedure must reject CNV trials containing drift deflection. Generally, trials exceeding ±100 μV in any channel, including EOG, must be removed. Further offline analysis can be performed to exclude ocular artifacts (eye movements/blinks). Several standard algorithms can be used to remove trials containing artifacts by computing the cross-covariance between the single-trial EOG waveform and a 200-ms step function and rejecting trials on which the maximum covariance exceeded a ±15 μV threshold. Lastly, the detection of artifacts could also be verified by visual inspection. When a sufficient number of CNV trials free from artifact has been analyzed (minimum 10–15 trials), the Grand Average of single trials is usually calculated. For each CNV wave (both a Grand Average or a single CNV trial), different parameters should be evaluated: the amplitude of CNV areas, the scalp distribution, the post-S2 components, and the performance measures. Regarding the CNV areas, the total CNV area is the main parameter for this wave, and many factors influence its value (see Note 2). Its amplitude is measured as a negative shift between S1 and S2 respect to the baseline. In the anterior-posterior axis, total CNV amplitude has been found maximal at vertex; in the lateral-lateral axis, total CNV wave appears bilaterally symmetrical over the two hemispheres [25–27]. For sufficiently long S1–S2 interval trial (>1,5 s) [14], two other main CNV areas may be identified, and their amplitudes can be acquired as negative shift compared to baseline: the early CNV between 500 and 700 ms after

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Fig. 1 (a) Grand averaged CNV waveforms, with early CNV, late CNV, and total areas highlighted, superimposed at three consecutive time points (T0, black lines; T1, red lines after 30 min; and T2, blue lines after 30 min from T1). W1: early CNV; W2: late CNV; S1: warning stimulus (flash); S2: imperative stimulus (tone; standard: 1000 Hz, target: 2000 Hz). The scalp potential maps at 600 ms (mean value of W1-CNV) for T0, T1, and T2 are also displayed. (b) ERPs trace in mid-line scalp locations for target and standard stimulus at T0, T1, and T2. The analysis epoch is 1.3 s with a 100 ms pre-stimulus baseline before stimulus. (Modified from Pauletti et al. [50])

S1, and the late CNV 200 ms preceding S2 [14, 28]. While the early CNV seems to be more represented at frontal sites, the late CNV has been found maximal at frontal-central scalp locations (see Fig. 1). Additional post-S2 components may be evaluated. In particular, the P3-like waves, as the largest positive deflection following the P200 wave, occur at least 250 ms after the S2 stimuli. Baseline to peak measures for post-S2 components may be computed (amplitude and latency) compared to the baseline commencing 100 ms before the S2-stimulus onset to avoid “CNV effects.” Another post-S2 activity is the post-imperative negative variation (PINV).

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The PINV is a prolonged negativity after S2 and, generally, is defined as the average amplitude of the event-related response between 2000 and 3000 ms after S2. Generally, this wave is not present in healthy subjects because the CNV wave returns to the baseline after the S2 stimulus. Sometimes PINV can also be elicited in normal subjects, such as after an unexpected temporary change in the S1-S2 paradigm [29]. Moreover, the PINV has been found in several psychiatric patients or after chronic dopaminergic stimulation [29–32]. Regarding the performance measures, reaction times (RTs) and the correct responses are acquired for a CNV task with a motor response. Responses are considered correct when RTs range between 180 and 1000 ms. For a CNV task without motor response, only the correct number of counted stimuli (or the number of errors) is acquired as the performance measure.

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Notes 1. A promising approach to characterize anticipatory attention emerged in the past few years by combining CNV elicitation and non-invasive brain stimulation (NIBS) techniques. NIBS methods, which include transcranial magnetic stimulation (TMS) and transcranial electric stimulation (tES), are used in cognitive neuroscience to induce transient changes in brain activity and thereby modulate the subject’s behavior. NIBS’ application aims to establish the role of a given cortical area in an ongoing specific motor, perceptual, or cognitive process [33, 34] that can also be evaluated with simultaneous EEG and ERPs technics. However, combining brain stimulation with online electrophysiological recording is related to a technical deficiency. Specifically, the TMS coil generates powerful, rapidly changing magnetic fields that induce impulsive, high-amplitude, longlasting artifacts on the EEG trace. Moreover, task-unrelated contaminations could emerge, consisting of auditory responses (due to the coil click occurring concurrently with the discharge of the magnetic stimulator) [35], of somatosensory responses (primarily due to trigeminal afferents or afferent responses after motor cortex stimulation) [36], of muscular responses (because of eye blink startle reflexes, eye movements induced by the coil click, or peripheral muscular contractions due to peripheral stimulation), or eventually movement of the electrodes due to coil vibration [37– 39]. Moreover, general arousal due to TMS or auditory facilitation by the coil click [40] might be present. All these effects should be eliminated whenever possible, and several strategies could be used [41–43]. The artifacts may be removed online

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during the EEG–TMS coregistration using complex electrical circuits, while a filtering process may be applied offline after the complete acquisition of the EEG–TMS trace. Since often these approaches do not guarantee satisfactory results, these artifacts should, as part of the experimental design, be reproduced in different conditions (i.e., via control stimulation at appropriate sites), and their effects should be taken into account/parceled out during data analysis [44]. 2. CNV amplitude depends on different factors related to the physiological or pathological state of the subject. Age is a significant physiological variable that influences the development of CNV waves. Many studies demonstrated that children tend to have smaller, less negative CNV components than adults [45, 46]. Additional data indicate that the CNV amplitudes gradually become more negative throughout development into young adulthood [47]. Moreover, a progressive amplitude reduction for CNV waves emerges in older subjects [48]. These data indicate that the developmental trajectory of the CNV and its components strictly reflects, on the one side, the maturation of the frontal lobe throughout childhood and adolescence and, on the other side, the early brain involutional processes related to a minimal and subclinical decrement of orienting, attentiveness, and response preparation capabilities. The subject’s mental state is also a crucial factor for the correct development of the CNV wave. The CNV amplitude is enhanced when subjects are instructed to concentrate and respond very quickly to S2 or, generally, receive instructions to heighten attention to S2. On the contrary, endogenous distractions such as mental fatigue may reduce CNV amplitude. Exogenous distractions such as conversations, irrelevant buzz between S1 and S2, music, and other rumors could also interfere with the physiological CNV development. Emotional factors, such as affective contents at S2 or the presence of a monetary incentive, may influence CNV amplitude and significantly speed up RTs [6, 49]. Arousal seems to have an invertedU shape relationship with CNV amplitude [3]. Finally, alcohol and caffeine intake, sleep deprivation, and acute and chronic drug administration should be investigated, given that these factors influence CNV amplitude. References 1. Brunia CH (1999) Neural aspects of anticipatory behavior. Acta Psychol 101(2–3): 213–242 2. Balkenius C, Fo¨rster A, Johansson B, Thorsteinsdottir V (2008) Anticipation in attention. In: Pezzulo G, Butz MV, Castelfranchi C, Falcone R (eds) The challenge

of anticipation, Lecture notes in computer science, vol 5225. Springer, Berlin/Heidelberg. ht t p s : //do i . o r g / 1 0. 1 0 07 / 97 8 - 3- 54 087702-8_4 3. van Boxtel GJM, Bo¨cker KBE (2004) Cortical measures of anticipation. J Psychophysiol 18(2–3):61–76

Contingent Negative Variation (CNV) 4. Creem-Regehr SH, Kunz BR (2010) Perception and action. Wiley Interdiscip Rev Cogn Sci 1(6):800–810 5. Walter WG, Cooper R, Aldridge VJ et al (1964) Contingent negative variation: an electric sign of sensori- motor association and expectancy in the human brain. Nature 203: 380–384 6. Tecce JJ (1972) Contingent negative variation (CNV) and psychological processes in man. Psychol Bull 77(2):73–108 7. Mento G (2013) The passive CNV: carving out the contribution of task-related processes to expectancy. Front Hum Neurosci 7:827 8. Corbetta M, Shulman GL (2002) Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 3(3):201–215 9. Gomez CM, Flores A (2011) A neurophysiological evaluation of a cognitive cycle in humans. Neurosci Biobehav Rev 35(3): 452–461 10. Rosahl SK, Knight RT (1995) Role of prefrontal cortex in generation of the contingent negative variation. Cereb Cortex 5(2):123–134 11. Mannarelli D, Pauletti C, Grippo A et al (2015) The role of the right dorsolateral prefrontal cortex in phasic alertness: evidence from a contingent negative variation and repetitive transcranial magnetic stimulation study. Neural Plast 2015:410785 12. Basile LF, Ballester G, de Castro CC et al (2002) Multifocal slow potential generation revealed by high-resolution EEG and current density reconstruction. Int J Psychophysiol 45(3):227–240 13. Giard MH, Perrin F, Pernier J et al (1990) Brain generators implicated in the processing of auditory stimulus deviance: a to- pographic event-related potential study. Psychophysiology 27:627–640 14. Gaillard AW (1976) Effects of warning-signal modality on the contingent negative variation (CNV). Biol Psychol 4:139–154 15. Loveless NE, Sanford AJ (1974) Slow potential correlates of preparatory set. Biol Psychol 1(4): 303–314 16. Masaki H, Yamazaki K, Hackley SA (2010) Stimulus-preceding negativity is modulated by action-outcome contingency. Neuroreport 21(4):277–281 17. Rohrbaugh JW, Gaillard AWK (1983) Sensory and motor aspects of the contingent negative variation. In: Gaillard AWK, Ritter W (eds) Tutorials in ERP research: endogenous components. North-Holland Publishing Co, Amsterdam

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18. Kornhuber HH, Deecke L (1965) Hirnpotential€anderungen bei Willku¨rbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale. Pflugers Arch Gesamte Physiol Menschen Tiere 284(1):1–17 19. Brunia CHM (1988) Movement and stimulus preceding negativity. Biol Psychol 26:165–178 20. Brunia CH, van Boxtel GJ (2001) Wait and see. Int J Psychophysiol 43(1):59–75 21. Damen EJ, Brunia CH (1987) Changes in heart rate and slow brain potentials related to motor preparation and stimulus anticipation in a time estimation task. Psychophysiology 24(6):700–713 22. Bares M, Nestrasil I, Rektor I (2007) The effect of response type (motor output versus mental counting) on the intracerebral distribution of the slow cortical potentials in an externally cued (CNV) paradigm. Brain Res Bull 71(4):428–435 23. Walter WG (1968) The contingent negative variation: an electro-cortical sign of sensorimotor reflex association in man. Prog Brain Res 22:364–377 24. Cohen J (1969) Very slow brain potentials relating to expectancy: the CNV. In: Donchin E, Lindsley DB (eds) Average evoked potentials; methods, results, and evaluations. US Government Printing Office, Washington, DC 25. Cant BR, Pearson JE, Bickford RG (1966) The mechanism of the expectancy wave in man. Electroencephalogr Clin Neurophysiol 21:622 26. Cohen J, Offner F, Blatt S (1965) Psychological factors in the production and distribution of the contingent negative variation (CNV). In: Proceedings of the 6th international congress of electroencephalography and clinical neurophysiology, Vienna, pp 251–254 27. Low MD, Borda RP, Frost JD et al (1966) Surface-negative, slow potential shift associated with conditioning in man. Neurology 16:771–782 28. Travis F, Tecce JJ (1998) Effects of distracting stimuli on CNV amplitude and reaction time. Int J Psychophysiol 31:45–50 29. Rockstroh B, Elbert T, Birbaumer N, Lutzenberger W (1982) Slow brain potentials and behavior. Urban and Schwarzenberg, Baltimore 30. Tecce JJ, Cattanach L (1982) Contingent negative variation. In: Niedermeyer E, Lopes da Silva F (eds) Electroencephalography, basic principles, clinical applications and related fields. Urban and Schwarzenberg, Baltimore, pp 543–562

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31. Timsit-Berthier M (1986) Contingent negative variation (CNV) in psychiatry. In: McCallum WC, Zappoli R, Denoth F (eds) Cerebral psychophysiology, studies in event-related potentials. Electroencephalogr Clin Neurophysiol, vol suppl 38. Elsevier, Amsterdam, pp 429–434 32. Amabile G, Fattapposta F, Pozzessere G et al (1986) Disease: electrophysiological (CNV) analysis related to pharmacological treatment. Electroencephalogr Clin Neurophysiol 64(6): 521–524 33. Hallett M (2000) Transcranial magnetic stimulation and the human brain. Nature 406:147– 150 34. Walsh V, Cowey A (2000) Transcranial magnetic stimulation and cognitive neuroscience. Nat Rev Neurosci 1:73–79 35. Nikouline V, Ruohonen J, Ilmoniemi RJ (1999) The role of the coil click in TMS assessed with simultaneous EEG. Clin Neurophysiol 110:1325–1328 36. Nikulin VV, Kicic D, Kahkonen S et al (2003) Modulation of electroencephalographic responses to transcranial magnetic stimulation: evidence for changes in cortical excitability related to movement. Eur J Neurosci 18: 1206–1212 37. Virtanen J, Ruohonen J, Naatanen R et al (1999) Instrumentation for the measurement of electric brain responses to transcranial magnetic stimulation. Med Biol Eng Comput 37: 322–326 38. Kahkonen S, Kesaniemi M, Nikouline VV et al (2001) Ethanol modulates cortical activity: direct evidence with combined TMS and EEG. NeuroImage 14:322–328 39. Kahkonen S, Wilenius J, Nikulin VV et al (2003) Alcohol reduces prefrontal cortical excitability in humans: a combined TMS and EEG study. Neuropsychopharmacology 28: 747–754 40. Marzi CA, Miniussi C, Maravita A et al (1998) Transcranial magnetic stimulation selectively impairs interhemispheric transfer of visuomotor information in humans. Exp Brain Res 118:435–438

41. Ilmoniemi RJ, Virtanen J, Ruohonen J et al (1997) Neuronal responses to magnetic stimulation reveal cortical reactivity and connectivity. Neuroreport 8:3537–3540 42. Thut G, Ives JR, Kampmann F et al (2005) A new device and protocol for combining TMS and online recordings of EEG and evoked potentials. J Neurosci Methods 141:207–217 43. Fuggetta G, Pavone EF, Walsh V et al (2006) Cortico–cortical interactions in spatial attention: a combined ERP/TMS study. J Neurophysiol 95:3277–3280 44. Bender S, Basseler K, Sebastian I et al (2005) Electroencephalographic response to transcranial magnetic stimulation in children: evidence for giant inhibitory potentials. Ann Neurol 58: 58–67 45. Jonkman LM (2006) The development of preparation, conflict monitoring, and inhibition from early childhood to young adulthood: a Go/Nogo ERP study. Brain Res 1097:181–193 46. H€ammerer D, Li SC, Mu¨ller V et al (2010) An electrophysiological study of response conflict processing across the lifespan: assessing the roles of conflict monitoring, cue utilization, response anticipation, and response suppression. Neuropsychologia 48:3305–3316 47. Segalowitz SJ, Davies PL (2004) Charting the maturation of the frontal lobe: an electrophysiological strategy. Brain Cogn 55:116–133 48. Zappoli R (2003) Permanent or transitory effects on neurocognitive components of the CNV complex induced by brain dysfunctions, lesions, and ablations in humans. Int J Psychophysiol 48(2):189–220 49. Hackley SA, Valle-Incla´n F, Masaki H et al (2013) Stimulus-preceding negativity (SPN) and attention to rewards. In: Cognitive electrophysiology of attention: signals of the mind. Elsevier Inc, pp 216–225 50. Pauletti C, Mannarelli D, Grippo A et al (2014) Phasic alertness in a cued double choice reaction time task: a contingent negative variation (CNV) study. Neurosci Lett 581:7–13

Chapter 3 The Study of Anticipatory Brain Activity in Cognitive Tasks by Means of Event-Related Potential, Frequency, and Time-Frequency Methods Valentina Bianco, Esteban Sarrias-Arrabal, Manuel Va´zquez-Marrufo, and Francesco Di Russo Abstract This chapter presents a series of studies on anticipatory brain activity in cognitive tasks acquired using electroencephalographic methods including event-related potential, frequency, and time-frequency analyses. The main aim is to provide an overview of the range of experimental methods that can be used to study the proactive capabilities of the brain in monitoring and regulating upcoming events and actions. The chapter is divided in sections taking into consideration basic cognitive functions as perception, attention, motor control, and inhibition. In each section, the most used experimental paradigms are described providing methodological information for their realization. Key words Anticipatory brain activity, Event-related potentials, Bereitschaftspotential, Motor-related cortical potentials

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Introduction Requin et al. [1] finely stated, “at any moment a large part of the present activity of an organism is devoted to preparing for subsequent behaviour.” This is because the brain is often challenged by changing environment, making it difficult or impossible to always react efficiently to external events. However, most of the events occur with a certain amount of predictability as a result of previous experience and with specific statistical regularities; this allows to formulate expectations about the most probable outcome of a given event and to update the predictions according to error signals [2]. Therefore, the brain represents an advanced anticipatory machine which aims to reach the best efficiency in terms of sensory, motor, and cognitive preparation to upcoming events.

Massimiliano Valeriani and Marina de Tommaso (eds.), Psychophysiology Methods, Neuromethods, vol. 206, https://doi.org/10.1007/978-1-0716-3545-2_3, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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Accordingly, a recent challenge in the neuroscience field is the understanding of how the preparatory brain activities can be linked to the performance of the following sensory, motor, and cognitive behavior. In this context, event-related potentials (ERPs), frequency and time-frequency analysis as temporal-spectral evolution (TSE) methods represent suitable tools to unveil the temporal dynamics of brain activities underlying the processes occurring during the anticipation of endogenous (i.e., internal) and exogenous (i.e., triggered by cues from the environment) events. In laboratory settings, ERPs frequency and TSE are recorded by means of electroencephalography (EEG) during the performance of specific tasks which mimic the perceptual, motor, and cognitive requirements of the sensory world. Accordingly, waiting for an upcoming stimulus or preparing a voluntary movement is accompanied by distinctive slow waves in the EEG as the result of a summation of several postsynaptic potentials in the cell columns of the cortical brain areas which are involved in the processing of the imminent event. The majority of ERP and TSE studies focused on the post-stimulus stage of processing, and the standard baseline to appreciate this activity is usually set 100–200 ms before stimulus presentation [3]. However, the implementation of larger EEG segmentations extending the left edge of the usual epoch to more than 1 s, thus including the pre-stimulus stage of processing (and anticipating the baseline), does represent a simple and crucial advance in cognitive neuroscience allowing to appreciate all the shades related to anticipatory processes, largely neglected in the previous decades. In this chapter, we will provide an overview of the most important preparatory ERP and TSE components emerging during paradigms aimed to disentangle sensory, motor, and cognitive processes preceding the anticipation of internal and external events. We will first cover the basis of anticipatory perception and attention, browsing ERP and TSE studies related to the anticipation of sensory events and with a focus on spatial attention. Then, we will introduce the motor and cognitive anticipatory ERP and TSE activity emerging during the preparation of self-paced movements vs. simple and complex tasks requiring motor/cognitive responses to external events. Last, we will acknowledge the existence of specific waves emerging during the waiting for external feedback about a past performance. EEG recording, ERP and TSE analyses involve the main following steps: amplification, digitization and online filtering of the EEG signal, offline filtering, artifact rejection, segmentation, baseline correction and averaging. For a detailed explanation of the entire ERP pre-processing and analysis pipeline, which is out of the scope of this chapter, please refer to Luck [3].

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Event-Related Potential Methods

2.1 Anticipatory Perception

The most straightforward way to study preparatory processes concerns investigation of neural areas involved in timeframes preceding perception of expected stimuli. In real-world settings, we are constantly prompted to perceive visual, auditory, somatosensory, and multimodal stimuli occurring in the surrounding environment which don’t necessarily need a direct cognitive or motor response. In laboratory settings, it is possible to ecologically recreate this situation by administering visual, auditory, somatosensory, and multimodal stimuli and asking participants exclusively to wait for perceiving the stimulus. A neurophysiological model for anticipatory behavior is the thalamo-cortical gating model [4, 5], which predicts that anticipatory cortical activation would stem from an enhanced thalamocortical transfer in the relevant sensory modality and would serve the function of pre-setting necessary physiological processes in the involved sensory cortex, in order to achieve a faster and/or more efficient processing of upcoming sensory input. In a purely passive task, no motor or cognitive actions are needed but sensory preparation occurs as the brain is prompted to be prepared to perceive an upcoming sensory stimulus, with or without full awareness. Prior to the appearance of exclusively visual, auditory, or somatosensory stimuli (not requiring any motor response and not having any affective or cognitive value), three slow-rising ERPs have been recently acknowledged: the visual negativity (vN) [6], the auditory positivity (aP), [7] and the somatosensory negativity (sN) [7]. The vN starts around 700 ms before the presentation of visual stimuli, peaks on parietal-occipital electrodes just before stimulus presentation, has source in bilateral extrastriate areas, and has been considered the electrophysiological correlate of visual anticipation. The aP initiates around 700 ms before the presentation of auditory stimuli, peaks on frontal electrodes just after stimulus onset, has source in bilateral temporal areas, and has been considered the electrophysiological correlate of auditory anticipation. Since the onset of these components is around 700 ms, an inter-stimulus interval (ISI) of a 1000 ms minimum is required in order to be sure to not interfere with proper ERP analysis. Interestingly, a very recent study [8] using unimodal and bimodal stimuli (i.e., visual plus auditory) showed that the vN and the aP had enhanced amplitudes and earlier onset (around 850 ms) when stimuli were presented simultaneously. These adjustments occurring in preparation of perceiving multimodal stimuli, i.e., the majority of real-world events occurring in everyday life, suggest that the brain prepares in advance multiple sources of sensory information to boost perception. For these types of paradigms, at least 32 electrodes should be used in order to cover the relevant areas of interest and the sampling rate should be of at least 250 Hz.

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A crucial aspect concerns the choice of the high-pass filter both during the online registration of the EEG signal and offline ERPs analysis. Indeed, being slow-wave potentials, the value of the highpass filter should always be less than 0.1 Hz, otherwise there is the risk of eliminating these low-frequency components. Additionally, for what concerns segmentation, in order to include a proper baseline, it is recommended to start the segmentation at least 1300 ms before the stimulus presentation. The right edge of the segmentation should always be set around post-stimulus presentation. For what concerns the analysis of early somatosensory evoked potentials (SEPs), in light of the rapid somatosensory pathways and the fast nature of these potentials [9], the EEG should be digitized at least at 1000 Hz. In a passive task involving somatosensory stimuli, the sN emerges as a slow negativity initiating approximately at -500 ms over parietal electrodes. Interestingly, this activity has its source in the contralateral somatosensory area to the stimulated arm and has been considered the ERP correlate of somatosensory anticipation. A crucial aspect to consider in order to appreciate reliable pre-stimulus slow wave components in passive tasks is the choice of variable instead of fixed ISI. Indeed, the occurrence of regular stimuli would generate the so-called “preferred” EEG phases [10] or steady-state evoked potentials [11] with substantial effects on ERP components. Accordingly, the use of variable ISI is highly recommended. In Fig. 1, an example of passive paradigms and associated ERP traces and scalp maps is presented. 2.2

Expectancy

Taking a step forward from the simple waiting of a sensory stimulus, a slightly more advanced way to call upon anticipatory processes is related to the preparation of a motor response following presentation of a cue signaling an imminent imperative stimulus. In this field, the contribution of ERPs is well represented by the study of the Contingent Negative Variation (CNV) [12]. In the standard paradigm eliciting the CNV, a first warning stimulus (S1) predicts the appearance of a second imperative stimulus (S2), signaling that one has to perform a specific action. Its distinctive name is due to the fact that this component is contingent on the statistical relationship between the warning and imperative stimuli. Accordingly, the CNV occurs as a central negative deflection in the scalp EEG during the S1–S2 interval (also known as the foreperiod, FP), develops during most of the inter-stimulus interval, and can last from about 300 ms to several seconds with magnitudes of up to 50 μV. Generally, this negativity ends sharply with the onset of the imperative stimulus. Several theories have been proposed to account for the cognitive processes underlying the CNV. For instance, its amplitude might be directly related to subjective probability or expectancy of the imperative stimuli [12] or, alternatively, might be related with the intention to perform an act [13]. According to Tecce [14], this component is related to both attention and

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Fig. 1 Typical passive tasks in the visual (a), auditory (b), and somatosensory (c) modalities. The right panel shows ERP traces and scalp maps of the pre-stimulus anticipatory components evoked in this stimulation modalities (aP anterior Positivity, sN somatosensory Negativity, vN visual Negativity, VEP visual evoked potential, AEP auditory evoked potential, SEP somatosensory evoked potential). (Taken with permission from Bianco et al. [7])

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Fig. 2 Early (O wave) and late (E wave) of the contingent negative variation. (Taken from Guo et al. [22])

arousal level, while Rebert and co-workers [15] believe that the CNV varies with the motivation of the individual to complete the task. Around 10 years later from its first discovery, the CNV has been further divided into an early orienting wave (O-wave, early wave) and a late expectancy wave (E-wave, late wave), in which the O-wave is associated with arousal and the orient-response, whereas the E-wave is related to motor preparation [16] (Fig. 2). In addition, they showed that occurrence of these two separate sub-components emerges as a function of ISI: the CNV measured with short ISIs (e.g., 1 s) is a combination of the O- and the E-waves, while it is necessary to use longer ISIs (e.g., 3 s) to appreciate the orienting and the expectancy waves separately. These two sub-components have also been associated with different scalp topographies in that the early wave is maximal over the frontal cortex while the later wave is maximal over the motor cortex [17]. Another issue in debate is the relationship between the CNV, especially the late CNV, and the Bereitschaftspotential (BP), a slow negative wave preceding voluntary movement [18] which will be discussed in the dedicated section of this chapter. Rohrbaugh and Gaillard [19] acknowledged the occurrence of the early CNV alone when motor responses are not required and proposed that the late CNV is equivalent to the BP in the case of preparation of a motor response. On the other hand, an alternative line of research showed that late CNV could be obtained also without motor responses [20], suggesting that the CNV is related

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to the anticipation of the imperative stimuli and that late CNV is not equivalent to the BP. Accordingly, Ikeda et al. [21] reported that BPs were abolished in some pathological states in humans in spite of persistent CNVs. However, a critical observation for what concerns CNV paradigms compared to the perceptual tasks treated in the previous section and to the externally triggered motor tasks which will be acknowledged in action anticipation section is the choice of a fixed ISI between S1 and S2 among trials, instead of variable ones. ISIs longer than 3 s are recommended to fully appreciate both the early and late components with the left edge of the segmentation covering at least -3.2 s before S2 in order to include a proper baseline selection. The right edge of the segmentation might coincide with S2 presentation till 200 ms later according to the specific experimental design. The sampling rate should be of at least 250 Hz. 2.3 Anticipatory Attention

The shift of attention to the expected location of an upcoming visual stimulus will improve its perception if it occurs there [23]. For what concerns ERP research, the neural correlates of attentional control are often investigated by examining the modulation of neural activity elicited by a symbolic cue (e.g., an arrow) which points at the location to attend in preparation for an upcoming target [24]. In a classic attention-cueing task suitable for recording ERPs, the cue is presented at fixation indicating the likely location of an impending target. After the presentation of the cue an early positive peak occurs at about 150 ms, predominant over occipital leads (cue-P1) followed by a late cue-P3 component which peaks at about 360 ms and with a parieto-occipital predominance. Besides, the perceptual improvement resulting from knowing in advance the spatial location of a stimulus is considered to be a consequence of attentional-control operations that are performed by frontal and parietal regions of the human brain [25]. In visuospatial attention tasks, three scalp ERP components have been commonly reported during the cue-target delay [26]: the early directing attention negativity (EDAN), occurring between 200 and 300 ms; the anterior directing attention negativity (ADAN), occurring between 300 and 400 ms; and the late attention directing positivity (LDAP) occurring around 500–800 ms. The EDAN peaks on parieto-occipital sites and might represent the directional cue meaning extraction and the attention shift. The ADAN peaks on frontal sites and might represent the scalp effect of the coordination occurring between frontal and parietal control regions in integrating control processes needed to shift attention with respect to both the immediate spatial instruction (i.e., the direction of the cue) and the overall task instruction (i.e., handling the target at the cued location). The LDAP peaks on parietal sites and should reflect a process of supra-modal control over spatial representations [27–29]. It must be noted that, to date, an ERP

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Fig. 3 Temporal dissociation between cue-related EDAN, ADAN, and LDAP and task-related vN. (Adapted from Di Russo et al. [31])

correlate of “proactive attention” occurring as slow-wave potential has not yet been acknowledged. Most research works on attention have focused on the effects of proactive attention on reactive ERP components [30], but not on the proactive attention per se. Recently, however, Di Russo and co-workers [31] designed a visuospatial task comparing the preparatory stages of processing during sustained vs. transient forms of attention (Fig. 3). The authors found that the sensorial ERP component associated with visual anticipation (i.e., the vN) was lateralized according to the attended side and was enhanced in the sustained task, as a result of the top-down nature of this endogenous task. Crucially, the vN was independent from the cue-related EDAN, ADAN, and LDAP activities. This, together with the finding that the sustained task was characterized by an increased amplitude of preparatory prefrontal activity, provides evidence of the task-related plasticity of top-down anticipatory attentional processes of the brain. As largely described in the section, this preparatory prefrontal slow wave corresponds to the prefrontal negativity (pN) [32], which is considered an ERP correlate of proactive top-down control. In general, the optimal design for investigating sustained and transient attention by means of anticipatory ERPs should involve ISIs (respectively fixed and variable) variable of at least 3/4 s and a wide left edge segmentation of at least 3 s. 2.4 Action Anticipation

One of the most important fields for anticipatory processes concerns the anticipation of voluntary movement, also in light of all the related clinical implications for movement disorders and motor rehabilitation [33]. Indeed, a proper anticipation has two main components: a perceptual component that ameliorates the

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processes at the input side of the organism and a motor component that enhances the processes at the output side of the perceptionaction cycle and is crucial for the subsequent action execution. One crucial aspect related to the study of motor preparation by means of the ERP technique concerns the choice of pre-stimulus vs. pre-movement segmentation. In the former, stimulus presentation is considered time 0 and the focus of interest is pre-stimulus time interval, while in the latter, movement onset is considered time 0 and analyses usually concern ERP activity occurring in pre-movement timeframe. The advantage of the pre-stimulus compared to the pre-movement segmentation is related to the possibility to investigate also the post-stimulus activity and to include in the average the anticipatory activities associated with all stimuli, regardless of being are followed or not by motor responses. The most important ERP related to motor preparation is the well-known Bereitschaftspotential (BP), a slow negative wave preceding voluntary movement that was first reported in the 1960s [18]. Overall, BP starts about 1–2 s before the movement onset, it is maximal at the midline centro-parietal area (vertex), and is widely distributed over the scalp regardless of the site of movement. The BP has been largely interpreted as an index of motor readiness [34]. The onset of the BP with respect to the movement onset significantly differs among different conditions of movement and among subjects. The BP, also referred to as readiness potential, reflects the progressive cortical excitability of the supplementary and cingulate motor areas in self-paced [35] and externally triggered [36] movements. Interestingly, the amplitude of this component is not particularly affected by the choice of pre-stimulus vs. pre-movement segmentation in visuo-motor tasks [37]. In the next section, we will provide a brief overview of the main anticipatory ERP components occurring in self-paced and externally triggered motor tasks. 2.5 Self-Paced Motor Tasks

A self-paced movement can be defined as motor activity following an individual volition to act in the absence of any external events. The concept of “pre-movement” is captivating in that this points at a time when no muscle movement is evident, but the cortex is adapted for the implementation of the action. The motor preparation subtending self-paced movements is usually studied by means of the so-called motor-related cortical potentials (MRCPs) [38]. As shown in Fig. 4, MRCPs usually include a slowly rising negativity at around 1–2 s, the BP (or early BP), followed by a steeper negativity starting about 400–500 milliseconds before movement onset, named the negative slope (NS’ or late BP) [39] and ending with the motor potential (MP), which occurs concomitantly with movement onset. After the MP, the activity rapidly shifts with a positive peak at around 150–200 milliseconds post-movement onset,

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Fig. 4 Typical motor-related cortical potential (MRCP) waveform preceding voluntary finger movements (BP Bereinshaftpotential, NS’ Negative Slope, MP Motor potential, RAP Reafferent Potential

named re-afferent potential (RAP). Overall, the BP emerges over frontal-central electrodes, has source in supplementary and cingulate motor areas, and is thought to reflect preconscious readiness for the upcoming action. The NS’ is more related to premotor areas activity and reflects a more conscious decision of action initiation. Approaching movement onset, the MP reflects activity of the primary motor area (M1), and the RAP has been related with afferent somatosensory inputs following action execution. MRCPs have been largely studied for finger flexion and extension [40]. Accordingly, a very interesting study compared MRCP emerging for different types of movements (finger, foot, tongue, and vocalization) recorded from subdural electrodes [41]. Authors not only showed a somatotopic distribution in the SMA according to the movement, but also pointed at the crucial role played by SMA in the organization of voluntary movements. MRCP are easy to obtain since it is sufficient to the participant to repeat a movement every 2–9 s and to record the movement onset using a mechanical transducer (e.g., a push button), or using a bipolar electrode pair along the interested muscle in order to record electromyographic activity. One more pre-movement ERP which has been largely investigated in the last decades is the so-called lateralized RP (LRP) [42], which emerges in anticipation of a movement performed with the left or right hand; while the BP is symmetric and represents the early part of the motor activity preceding movements, the LRP represents a later phase and is lateralized to the hemisphere

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Fig. 5 MRCP for right finger flexions. Lateralized readiness potential (LRP) is defined as a difference in ERP activity between signal recorded at electrode positions over the primary motor cortex contra- and ipsilateral to the side of movement (LRP difference between C3 and C4)

contralateral to the side of movement (Fig. 5). The LRP is usually calculated using Coles’ eq. [43]: LRP = left hand (C4 - C3) right hand (C4 - C3). The LRP has been largely used to measure covert preparatory processes [44] and preliminary activation of a response that is never actually produced [45]. The LRP, together with the BP, has long been in the center of the discussion around the neural correlates of the free will; indeed, with its famous Clock experiment, Libet et al. [13] proposed that “the initiation of the free voluntary act appears to begin in the brain unconsciously, well before the person consciously knows he wants to act!”. However, Libet’ s concept of conscious intention has been largely challenged [46]. More recent views indeed claim that the existence of preparatory brain activities reflects the intention to act (but are not causing it) and can provide a measure of the quality of the upcoming motor movement instead of reflecting the unconscious will to act. Under this vein, Bianco and co-authors [47] designed a novel paradigm in which participants had to freely decide in advance to respond or not to an upcoming stimulus and found that, by just observing the occurrence of motor and cognitive anticipatory ERPs, it was possible to unveil participant’s choice. In addition to self-initiated praxic actions, MRCPs have been largely employed in studies using action observation [48], incompatible or impossible movements [49, 50], and movements aimed at self-administering emotional pictures [51].

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2.6 Motor Response Tasks

Often, we are required to react to an external event in the environment. Sometimes, this implicates exclusively to prepare a fast response without the necessary need of cognitive processes. This situation can be replicated in research settings with a paradigm known as simple reaction task (SRT) in which the participant is required to respond to any presented stimulus as fast as possible. Thus, the SRT measures the basic processes of perception and response execution. In visual SRT, a strong motor preparation at SMA and CMA level reflecting the well-known BP component has been acknowledged [32]. More recently, Di Russo and co-authors [6] showed that the vN emerged also during this task, reflecting visual anticipation and presumably ensuring, together with the BP, the best perceptual and motor response to the upcoming stimulus (Fig. 6). In order to build a proper SRT for studying anticipatory ERPs, it is recommended to use a sample frequency of at least 250 Hz, a variable ISI of minimum 1 s, and a left edge segmentation of at least 1.1 s to ensure proper baseline-referenced epochs. However, in real-world settings, we are usually exposed to complex events, which require us to prepare not only a quick but also a cognitively efficient response. A typical experimental paradigm that challenges cognitive and motor processes is represented by the discrimination response task (DRT), which prompts the individual to choose between possible alternatives. If the alternatives are represented by different response options (i.e., to use the index or the middle finger to react to different types of stimuli), the paradigm takes the form of a choice reaction task (CRT); otherwise, if the alternatives refer to the options of responding (go) vs. refraining from response (no-go), the paradigm takes the form of a go/ no-go task. This type of DRT not only allows the study of the BP, which is identical in go and no-go trials because the participant can decide whether to move or not only after stimulus presentation, but also provides the possibility to explore the cognitive processes related to action/inhibition anticipation. A recently discovered ERP reflecting cognitive anticipation is the prefrontal negativity (pN) [32], a slow-rising negative potential emerging in its early phase over lateral prefrontal sites with bilateral radial topography and in its later phase on medial prefrontal sites with medial radial distribution. The pN starts around 800 ms before stimulus onset and peaks concomitantly to the stimulus presentation. The presence of a negative component emerging during discriminative response tasks has been pioneered by Berchicci and co-authors [52] comparing a sample of older vs. younger participants: they showed that this activity occurred earlier and was enhanced in the former, suggesting that the elderly prepare to react with faster anticipation and higher resources recruitment. The pN source has been localized in the pars opercularis of the inferior frontal gyrus (iFg) [32] and has been associated with top-down control (bilateral distribution) and proactive inhibition (right-side enhancement) of

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an upcoming response in an externally triggered tasks [37, 47]. The existence of the pN has also been acknowledged in auditory and somatosensory go/no-go tasks [53], in the Stroop Test [54], and in the choice reaction task [55]. In general, the recommendations for a proper task design includes sampling rate of at least 250 Hz, low cut-off filter not more than 0.01 Hz, the use of variable ISI and a left-edge segmentation of at least 1 s. 2.7 Anticipation of External Feedback (SPN)

The last class of anticipatory ERPs treated in this methodological chapter concerns anticipatory processes related to the waiting for an external source of information on the correctness of a previous response/decision. The stimulus-preceding negativity (SPN) [56] is a negative slow potential occurring between an action and the knowledge of the quality of the associated performance (Fig. 7). This is commonly provided by an external feedback signal which encourages behavioral adjustments [57] or includes affective/ motivational aspects [58]. The typical paradigm eliciting the SPN is the time estimation task, in which participants are subsequently informed about the correctness of their time estimation by a stimulus providing knowledge of results (KR) [59]. For instance, participants are asked to press a button a predetermined number of seconds (e.g., 4) after the presentation of a visual/auditory warning signal. Afterward, they are informed with specific cues of KR (e.g., a minus sign, a horizontal bar, or a plus sign) signaling if the response was anticipated, correctly timed, or delayed. To elicit the slow SPN, a minimum of 2 s between the response and the KR stimulus should be considered. For what concerns the SPN scalp topography,

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affective studies showed that its amplitude increases over frontal regions, reflecting motivational anticipation of outcomes that is induced by monetary reward or punishment [60]. Further, the SPN shows a right hemisphere preponderance [61] as a result of activities of the right anterior insula cortex [62], which underlies the affective-motivational aspects of the SPN. In general, there is broad consensus in considering the scalp-recorded SPN as the result of the activity of an extended network including the anterior cingulate cortex (ACC), the parietal cortex, and the insular cortex [63].

3

Frequency and Time-Frequency Methods

3.1 Methodological Bases

In the analysis of frequency bands, there are two types of technical procedures that differ in the type of information they provide. One is the frequency analysis that allows extracting from the EEG signal information about the spectral power contained in the brain activity of each band. On the other hand, time-frequency analysis allows to observe the temporal dynamics of the different frequency bands, with millisecond resolution, during information processing [64, 65]. Probably, the most widely used and well-known technique of frequency analysis is the fast Fourier transform (FFT) [66– 69]. However, FFT does not provide information on the behavior over time of the frequency bands [70]. Another problem with the FFT is that it starts from the assumption that EEG is a stationary signal [71]. With respect to time-frequency analysis it must be noted that, in addition to being able to observe the temporal dynamics of brain electrical activity, it allows the study of non-phase (induced) activity. However, this information is not obtained with all the techniques that fall under time-frequency analysis, as we will see below. Before describing the main timefrequency analysis techniques, it should be pointed out that for any filtering or procedure performed on the EEG signal, some information is lost. The aim of this chapter is not to go into the analysis techniques more in details. For this reason, we provide an overview of the most fundamental characteristics of each of them. The main techniques used in time-frequency analysis are wavelets [68, 71], event-related desynchronization (ERD) [72, 73], Hilbert transform [74, 75], and temporal-spectral evolution (TSE) [70, 76–78]. Given that compared to other techniques TSE is barely known, we will proceed to describe it briefly. TSE is a time-frequency analysis method first introduced by Salmelin and Hari [76] and since its design in 1994 only a few studies have used this technique. The TSE analysis pipeline consists of the following procedure: filtering EEG activity in the desired frequency range, rectifying the signal before averaging to avoid

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cancellation of non-phase activity and, finally, averaging the signal (Fig. 8) [70, 79–81]. According to Hari et al. [82], the advantage of TSE over frequency power analysis methods, such as FFT, is that it respects the original amplitude of the signal and improves the spectral signal analysis by introducing the time variable [70, 83]. Sometimes, an envelope of the frequency modulation is calculated using a low-pass filter to smooth the signal obtained after TSE [84]. Finally, to calculate the non-phase (induced) activity, it is necessary to extract the evoked activity from the original EEG signal using a different sequence of steps (segmenting and averaging the EEG signal and filtering at the desired frequency). Once both protocols (TSE and spectral evoked activity) have been applied, induced activity is obtained as the result of subtracting evoked activity from the TSE activity [81, 85–91]. 3.2 Methodological Issues

Several issues can arise in the frequency analyses of EEG signal. We will provide a brief summary in this section, but more details can be found in specialized literature [71]. One of the first issues to be considered is the type of artifacts that affect frequency analysis compared to ERP analysis. Given that specific filtering limits can be set (i.e., alpha study, 8–13 Hz), some artifacts would be cleared in the data. However, other conditions can arise based on the application of the filtering (see “filter ripple” in Fig. 9). Another setback in frequency analyses concerns the potential modulations of the natural oscillation of electrical/electronical devices (50 Hz in Europe and 60 Hz in USA). In the case of ERPs analyses, a notch filter is usually applied to prevent this interference, but this does not apply to frequency analysis for obvious reasons. Electromyography (EMG) activity (i.e., jaw or neck) or mechanical noise could cause broadband effects (Fig. 9). Fortunately, muscle artifacts are usually concentrated in high spectral bands and do not represent a serious interference for slow bands as theta or alpha. However, special attention is needed for fast frequencies such as beta or gamma. In the specific case of the FFT and the calculation of power spectral density, it is mandatory to apply a logarithmic transformation to power values in order to make them usable for statistical analyses (normal distribution). In time-frequency analysis, we must be especially cautious with gamma studies. Due to signal distortion from the skull and scalp, high-frequency activity (>100 Hz) has a low signal-to-noise ratio and may require many trials and special analysis techniques to enhance signal-to-noise ratio. Besides, when analyzing gammaband activity in the EEG signal, it is mandatory to control for the effects produced by saccades that can produce an induced gamma band response (iGBR) [92].

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Fig. 9 Some methodological issues in time-frequency analyses (EMG electromyogram, locs locations). (From Cohen [71])

An additional issue concerns the handling of edge artifacts. Indeed, the application of filters is compromised at the beginning and the end of the interval of interest. In FFT, it is possible to apply “windows procedures” that reduce this artifact. In time-frequency methods, it is possible to avoid the edge effects by increasing the length of epoching. The widening depends on the frequency of the analyzed band. The smaller the frequency, the longer the length of segmentation. This is because edge effects last for two or three cycles of band filter applied. For instance, in alpha band this would correspond to 200–300 ms, but in gamma band, this is just 50–75 ms [71]. Another major concern in the frequency analysis is the stationary condition for the EEG signal. Some of the frequency and timefrequency analyses assume that the signal under analysis is stationary, thus implying that some nodes (time points of the signal) are steady along the recording. That is not necessary in the case of the EEG signal. New approaches in time-frequency analyses have been developed to avoid this requirement, such as Hilbert-Huan transformation or wavelets.

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Temporal filters can produce high-amplitude broadband power artifacts during hundreds of milliseconds at first and the end of the epoch. Therefore, it is highly recommended to create longer epochs to edge artifacts emerged in time windows of little interested. How long must be the epochs? It depends on the frequencies you want to analyze. Edge artifacts last two or three cycles. Therefore, the lower the frequencies, the larger the epochs you have to create. Finally, be cautious with overlapping trials that create longer epochs to avoid edge artifacts. One option to create longer epochs without overlapping the signal EEG is to use a reflection approach (Fig. 10) [71]. Another possibility is applying filters on raw data, not in segmented data. In this way, edge artifacts will only arise at the beginning and at the end of the EEG signal. 3.3 Anticipatory Perception

To our knowledge, alpha activity has been the most studied spectral frequency. In fact, most studies that have attempted to describe alpha functionality have focused on its topographic distribution in occipital regions. Rommer et al. [93] presented sentences with high or low restrictions to healthy control individuals to study the spectral modulation prior to the final word, which was expected or not based on the linguistic context of the sentence. Applying FFT, they observed a decrease in alpha activity prior to the critical word in those sentences that allowed a better construction of the context, facilitating the prediction process. The authors suggest that this decrease in the alpha band would be necessary to recruit the cognitive resources needed to process the anticipated stimulus more efficiently. Previously, Hanslmayr et al. [94] had found a correlation between alpha level prior to onset stimulus and performance during

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a detection task. The authors observed that the lower the desynchronization or decrease in alpha prior onset of stimuli, the worse the perceptual performance in the task. On the other hand, BidetCaulet et al. [95] in a similar task using nonverbal stimuli also found a larger decrease in alpha activity when the context was more predictable. These authors associated the decrease in alpha to the process of attentional readiness and expectancy. 3.4 Anticipatory Attention and Expectancy

Consistent with the attentional readiness and expectancy, Fan et al. [96] developed the Attention Network Test (ANT) to get a measure of attentional networks described by Posner and Petersen [97]. Fan et al. [98, 99] used the ANT to analyze spectral modulations in the expectancy interval using wavelet analysis. These authors observed a decrease in alpha activity in conditions where the target stimulus location was cued. However, this decrease was also observed when the cue was central, which does not provide spatial but temporal information. This finding was interpreted as further evidence that the decrease in alpha activity is associated with the expectancy mechanism. Years later, Va´zquez-Marrufo et al. [78] applying TSE found the same pattern, i.e., a sustained decrease in parieto-occipital alpha activity 500 milliseconds (ms) before the onset of the target stimulus (Fig. 11). However, these authors argued that the decrease in the alpha band would represent the reduction of neural noise and that other spectral bands, such as theta and delta, would be associated with the process of expectation and attentional preparation. In addition, an interesting finding of this work was that this decrease in alpha activity occurred in non-phase activity. This suggests that the processes associated with the different pre-stimulus ERPs (phase activity) associated with preparation and expectation should be represented in other spectral bands, possibly delta and theta. Recently, employing an artificial intelligence technique such as machine learning (ML) a study has yielded data in favor of using alpha activity (both its spectral power and its topographic distribution) as an index of attentional expectancy and preparation process [100]. Riels et al. [100] found that the topographic distribution of alpha activity prior to the onset of the target stimulus predicted on which trials participants performed better [100]. This finding was interpreted as evidence of the fact that that an improved set up of expectancy and attentional readiness “predisposes” to a better performance, and this can be observed in the distribution (occipital regions) and spectral power of the alpha band (decrease or desynchronization). Attention has been widely studied since the discovery of EEG [101], especially the alpha band. The Posner’s visual cueing task has been useful to describe the spectral modulations occurring before the onset of stimuli. Verleger Heide et al. [102] observed a decrease of alpha activity at right parietal recording site in patients with

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Fig. 11 Evoked (phase) and induced (non-phase) alpha activity in the expectancy and target response intervals (MS multiple sclerosis, HC healthy controls, NC no cue, CC Central cue, SC spatial cue, ms milliseconds). (Extracted from Va´zquez-Marrufo et al. [78])

lesions of the right parietal cortex when the cue was displayed on left visual field. The authors suggested that alpha was related to processing of the stimuli on relevant areas for the task (contralateral parietal). Later, the study of Freunberger et al. [101] showed an increase of alpha activity in regions irrelevant for the task. The authors argued that the synchronization of alpha represented an active inhibition enhancing the possibility of detecting stimuli more effectively by relevant regions for the task. Recently, Ikkai et al. [103] observed a decrease in occipital region contralateral to the attended visual field, whereas the decrease was bilateral when no cue preceded the onset of the target stimulus. These results were interpreted as a spectral index of the neural mechanisms of attentional control [103–108]. Under this vein, using a task that does not present lateralized stimuli, but above or below the fixation cross, several studies further provide evidence of spectral modulation prior to the onset of the stimuli as a function of visuospatial attention [78, 98, 99]. Fan

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Fig. 12 Evoked (phase) and induced (non-phase) gamma activity in the expectancy and target response intervals. (MS multiple sclerosis patients, HC healthy controls, CC Central cue, SC spatial cue, TG target, EXP expectance). (From Va´zquez-Marrufo et al. [78])

et al. [98, 99] observed by spectral power analysis an increase in gamma activity around 200 ms after the onset of the spatial cue. This increase was not observed in the central cue (temporal information) and neutral cue (neither temporal nor spatial information) conditions. The authors argued that the increase in gamma activity could be related to cognitive processes occurring during the translation of attentional focus. Also applying ANT and the TSE, Va´zquez-Marrufo et al. [78] described the same increase of the gamma band in the condition with spatial cue. However, these authors provide more information in this regard, since the increase was found in the non-phase activity of the gamma band (Fig. 12). Therefore, in line with the results described in the section on expectancy and attentional readiness, the gamma activity appears to represent processes that are not observable in ERP analyses.

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With respect to the role of beta band in anticipatory attention and expectancy tasks, Fru¨nd et al. [109] applying wavelets observed an early frontal increase of beta activity in cued trials compared to uncued trials prior of the onset of stimulus. The authors suggested that the increase of beta band represents the synchronization of local neural population for a better performance when stimulus is displayed. It is possible that this increase of alpha represents the motor readiness. As far as we know, the contribution of slower frequencies (delta and theta) to the attentional system prior to the onset stimuli has been barely studied. However, it is widely known which ERPs pre-stimuli are observed (e.g., CNV, BP, pN, and vN) [6, 18, 34, 47, 52, 53, 110, 111, 112]. As suggested by Va´zquez-Marrufo et al. [78], relying on a study by Va´zquez-Marrufo et al. [112], it is possible that processes associated with pre-stimulus ERPs are represented in phase or evoked activity of slower spectral frequencies such as delta and theta. In fact, Barry et al. [113, 114] observed, using a method different from TSE, that prior to the presentation of the imperative stimulus, both delta and alpha bands well explain the CNV wave in the same interval. Another line of research is referred to the influence of the pre-stimulus spectral content and ERPs to post-stimulus processes. In a series of studies using CNV paradigms with go/no-go tasks on the imperative stimulus, De Blasio et al. [115–117] found that delta activity positively correlated with the amplitude of the P2, N2, and P3 (go trials) and P1, P2, and P3 (no-go trials). However, delta power negatively correlated with the latency of N1 (go trials). Besides, theta activity in the CNV period positively correlated with the amplitude of the P1, P2, and P3 (go trials and no-go trials) and positively correlated with the N2 and N1 for both go and no-go trials, respectively. In fact, according to their results, theta band modulates the amplitude of the P3. On the other hand, the level of alpha during the CNV period directly influences the amplitude of the P1, P2, and P3 (go and no-go trials). Further, pre-stimulus beta band activity directly modulates the amplitude of the P1, N1, and P2 in both go and no-go conditions. Finally, pre-stimulus beta activity also correlates negatively with N1 latency in go trials. It seems that frequency and time domains are certainly related but at the same time dissociable in their psychophysiological roles and for what concerns the use of techniques to observe them. Finally, we would like to emphasize that the study of alpha activity has been useful in providing evidence for the described attentional theories of hemispheric dominance [118]. The two main theories are the “interhemispheric competition” theory of Kinsbourne [119] and the hemispatial theory of Heilman and Van den Abell [120]. However, through a paradigm of spatial cues, the study of Gallotto et al. [118] has yielded evidence in favor of both theories. By applying a Morlet wavelet, the authors observed a

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lower alpha power in the left hemisphere in the neutral cue condition. This finding is in line with the hemispatial theory, which suggests that the right hemisphere is involved when attending to the left and right hemifields. Nevertheless, in the spatial cue conditions, the alpha desynchronization (decrease) of its power was higher in the hemisphere contralateral to the visual hemifield where the target stimulus was displayed [118]. This result supports the interhemispheric competition theory that proposes a competitive interaction between hemispheres, which leads to the prioritization of contralateral hemisphere over the ipsilateral hemisphere. 3.5 Action Anticipation

Action anticipation is not only in reference to self-movements but is a cognitive-perceptual process that may be involved also in observing and predicting movements of other people in order to adapt our behavior to the situation [121]. In most studies investigating motor activity, experiments have focused on frontal mu (which oscillates in the frequency of alpha (8–13 Hz)) and beta activities (15–25 Hz) [122]. Many investigations have tried to associate the modulations in the mu and beta bands with the functioning of the mirror neuron system [123]. Therefore, sport provides a good opportunity to study spectral activity prior to movement execution and associated processes, such as anticipating the consequence of the opponent’s movement in order to perform a movement. In this regard, Denis et al. [124] compared mu (8–13 Hz) and beta (15–25 Hz) activities prior to movement execution between expert and novice tennis players. The authors observed differences in the desynchronization of both bands (mu and beta) in frontal regions, being earlier and more intense in expert tennis players. The authors postulated that a larger desynchronization of mu and beta facilitates the process of anticipation of movements to be executed increasing the accuracy of the movements [124]. Recently, Simonet et al. [125] have observed stronger alpha desynchronization over occipital (mostly) and frontocentral areas in experts (cricket players) compared to novices when they had to anticipate the action from a video scene in different conditions (contextual information, kinematic information, and both) (Fig. 13). The authors suggested that this alpha ERD is associated with preparatory attentional mechanisms. However, this finding was not only observed in sports activities. Koelewijn et al. [126] used a lateralized cue paradigm to calculate the spectral power of the beta band (15–35 Hz) by means of an FFT. These authors found a larger decrease in beta activity on trials where subjects responded correctly compared to erroneous trials. Therefore, this work points toward the same direction as that described above [124]. Thus, it seems feasible to claim that decreasing beta activity facilitates the anticipatory process and enhances the preparation of a more functional response [124, 126].

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Fig. 13 The scalp topographies averaged for each condition (i.e., game situation, feld setting, and visual cues) across the expert and the novice groups. (From Simonet et al. [125])

However, beta desynchronization is also present in other cognitive tasks. Pinet et al. [127] using a language paradigm observed a bilateral decrease of beta (15–30 Hz) prior to the execution of typing after presenting a picture on the screen. In addition, there are studies that provide evidence for hemispheric dominance of language by analyzing spectral activity. Pinet Dubarry and Alario [128] carried out a manipulation of the paradigm used in a previous study. This version consisted of presenting pictures whose names begin with letters located on the left or right side of the keyboard. Following this paradigm manipulation, the authors observed that regardless of the location of the key on which the word begins, beta desynchronization was always larger in the left hemisphere than in the right hemisphere. The authors interpreted this finding as evidence for left hemisphere dominance in the preparation and planning of linguistic responses [128]. Therefore, beta-band desynchronization prior to movement execution seems to be related to a better ability to anticipate and plan a motor response, in both linguistic and non-linguistic tasks.

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Fig. 14 Mean topographic representations (4 maps on the left) of the 12-Hz ERD from 1050 to 550 ms before the movement onset and of the posterior dominant rhythm (PDR) power at 10 Hz (1 map on the right) across all subjects. The black dots indicate the EEG electrodes positions. The colour indicates power differences from the baseline in log scale. (From Fumuro et al. [129]) 3.6 Self-Paced Movement

For what concerns self-paced movements, Fumuro et al. [129] found larger ERD upper alpha (10–12 Hz) in self-paced goaldirected movement (reaching objects) compared to self-paced not-goal-directed movement (wrist extension) (Fig. 14). The ERD upper alpha was widely distributed on contralateral parietoposterior cortex (PPC). Besides, this ERD upper alpha was observed earlier (1.2 s prior of the onset stimulus) in self-paced goal directed movements than not-goal directed movements (0.7 s). The authors suggested that ERD upper alpha on PPC represents an index of the preparation of praxis movement. These results support Hallett and Bai [130] work that describes alpha desynchronization 1 s before self-paced movements. An EEG study using self-paced movements found that during movement preparation, alpha and beta band activities are both present in the supplementary motor area [80], presumably generating the BP. Moreover, Kim et al. [131], applying a self-paced hand grasping task, also suggested that beta contributes to the generation of the BP. In fact, Shibasaki and Hallet [34] reported increase of desynchronization in beta band on central regions before movement onset. However, it is important to acknowledge that the spectral features of pre-stimulus ERPs may influence poststimulus ERPs [71, 132].

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Lastly, alpha band over posterior regions has been also investigated. Stenner et al. [133] used a self-generated visual stimulation task, where participants can decide when and where the stimulus is displayed. Applying FFT, they observed enhanced alpha oscillations in visual cortex during anticipation. The authors suggested that the increase in alpha power has the function of reduced excitability to inputs (sensory attenuation) when a motor-induced anticipation occurs.

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Conclusions From this brief overview of the main anticipatory ERP components associated with perceptual, motor, and cognitive processes, it emerges that, in order to properly leverage the EEG technique for the investigation of anticipatory processes, it is crucial to define clearly the objectives of the study and then to choose the most suitable paradigm accordingly. First, the sensory modality of the used stimuli is going to largely affect the electrodes of interest for further analysis. Second, the use of abstract or emotional stimuli or related to specific categories (e.g., sport, food) will differently affect the modulation of specific ERP components; therefore, it is crucial to have a deep knowledge of previous literature using stimuli related to the experimental condition of interest. Third, the choice of variable vs. fixed ISI should depend on the specific type of anticipatory ERP to be investigated, in that the first will prevent the occurrence of unwanted steady state potentials, while the latter is essential for studying the foreperiod between a cue and an imperative stimulus. For what concerns EEG recordings, we recommend using EEG caps of at least 64 electrodes in order to increase spatial resolution and consider electrodes that have largely neglected in the last decades, as in the case of prefrontal leads. Further, a special attention has to be directed at the choice of proper online filtering; since anticipatory components are slow waves the high-pass filter should always be of at least 0.01 Hz to avoid cutting our relevant low frequencies contributing to the ERP waveform. Regarding ERP and TSE analysis, for each experimental condition, it is highly recommended to work on averages of at least 100 “good” segments. In other words, this means that at least 100 selected segments should survive the artifact rejection procedure when performing offline processing of ERP and TSE data. Accordingly, the general experimental design should opt for recording at least 200 trials for each condition of interest, independently from the specific task. Considering the low spatial resolution of the EEG technique, source analysis procedures should be conducted to confirm the presumed neural origin of the components of interest. However, several steps are needed to pass from the recording of

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the EEG to three-dimensional images of neuronal activity and each type of source reconstruction has its own features, which are all well described in the recent review by Michel and Brunet [134]. Using TSE is possible extracting from EEG signal activity that is not in phase with the stimuli, but which is modulated by the demands of the task. This opens a wide field for study and knowledge because it allows us to describe cognitive, motor, and/or sensory processes that are not represented in the ERPs, since these represent phase activity with the stimuli. It is true that using techniques as the FFT, it is possible to observe increases or decreases in the power of the induced activity, but it is not possible to discern whether the increase or decrease that is observed occurs in phase or non-phase activity. This drawback, which a priori does not seem important, could have important implications when it comes to explaining the functioning of the intrinsic properties of neural networks and to answering the following question: what does it imply that some psychological processes are reflected in phase activity and others in non-phase activity? What consequences does it have on neural functioning? Future must clarify the psychophysiological role of phase and non-phase modulations in all frequency bands in the processing of information. Further, it must be acknowledged the utility of ERPs and TSE for brain computer interface (BCI) systems. The detection of voluntary movement intention prior to its actual execution may advance the current state of the art in BCI and neurorehabilitation. A BCI can translate brain signals measured from an individual into specific features suitable for driving an external device (i.e., a robotic arm). Accordingly, anticipatory ERP potentials seem an interesting marker both to further explore plastic changes occurring in clinical movement conditions (e.g., Parkinson, multiple sclerosis, trauma, stroke) [135] and to consider for implementation in BCIs, aiming to restore functional mobility in patients with movement disorders. Indeed, decoding anticipatory potentials suits naturally in the design of more advanced protocols where patients need to execute goal-directed actions instead of repetitive ones [136]. References 1. Requin J, Brener J, Ring C (1991) Preparation for action. In: Jennings JR (ed) Handbook of cognitive psychophysiology: central and autonomic nervous system approaches. Coles MG, Chichester, pp 357–448 2. Friston K (2010) The free-energy principle: a unified brain theory? Nat Rev Neurosci 11(2): 127–138

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Chapter 4 qEEG Methods to Probe Abnormal Brain Rhythms Related to Quiet Vigilance in Patients with Dementia Due to Alzheimer’s, Parkinson’s, and Lewy Body Diseases Claudio Babiloni, Bahar Gu¨ntekin, Go¨rsev Yener, and Claudio Del Percio Abstract Here, we discuss relevant literature findings on abnormal resting-state scalp-recorded electroencephalographic (rsEEG) rhythms in old patients with severe cognitive deficits and disabilities in activities of daily living (i.e., dementia) due to Alzheimer’s (ADD), Parkinson’s (PDD), and Lewy body (DLB) neurodegenerative diseases. Furthermore, we described a modern quantitative EEG (qEEG) methodology to explore those rhythms and related vigilance disorders. The reviewed findings unveil consistent abnormalities in topographic and frequency (most in alpha-reactive Nold was fitted by the frontal and limbic alpha 1 source activity ( p < 0.0001) and (2) the discriminant pattern alpha-reactive ADD < alpha-reactive PDD and DLB < alpha-reactive Nold was fitted by the occipital alpha 2 source activity ( p < 0.05)

open condition [69, 113]. On the contrary, the percent positive values (i.e., greater posterior rsEEG BGF 2 source activity during the eyes-open than the eyes-closed condition) indexed an increase in the posterior rsEEG BGF 2 source activity from the eyes-closed to the eyes-open condition. In the present study, we included only Nold and ADD participants presenting a significant reduction in the posterior rsEEG alpha source activity—a reactivity (%) of the central-parietal-occipital rsEEG (eLORETA) source activity higher than -10 %. 4.7 Statistical Analysis of rsEEG Source Activities

Figure 2 shows the mean values of the regional eLORETA source activities computed from the resting-state eyes-closed rsEEG alpha rhythms in the alpha-reactive Nold, ADD, PDD, and DLB patients. The distribution of those source activities differed across the patients’ groups. In the alpha-reactive Nold group (as a physiological reference), the parietal and occipital (eLORETA) rsEEG alpha 2 and 3 source activities showed dominant values among all cortical macroregions and frequency bands. Regional rsEEG alpha 1 source activities were characterized by relatively low values in all regions of interest. As compared to the alpha-reactive Nold group, the alpha-

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reactive ADD, PDD, and DLB groups exhibited a substantial decrease in occipital rsEEG alpha 2 and alpha 3 source activities. That decrease was higher in the alpha-reactive ADD than the alphareactive PDD and DLB groups, with maximum differences observed in the occipital rsEEG alpha 2 source activity. Overall, the differences in the posterior cortical sources of the rsEEG rhythms at the alpha 2 and alpha 3 sub-bands may subtend different levels of alterations in the neurophysiological regulation and maintenance of the quiet vigilance in ADD, PDD, and DLB patients.

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Conclusions This chapter discussed relevant literature findings on abnormal scalp-recorded rsEEG rhythms recorded in ADD, PDD, and DLB patients and proposed a qEEG methodology to explore those rhythms at the alpha frequency band, possibly reflecting vigilance disorders. These literature findings unveiled consistent abnormalities in rsEEG delta, theta, and alpha rhythms ( A + V) is considered a marker of multisensory integration at behavioral and neurophysiological levels, and it typically occurs when the constituent unimodal stimuli are congruent in space and time (i.e., spatial and temporal rules) and minimally effective in evoking responses (i.e., principle of inverse effectiveness). Adapted in part from [13]

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Fig. 2 Schematic representation of neural pathways mediating multisensory interplay. (a) Feedback connections from higher-level multisensory regions, back to lower-level modality-specific areas. For instance, visual (V, in red) and tactile (T, in green) sensory inputs may interact (visuo-tactile interactions, VT) via subregions of posterior parietal cortex (PPC) that receive afferent input from both modalities and send feedback projections to each (i.e., primary visual - VC - and somatosensory - SC - cortices). In the same vein, auditory (A, in blue) and visual modalities may interact (visuo-auditory interactions, VA) in posterior superior temporal sulcus (STS) and send feedback projections to sensory-specific auditory (AC) and visual cortices. (b) Direct feedforward influences between visual, tactile, and auditory processing via sparse cortical-cortical connections directly between auditory cortex (AC, in blue), visual cortex (VC, in red), and somatosensory cortex (SC, in green). Direct feedforward influences between visual, somatosensory, and auditory processing might also arise subcortically at thalamic levels (not shown here)

At a system level (see Fig. 2), two main mechanisms have been proposed, by which multisensory integration can affect perception. First, feedback projections from heteromodal brain areas (i.e., areas containing multisensory neurons) to modality-specific sensory areas may provide a way for information regarding one modality (e.g., audition) to influence activity in areas dedicated to a different modality (e.g., vision) [16–18]. However, multisensory integration can occur also at low-level stages of sensory cortical processing and even in subcortical structures. Indeed, stimulation in one sensory modality can activate brain regions typically ascribed to process a different modality, via direct anatomical connections between sensory-specific brain areas [17, 19–22]. Sensory-specific thalamic structures may act as crucial processing nodes of multisensory interplay in addition to their traditional role as sensory relaying structures [23].

3 Behavioral Assessment of Multisensory Integration 3.1 Enhancement Effects

The primary advantage of multisensory integration consists in the enhancement of the salience of sensory stimuli, which, in turn, facilitates behavioral responses to them, such as fastening spatial orienting, optimizing sensorimotor control and even improving cognitive processes, such as memory, face recognition and language comprehension [24]. A common method to test for the facilitatory effects of multisensory integration is by contrasting behavioral responses when presenting unimodal stimuli only (e.g., an auditory

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or visual stimulus alone) to responses to bimodal stimuli (e.g., an audio-visual stimulus), as typically done in electrophysiological recording. For example, participants may be asked to respond by depressing a key or button as fast as possible when a visual stimulus is presented. The multisensory condition would entail responding as quickly as possible when the visual stimulus is paired with a stimulus from another sensory modality, such as a sound or a touch (see [25, 55] for examples). Following the rules governing multisensory integration at the neural level, the spatial and temporal alignment of the visual and auditory stimuli will determine the enhancement effect in terms of increased response accuracy and speed the closer the visual and auditory stimuli are to each other [26]. Moreover, due to the ‘inverse efficacy rule’, the greater the difficulty in detecting a unimodal stimulus, the greater the response facilitation brought by a paired cue from a different sensory system [26, 27]. However, while it may seem that any mean reduction in response time (RT) in the multisensory compared to the lowest unimodal condition reflects multisensory integration, this is not always the case. This is because RTs to multisensory (bimodal or trimodal) stimuli will be reduced compared to unimodal stimuli alone simply due to statistical facilitation or what can be referred to as the redundant signal effect (RSE). That is, a reduced RT is predicted in the multisensory condition regardless of whether multisensory integration took place or not. Therefore, special care needs to be taken when using RTs as a proxy of multisensory integration to determine whether a reduced RT is due to statistical facilitation or integration processes. The nature of the multisensory fastening of RTs can be inferred with a mathematical procedure introduced by Miller [28, 29], which allows to determine whether the speed advantage with redundant stimuli is related to a probabilistic facilitation, resulting from signals traveling through separate information channels (race model), or to coactivation in a neuronal pool where redundant signals converge (coactivation model). The latter mechanism implies a specific neural structure, whereas the former simply postulates that the fastest among the signals conveyed by different channels triggers the response. In particular, RTs for each condition (e.g., visual alone, auditory alone, and audio-visual) need to be fit with a cumulative distribution function (CDF). The CDF represents the probability of a response at a given time point after the stimulus has been presented. Then the CDF is fit to the ‘race model’ (i.e., values predicted by statistical facilitation) by calculating the joint probability of presenting the unimodal stimuli together [28]. The joint probability for each time point is calculated as follows: ðP A þ P V Þ - ðP A × P V Þ

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where PA is the probability that a response has been made when an auditory stimulus was presented alone and PV is the probability that a response has been made when a visual stimulus was presented alone. According to this race model, the joint probability will always be higher than either unimodal probability. Differences in the CDF between the multisensory condition and the race model CDF will determine whether multisensory integration has occurred; this is the Miller’s inequality test [28]. Specifically, if the probability of a response in the multisensory condition is significantly greater than the probability of a response predicted by statistical facilitation, the race model is deemed to be violated suggesting that integration between the stimuli has occurred. Thus, the reduction in RT in the multisensory condition should go beyond that could be predicted by statistical facilitation, and it should be modulated by the spatial and temporal congruency of the combined stimuli, in strict adherence with the multisensory neuronal responses found in the cat’s superior colliculus [30] (see Fig. 3). 3.2 Simultaneity Perception

Another reliable index of multisensory integration can be derived by measuring the so-called temporal binding window (TBW), which is the time window within which multisensory stimuli are highly likely to be bound. A standard behavioral paradigm to measure the multisensory TBW is the 2-alternative forced-choice simultaneity judgment (SJ2) [31]. During the SJ2 task, participants are required to judge whether two stimuli of different sensory modalities, presented with different temporal delays (stimulus onset asynchronies), are perceived as simultaneous or not. When observers judge the perceived simultaneity of events separated by a variable temporal interval, a peaked function of stimulus onset asynchronies is revealed. The width of that function estimates the TBW, that is, the tolerance of temporal mismatch (i.e., temporal acuity of the simultaneity judgment; [32]). Additionally, the SJ2 task allows the determination of individual points of subjective simultaneity (PSS), namely the exact point in time at which an individual is most likely to perceive two inputs of different sensory modalities as synchronous. The TBW and PSS respectively assess the precision and accuracy of multisensory temporal perception [33].

3.3 Sensory Weighting in Multisensory Conditions

While multisensory enhancement and simultaneity perception rely on the spatial and temporal congruence of the stimuli, in everyday life we also encounter situations featured by conflicting multisensory information, such as hearing a bark, but visually detecting a dog that is not barking at all. Paradigms in which conflicting sensory stimuli are presented allow for the investigation of how humans weigh sensory modalities when integrating different sensory information.

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Fig. 3 (a) Redundant signal paradigm. Participants keep fixation centrally and give speeded manual responses to visual (V), auditory (A) or audio-visual (AV) targets. Visual stimuli are lateralized, in this example at 20° from the central fixation (central white circle) and the loudspeakers producing white noise targets are at the lateral sides of the monitor, adjacent to the visual stimulus locations. To verify whether the superior colliculus is important for the coactivation of bimodal stimuli in this paradigm, red (Vr) or purple (Vp) visual stimuli can be used, since the last (Vp) are mainly detected by the S-cones that do not project directly, or do so only in a sparse manner, to the SC (see [30] for details). In this example, two unimodal targets are shown on the left: a red left-sided visual target, and below a left-sided auditory target. Centrally, bimodal spatially congruent stimuli are shown, consisting of pairs of left-sided visual (red or purple) and auditory targets (AVrC, AVpC respectively). On the right, the bimodal spatially incongruent stimuli: a left-sided visual (red or purple) target paired with a right-sided auditory target (AVrI, AVpI, respectively). (b) Redundant signal effect (RSE): mean reaction times (RTs, in milliseconds) to unimodal auditory (A, white bar), visual red (Vr, light grey bar), and visual purple (Vp, dark grey bar) targets, and to bimodal audio-visual targets: spatially congruent with red (red bar, AVr-C) or purple (purple bar, AVp-C) visual stimuli, and spatially incongruent with red (red-white bar, AVr-I) or purple (purple-white bar, AVp-I) visual stimuli. Note: in every bimodal condition, RTs are faster than in unimodal conditions (i.e., RSE). (c) Magnitude of the Miller inequality violation. The y-axis shows the difference between the cumulative probability of RTs in bimodal conditions, and the summed cumulative probability of response in unimodal conditions, at different percentile values (x-axis). Values above the zero line indicate a violation of the inequality. The yellow area highlights significant violations: the RSE explainable by neural coactivation occurs only with spatially congruent audio-visual red stimuli, in strict adherence with the multisensory responses in the SC of the cat. (Modified from [30])

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Behaviorally, humans have been shown to integrate multisensory information in a statistically optimal fashion by averaging the individual unisensory estimates according to their relative reliabilities. This form of integration, also referred to as maximum likelihood estimation (MLE), is optimal in that it yields the most reliable multisensory percept, that is, the percept associated with the least variance [34, 35]. Thereby, the brain achieves the highest possible precision in estimating the environmental property, higher than when using any of the individual sensory modalities alone. For example, a bimodal, audio-visual, event may convey auditory and visual information related to the location of the event, and both modalities will give us a slightly different estimate of the location. The multisensory estimate of the audio-visual event can be predicted as follows: s B = wA s A þ wV s V where s B is the integrated bimodal estimate, wA is the weight of the auditory estimate, wV is the weight of the visual estimate, s A is the auditory estimate, and s V is the visual estimate. The weight given to each sensory estimate is proportional to its relative reliability, with the sum of the reliabilities equal to 1. Sensory weight for the auditory estimate is calculated as follows: rA wA = rA þ rV where rA is the reliability of the auditory estimate and rV is the reliability of the visual estimate. Lastly, reliability is inversely proportional to the variance in the sensory estimate when presented alone and the reliability for the auditory estimate is calculated as follows: rA =

1 σ 2A

where σ 2A is the variance of the auditory estimate. In summary, if the variance in the auditory estimate is higher than the variance in the visual estimate, the weight for the visual estimate will be higher, and the predicted multisensory estimate based on the MLE model will be closer to the location of the visual estimate. Importantly, the integrated sensory estimate is considered optimal as the reliability of the integrated estimate is the sum of the individual reliabilities: rB = rA þ rV where rB is the reliability of the integrated bimodal estimate [36]. Note that the formulas for sensory weight and reliability would also be applied for the visual estimate but are not included here for simplicity. In an experimental setting, this model has been validated by presenting two sensory estimates with conflicting spatial [34] and size [35] information. In relation to the above-mentioned audio-

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Fig. 4 Comparison of actual and predicted thresholds for audio-visual localization. (a) Visual, auditory, bimodal, and predicted localization errors (in degrees) of the participants. (b) Averages of the participants, after normalizing to unity for the predicted value (so the error in that condition is zero). The improvement in the bimodal condition over the average unimodal conditions is of 1.425, compared with a predicted value of 1.414. (Modified from: [34])

visual example, participants are first presented with visual and auditory location stimuli alone to determine the reliability in discriminating location information with either modality (i.e., calculated using the just noticeable difference of the associated psychometric curve). Once the reliabilities are determined, conflicting audiovisual information is presented and participants are asked to compare the perceived location to an audio-visual event where the auditory and visual information are aligned (e.g., was the first [conflicting] event to the left or right of the second [aligned] event; see Fig. 4). According to the MLE model, the estimated location of the audio-visual event will be closer to sensory estimate with the higher reliability (see Fig. 5a). The actual integrated estimate and reliability can be compared to the integrated estimate and reliability predicted by the MLE model for optimality comparisons. Further, the individual reliability of the visual estimate, for example, can be changed by introducing noise or blur into the visual environment to change the integrated sensory estimate [34, 35].

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Fig. 5 An example of paradigms presenting conflicting sensory stimuli in perceptual (a) and movement (b) tasks. In (b), the hand is outlined by dashes lines to indicate that it is not visible to the participant, but rather only the green cursor is visible. Notice in each task that the location of the integrated estimate differs depending on the weight of the individual modalities (wV is the weight of the visual estimate and wP is the weight of the proprioceptive estimate)

Although these measures have been presented in relation to perceptual tasks thus far, these models and rationale can also be applied to movement tasks as well, taking into account proprioceptive estimates (e.g., [37–40]; see Fig. 5b for an upper-limb reaching example). 3.4 Crossmodal Illusions

Statistically optimal combination of inconsistent sensory information often results in crossmodal perceptual illusions, with inputs from the dominant, most reliable modality, distorting the percept of other sensory inputs and thereby generating a non-veridical perceptual experience. Seeing can generate illusory tactual impressions, and hearing can cause visual illusions. Crossmodal interactions of this sort are rampant in daily life. For instance, in the spatial domain, vision strongly dominates over audition giving rise to the ventriloquism illusion, by which the perceived location of sound is captured by the location of the visual stimulus if they are spatially incongruent [41]. This occurs because of the higher spatial resolution of the visual system. Visual capture of location also occurs in relation to proprioceptive and tactile modalities [42]. Even in speech perception, vision has been shown to strongly alter the quality of the auditory percept [43]. For example, pairing the sound of syllable /ba with the video of lips articulating syllable / ga, will induce the percept of syllable/da. This effect is known as the McGurk Illusion [43]. These illusions can be reduced by reducing the reliability of the visual cues [34]. Instead, in the temporal domain, audition dominates over vision and touch, since the auditory modality has much better temporal resolution. For example, when a single flash of light is accompanied by two or more auditory beeps, observers often report seeing two or more flashes. This effect is known as sound-induced flash illusion–fission effect. The reverse illusion can also occur, in which two flashes that are accompanied by a single beep are perceived as a single flash, the so-called fusion effect [44]. The susceptibility to the sound-induced flash illusion

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can be used to assess the magnitude of audiovisual binding and can easily be applied to different tasks (e.g., reaching [45–47]). Crossmodal illusions can even affect the own body representation and self-consciousness. The most famous example is likely the rubber hand illusion, typically used to assess the sense of body ownership [48]. This illusion is induced by brushing a person’s hand, hidden from view, while synchronously brushing a visible rubber hand. This results in the shift of sense of ownership from one’s own hand to the rubber hand, and in a projection of sensations from the brushed rubber hand to the person. The rubber hand illusion is also associated with a measurable proprioceptive drift in the perceived location of the hand towards the rubber hand, changes in reaching movements performed with the stimulated hand [49, 50], and it may also alter the perceived size of the own body parts [51]. By applying a visuo-tactile stimulation to the trunk it is also possible to elicit the sensation of ownership towards a different whole body (i.e., body swap illusion; [52]), or a dislocation of the ‘self’ towards a position external to one’s body (i.e., fullbody illusion; [53]). These and other crossmodal illusions represent effective strategies to assess the magnitude of multisensory binding in cases of inter-sensory conflicts.

4

Neurophysiological Assessment of Multisensory Integration

4.1 Electroencephalography (EEG)

Fundamental electrophysiological markers of multisensory integration can be derived using the EEG and measuring the event related potentials (ERPs). Figure 6 presents a didactic comparison of multisensory enhancement effects as revealed through different electrophysiological and neuroimaging techniques. Usually, the magnitude of ERPs induced by multisensory stimuli is compared to the magnitude of the sum of the unimodal ERPs, considering a deviation above the summation as an index of multisensory integration [55–57]. For example, Foxe and colleagues [56] compared ERPs to unisensory pure-tone auditory stimuli, median-nerve electrical tactile stimulation, and combined audio-tactile stimulation. Presentations of simultaneous auditory and tactile stimuli led to significantly larger ERP responses relative to summed responses (i.e., superadditivity criterion, AV > A + V), suggesting the integration of auditory and tactile multisensory information. Another example is offered by Molholm and colleagues [55], who recorded EEG activity during a redundant target paradigm. Cortical interactions were detected at 46 ms at posterior electrodes over the visual cortex corresponding to the initial visual responses in the visual cortex occurring at around 40–55 ms poststimulus in the form of the C1 ERP wave [58]. These effects show

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Fig. 6 Multisensory interactions showcased using single neuron, fMRI, and EEG responses. All three panels show superadditive responses (i.e., the response to a multisensory stimulus is greater in magnitude than the sum of the responses from the component stimuli presented alone). The superadditive response has been considered as a marker of multisensory integration at behavioral and neurophysiological levels. Top left: Single cell discharge from a superior colliculus neuron during the presentation of a visual (V), auditory (A), or audio-visual stimulus (AV). Note that the mean impulses during the multisensory presentation are greater than the sum of the unimodal responses and the percentage increase compares the multisensory response to the maximum unisensory response. (Modified from [14]). Top right: Relative BOLD response changes in a heteromodal association cortical site (e.g., superior temporal sulcus) when congruent or incongruent audiovisual speech stimuli are presented. Positive differences from the summed unimodal responses (100%) are indicative of superadditivity. (Modified from [54]). Bottom: The graph on the right compares ERP responses to the simultaneous audio-visual stimuli to the summed auditory and visual ERP responses, with the difference shown in green. (Modified from [55]). The increased magnitude of the simultaneous ERP compared to the summed ERP is indicative of a superadditive response. The vertical black line corresponds to the voltage map shown on the left, with the electrode location depicted by the black dot and the voltage range indicated in grey

that multisensory integration can occur as early in the processing hierarchy as the sensory analysis stage in the primary visual cortex. It should be noted that it may be difficult to rely on the superadditive criterion when assessing multisensory integration due to biases in the signals contributing to the ERP response [13]. A main

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biasing factor is the common activation, which is an activation common to all sensory conditions contributing to the overall ERP response but not directly related to sensory processing. Common activation can take the form of activation related to simple motor processes (i.e., related to a button press), target processing, or response selection [59, 60]. Moreover, when summing the magnitude of the ERPs in unimodal conditions, the common activation from each unimodal condition is also added together, but no such summation exists for the multisensory condition (i.e., the common activation is only represented once). Therefore, common activation may bias results toward subadditive multisensory interactions [13]. To account for common activation, and reduce its biasing effect, there are a few methods that can be employed. One method is to restrict the ERP analysis to the very early post-stimulus presentation times (below 200 ms; [13, 60]). This is because non-sensory related activation (i.e., those sources contributing to common activation) are thought to arise after 200 ms [61]. One disadvantage, however, is that the analysis can be limited in the time with which multisensory interaction can occur. Another method requires quantifying the common activation and subtracting it from both the unimodal and multisensory conditions. That is, the introduction of no sensory control trials in addition to unisensory and multisensory ones. In these control trials, participants would still be required to make a response (e.g., a button press), but in the absence of the auditory or visual stimulus [62]. This may give an indication of the common activity in the absence of the sensory stimuli used in the experiment and can be subtracted from each condition before comparing the multisensory ERP response to the summed unimodal responses. In this way, any differences can be more closely attributed to differences in sensory processing [60]. A last method involves manipulating a component (e.g., intensity) of each unimodal stimulus as well as the multisensory stimulus. For example, the participant can be presented with a weak or strong auditory stimulus, a weak or strong visual stimulus, and a weak of strong audio-visual stimulus. To understand if there is a multisensory integration, the relative effect of changing the stimulus intensity on the unisensory responses (i.e., [A high – A low] + [V high – V low]) can be compared to the effect of changing the stimulus intensity on the bimodal response (AV high – AV low). If there is indeed a multisensory effect, changing either unisensory stimulus should have a greater influence on the bimodal response [13, 63]. Further, the common activation is removed or reduced by comparing the relative differences between the unimodal conditions and the bimodal condition. A main technical limit of EEG systems is the low spatial resolution [13, 64–66]. This makes it difficult to localize what brain areas contribute to and are responsible for the neural activity recorded

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using EEG. However, sophisticated analyses have been developed to implement EEG as a neuroimaging tool by using reference-free spatial information from high-density EEG systems [65]. In this way, these analyses can be used to understand modulations in topographic responses in addition to modulations in strength and latency already afforded by typical peak-to-peak analyses of ERPs. In relation to multisensory integration studies, this can be implemented to better understand differences in neural generators and the timing of their activation when comparing responses to unimodal and bimodal stimuli [13, 67, 68]. In conjunction with measuring behavioral responses, employing EEG during the presentation of conflicting sensory information provides a unique opportunity to assess the neural correlates of crossmodal illusions. A main approach is to compare EEG responses in illusory trials (i.e., trials when the illusion is reported) to those in non-illusory trials [69–71]. For example, Mishra and colleagues [72] presented participants with the above-mentioned sound-induced flash illusion while measuring ERPs. The authors isolated neural activity associated with the illusory second flash and found an early modulation of visual cortex activity at 30–60 ms after the second sound. 4.2 Functional Magnetic Resonance Imaging (fMRI)

With respect to fMRI, again the first guideline established for studying multisensory phenomena specific to population-based blood oxygenation level-dependent (BOLD) activations is the superadditivity criterion. In a seminal fMRI study [54], audio and visual presentations of speech (talking heads) revealed an area of the superior temporal sulcus that produced BOLD activation with a multisensory speech stimulus that was greater than the sum of the BOLD activations with the two unisensory stimuli (AV > A + V). The use of the superadditive criterion is based on the premise that the BOLD activation can be modeled as a time-invariant linear system. That is, activation produced by two stimuli presented together can be modeled by summing the activity produced by those same two stimuli presented alone [73–76]. The presence of multisensory neurons can be then inferred if BOLD signal with multisensory stimuli exceeds the additive criterion (i.e., AV = A + V). A main issue of using the superadditive activations as index of multisensory integration is that BOLD data could produce falsepositives in brain regions containing only two pools of unisensory neurons and no multisensory neurons. That is, if a single voxel contained only unisensory neurons and no neurons with multisensory properties, the BOLD response would still exceed the sum of the BOLD activations brought about by the two unisensory stimuli, without proving their interaction. On the other hand, although neurons that present superadditive responses are, by definition, multisensory, the majority of multisensory neurons are not

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truly superadditive [77–79]. Indeed, superadditivity is not a criterion for identifying multisensory enhancement even in single-unit studies as it is just used to classify the degree of enhancement. On the other hand, it is worth mentioning that neuronal populations within multisensory brain regions contain a mixture of unisensory neurons from different sensory modalities in addition to multisensory neurons [3, 14, 80–88]. Without being able to account for the heterogeneity of neuronal populations, the superadditive criterion is likely to produce false-positives when used with a populationbased measure such as fMRI. For these reasons, the adoption of an additive criterion for assessing multisensory integration in fMRI studies has been proposed. However, even the additive criterion seems not entirely optimal, being susceptible to false negatives. Indeed, while some fMRI studies successfully identified brain regions that met the additive criterion [54, 89], other studies did not find evidence for additivity even in renowned multisensory brain regions [90–94]. Another established principle of multisensory single-unit recording is the above-mentioned inverse effectiveness rule. Accordingly, if the average level of multisensory enhancement of neuronal activity increases when the combined unimodal stimuli are degraded, then the BOLD activation could exceed the additive criterion when degraded stimuli are used [95]. Although there is some empirical evidence from neuroimaging showing an increased likelihood of exceeding the additive criterion as stimulus quality is degraded [94, 96], even this method has intrinsic limitations related to the use of degraded sensory stimuli and the fact that the absolute BOLD percentage signal changes are measured on an interval scale with no natural zero. In fMRI experiments, raw BOLD values are transformed to percentage signal change values by subtracting the mean activation for the baseline condition and dividing by the baseline. Thus, for BOLD measurements, ‘zero’ is not absolute, but is defined as the activation produced by the baseline condition chosen by the experimenter [97, 98]. Because the activation values are measured relative to an arbitrary baseline, the value of the baseline condition can have a different effect on the summed unimodal activations than on the multisensory activation (i.e., the value of the baseline is subtracted from the additive criterion twice, but is subtracted from the multisensory activation only once; e.g., [90]). To potentially overcome these issues, the use of relative BOLD differences instead of absolute BOLD measurements for assessing multisensory integration has been recently proposed [99]. The investigation of the neural substrate of multisensory integration may also take advantage of the neuronal adaptation phenomenon in fMRI. Neuronal adaptation consists in a suppression of the response of those neurons that are selective to one characteristic of the stimulation, when a stimulus featuring such

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characteristic is presented repeatedly [100]. It is based on the premise that the repeated presentation of identical stimuli leads to a reduction in the measured signal from neuronal populations selective to specific stimulus features. The main advantage of fMRI adaptation compared with traditional fMRI methods is the capacity to reveal subpopulations of neurons within a single voxel that exhibit selectivity to such features [101]. In the field of multisensory integration, fMRI adaptation may be used to identify multisensory neural representations showing, regardless of the changes in sensory modality, adaptation when presented with identical stimuli in different sensory modalities. For example, a region that integrates visual and auditory stimuli is expected to have an increased visual adaptation, if the auditory feature is also repeated [102]. This crossmodal adaptation effect has been used to identify multisensory phonological [103, 104] and space [105] representations.

5

Conclusion From inspirational electrophysiological recordings in the cat’s brain to subsequent experiments in humans, research on multisensory integration has gradually expanded to including a variety of dimensions thanks to the development of a multiplicity of empirical and computational techniques [4] that has increased the knowledge of multisensory phenomena and its neural underpinnings in the human brain. Beyond the main behavioral, electrophysiological, and neuroimaging methods reviewed in this chapter, non-invasive brain stimulation approaches are also greatly advancing our understanding of the neural mechanisms underlying multisensory integration from a causal manner [106], and future investigations combining neurostimulation approaches with simultaneous recording and imaging will further allow the deepening of the architecture of multisensory networks, uncovering also their functional properties [107]. These advancements will be crucial not only to enhance our understanding of multisensory processes active under healthy conditions, but also of the requirements for their development, plasticity, and susceptibility to brain diseases [4]. In this regard, it is worth mentioning the increased interest in the study of the origins and early development of multisensory processing in humans. From birth, the infant’s brain seems to be already tuned to multisensory processing, as evidenced by fMRI studies showing that regions of the neonate brain exhibit functional connections with cortical representations of different sensory modalities [108]. Specifically, resting state fMRI shows that the intraparietal sulcus and the superior temporal sulcus, typical regions of multisensory convergence in adults, possess multiple connections with visual, somatosensory, and auditory areas, thus suggesting that the connectivity substrate of multisensory processing is already

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present at birth. This neurophysiological evidence converges with behavioral studies revealing that, early in life, infants display high sensitivity for multisensory stimuli and seem to attend, remember, and learn more from information presented from two or more sensory modalities than from a single sensory system [109, 110]. Research on the development of multisensory integration represents an important field to understand neurodevelopment disorders, such as autism (for review see: [111, 112]), but also to psychiatric disorders that show the first symptoms of mental illness in late adolescence, such as schizophrenia [113, 114]. In both cases, there is evidence of disrupted multisensory processing, both in temporal and spatial domains, that seems to be associated to the clinical symptomatology [115]. But there is also evidence showing a high degree of flexibility of multisensory functions throughout the human life span, with chances for their training and restoration, hence with implications for clinical rehabilitation [8]. Many behavioral and neurophysiological paradigms can be easily applied to study multisensory functions and dysfunctions in clinical conditions, and they have also been used to uncover the link between multisensory processing and the pathophysiology of neurological diseases [116]. This is the case of migraine, for example, in which a simple crossmodal illusion, the sound-induced flash illusion mentioned earlier, has allowed to gain a new insight into the role of pathologic cortical hyperexcitability state [117] and medication overuse in this disease [118]. On the other hand, migraine itself has also provided a new model for the study of how abnormal level of cortical excitability impacts multisensory processing. This example underscores the importance of assessing multisensory processing beyond basic empirical research purposes. References 1. James W (1890) The principles of psychology. Henry Holt & Co, New York 2. Molyneuxm M (1688) Letter to John Locke. In: de Beer E (ed) The correspondance of John Locke. Clarendon Press, Oxford 3. Stein BE (1993) Meredith MA. MIT Press, The merging of the senses 4. Stein BE, Stanford TR, Rowland BA (2020) Multisensory integration and the society for neuroscience: then and now. J Neurosci 40 (1):3–11 5. Boring EG (1942) Sensation and perception in the history of experimental psychology. Century Company, New York 6. Bolognini N, Russo C, Vallar G (2015) Crossmodal illusions in neurorehabilitation. Front Behav Neurosci 9(212):1–6

7. Bolognini N, Vallar G (2020) Hemainopia, spatial neglect, and their multisensory rehabilitation. In: Sathian K, Ramachandran VS (eds) Multisensory perception: from laboratory to clinic. Elsevier/Acamdemic Press, London, pp 423–447 8. Santhian K, Ramachandran VS (2020) Multisensory perception: from laboratory to clinic. Elsevier/Acamdemic Press, London 9. Pourtois G, De Gelder B, Bol A, Crommelinck M (2005) Perception of facial expressions and voices and of their. Cortex 41:41– 59 10. Stanford TR, Quessy S, Stein BE (2005) Evaluating the operations underlying multisensory integration in the cat superior colliculus. J Neurosci 25(28):6499–6508

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Chapter 12 Psychophysiology of Sleep Maria P. Mogavero, Giuseppe Lanza, Lourdes M. DelRosso, and Raffaele Ferri Abstract Sleep is a complex behavior, neurophysiological, psychological, and neurochemical phenomenon, which shows striking phylogenetic and ontogenetic changes. Among its numerous and vital functions, research has identified energy conservation, brain waste clearance, immune response modulation, development, learning, cognition, optimal performance, recovery from disease, modulation of vigilance, and several other mental/psychological processes. This chapter provides a condensed overview of the normal and pathological human sleep, and of the main neurophysiological tool (polysomnography) that has been for decades the gold standard for its study. This chapter is intended as an introduction to this complex and fascinating topic, especially in the field of Psychophysiology, and provides a basis for needed future research combining the “physical” and “psychic” aspects of sleep, especially concerning the origin of sleep and its stages, as well as the psychophysiological characteristics of normal sleep and their changes occurring in sleep disorders. Key words Sleep, Polysomnography, Sleep stages, Sleep architecture, Sleep disorders, Normal sleep

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Introduction Sleep is a complex behavior, neurophysiological, psychological, and neurochemical phenomenon which shows striking phylogenetic and ontogenetic changes. Consequently, its functions are many different and, probably, not completely known. Among them, the following are some of the most important: energy conservation, brain waste clearance, immune response modulation, development, learning, cognition, optimal performance, recovery from disease, modulation of vigilance, and other mental/psychological functioning [1]. For all these reasons, sleep is definitely one of the essential functions of living beings, similar to feeding, drinking, breathing, etc. Sleep science has developed strikingly in the last decades and we report here a short coverage of some of the tools available for its assessment, especially for psychophysiological studies and of some areas of application in healthy individuals and in patients with different sleep disorders.

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Polysomnography The main tool to assess normal and pathologic sleep architecture is polysomnography (PSG) which is performed by recording multiple different signals through electrodes and transducers placed in specific positions over the body. The main goal of this exam is to assess and quantify sleep stages; these are then classically graphically depicted by the “hypnogram”. By doing this and by recording multiple parameters, PSG enables to assess the presence and severity of a sleep disorder. Full PSG is usually recorded in the sleep laboratory, along the whole night with a continuous supervision and control by a specifically trained and certified technician. Three main basic types of physiological signals are included in a PSG recording for a correct staging of sleep: (1) the electroencephalogram (EEG), obtained from a minimum of three derivations from the frontal, central, and occipital scalp areas of one hemisphere, referred to the contralateral earlobe or mastoid (for example F3-M2, C3-M2, and O1-M2); (2) the electrooculogram (EOG), obtained from a minimum of two channels from specific locations close the lateral canthus of both eyes, both referred to the same earlobe or mastoid, and (3) surface electromyogram (EMG) from the chin; however, EMG from the tibialis anterior muscles and electrocardiogram (ECG) are also commonly recorded [2]. Sleeprelated breathing disorders (e.g., sleep apnea) are assessed by recording signals exploring the respiratory functions, such as oral and nasal airflow and respiratory effort; in addition, also peripheral pulse oximetry, capnography, and snoring sound by a microphone are obtained. The full laboratory PSG is not always required and less complex tests can be performed, such as cardiorespiratory monitoring, usually including only the respiratory channels and ECG, and other unsupervised “Out of Center Sleep Testing” (OCST). The American Academy of Sleep Medicine has published the current criteria for sleep staging [2], which describe the detailed procedures and rules to identify wakefulness (stage W), three non-REM (NREM) sleep stages (N1, N2, and N3), and REM sleep (stage R). These rules have replaced the criteria previously defined by Rechtschaffen and Kales in 1968 [3]; however, the new stages correspond, to a large extent but not completely, to the previous ones [4, 5]. PSG provides important information regarding both sleep architecture (e.g., sleep latency, REM sleep latency, awakenings after sleep onset, total sleep duration, percentages and the duration of each sleep stage, etc.) and clinically relevant features which are potentially able to allow or supporting the diagnosis of several disordered sleep conditions, such as movements, behaviors, respiration, cardiovascular, etc.

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Moreover, PSG can include the recording of a large number of different parameters that can be chosen on the basis of the clinical characteristics of each individual patient (for example, additional EMG channels, such as masseter muscle activity for sleep bruxism, body temperature, esophageal pH, etc.). This allows a detailed description of the individual sleep problem and complies with the principles of personalized precision medicine. PSG can be enriched by the addition of synchronized video recording; in such a case, it is called video-PSG (vPSG). The video recording of the movements or behaviors accompanying the neurophysiological signals recorded by PSG represents an extremely powerful tool for the assessment of several sleep disorders, including NREM and REM sleep parasomnia, such as REM sleep behavior disorder (RBD) and sleep-related seizures.

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Normal Sleep Normal sleep has a regular alternation of NREM and REM periods constituting “sleep cycles”. After falling asleep, the subject progressively passes from stage 1 (N1) of NREM sleep to stage 2 (N2), then reaches the most profound NREM stage (N3) [2], also known as slow-wave sleep (SWS). In physiological conditions, between 70 to 100 min after falling asleep, the first REM sleep period occurs. The first sleep cycle ends with the end of the first REM sleep period. After the first cycle, other cycles follow one another with a rather constant duration, although REM sleep tends to increase in duration at the expense of NREM sleep, in particular SWS which become shorter. In young healthy adults, REM sleep occupies approximately 20–25% of the total sleep time. Short awakenings are also usually present during the sleep period.

3.1 Wakefulness after Sleep Onset (WASO)

This term refers to the usually brief periods of wakefulness occurring within the sleep period, after sleep onset, and before the final awakening. During wakefulness the EEG shows, with eyes closed, a condition of “synchronization”, characterized by alpha activity (typically at 8–12 Hz), better recorded over the parietal and occipital areas; with eye opening, a “desynchronized” EEG pattern is typical, characterized by waves of low voltage (10–30 μV) and high frequency (16–25 Hz). At the same time, both fast and slow eye movements and muscle tone of medium to high amplitude are present.

3.2 Non-REM Sleep (Stages N1, N2, and N3)

During N1, alpha activity decreases and is substituted by low-voltage waves of mixed frequency, mostly between 3 and 7 Hz. Eye movements are mainly slow and the chin EMG shows persistent tonic activity although of a lower intensity than when awake.

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In stage N2, background EEG shows a low-voltage mixedfrequency activity similar to that described above; however, two specific transient EEG patterns characterize this stage: (a) the so-called K-complexes and sleep spindles (or simply spindles). Eye movements are slow or absent, while chin EMG is further reduced in amplitude. In stage N3, at least 20% of an epoch, corresponding to 6 s (each epoch is 30 s long) must contain high-amplitude (>75 μV) slow waves (0.5–4.0 Hz, but typically 0.5–2.5 Hz). Muscle tone is further reduced and eye movements are absent. Sleep spindles may or may not occur, as well as K-complexes, although they are often difficult to distinguish from delta waves. 3.3

REM Sleep

3.4 Other Physiological Characteristics

REM sleep is also characterized by a low-voltage mixed-frequency EEG, very similar to that of N1; however, it also contains characteristic “sawtooth waves”. The acronym REM stands for “rapid eye movements”, which are typical of this stage and are accompanied by a very low tone of the chin muscles, lower than in any other sleep stage. Dreams occur predominantly, but not exclusively, during REM sleep. This stage is also characterized by an instable control of the vegetative functions with the increases and sudden fluctuations in blood pressure, also heart rate increases and extrasystoles may appear, respiratory rate increases and becomes more irregular, thermoregulation is less effective. REM sleep is maximal in infants and tends to gradually decline with age in favor of NREM sleep. Respiration is under a double voluntary and automatic control by the autonomic nervous system. During the day it is highly irregular and connected with the different metabolic demands by our activities. During the night, respiration in mainly automatic and during NREM sleep breathing rate is slower and regular. Conversely, during REM sleep breathing rate increases and becomes shallower and irregular. In general, during sleep, oxygen levels in the peripheral blood are slightly lower and carbon dioxide levels slightly higher. During sleep, a general decrease in heart rate and blood pressure occurs and, particular in NREM sleep. Heart rate and blood pressure decrease by up to 10% and subjects who do not show this decrease, because of different disorders, are generally called “nondippers”. These changes are gradually more evident with deeper NREM sleep, especially during N3, and are sustained by a fall in sympathetic activity, accompanied by an increase in parasympathetic (vagal). This change in balance between the parasympathetic and sympathetic activity, with a predominance of the parasympathetic counterpart is also detected by the analysis of heart rate variability during sleep, showing an increase in the high-frequency band and a markedly reduced low-frequency band of heart rate variability.

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Body temperature shows physiological fluctuations during both day and night; however, during sleep it shows a dip of approximately 1–2 °C. It is interesting to note that the decrease in body temperature starts before bedtime and this is believed to favor falling asleep. Similarly, body temperature starts to rise again towards morning, before awakening. Spontaneous electrodermal activity (EDA) is also under the autonomic nervous system control; it is most abundant during N3 and usually decreases dramatically REM sleep. In normal adults, EDA is not distributed regularly along the night and spontaneous skin potential responses are less likely to occur during the first sleep cycle, compared to the latter cycles. However, they seem to be less frequent also in the second half of sleep, especially towards the end of the night. It has been observed that EDA decreases during the 6 min preceding a REM sleep period and when it occurs in in this sleep stage, it is typically associated to bursts of REM activity. Moreover, sleep exerts a potent inhibitory effect on gastrointestinal motility and both stomach and colon show very little motility during this state. Normally, healthy adults do not micturate at night or they do it once during their night sleep. The need to urinate triggers peripheral reflexes awakening the subject, who is able then to go to the toilet. Glomerular filtration rate shows a clear circadian pattern, with a maximum during the day (around 2–3 p. m.) and a minimum in the middle of the night. This is associated time intervals between urination longer at night than during the day. In children, bladder control is reached usually before the age of 3 years, when they stop bedwetting.

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Psychological Characteristics

4.1 Consciousness and Attention

Arousal is the most basic state of wakefulness. Arousal is achieved through an orchestrated release of neurotransmitters in the brainstem and cortex, via the ascending reticular activating system (RAS). Various nuclei and neuronal projections originate in the reticular formation of the brainstem, including the locus coeruleus, dorsal raphe, median raphe, pedunculopontine nucleus, laterodorsal tegmentum, and parabrachial nucleus. Non-brainstem nuclei include thalamic nuclei, hypothalamus, and basal forebrain [6]. Neurons from the pedunculo-pontine nucleus (PPN) and the laterodorsal tegmentum (LDT) synthesize acetylcholine and generate beta and gamma activity during wakefulness via membrane oscillations, which are mediated by voltage-dependent calcium channels and modulated by G proteins [7]. From the PPN, signals travel via two pathways: the dorsal pathway (or thalamo-cortical transmission [8]) and the ventral pathway, which projects to the hypothalamus and basal forebrain. Arousal is therefore initiated by these projections to the thalamus, hypothalamus, and forebrain.

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Arousal is also important in the regulation of consciousness and attention. Consciousness refers to the awareness of oneself and one’s surroundings and involves an interaction between many areas in the brain, both cortical and subcortical. With sleep onset, consciousness is altered. Meta-awareness is absent during dreaming, meaning that there is no awareness of dreaming. When awareness of dreaming is preserved, the dreamer is experiencing a so-called “lucid dream” [9]. Attention refers to the process by which we filter unwanted stimuli, in order to focus on relevant information. Various theories have postulated the neuronal networks involved in attention, but the frontal lobe and dopamine play a significant role [10]. During sleep, stimuli from the external world are blocked or greatly attenuated, creating an environment in which synaptic plasticity will support attention, memory, and learning [11]. 4.2 Sensory Responsiveness

Sleep is characterized by diminished response to external stimuli and lack of volition. However, during sleep, sensory inputs continue to be received and processed, visceral and vascular receptors continue homeostatic control, and proprioceptive receptors send information about muscle tone and body posture. This signal processing is evident during periods of arousal secondary to sound, touch, pain, or other stimuli. During NREM sleep, for example, thalamic and cortical auditory evoked potentials show greater amplitude than during wakefulness and REM sleep [12]. Nociceptive responses also decrease during sleep, although the underlying process is still unclear.

4.3 Cognitive Processing

Sleep plays an important role in memory and executive functions. Studies assessing cognitive and executive function have demonstrated that increased amount of WASO have detrimental effects on inhibition, verbal fluency, and delayed recall; however, good sleep manifested by lower amounts of WASO is associated with better performance [13]. Sleep has also been studied for a long time as a contributor to memory consolidation. The memory process includes mainly three important stages: encoding, consolidation, and retrieval of information. There are three areas of the brain involved, within the hippocampus, the neocortex, and the amygdala. The hippocampus, located in the brain’s temporal lobe, is where episodic memories are formed. During sleep, thalamic activity enables hippocampal-cortical communication with subsequent transfer of information to the neocortex for long-term storage. Not lastly, REM sleep plays an important role in emotional memory. When selectively deprived of REM sleep, healthy individuals do not process negative memories in the same way than individual with intact REM. In a study testing the overnight amygdala adaptation to unpleasant experiences, it was demonstrated that

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interrupted REM sleep impairs emotional processing and that emotion processing is proportional to the duration of sound REM sleep and fails when REM sleep is disrupted [14]. 4.4 Dreams and Mentation

5

Dreams are phenomena that occur during sleep characterized by hallucinatory-like activity which can be accompanied by recall. Various theories have been developed to explain the neurological origin of dreams. In the activation-input-modulation, dreams are generated by the interaction between the brain global level of activity, the source of stimuli, and the type of neurochemical modulation (cholinergic or adrenergic) [15]. The ability to remember dreams develops at 3–4 years of age, whereas prior to this age children do not seem to have the ability to recall dreams. When awakened from sleep, children have a dream recall rate of 20%, while in adults the recall rate is 80% [16]. Dreaming can occur during both NREM and REM sleep, with diverse characteristics although dream mentation is estimated to be 84% in REM and 54% in NREM. The length of the dream or the report appears also to be longer in REM than in NREM. REM-related dream content is reported to be more vivid, bizarre, emotional (anxiety or fear), dramatic, physically, and more involved while dreams reported after NREM sleep are more humdrum and thought like.

Sleep Disorders

5.1 Insomnia and Sleep Deprivation

According to the ICSD-3 criteria [17], chronic insomnia is a sleep disorder characterized by difficulty in falling asleep, in maintaining sleep, or by early awakening, with a negative impact on daily performance, manifesting itself with fatigue, excessive daytime sleepiness (EDS), and difficulty concentrating and attention, for at least 3 times a week and a duration of at least 3 months, not attributable to other sleep disorders or medical or pharmacological conditions. Insomnia in early childhood can manifest itself with various phenotypes, which can be translated into different pharmacological approaches: insomnia with motor restlessness; insomnia without difficulty falling asleep but characterized by long-lasting morning awakenings; insomnia with multiple nocturnal awakenings and difficulty falling asleep [18]. Insomnia has a high prevalence in the general population, from 6 to 20% [19], with increased risk of comorbidity and healthcare costs [20]. Insufficient sleep syndrome (ISS) [17], a very frequent cause of EDS, is a chronic failure to get the amount of sleep necessary to maintain normal alertness and wakefulness. Changes in lifestyle caused by modern society have induced, in recent years, a steady increase in its prevalence; pressing rhythms imposed by work [21] and academic [22] demands, and environmental factors, including

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light pollution [23], play a major role. When assessing ISS, both individual chronotype (i.e., the predisposition to be active at a certain time of the day) and hypnotype (i.e., the amount of sleep a person needs to get a sleepful sleep) must be taken in due consideration. Both insomnia and insufficient sleep can have a negative effect on health and, indeed, good sleep helps preventing cardiovascular disease [24] and cancer [25], reinforces the immune system [26], and supports cognitive performance [27]. Chronic sleep deprivation is believed to induce epigenomic sequelae through DNA methylation and histone changes, and RNA coding, thus impacting negatively on learning and memory [28]; it can also favor the onset of depression [29] and obesity [30]. An accurate diagnostic classification of these disorders is therefore of pivotal importance for the health of those affected, as well as for establishing the therapeutic approach aimed at their treatment, e.g., through cognitive-behavioral therapy (CBT) or drugs. In particular, the identification of the different insomnia phenotypes could allow targeted therapies and reduction of healthcare costs [31]. 5.2 Circadian Rhythm Disorders

Circadian rhythm disorders are a category of sleep disorders characterized by a chronic misalignment between the endogenous circadian rhythm (typical of the body) and the desired or required rhythm by the surrounding environment or by social/work programs. Consequently, this disorder can cause symptoms of insomnia, EDS, or both, with possible impairment of the cognitive, social, educational, and work spheres, among others [17]. Circadian rhythm disorders have an estimated prevalence of around 3% in the general population, although this condition is often misdiagnosed with other sleep disorders and, therefore, a prevalence of 10% has also been reported [32]. One type of circadian rhythm disorder is the advanced phase disorder, more commonly observed in elderly people, in which there is an anticipation of the onset of night sleep and morning awakening of at least 2 h compared to a conventional sleep-wake cycle. Conversely, in the delayed phase circadian rhythm disorder, typically observed in younger age groups, the patient tends to fall asleep later at night and therefore postpone waking up in the morning, with a difference of at least 2 h compared to conventional times. Finally, in the irregular sleep-wake rhythm disorder, the sleep-wake cycle is so variable and unstable as to completely subvert the normal circadian trend [33]. The jet-lag syndrome during a transmeridian trip, or shift work disorder in the case of shift workers, also falls into this category of disorders [33]. Treatment varies according to the disorder, from the use of melatonin at variable dosages and times (depending on the clinical case in delayed phase circadian rhythm disorder) to light therapy

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(with different modalities depending on the disorder), to CBT. In any case, the treatment must be personalized, following an accurate diagnosis that also uses objective methods, such as actigraphy or, in order to exclude other sleep disorders, PSG/vPSG. Regardless of the type of disorder, these conditions can exert a very negative impact on the health and quality of life (QoL) of those affected, especially if they persist chronically. The circadian system is driven by a self-regulating feedback circuit of transcriptional activators and repressors, located in the hypothalamic suprachiasmatic nucleus and regulated by light stimulation: the genes of the circadian clock, consisting of the activators CLOCK and BMAL1 and the repressors PER and CRY, which employ approximately 24 h to complete a full cycle; a second feedback loop, involving CLOCK and BMAL1, also regulates the transcription of genes for the REV-ERBa and REV-ERBb nuclear receptors; a third feedback loop is mediated by CLOCK/BMAL1-mediated transcription and ROR/REV-ERB-mediated transcription [34]. The circadian clock of the suprachiasmatic nucleus, in turn, regulates all the peripheral clocks, contained in every cell of our body, which play an important role in the biological functions of the organ to which they are part [35]. It follows that a dysfunction of the circadian rhythms can be connected to a wide range of cardiovascular, immunological, inflammatory, psychiatric, metabolic, endocrinological, neurodegenerative, and tumor pathologies [36]. An aberrant light stimulation, due to light pollution or excessive and improper use of electronic devices, can greatly contribute to the risk of circadian rhythm disturbances, as it occurs above all for the adolescent population [37, 38]. 5.3

Parasomnia

The International Classification of Sleep Disorder (ICSD-3) [17] defines parasomnias as undesirable physical events or experiences that occur during entry into sleep, within sleep, or during arousal from sleep, whereas in the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) [39] they are defined as recurrent episodes of incomplete awakening from sleep, with usual amnesia of the episode, and little or no dream imagery and distress or social impairment. Overall, parasomnias include several disorders sharing distinctive psychophysiological and clinical features: (i) correlation with age; (ii) symptoms associated with muscular activity; (iii) unassociated medical problems; (iv) absence of specific PSG abnormalities; and (v) spontaneous resolution [40]. Parasomnias are classified based on the sleep stage during which they occur: NREM sleep-related parasomnias, REM sleep-related parasomnias, and other parasomnias (such as sleep-related hallucinations and sleep enuresis). Most parasomnias have a benign evolution, with spontaneous resolution during the adolescence, although they can substantially impact QoL of patients and their relatives [41, 42].

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PSG or, better, vPSG, is not always recommended for the diagnosis or evaluation of typical parasomnias, but it is indicated for injurious events and when nocturnal seizures or other comorbid sleep disorders are suspected [40]. NREM sleep-related parasomnias, also known as disorders of arousal (DOAs), are defined in the ICSD-3 [17] as recurrent episodes of incomplete awakening from sleep, characterized by inappropriate or absent responsiveness to efforts of others to intervene or redirect the person during the episode, with limited or no associated cognition or dream imagery, and with partial or complete amnesia for the episode. DOAs include confusional arousals, sleep terrors, sleepwalking (also called somnambulism), and sleeprelated eating disorder. Typically, there is only one or very few events per night and the individual may continue to appear confused and disoriented for several minutes, or even for longer, following the episode. Some individuals may experience more than one type of arousal parasomnia [41]. In some cases, differential diagnosis is challenging, also because DOAs may mimic some nocturnal seizures or RBD. As a whole, NREM sleep parasomnias are common and benign sleep disorders, typically occurring in childhood and that tend to decrease in frequency across development and in adulthood, possibly because of the decrease in SWS with aging [43]. Pathophysiologically, DOAs seem to result from a NREM sleep-wake state dissociation, since patients appear to be simultaneously awake (with retention of their motor and behavioral functions) and asleep (with impairment of cognition, judgment, and memory for the events) [44–46]. The variable level of awareness during NREM sleep parasomnia may depend on the quantity and position of the local persistence of the slow-wave activity [44]. NREM sleep parasomnias can also occur or be worsened by [40]: (i) conditions increasing slow wave sleep, such as sleep deprivation and some drugs; (ii) conditions causing repeated cortical arousals and subsequent sleep fragmentation, such as noise, fever, physical activity late in the day, emotions, distress, and anxiety; (iii) impaired arousal mechanism and persistence of sleep drive, resulting in a failure of the brain to fully transit into wakefulness; (iv) genetic factors [47]. Parasomnias normally do not require treatment, but only prevention, safety measures, and bystander interventions [40]. CBT, relaxation before sleep, and hypnosis can be helpful [48]. Another technique is anticipatory or scheduled awakenings, which consist of awakening the child about 15 min before the presumed time when the episode occurs; this may shift the child into a lighter state of sleep, thereby aborting the event [49]. Pharmacotherapy, such as L5-Hydroxytryptophan or melatonin [40, 50], should be considered only when episodes are frequent or dangerous to the patient or others, or when they cause undesirable secondary consequences, such as EDS or distress to the patients or their families [49, 51].

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REM sleep-related parasomnias. The main differences between NREM sleep-related parasomnias and REM sleep-related parasomnias are as follows: (i) the occurrence during REM sleep; (ii) the occurrence during the second half of the night, when REM sleep is more prevalent; (iii) dream enactment behaviors; and (iv) absence of mental confusion on awakening. On very rare occasions, some patients meet the diagnostic criteria for both NREM sleep and REM sleep parasomnia and are diagnosed with parasomnia overlap disorder [52]. RBD is characterized by complex behaviors with enactment of dreams that are often unpleasant, action-filled, and violent, thus causing sleep disruption and sometimes injuries to the patients or their bedpartners. Pathogenesis is linked to the absence of the REM suppression of muscle tone (atonia) [53]; therefore, patients can enact their dreams, with behaviors that vary from small hand movements to violent activities (such as punching, kicking, or leaping out of bed) [52]. The patient usually remembers the dream. RBD in childhood and adolescence is rare and usually associated with narcolepsy, idiopathic hypersomnia, neurodevelopmental disorders, or structural brainstem abnormalities [40], whereas in adulthood it is a strong predictor of neurodegeneration [54–56]. RBD can also represent a side effect of pharmacologic agents, such as selective serotonin reuptake inhibitors [57]. Nightmare disorder is characterized by recurrent, highly dysphoric dreams, that generally occur during REM sleep and often result in awakening [17]. On awakening, the person rapidly becomes oriented and alert. Nightmares can also occur in children with posttraumatic stress disorder [58]. Patients with nightmare disorder may be scared, but usually manage to report the dream and are well oriented, with an intact sensorium; parental intervention is accepted well. During the nightmare, there is little motor activity and the child does not move out of bed (because of REM atonia) and there is no dream enactment. Emotional contents are characteristically negative, with anxiety and fear, but also anger, rage, embarrassment, and disgust [40]. Exposure to violent content (e.g., television, tablet, or computer programs) may contribute to nightmares and, therefore, should be avoided [59]. Recurrent isolated sleep paralysis is defined as a period of inability to perform voluntary movements, which occurs at the beginning of a sleep period (hypnagogic) and/or after waking up (hypnopompic). Each episode lasts from seconds to a few minutes and causes clinically significant distress, including bedtime anxiety or fear of sleep. The individual experiencing sleep paralysis is conscious and alert, but feels paralyzed; all muscle groups are involved, with the exception of the diaphragm and the extrinsic muscles of the eyes. The pathogenesis of this disorder is linked to the persistence of REM atonia into wakefulness, thus normal mental activity occurs in the presence of body paralysis. Sleep paralysis is also a symptom

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commonly seen in narcolepsy: if other suggestive symptoms, such as EDS, cataplexy, or hallucinations, are reported, referral to a sleep specialist is recommended [40]. 5.4 Central Hypersomnia

The central disorders of hypersomnolence include narcolepsy type 1 (NT1), narcolepsy type 2 (NT2), idiopathic hypersomnia (IH), and Kleine Levine syndrome (KLS) [17].

5.4.1

This disorder is characterized by persistent EDS (>3 months), with uncontrollable sleep attacks and short naps, with patients often reporting a dream, dream-like experiences (most often visual) occurring at the sleep-wake transition (called hypnagogic and hypnopompic hallucinations), transient episodes with impossibility to move (called sleep paralysis), and cataplexy (characterized by sudden and typically brief loss of muscle tone and preserved consciousness), very often induced by emotions. Lumbar puncture for the measurement of the orexin A levels in the cerebrospinal fluid (CSF) is very important for the diagnosis of NT1 and its differential diagnosis from the other hypersomnia; in 95% of patients with NT1, orexin A levels are significantly reduced (30 days) and about 15% have persistent episodes after more than 20 years from onset [67]. Also, the symptoms cannot be attributed to another medical condition or substance abuse [68]. KLS is a definitely rare disorder with onset typically during adolescence, with only few cases observed before 9 years of age [67]. KLS is more frequent in males than in females.

5.5 Sleep-Related Movement Disorders

According to the criteria reported in ICSD-3, the main sleeprelated movement disorders are: Restless Legs Syndrome (RLS), Periodic Leg Movement Disorder (PLMD), and Sleep Bruxism. Restless Sleep Disorder (RSD) [17] is a recently identified condition of childhood.

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5.5.1 Restless Legs Syndrome

According to the diagnostic criteria of the International Restless Legs Syndrome Study Group (IRLSSG) [69], RLS can be defined as an urgent need to move the legs, often but not always accompanied or perceived as a consequence of unpleasant sensations; this need to move the legs and any associated unpleasant sensation begins or worsens during periods of rest or inactivity such as sitting or lying down; the symptoms are partially or totally eliminated with movement, such as walking or stretching the legs, at least as long as the activity persists; the symptoms occur or worsen in the evening or at night, compared to the day and are not attributable to any other medical condition (e.g., myalgia, venous stasis, arthritis, edema or leg cramps, etc.) [69]. Supporting criteria are: a positive family history (mutations have been found in the genes BTBD9, MEIS1, and PTPRD [70]; the improvement of symptoms after dopaminergic therapy and the presence of PLMS, which can be observed in 70–90% of cases [69]. The prevalence in the general population is estimated from 0.1 to 11.5%; in the large majority of cases, RLS is present as an idiopathic form, whereas in 20–30% of cases, it can be secondary to anti-dopaminergic drugs or be associated with different conditions, such as pregnancy, iron depletion, diabetes mellitus, liver failure, polyneuropathy, bone marrow disorders, rheumatoid arthritis, and kidney diseases [71]. The etiopathogenetic mechanisms of RLS have yet to be defined, although numerous studies have demonstrated the involvement of specific structures of the central nervous system, such as the hypothalamic dopaminergic nucleus A11 [72], the dopaminergic system, and central iron metabolism disorder [71]. Recently, an involvement of the mesolimbic dopaminergic pathway and of the gray subcortical structures has also been proposed [73, 74]; on the other hand, a brain connectivity study showed that the increased thalamic connectivity to the prefrontal regions in patients with RLS receiving dopaminergic therapy suggests an effect of the treatment on the thalamus [75, 76]. Although dopaminergic treatment was previously the first-line therapy, prolonged use can result in a severe worsening of symptoms known as “augmentation”. Clinical studies of pregabalin, gabapentin, oxycodone-naloxone, and iron preparations have provided new therapeutic options, although most patients still report inadequate long-term symptom management [71], thus paving the way also to some non-pharmacological approaches [77–79].

5.5.2 Periodic Limb Movement Disorder

PLMD is defined as PLMS with a rate > 15 h (in children >5 h), associated with disturbed sleep or a complaint of daytime consequences that cannot be better explained by another cause [17]. Beside the assessment of PLMS, PSG allows for the identification/exclusion of comorbidities possibly accounting for the daytime symptoms, such as obstructive sleep apnea. PLMD has a prevalence of 4–11% in adults [80], while PLMS are even more prevalent in the general population (28.6% in the HypnoLaus study

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[81]). Age, male sex, use of antidepressants, RLS, and the presence of BTBD9 rs3923809, TOX3 rs3104788, and MEIS1 rs2300478 alleles are independent predictors of a PLMS index >15 h [81]. Increased PLMS have been reported in 5–6% of children referred to a sleep center [82], with a male prevalence [83], but PLMD in only 0.3% of children. Different studies, probably using less strict criteria, have found a prevalence as high as 14% [84]. It is believed that, similar to RLS, central dopamine pathways may play a crucial role for PLMD, in consideration of the efficacy on PLMS of L-dopa and dopamine agonists [72]. Alcohol, smoking, and caffeine may favor PLMD [85], as well as some medications, such as antidepressants [80, 86], and iron deficiency anemia [87]. Treatment of PLMD and RLS basically overlaps and includes oral or intravenous iron supplementation, dopamine agonists, gabapentin, opioids and, sometimes, benzodiazepines [88]. 5.5.3

Sleep Bruxism

The ICSD-3 criteria for sleep bruxism include anamnestic report of family members of nighttime grinding noise in the last 6 months for at least 3 nights per week; abnormal wear of the teeth; hypertrophy of masticatory muscles; often, temporal headache or orofacial pain and temporomandibular joint malfunction [17]. According to an updated international consensus, bruxism is a masticatory muscular activity that can occur during sleep (characterized as rhythmic or non-rhythmic) and/or wakefulness (characterized as repetitive or prolonged tooth contact and/or strengthening or pushing of the jaw). The prevalence of sleep bruxism is 10–13% in adults and 40–45% in the pediatric and adolescent population. The diagnosis makes use of PSG, in order to plan the most appropriate treatment, e.g., through the use of bites or some drugs (i.e., benzodiazepines, muscle relaxants, botulinum toxin) [89].

5.5.4 Restless Sleep Disorder

RSD has been recently identified in children and adolescents and is defined as restless sleep complaint reported by the parent of caregiver, frequent movements involving large muscle groups and occurring during sleep at a rate of at least three nights per week for at least 3 months. vPSG must show at least five movements or repositioning per hour of sleep. Moreover, daytime symptoms of sleepiness, hyperactivity, mood disturbance, difficulty concentrating, or other daytime impairment attributed to the poor sleep quality must be present; the disorder sleep must not be secondary to another disorder or medication [90]. RSD has been found in 7.7% of children referred to a pediatric sleep center [91]. Increased nocturnal sympathetic predominance [92], increased indices of NREM sleep instability [93], iron deficiency [94], comorbid parasomnias [95], and attention deficit hyperactivity disorder (ADHD) have been reported [96, 97]. Treatment with iron, orally or intravenously, seems to improve both nighttime and daytime symptoms [94].

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5.6 Disorders of Breathing During Sleep

The term disorders of breathing during sleep encompasses the wide spectrum of sleep-related respiratory disturbances. As such, sleeprelated breathing disorders (SRBD) represent the subset that meets the ICSD-3 criteria [17], whereas other abnormalities (such as snoring or catathrenia) are not included. The ICSD-3 [17] describes four different but interrelated SRBD: obstructive sleep apnea syndrome (OSAS), central sleep apnea syndrome, sleeprelated hypoventilation disorders, and sleep-related hypoxemia disorder. SRBD are prevalent disorders in the adult population and are often associated with a variety of comorbid conditions. The degree of airway narrowing can range from snoring to complete collapse of the airway and cessation of airflow. As such, SRBD may manifest as abnormal airflow, oxygen desaturation, and hypercapnia, that can be associated with EDS [98]. EDS is a frequently reported symptom and has been included as a possible clinical feature in the ICSD-3 diagnostic criteria for OSAS, although a significant number of these patients do not report it [99, 100]. SRBD are known to be associated with several comorbidities and complications. Among others, SRBD have been found to negatively impact psychomotor efficiency and cognition, especially attention, executive function (particularly processing speed), and memory (especially declarative memory) [101–103]. In the elderly, it has been postulated that age may exert a synergistic effect with SRBD to affect cognitive decline, although more studies are needed to clarify this relationship [104, 105] and genetic factors certainly play a role [106]. SRBD have been shown to negatively impact QoL, including general health perception, physical functioning, social functioning, work performance, and vitality [107]. In the elderly, OSAS has also been associated with depression, thus leading to lower QoL [108]. It has been shown that use of continuous positive airway pressure (CPAP) in patients with moderate-to-severe OSAS can improve QoL measures, as indexed by a decrease in sleepiness, anxiety, and depression [109]. The association between OSAS and mortality has been studied as well. Overall, there is an increase of associated cardiovascular mortality in these patients [110], whereas the treatment with CPAP may decrease the risk of all-cause and cardiovascular mortality [110–112]. The treatment of choice for OSAS remains the positive airway pressure (PAP), which can be delivered as CPAP, bilevel PAP, or auto-titrating PAP [98]. Behavioral treatment includes weight loss, positional therapy, and avoidance of alcohol and sedatives before bedtime. Oral appliances and mandibular advancement devices have been shown to improve OSAS, although CPAP generally provided more benefits in severe cases [113, 114]. The surgical approach is considered when these therapies do not provide

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adequate response [98]. Surgery is directed at the site of obstruction and is often staged: nasal surgery, procedures for palatal obstruction, hypopharyngeal surgery, etc. [99]. Newer technologies, such as hypoglossal nerve stimulation, are becoming appealing as alternatives to CPAP because of their lower surgical-related morbidity, good clinical outcomes, and multilevel effects on airway obstruction [115, 116]. 5.7 Other Medical and Psychiatric Conditions

6

Sleep is essential for the cognitive, immunological, and cardiovascular functions, for health and well-being. In recent decades, the hours of night sleep have progressively decreased, due to biological and psychological factors and, above all, to the excessive consumption of electricity (including the use of light emitting devices even at night), resulting in the onset of behavioral and cognitive effects and possible occurrence of inflammatory and metabolic deficits [117]. Moreover, many sleep disorders can be associated with medical conditions: sleep deprivation and insomnia are frequently associated with psychiatric problems (especially anxiety and depression), bidirectionally [118, 119]; on the other hand, movement disorders, such as RLS or PLMS, can also be associated with psychiatric disorders, both depression and ADHD, as examples [120– 122], and various antidepressants are associated with the onset of RLS or PLMS [86]. Finally, among hypersomnia, KLS has some clinical features in common with bipolar disorder or schizophrenia and, recently, a common genetic component for KLS and bipolar disorder has also been identified [123]. Lastly, some symptoms of type 1 narcolepsy, such as hallucinations and cataplexy, may be a challenging differential diagnosis with some psychiatric diseases, especially in children [124, 125]. Overall, a detailed knowledge of the most common sleep disorders is essential for an appropriate management of the patient with associated comorbidities, in order to be able to setup the most appropriate therapy and reduce the risk of side effects.

Conclusions In this chapter, we have summarized the main neuro- and psychophysiological aspects of sleep and its implications for the diagnosis and treatment of the major sleep disorders. Overall, sleep can be viewed as a complex neurobiological phenomenon allowing for homeostatic optimization of neural networks and the replay-based consolidation of specific circuits, especially important for cognition, psychology, and behavior. The importance of sleep to the brain requires a state that cannot be available during wake and allows for the optimization of homeostasis and consolidation of specific circuits. Accordingly, recent evidence points out that sleep-associated plasticity is focused not on acquiring new information, but rather

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INDEX A Advances ................................................. 1–17, 34, 35, 38, 43, 60, 70, 145, 172–174, 206 Alzheimer’s Disease Dementia (ADD) ............. 68, 70–82 Anticipatory attention.......................................24, 25, 28, 38–40, 52–56

B

Electroretinogram (ERGs/PERGs).......... 121–125, 128, 138–145 Enhancement.............................................. 2, 13, 44, 215, 243–246, 251, 255 Event related potential (ERP) ........................ 1–9, 12–17, 28, 34–38, 40–45, 48, 52, 54, 55, 58–60, 131, 144, 162, 165, 170, 208, 224, 251–254 Expectancy.......................................24–26, 36, 38, 52–55

Brain-computer interface (BCI)..................196, 204–234

F

C

Functional near-infrared spectroscopy (fNIRS) ....................................181–198, 204, 205

Clinical .......................................................... 5, 40, 69, 93, 119, 169, 182, 204, 257, 265 Cognition ..................................................... 25, 110, 111, 165, 170, 172, 228, 263, 272, 278, 279 Cognitive neuroscience.....................................24, 28, 34, 109, 165, 170, 174 Color...................................................116–119, 128, 132, 134, 136–138, 140, 141, 143–145, 189 Contingent negative variation (CNV) ........23–30, 36, 38 Contrast sensitivity..................................... 127, 134–136, 140, 141, 143, 144 Cortical functional connectivity ..................................... 69 Cortical information flow ............................................. 112 Crossmodal.................................248, 251, 254, 256, 257

D Deep learning ......................................174, 206, 207, 219 Dementia with Lewy bodies (DLB)....................... 68, 70, 71, 74–82

E Electroencephalogram (EEG) ................... 2, 3, 9, 12, 25, 27, 28, 30, 34, 36, 47, 48, 50–52, 58–60, 68–71, 73, 74, 77–80, 94, 95, 100, 103, 104, 109–112, 119, 120, 126, 128, 144, 145, 157, 159–166, 170, 172–174, 181–198, 204–219, 221–224, 226–228, 230, 232–234, 251–254, 264–266 Electrophysiology......................................... 70, 110, 121, 122, 129, 138–145, 280, BNF–144

I Imperative stimulus........................ 24, 26, 28, 36, 55, 59

L Laser evoked potential (LEP).................................92–103

M Machine learning (ML) ............................... 52, 174, 206, 215, 219, 220, 233 Magnetoencephalography (MEG) ........ 4, 131–133, 145, 157–174, 204, 205, 231 Mild Cognitive Impairment (MCI) ...............5, 6, 12–16, 70, 72, 74, 130 Mismatch negativity (MMN) ............................ 1–17, 170 Motor preparation ....................... 24, 25, 38, 41, 44, 196 Multisensory......................................................... 241–257

N Neuroimaging ...................... 70, 78, 132, 141, 145, 170, 172, 181, 192, 194, 205, 208, 251, 254, 255 Neurology.........................................................5, 129, 280 Neuromodulation ................................................ 118, 144 Neuroscience ....................... 34, 158, 172–174, 195, 198 Normal sleep ................................................265–267, 280

O Olfactory systems ................................................. 109–111

Massimiliano Valeriani and Marina de Tommaso (eds.), Psychophysiology Methods, Neuromethods, vol. 206, https://doi.org/10.1007/978-1-0716-3545-2, © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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PSYCHOPHYSIOLOGY METHODS

288 Index P

S

Pain .................................. 92, 93, 95–104, 196, 268, 277 Parkinson’s Disease Dementia (PDD)................... 68, 70, 71, 74–82 Perception................................24, 34, 35, 38, 44, 49–52, 95, 96, 100, 103, 109–112, 116, 117, 137, 140, 186, 196, 228, 241, 242, 244, 245, 248, 278 Pheromones.......................................................... 109–112 Polysomnography ................................................ 264–265 Post Imperative Negative Variation (PINV) ...........................................................28, 29 Psychiatry.....................................................................5, 17 Psychophysiology ..........................................24, 115–146, 170, 174, 263–280 P300.......................................................... 1–17, 144, 162, 208–210, 221, 222, 225–229, 233

Sleep...............................30, 75, 130, 162, 186, 233, 263 Sleep architecture .......................................................... 264 Sleep disorders............................ 233, 263–265, 269–280 Sleep stages .................................222, 264, 266, 267, 271 Slow cortical potentials ....................................25, 26, 210

R

W

Readiness potential ..................................... 25, 41, 43, 46 Resting-state eyes-closed electroencephalographic (rsEEG) rhythms .................................... 68–80, 82

Warning stimulus ........................................ 24, 26, 28, 36

T Transfer entropy (TE)................................................... 112

V Vision ..................................................115–119, 121, 122, 128–130, 132, 134–146, 244, 248 Visual evoked response (VEP) ........................... 119, 120, 122–130, 133, 135, 139–145, 208, 209, 223