Adjusting deviant oscillatory dynamics in Schizophrenia via computerized-cognitive training

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Adjusting deviant oscillatory dynamics in

Schizophrenia via computerized-cognitive training

Dissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.)

vorgelegt von Petia Popova an der

Mathematisch-Naturwissenschaftliche Sektion Fachbereich Psychologie

Tag der mündlichen Prüfung: 27.06.2016 1. Referent: Prof. Dr. Brigitte Rockstroh 2. Referent: Prof. Dr. Christian Wienbruch

Konstanzer Online-Publikations-System (KOPS) URL: http://nbn-resolving.de/urn:nbn:de:bsz:352-0-350957

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DANKSAGUNG

This doctoral thesis was only possible with the support of Prof. Dr. Brigitte Rockstroh.

She made my PhD real, offered me excellent conditions for productive working, confi- dence, exciting and constructive discussions and understanding.

I would like to thank Gregory Miller for his advices and help on the studies drafts, my colleagues and friends for their contributions, exciting dialogues and ideas and for mak- ing my time at ZPR Reichenau enjoyable: Anne Schawohl, Marina Widmann, Almut Carolus, David Schubring, Vanessa Hirt, Thomas Kustermann, Andreas Mühlherr, Johanna Fiess and Kienley - Thanx people! A word of thanks also goes out to Ursula Lommen, Dagmar Moret and Barbara Awiszus for their patience help and humor. I would like to thank Prof. Dr. Chrisitan Wienbruch for developing the FAT training and his helpful and insightful comments. I am grateful that Prof. Dr. med Chrisitan Dett- mers and Prof. Dr. Christian Wienbruch agreed to supervise and review the thesis.

I would like to offer a very special thanks to Station 33 Psychiatry Center Reichenau.

Especially Dr. med Karl Pröpster, Dr. Michael Odenwald, Dr. Inga Schalinski, Anne Schawohl and Marina Widmann but of course to all staff members and every partici- pant in the studies.

I am particularly grateful for the indulgence and support of my husband Tzvetan, his help in analytic questions and his advices.

I am grateful for the support of my parents Stoynka and Ivan and family members Fil- inka and Mincho.

I would like to dedicate this thesis to my husband, friend and confident Tzvetan and my wonderful and funny sons Filip and Emil.

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Schizophrenia disease is characterized by severe impairments in cognitive and perceptual functions. Recent findings demonstrate that neural oscillatory mechanisms might play a fundamental role in a number of cognitive functions in the disease, suggesting a redefinition of Schizophrenia as disorder of brain dynamics. Appropriate treatment requires characterization of deviant oscillatory mechanisms and their temporal dynamics as well as the evaluation of possible relationship to cognitive behavior.

Present thesis focused on the impact of cognitive training interventions on cognitive functioning and underlying neural oscillatory mechanisms under task- and resting state task-negative conditions.

Conceptually, the present thesis can be divided into three parts. Part 1 summarizes the findings that unveil schizophrenia as disease of severe cognitive abnormalities and impaired temporal coordination and dynamics. Part 2 places these observations into a broader methodological perspective and addresses the question of cognitive ameliora- tion possibilities in response to neuroplasticity- based training procedures. Thereby the extensiveness and boundaries of training effects were explored in studying the brain- state dependent functioning and relevance for intact cognitive functioning by examining oscillatory activity and fundamental mechanisms observed at rest in schizophrenia and health; and evaluating their modulation potential in schizophrenia after -- based intervention. Part 3 will finally recapitulate the findings in the present thesis and suggest how the studies on hand contribute to a deeper understanding of neural oscillations and how timing precision and coordination in schizophrenia might be related to deficient behavior output.

Study 1 Deficits in social cognition including facial affect recognition and their detrimental effects on functional outcome are well established in schizophrenia. Structured trainings have substantial effects on social cognitive measures including facial affect recognition.

Elucidating training effects on cortical mechanisms involved in facial affect recognition may identify mechanisms of dysfunctional facial affect recognition in schizophrenia and foster remediation strategies. In the present study, 57 schizophrenia patients were ran-

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ii domly assigned to (a) computer-based facial affect training that focused on affect discrimination and working memory in 20 daily 1-hour sessions, (b) similarly intense, targeted cognitive training on auditory-verbal discrimination and working memory, or (c) treatment as usual. Neuromagnetic activity was measured before and after training during a dynamic facial affect recognition task (5 s videos showing human faces gradually changing from neutral to fear or happy expressions). Effects on 10-13 Hz (alpha) power during the transition from neutral to emotional expressions were assessed via MEG based on previous findings was analyzed based on previous findings that alpha power increase is related to facial affect recognition and is smaller in schizophrenia than in healthy subjects. Targeted affect training improved overt performance on the training tasks. Moreover, alpha power increase during the dynamic facial affect recognition task was larger after affect training than after treatment-as-usual, though similar to that after targeted perceptual- cognitive training, indicating somewhat nonspecific benefits. Alpha power modulation was unrelated to general neuropsychological test performance, which improved in all groups. Results suggest that specific neural processes supporting facial affect recognition, evident in oscillatory phenomena, are modifiable. This should be considered when developing remediation strategies targeting social cognition in schizophrenia.

Study 2 Neuronal oscillations provide an efficient means of communication, fostering functional neural states supporting action and reaction. High in the action hierarchy, cognitive abilities are severely compromised in schizophrenia. Current thinking highlights a clocking mechanism provided by the phase of an ongoing slow oscillation that offers a temporal frame for coding of perceptual and computational elements. Yet unclear is whether and how a dysregulated clocking mechanism accounts for diminished cognitive performance in schizophrenia. Neuromagnetic oscillatory activity was related to cognitive performance assessed by the MATRICS Consensus Cognitive Battery in 46 schizophrenia patients (SZ) and 58 healthy comparison individuals (HC). HC showed a corre- lation between high-frequency gamma-band oscillations (> 40Hz) and working memory performance. This relationship was disrupted in several ways in SZ. First, patients evi- denced less gamma power, poorer working memory performance, and no relationship between these measures. Second, beyond gamma, the power spectra were dominated by

~10 Hz alpha oscillations with no group differences in amplitude. However, analysis of

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iii phase-to-amplitude coupling (PAC) revealed exaggerated clocking of gamma activity by alpha phase in SZ, associated with poor working memory performance. Third, despite entrainment by the same 10 Hz clock, gamma amplitude was abnormally distributed across the duty cycle, a potential consequence of compromised interneuron inhibition.

Fourth, SZ showed over-engagement of a fronto-parietal network measured by gamma phase coherence, suggesting a brain state hindering cognitive output. Such an endoge- nous temporal organization may be a core dysfunction in SZ: a segregation / integration input imbalance fostering compromised behavioral output and reduced cognitive performance.

Study 3 A lower than normal amplitude of gamma oscillations is a common finding in schizophrenia and has been interpreted as an indication of maladaptive temporal organization and functional network architecture. While modulation of task-related oscillatory activity by neuroplasticity- based training has been reported, training effects on spontaneous oscillations at rest should complement evidence of reorganizational capacity of schizophrenic brains. The present study evaluated resting-state oscillatory brain activity and cognitive test performance in 59 schizophrenia patients before and after a random as- signment and accomplishment of either four-week neuroplasticity-based training or treatment-as –usual intervention (TAU). Pre-training gamma power increased after training but not after TAU. This increase was related to improved cognitive test performance. Source analysis confirmed the increase in gamma activity confident to the fronto-parietal network. Results suggest that psychological intervention affects maladaptive temporal organization and functional network architecture. The relationship between training-induced gamma power changes and cognitive test performance further strengthens the potential implementation of task-independent neural network communication in higher-order cognitive processes.

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Zusammenfassung

Schizophrenien sind schwere psychische Erkrankungen, die durch schwere Einschrän- kungen im Bereich der kognitiven Leistungsfähigkeit gekennzeichnet sind. Die diesen kognitiven Dysfunktionen zugrunde liegenden Prozesse stehen nach wie vor im Zent- rum neurowissenschaftlicher Forschung. Aktuelle Forschungsergebnisse belegen, dass primär oszillatorische Phänomene eine zentrale Rolle in der Vermittlung typischer kognitiver Defizite spielen könnten und legen die Beschreibung von Schizophrenien als Stö- rung neuronaler Hirndynamik nahe. Sollte die Beeinflussung dieser Dynamik auf Ziel von Behandlungsstrategien sein, so ist die genauere Beschreibung abnormaler oszillatorischen Aktivität und Synchronisierungsmechanismen und deren Bedeutung für kognitive Prozessen und Defizite ein oberstes Gebot.

Gegenstand der vorliegenden Arbeit ist die Auswirkung computer-gestützte kognitive Intervention (Training) auf kognitive Testleitung bei Schizophren-Erkrankten und auf Korrelate der kortikalen oszillatorischen Netzwerkdynamik.

In theoretischen Teil der Arbeit werden neuere Erkenntnisse über kortikale Oszillatio- nen allgemein und in Verbindung mit Schizophrenien als Störung der kortikalen Netz- werkarchitektur zusammengefasst. Daraus ergeben sich Fragestellungen, die in drei auf- einander aufbauenden Studien untersucht wurden und anhand von 3 Publikationen dargestellt werden. Neuromagnetische Aktivität während der Verarbeitung visueller Stimuli (Gesichter) und unter Ruhebedingungen wurden aufgezeichnet und Mecha- nismen der Perzeptionsverarbeitung und Kommunikation analysiert. Ferner wurde das Plastizitätspotenzial Schizophren Erkrankter anhand der Wirkungen eines computer- gestützten kognitiven Training erforscht. Im letzten Abschnitt werden die Haupter- kenntnisse der vorliegenden Arbeit anhand der Leitfragen diskutiert, inwiefern die hier dargestellten Studien zu einem besseren Verständnis der Bedeutung kortikaler Oszillati- onen beitragen und welche Bedeutung zeitliche Präzision und Koordination neuronaler Ereignisse für das Verständnis der kognitiver Defizite bei Schizophrenien haben könn- ten.

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Studie 1 Defizite in den Bereichen sozialer Kognition und Emotionswahrnehmung sind oft repli- zierte Befunde bei Schizophren Erkrankten. Neuere Erkenntnisse deuten darauf hin, dass strukturierte Trainingsverfahren diese Defizite beeinflussen können. Die Erfor- schung von Trainingseffekten auf kortikalen Mechanismen, die mit sozialer Kognition in Zusammenhang stehen, könnte potenziell nicht nur Mechanismen der Affektwahr- nehmung bei Schizophrenien aufdecken, sondern auch Behandlungsstrategien beeinflussen. In der vorliegenden Studie wurden 57 Schizophrenie Patienten randomisiert entweder a) einem computer-gestütztem Training mit Fokus auf Affektdiskrimination und Arbeitsgedächtnis oder b) auditorisch-verbale Diskrimination und Arbeitsgedächt- nis oder c) Standardbehandlung zugeteilt. Beide Trainings umfassten jeweils 20 Sitzun- gen. Vor und nach der entsprechenden Intervention wurde die neuromagnetische Akti- vität in einem Paradigma zur Affektdiskrimination gemessen (Videos in denen sich über 5 Sekunden Gesichter graduell von einem neutralen zu einem emotionalen (glücklich oder ängstlich)Ausdruck veränderten). Vorausgegangene Studien haben gezeigt, dass der bei gesunden Probanden während dieser Affektdiskrimination beobachtete Anstieg der Alpha Aktivität (Power, 10-13Hz) bei Schizophren Erkrankten signifikant geringer ausfällt. Die Ergebnisse zeigen, dass gezieltes und intensives Training zusätzlich zu einer Verbesserung der Erkennungsleistung die Power im Alpha Frequenzbereich signifikant gegenüber stationärer Standardbehandlung erhöht. Demgegenüber unterschied sich die verbesserte Alpha Modulation nicht zwischen den beiden Trainingsvarianten. Un- abhängig von Trainingseffekten verbesserten sich kognitive (neuropsychologische) Test- leistungen bei allen drei Gruppen über die Zeit hinweg. Die Ergebnisse legen nahe, dass oszillatorische Mechanismen, die affektive Gesichtsdiskrimination zugrunde liegen, durch gezielte und intensive Intervention positiv veränderbar sind. Dies wiederum sollte bei der Planung von Behandlung Strategien berücksichtigt werden.

Studie 2 Neuronale oszillatorische Phänomene stellen ein effizientes Mittel kortikaler Netzwerk- kommunikation dar. Sie bieten die funktionale Grundlage für effektive Informations- verarbeitung und Handlung. Als Funktion oszillatorischer Phänomene wird ein „taktge- bender“ Mechanismus für die Definition des zeitlichen Rahmens für die Kodierung von kognitiven und perzeptuellen Elementen vermutet. Dieser Mechanismus ist über die Phase langsamer Oszillationen definiert. Noch nicht ausreichend untersucht ist die Fra-

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vi ge, ob und wie ein fehlregulierte „Taktgeber“ bei Schizophrenien zum Verständnis schizophrenie-typischer kognitiver Dysfunktionen beitragen könnte. Um dieser Frage näher zu kommen, wurden in der vorliegenden Studie neuromagnetische oszillatorische Aktivität und neuropsychologische Testergebnisse (MATRICS Consensus Cognitive Battery, MCCB) hinsichtlich ihrer Zusammenhänge untersucht. Die Daten von 46 Schizophren Erkrankter und 58 gesunde Kontrollprobanden wurden analysiert. Wäh- rend Gesunde durch die Korrelation zwischen hochfrequenter Gamma-Band Oszillati- onen (> 40Hz) und der Leistung aus dem Arbeitsgedächtnis Untertest in der MCCB charakterisiert waren, war dieser Zusammenhang bei Schizophren Erkrankten beein- trächtigt: Patienten zeigten niedrige Amplituden im Gamma-Band, schwächere Arbeits- gedächtnisleistung und keine Korrelation zwischen den beiden Merkmalen. Für, ~10 Hz dominante Alpha-Aktivität wurden keine Gruppenunterschiede festgestellt. Jedoch ergab die Analyse des „taktgebende“ Mechanismus mittels Phasen-Amplituden Kopp- lung (PAK) eine überstark getaktete Gamma-Amplituden - Alpha-Phasen-Beziehung bei Schizophren Erkrankten, die in korrelativem Zusammenhang mit reduzierter Arbeits- gedächtnisleistung stand. Schließlich waren bei Patienten trotz vergleichbarer Alpha- Taktung die Gamma-Amplituden anormal über den 10-Hz Zyklus verteilt, was als Kon- sequenz eines beeinträchtigten (Interneuron) Inhibitionsmechanismus diskutiert wurde.

Gamma- Phasensynchronisation wies ferner bei schizophren Erkrankten auf Überakti- vierung des frontal-parietalen kortikalen Netzwerks hin, was als fehlerhafter, möglich- erweise kognitive Leistung einschränkender, Gehirnzustand interpretiert wurde. Es er- scheint möglich, dass eine gestörte zeitliche Auflösung (Präzision) einen pathophysiologischen Faktor bei Schizophrenien darstellt, der die Zerlegung und In- tegration von Einzelreizen (segregation-integration) beeinflusst und darüber zu einer Dysbalance der Input-Informationen beiträgt, die letztendlich höhere kognitive Prozesse bei Schizophren Erkrankten modulieren.

Studie 3 Eine pathologisch niedrige Amplitude der hochfrequenten Gamma-Oszillationen ist wiederholt in Verbindung mit Schizophrenien berichtet worden. Diese Defizite wurden in der Literatur als Indikator für fehlerhafte zeitliche Koordination und Funktionseffizi- enz auf lokaler und Netzwerk Ebene interpretiert.

Während die Veränderbarkeit (Plastizität) oszillatorischer Aktivität nach computer- gestützten kognitiven Trainings berichtet wurde, ist die Modulation der kontinuierlichen

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vii Ruheaktivität bisher kaum erforscht. Ziel der vorliegenden Studie war die Untersu- chung oszillatorischer Plastizität nach computer-gestützten kognitiven Trainingsverfah- ren (im Vergleich zu Standardintervention). Insgesamt nahmen 59 Schizophrene Pati- enten vor und nach der jeweils 4-wöchigen Interventionsphase an einer MEG-Messung (5-min Ruhemessung) und neuropsychologischen Tests teil. Die Zuteilung zu Training (N=39) oder Standardbehandlung (N=20) erfolgte randomisiert. Die Ergebnisse zeigten einen signifikanter Anstieg der Gamma-Aktivität in der Trainingsgruppe, jedoch nicht in der Gruppe, die Standardbehandlung erfuhr. Weiterhin ergab sich nur in der Trai- ningsgruppe ein Zusammenhang zwischen der Verbesserung der kognitiven Leistungs- fähigkeit mit der Verbesserung in Gamma Powe. Eine Analyse der Gamma-Aktivität im Quellraum bestätigte die Lokalisation des Trainings Effektes in frontal-parietalen kortikalen Netzwerken. Die vorliegenden Befunde legen eine Verbesserung in der zeitlichen Koordination und somit Netzwerk-Kommunikation nahe und bestätigen damit ein Re- organisationspotential auch für Spontanaktitivät. Dieses Reorganisationspotential könn- te sich zugleich positiv auf die Defizite in höheren kognitiven Prozessen auswirken.

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viii Abbreviations

ANOVA Analysis Of Variance BCI Brain-Computer-Interface BFP Brain Fitness Program

CE Cognitive Exercises

CFC Cross-Frequency Coupling

CPZ Chlorpromazine

DICS Dynamic Imaging of Coherent Sources DLPFC Dorsolateral Prefrontal Cortex

DMN Default Mode Network

DNC Dynamic Network Connectivity DSM Diagnostic and Statistical Manual EEG Electroencephalogram

E/I Excitation/Inhibition

e.g. For example (Latin: exempli gratia) ERD Event Related Desynchronization ERF Event Related Field

ERS Event Related Synchronization et. al. And others (Latin: et alii) FAT Facial Affect Traiing

fMRI Functional Magnetic Resonance Imaging GABA Gamma Aminobutyric Acid

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GAF Global Assessment of Functioning HC Healthy Control

Hz hertz

IAF Individual Alpha Frequency ICA Independent Component Analysis ICD International Classification of Diseases

M100 Evoked magnetic field approx. 100 ms post stimulus

MATRICS Measurement And Treatment Research to Improve Cognition in Schizophrenia

MCCB MATRICS Consensus Cognitive Battery MEG Magnetoencephalogram

MNI Montreal Neurological Institute MRI Magnetic Resonance Imaging

ms milliseconds

NMDA N-methyl-D-aspartic acid PAC Phase Amplitude Coupling

PANSS Positive And Negative Syndrom Scale

PV Parvalbumin

RSN Resting State Network

SZ Schizophrenia

TAU Treatment as usual

TFR Time Frequency Representation of Power

vs. Versus

WM Working memory

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Conducted Studies & Own Research Contributions

The studies in the present thesis were co-authored and supported by a number of colleagues. Those are listed bellow together with my own research contributions.

Study 1: Changing facial affect recognition in schizophrenia: Effects of training on brain dynamics

Authors: Petia Popova, Tzvetan G. Popov, Christian Wienbruch, Almut Carolus, Gregory A. Miller, Brigitte Rockstroh

Published in NeuroimageClinical

Data Collection: Recruitment of participants, supervision of training interventions, neuropsychological testing and MEG assessments. Data Analysis and Manuscript:

behavioral and MEG data analyses and drafted the manuscript.

Study 2: Same clock, different time read-out: Spontaneous brain oscillations and their relationship to deficient coding of cognitive content Authors: Tzvetan Popov and Petia Popova

Published in NeuroImage

Data Collection: Recruitment of participants, neuropsychological testing and the MEG measurements. Data Analysis and Manuscript drafting : behavioral and MEG data analyses and drafted the manuscript.

Study 3: Cognitive training affects spontaneous gamma oscillations and cognitive performance in schizophrenia

Authors: Petia Popova, Tzvetan Popov, Christian Wienbruch, Almut Carolus and Brigitte Rockstroh

Submitted in Clinical Neurophysiology

Data Collection: Recruitment of participants, supervision of training interventions, neuropsychological testing and the MEG measurements. Data Analysis and Manuscript drafting : behavioral and MEG data analyses and drafted the manuscript

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“[…] If someone aims to study cognition, he needs to first understand the mechanisms underlying it in encoding the syntax of processing and organ- ization […] ” ¹

Beside hallucinations, delusions and disordered reasoning, patients suffering from schizophrenia are often characterized by a profound deficiency in cognitive skills. One goal in cognitive neuroscience is to understand the pathophysiological mechanisms underlying these deficits. Although progress has been made in reducing concepts such as attention and working memory and associated deficits to empirically quantifiable entities (Nuechterlein et al., 2008), a direct mapping of the links between the variety of the ob- servable deficits and unifying cortical mechanisms is still not possible.

Understanding aspects of cognition by studying brain function is a challenging endeav- or. Cognition is a complex concept that requires complex processing mechanisms beyond the boundaries of cerebral communication alone (Miller, 2010). Nevertheless, ad- vancing our knowledge of cerebral communication holds great promise for a better understanding of the aspects of cognitive processing.

The present thesis reports on three experimental observations that contribute to the ongoing scientific discourse on the neurobiology of cognition. Each of these observations provides a different view of large-scale neuronal computation during cerebral communication. These are examined in the context of group studies on healthy adults and patients diagnosed with schizophrenia. Thereby the main focus lies on the modulation capacity of neuroplasticity-based cognitive trainings on stimulus-induced and spontaneously generated brain rhythms.

The introduction provides an overview of the concept of “brain oscillations”. Some of the theoretical approaches and experimental applications that have led to a better understanding of the mechanisms underlying brain oscillations are outlined. Finally, current indications for the modulatory effects of neuroplasticity-based training approaches in the context of impaired cognitive abilities and oscillatory characteristics in schizophrenia are summarized as a prelude to the three main studies of this thesis.

1 (György Buzsaki, http://thesciencenetwork.org/programs/ultimate-block-party-the-arts-and-sciences-of-play/gyorgy-buzsaki- part-1)

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The concept of brain oscillations 1

1 Background

1.1 The concept of brain oscillations

Periodic patterns are essential for all organisms. Many vitally important functions, such as respiration, heartbeat, body temperature, metabolic processes, and hormone regulation, are rhythmically organized, and so is neuronal activity, as reflected in brain oscillations. These emerge from periodic fluctuations on the cell membrane of myriads of neurons and reflect specific patterns of electrical activity over a wide range of spatial and temporal scales (Varela et al., 2001; Buzsaki and Draguhn, 2004). Periodic fluctuations of synchronous neuronal firing are generated spontaneously and in response to stimuli (Lakatos et al., 2008; Basar, 2013b).

The flow of surrounding information is continuous. So how does the brain achieve its internal communication? In order to cope with sensory demands, input information needs to first be “segmented” and subsequently integrated in a hierarchically organized construct (Buzsaki, 2010). One suggestion is that the “segmentation in the brain is done by the multiple rhythms it generates”.² The exact mechanisms allowing for the complex dynamics of neural networks, the syntactic rules of organization and communication are still not well understood, but several hypothetical mechanisms are discussed in the literature.

The term “excitation/inhibition balance” (E/I) in the neural microcircuit refers to the successful interplay of excitatory and inhibitory synaptic inputs affiliated to some neuronal event: oscillation or response evoked by stimuli (Okun, 2009). In the cortex, inhibition is provided by gamma amino butyric acid (GABAergic) interneurons. It is hypothesized that a subtype of GABAergic interneurons, called Parvalbumin positive (PV⁺) interneurons, comprise the largest source of inhibitory current for spontaneous and stimulus-evoked activity (Lewis et al., 2005; Gonzalez-Burgos and Lewis, 2008).

2(György Buzsaki: http://thesciencenetwork.org/programs/music-science-medicine-at-the-new-york-academy-of-sciences/neural-syntax-what- does-music-offer-to-neuroscience-and-vice-versa)

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The concept of brain oscillations 2

This subtype of interneurons is also found to have an important role in the generation of network oscillations, in particular those in the gamma band frequency (30-90Hz) (Gonzalez-Burgos and Lewis, 2008). It is assumed that in regulating the activity of principle cells, interneuron inhibition serves as a mechanism to increase the selectivity, precision and speed of cortical responses and is indispensable for the temporal integration of sensory inputs – and thus cortical computation (Jonas, 2007; Okun, 2009).

The interplay between excitation and inhibition often gives rise to rhythmic behavior.

Principal cells are controlled by various rhythmic patterns. Instead of firing arbitrarily, their activity becomes framed into determined “windows of opportunity”: they become synchronized (Jonas, 2007). On the network level the synchronous activity of otherwise spatially distant neurons is accompanied by an oscillatory behavior of the neurons involved (Buzsaki, 2010). Thus, it was recognized that oscillatory activity on the single neuron level or even on the level of neuronal groups could act as a means of establishing synchrony between neurons (Engel et al., 1992; Sturm and Konig, 2001).

Currently a comprehensive body of literature suggests that some oscillatory patterns can be viewed as the periodic succession of the synchronized firing currents, which in turn reflect the temporal envelope of spiking activity (Fernandez-Ruiz and Herreras, 2013) and thus offer a mechanisms for neuronal communication (Fries, 2005, 2015). Synchro- nization in that case reflects how the brain achieves large-scale integration of temporally simultaneous but spatially distributed information processing, allowing coherent cognition and behavior (Ward, 2003). Although the relationship between field oscillations and neuronal firing activity is not yet completely clarified, this hypothesis assumes brain rhythms to be concrete, available mechanistic representations of neural activity enabling the study of information processing in cognition and behavior (Fernandez-Ruiz and Herreras, 2013).

Neural synchronization is associated with a variety of rhythmic oscillations (Buzsaki and Draguhn, 2004; Buzsaki, 2006b), and the precise timing of neuronal-spike discharges is believed to be important for the coding of information (O'Keefe and Recce, 1993;

Buzsaki and Chrobak, 1995; Konig et al., 1995; Singer and Gray, 1995; Buzsaki and Wang, 2012). It is believed that such rhythmic brain activity functions as a “clocking mechanism” that helps coordinate the timing of neural firing (Jones and Wilson, 2005),

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The role of alpha and gamma oscillations in cognition 3

thereby enabling the organization and execution of cognitive operations (Singer and Gray, 1995; Fries, 2005, 2009; Wang, 2010).

The subtle changes that result from the modulation of large-scale, synchronous neocortical oscillations can be detected on the scalp surface (Kahana, 2006). One major goal of contemporary neuroscience is to decode how complex cognitive processes are related to brain dynamics. A common approach to determine such a relationship is to study the interdependency of oscillatory activity measured with magneto- and electroencephalog- raphy (MEG/EEG) and the dynamics of cognitive performance. Previous studies have detected a spectrum of neuronal oscillations. Typically, the name of these oscillations indicates a frequency band rather than a particular frequency, such as delta (1-4 Hz), theta (4-8 Hz), alpha (8-14 Hz), beta (14-30 Hz), gamma (30-90 Hz) and HFO (high frequency oscillations, >90 Hz) (Buzsaki and Draguhn, 2004). Two of these oscillatory bands seem to be particularly relevant for processing information: the alpha and gamma band oscillations. These are also the basis of the investigation in the present thesis, and the following chapters will therefore focus on alpha and gamma band oscillations in more detail.

1.2 The role of alpha and gamma oscillations in cognition

So how exactly do these two oscillations contribute to cognitive functioning? Alpha oscillations, ranging from 8 to 14 Hz (mean frequency at 10Hz), are the most dominant oscillatory activity that can be observed with MEG/EEG techniques (Jensen and Mazaheri, 2010). It is hypothesized that alpha oscillations regulate the de/activation of a given cortical region in accordance with the demands of a task. Contrary to the initial view that alpha oscillations reflect the unoccupied/idling state of the brain, today these oscillations are recognized to play a central role during cognitive processing (Palva and Palva, 2007; Basar and Guntekin, 2012). They are not only largely present at rest but are also modulated by a stimulus/task. In the latter case, they respond to a stimulus and/or task with either a decrease (ERD, event-related desynchronization) or an increase (ERS, event-related synchronization) of amplitude (Klimesch, 2012).

These two types of alpha response follow a somewhat reverse logic that goes against the assumption that the impact of an oscillation is proportional to its magnitude (as in ERS).

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In fact, it was demonstrated that the “negative sort of think”³ – alpha ERD – could be seen as the active response during information processing (Klimesch et al., 2007). The underlying hypothesis is that alpha ERS is related to inhibition and alpha ERD to the release from inhibition. In line with this hypothesis, a substantial body of research has demonstrated that brain regions that are task-relevant exhibit alpha ERD while other regions not directly involved in the “processing schedule” manifest alpha ERS.

The “alpha inhibition timing hypothesis” has been proposed on the basis of such observations (Klimesch et al., 2007). This hypothesis relates the phase of alpha oscillations to cognitive processing. Because the definition of an oscillation implies rhythmicity, and given the notion of alpha as an active inhibitory mechanism in the brain, this assumption needs to be understood as a fluctuation of alpha activity between a minimum and a maximum of inhibition (Klimesch et al., 2007). Within the framework of this hypothesis it has been assumed that the alpha inhibition could serve as either a selective activation or as a blocking mechanism for information processing. Considering the working brain as a continuously linked network, the mechanism of alpha inhibition could be regarded as a mechanism for gating information between different brain regions (Jensen and Mazaheri, 2010), effectively fostering perception (Ai and Ro, 2014). More specifically, this assumption implies that at early stages of perception alpha “directs the flow of information” to those neural structures that represent information relevant for the encoding (Klimesch et al., 2011). This gives rise to a rhythmic behavior providing pulsing moments of inhibition, which in turn constitute “windows of opportunity” for the processing of information in a given area (VanRullen and Koch, 2003; Jensen and Mazaheri, 2010; Mazaheri and Jensen, 2010). In summary, the “inhibition timing hypothesis” offers an empirically testable framework accounting for the cerebral network dynamics.

As mentioned above, cognitive functions require the large-scale coordinated interaction of neurons distributed across the brain (Tallon-Baudry and Bertrand, 1999; Buzsaki and Watson, 2012). It has been demonstrated that activated neuronal groups synchronize in the gamma frequency (30-90 Hz) independently of brain anatomy and across various

3 In 1934 Adrian and Matthews confirmed Berger’s observations on alpha event-related desynchronization during cognitive demand; the latter was called by Walter “negative sort of think”: Walter G (1934) Thought and Brain: A cambridge experiment. The spectator, London:478-479, Ros T, B JB, Lanius RA, Vuilleumier P (2014) Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework. Frontiers in human neuroscience 8:1008.

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species and under different conditions (Fries et al., 2007; Fries, 2009; Buzsaki et al., 2013). Local active processing within a given cortical region is typically dominated by synchronous gamma frequency (>30 Hz) activity (Palva and Palva, 2007; Kujala et al., 2015). Gamma oscillations have been shown to be modulated by stimulus properties and cognitive parameters such as attention and memory and relate to conscious perception (Fries, 2005; Palva and Palva, 2007; Ray and Maunsell, 2015). Thus, they are considered a reflection of active engagement and processing within a cortical area (Jensen and Mazaheri, 2010). Yet, their description and characterization from a mechanistic perspective is still debated. Several theoretical accounts currently present in the literature are outlined below.

Theoretical frameworks like binding-by-synchronization (Singer, 1993; Singer and Gray, 1995; Singer, 1999) or communication through coherence (CTC) (Fries et al., 2007; Fries, 2009) were concerned with a biologically plausible answer to the question how a highly distributed system with parallel processing operations achieves coordination and consol- idation of associated information to accomplish coherent percepts or action. That is, presumably appropriately routed (by virtue of alpha oscillations, see above), how is information encoded in the brain? The synchronization of neuronal activity by phase locking of self-generated network oscillations (binding-by-synchronization) appeared to be a suitable answer. The experimental underpinning of this theory consists in the observation that groups of spatially distributed neurons engage in synchronous oscillatory activity at approximately 40 Hz when excited by visual stimulation. In turn, this synchronization enables neuronal discharge with high temporal precision, thus theoretically engaging a hypothetical sender and receiver into a brief (e.g. 25 ms) yet common temporal frame advantageous for information transfer. This linking of simultaneously active neurons into “coalitions” or assemblies potentially increases their representational capacity, thus enabling precise and fast processing (Fries, 2015).

The gamma cycle and communication through coherence (CTC) hypothesis (Fries, 2005; Fries et al., 2007) extends this by arguing that only coherently oscillating (in phase or phase- locked) neuronal groups are able to communicate effectively. The reason for this is that the “windows of opportunity” for the respective input and output are available at the same time. Central to this extended version is the idea of coordination by inhibitory interneurons, where a computational mechanism for timing control converts firing rate into temporal code, enabling transmission and read-out.

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In their essence the theories outlined above are not mutually exclusive. While the binding by synchronization theory suggests a mechanism for representational coding on a more local level, the communication through coherence and gamma-cycle theories focus on mechanistic implements for cognitive dynamics providing flexible neuronal coherence patterns for dynamic long-range communication. Although no single theory is able to consider all functional consequences at once (Fries et al., 2007), gamma oscillatory activity during cognitive tasks has been confirmed by a large body of literature, which makes the relationship between gamma band activity and cognition a realistic scenario for a functional mechanism (Tallon-Baudry and Bertrand, 1999; Varela et al., 2001; Jensen et al., 2007;

Fries, 2009; Uhlhaas et al., 2011).

Cross-frequency interactions

Cognitive operations are too complex to be ascribed to and provided by a single particular brain rhythm. Investigations of strictly defined frequencies in isolation, ignoring the functional interplay between frequencies, appear incompatible with the complex oscillatory and functional architecture of the brain (Klimesch, 2012). Flexible changes in the functional architecture are required, and as such they need to rely on some dynamic interactions (Jensen and Mazaheri, 2010). It was proposed that an appropriate mechanism underlying cognitive operations should take into account the simultaneous occurrence and interplay of various frequencies (Engel and Singer, 2001; Palva and Palva, 2007).

Recent findings indicate that the magnitude of gamma oscillations is modulated by slower rhythms (< 12 Hz). The mechanism is known as cross-frequency coupling (CFC) or phase-to-amplitude coupling (PAC). CFC between gamma and slow rhythms within the same or different brain regions are well documented (Palva et al., 2005; Cohen et al., 2009; Canolty and Knight, 2010; Fell and Axmacher, 2011). Such a mechanism is hypothesized to allow the functional configuration of distant cortical circuits (Osipova et al., 2008; Voytek et al., 2010; Buzsaki and Wang, 2012). Thus the co-occurrence of multiples brain rhythms supports the E/I balance, leads to increased sensitivity and routing of information through the brain space and offers an economic computational principle for information processing enabling selective read-out of relevant information (Jensen et al., 2012).

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Spontaneous brain oscillations – the brain as system of oscillators 7

Based on the pulsed inhibition hypothesis of the slow alpha oscillations an important consequence of such an assumption is that the magnitude of alpha ERS controls the duty cycle, i.e. the time window within which gamma amplitude increases (Jensen et al., 2014).

In this scenario, slow, long-range oscillations act as a “clock” generator and fast oscillations emerge at a local scale, enabling computation with very high temporal precision.

Another interpretation of this oscillatory cooperation in the visual modality was ex- pressed in VanRullen and Koch (VanRullen and Koch, 2003). They postulated that CFC of alpha and gamma oscillations leads to a discrete perception, so that “snapshots”

of the perception are coded in the gamma waves, with the entire percept being concili- ated by alpha waves.

1.3 Spontaneous brain oscillations – the brain as system of os- cillators

As mentioned above, periodic fluctuations of synchronous neuronal firing are not only generated in response to stimuli but can also arise spontaneously (Lakatos et al., 2008;

Basar, 2013b). In the previous sections it was demonstrated that time coordination of spiking activity through oscillatory activity (power) and/or dynamics (phase-related) could offer various possibilities and mechanistic tools to achieve neuronal cooperation over local and/or long distances.

An important fact that was ignored for a long time is the intrinsic, spontaneously generated internal noise activity of the brain. In recent years an increasing body of literature has begun to uncover and characterize the brain’s spontaneous activity with regard to its functional significance. It is hypothesized that the unoccupied brain activity has an important functional role in operational activities, providing temporal constraints for communication (Raichle, 2015b). The active processing of external information is thus added to the background of intrinsic activity going on continuously. In 1914, Brown explicitly stated that the operations in the brain are mainly intrinsic, and not only serve the response to, acquisition and maintenance of upcoming information but also its interpretation and even prediction (Brown, 1914; Raichle, 2011). This is supported by observations pointing to intrinsically spontaneous, well-organized temporal and spatial patterns in the brain, called resting state functional connectivity (Biswal et al., 1995;

Linkenkaer-Hansen et al., 2001; Raichle et al., 2001; Buzsaki and Draguhn, 2004;

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Spontaneous brain oscillations – the brain as system of oscillators 8

Buzsaki, 2006b; Mantini et al., 2007; Chen et al., 2008; de Pasquale et al., 2010;

Raichle, 2011; Leopold and Maier, 2012; Ghuman et al., 2013; Harmelech and Malach, 2013; Botcharova et al., 2015).

A unique property of spontaneous brain activity seems to be the consistent segregation of activity into resting-state networks (RSNs). The latter coincide anatomically with the major functional systems of the brain during tasks (Raichle et al., 2001; Fox et al., 2007;

Smith et al., 2009; Biswal et al., 2010; Smith, 2012; Florin and Baillet, 2015). Certain brain regions are found to have inverse activation during resting state vs. cognitive task conditions. Specifically, these regions demonstrate a decrease in activity during tasks vs.

greater activity during rest and thus were supposed to constitute an organized network supporting a default mode of brain function (Raichle et al., 2001; Greicius et al., 2003). It bears mentioning that resting state, and specifically DMN (Default Mode Network) state activity at rest have been extensively studied based on hemodynamic fluctuation analysis, but little is known about its electrophysiological spatial and spectral characteristics.

Chen and colleagues investigated the spatial characteristics of spectral power distribu- tion in EEG resting state eyes-open (EO) vs. eyes-closed (EC) conditions (Chen et al., 2008). They reported a strong modulation of fronto-central theta (4-7 Hz), low beta (13- 23 Hz), and middle (7.7-9.5 Hz) and high (10-12 Hz) posterior alpha in relation to the resting state condition (EC/EO), where high beta and gamma both with prefrontal dis- tribution exhibited no changes across conditions.

Thus the available literature to date recognizes that spontaneous brain activity, reflected in rest activity vs. task-deactivation of interconnected brain areas (DMN) is, firstly, present and preserved through different evolutionary paths between rodent and primate brains, attesting to its functional significance in the mammalian brain (Lu et al., 2012);

secondly, is comparable to task-related spatio-temporal organization (Smith et al., 2009;

Arviv et al., 2015); and thirdly, is modulated depending on external demands (Greicius and Menon, 2004). It is thus suggested that both task-driven neuronal responses and behavior are reflections of the ongoing dynamic and functional organization of the brain (Fox et al., 2005; Fox et al., 2006), and their relationship needs to be further examined in normal and pathophysiological states of the brain.

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Cognitive and related oscillatory dysfunction in schizophrenia 9

1.4 Cognitive and related oscillatory dysfunction in schizo- phrenia

Schizophrenia is defined as a severe disorder with the occurrence of hallucinations, delusions, disordered thinking and behavior, lack of normal everyday functioning and social withdrawal. In spite of certain evidence that numerous additional cognitive impairments form a constant part of the illness, translating into effects on real-life functioning, they are still not a part of the DSM-V (Diagnostic and Statistical Manual of Mental Dis- orders) rules for schizophrenia diagnostics. This chapter will summarize the main findings concerning cognitive functioning in schizophrenia and the relation to underlying deficient brain activity. Both are discussed in the literature as potential predictors of functional outcome (Green et al., 2004) and as targets for remediation (Keefe and Harvey, 2012; Mehta et al., 2013).

Cognitive deficits in schizophrenia

The findings related to the cognitive impairments in schizophrenia have remained stable over decades and across different regions and cultures of the world (Schaefer et al., 2013). Despite profound effects on psychotic symptoms, the impact of antipsychotic medication on cognitive deficits remains negligible (Keefe and Harvey, 2012). The evidence from research strongly points towards a general deficit with significantly reduced performance across all cognitive tests in comparison to healthy subjects (Schaefer et al., 2013). The impairments are found to occur predominantly, but to a varying extent, in the domains of neuro-cognition and social cognition (Mehta et al., 2013; Peyroux and Franck, 2014). Both domains are supposedly related, yet they are treated as distinct con- structs in schizophrenia (Penn et al., 1997a; Penn et al., 1997b; Allen et al., 2007; Mehta et al., 2013). Neuro-cognitive disabilities in schizophrenia affect domains dealing with information encoding and processing, like inhibition processes and sensory processing, speed of processing, attention, working memory, and executive functioning (McGurk et al., 2007; Medalia and Choi, 2009; Barch and Ceaser, 2012).

On the other hand, impaired social cognition is one of the hallmarks of schizophrenia and also one of the most important unmet treatment needs for people suffering from this mental disease (Kern et al., 2009; Peyroux and Franck, 2014). Social cognition generally refers to a large range of skills that allow people to perceive and interpret social stimuli

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and guide daily social interactions (Frith and Frith, 2007; Green and Leitman, 2008;

Green et al., 2008; Lavoie et al., 2014). A major component of social cognition is the processing of emotional information. Emotion perception impairment in schizophrenia is a robust finding (Hooker and Park, 2002; Sachs et al., 2004; Kohler et al., 2010). Of particular importance, the deficits in facial emotion recognition in schizophrenia are also well established (Edwards et al., 2002; Hooker and Park, 2002; Sachs et al., 2004;

Johnston et al., 2010).

Because cognitive dysfunction in schizophrenia was recognized to be perhaps one of the most critical determinants for quality of life and functional outcome, in the past few decades an increasing body of literature has focused on the pathophysiology of these deficits (Johnston et al., 2010; Lesh et al., 2011; Barch and Ceaser, 2012). Encoding, maintaining and manipulating sensory information requires a rigid temporal coordination of neuronal activity (Lozano-Soldevilla et al., 2014). As summarized in section 1.1, brain oscillations are considered central in the above-mentioned operations, enabling coordination and mediating cognitive performance. A large body of literature over the last decades has demonstrated that schizophrenia patients (SZ) exhibit impaired oscillatory dynamics in different frequency bands related to various cognitive deficits (Basar and Guntekin, 2012; Basar, 2013b, a; Basar et al., 2013; Basar and Guntekin, 2013). In section 1.2 I provided a brief overview of the current hypothesis concerning two brain rhythms that are shown to act in neuronal dynamics and coordination. More explicitly, the putative role of alpha and gamma oscillations in this context was mentioned, suggesting that alpha-oscillations play a regulatory “clock” role in controlling the routing of information in accordance with task demands, enabling local processing reflected in the gamma band frequency activity. Their synchronization and dynamic pattern interaction, allowing simultaneous brain operations at multiple temporal and spatial scales, were also briefly mentioned. Considering the relevance of these cortical rhythms for cognitive abilities, their dysfunction in schizophrenia is outlined in the next section.

Impaired oscillatory activity in schizophrenia

Despite the heterogeneity of schizophrenia symptoms it is assumed that the underlying or mediating neural oscillatory activity could unify at least some of the disease’s characteristics (Uhlhaas and Singer, 2006, 2015b). The literature describes schizophrenia as an illness of neurotransmitter system distortion (dopamine, glutamate, GABA) that leads to

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changes in brain structure, functioning, physiology and connectivity and is manifested in affective, cognitive and psychotic symptoms (Schaefer et al., 2013). In the following par- agraphs I will briefly summarize the findings that reveal schizophrenia-related dysfunctions in cortical circuits, before devoting more attention to the resultant deviant oscillatory patterns.

It is assumed that during normal brain functioning the generation of coherently organized large-scale networks is critically dependent upon the activity of gamma- aminobutyric acid (GABA) inhibitory interneurons. These express the calcium (Ca²⁺) binding protein parvalbumin (PV) and glutamatergic activation of PV interneurons, leading to rhythmic fluctuations of neuronal excitability at low and high frequency ranges (Sohal et al., 2009; Rivolta et al., 2015). GABAergic interneurons have been dis- covered to play a crucial role in the regulation of synchronization in neuronal popula- tions (Gonzalez-Burgos and Lewis, 2008; Gonzalez-Burgos et al., 2015). This applies to both spontaneous and sensory-evoked brain activity and thus is considered a key mechanism for the generation of network oscillations, in particular those in the gamma band frequency (30-90Hz) (Gonzalez-Burgos and Lewis, 2008; Buzsaki and Wang, 2012).

In schizophrenia research the findings to date point towards a general alteration of GABA-mediated inhibition processes (Uhlhaas and Singer, 2010; Gonzalez-Burgos et al., 2015). Consequently, because of both reduced GABA levels (Marsman et al., 2014) and reduced gamma amplitude during processing of sensory information, attention and working memory (WM) (Cho et al., 2006; Uhlhaas and Singer, 2013), it was supposed that cognitive deficits in SZ arise from alterations in the cortical circuitry reflected in frequency-specific deficits in the generation and maintenance of coherent gamma oscillations (Lewis et al., 2005; Light et al., 2006; Basar and Guntekin, 2008; Gonzalez- Burgos and Lewis, 2008; McNally et al., 2013; Lewis, 2014; Lozano-Soldevilla et al., 2014). Schizophrenia could be thus related to impaired dynamics of excitatory and inhibitory networks (E/I balance), which leads to deficits in the generation of appropriate oscillatory dynamics, which in turn are indispensable for cognitive functioning, effectively manifesting in certain symptoms in schizophrenia (Yizhar et al., 2011). However, the exact role of fast synaptic inhibition of neocortical circuits in schizophrenia is still not precisely defined (Gonzalez-Burgos et al., 2015).

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Recent findings indicate considerable abnormality in oscillatory dynamics during task- related and spontaneous brain activity in patients with schizophrenia (Uhlhaas and Singer, 2015b). The available literature dealing with oscillatory activity and cognitive functions is less decisive. For instance, for of the same cognitive function there are con- flicting views about which oscillatory rhythms are “in charge” (Basar, 2013a). An emerg- ing question is whether cognitive dysfunctions in schizophrenia are related to low or high frequency oscillations or to both (Moran and Hong, 2011).

The prevalent opinion in the scientific community views alpha oscillations as central for effective sensory processing, as outlined in section 1.2. Schizophrenia patients exhibit impaired alpha band oscillations, related to sensory processing (Turetsky et al., 2007;

Brockhaus-Dumke et al., 2008; Popov et al., 2011a; Popov et al., 2012; Carolus et al., 2014; Popov et al., 2014). The findings related to alpha deficits are interpreted as dis- turbed integration of perception (Basar-Eroglu et al., 2013) or maintenance of coherent perception (Basar-Eroglu et al., 2015) or as deficits in information sampling spreading to deficits in higher cognitive functioning (Carolus et al., 2014). Alpha power activity has also been related to alterations in social interaction in schizophrenia (Popov et al., 2014;

Billeke et al., 2015). In Popov et al., 2014, schizophrenic patients’ deficits in affective facial recognition were investigated on the basis of cortical oscillatory activity. It was demonstrated that SZ patients exhibit deficits in the stimulus evaluation, probably re- flecting impaired readiness and abnormal dynamics during facial affect recognition (Popov et al., 2014). Deviant pre-stimulus alpha power related to behavioral accuracy is equally and extensively reported (Hanslmayr et al., 2007; Basar-Eroglu et al., 2009; Koh et al., 2011; Abeles and Gomez-Ramirez, 2014; Lou et al., 2014; Popov et al., 2014).

As well as the frequently reported relationship between alpha oscillatory activity and processes of encoding and perception, gamma band oscillatory activity is also thought to play a role, particularly in perception. Impaired gamma in early sensory processes (Uhlhaas et al., 2006a; Ford et al., 2008; Hirano et al., 2008) as well as in auditory and visual processing (Johannesen et al., 2008; Popov et al., 2011a; Popov et al., 2013; Sun et al., 2013; Taylor et al., 2013) have been reported. Lower gamma band synchronization in the perception of Gestalt or Moony faces was related to deficits in the neural circuitry and psychopathology (Spencer et al., 2004; Grutzner et al., 2013; Sun et al., 2013; Spencer and Ghorashi, 2014).

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Sufferers of schizophrenia exhibit deficits in a large range of cognitive domains. Certain- ly each single domain contributes to the overall cognitive dysfunction in schizophrenia.

Yet, one cognitive construct seems to have a greater impact, and thus is of particular note here. This is the inability to actively represent goal information in working memory (WM) (Barch and Ceaser, 2012). Gamma band oscillations, being the embodiment of

“active” processing (Jensen and Mazaheri, 2010), are related to WM (Ward, 2003) and are altered in SZ (Haenschel et al., 2009; Uhlhaas and Singer, 2010; Williams and Boksa, 2010; Basar, 2013a; Tan et al., 2013; Uhlhaas and Singer, 2013). The power of gamma activity has been demonstrated to be reduced in SZ, accompanied by a lower WM load (Haenschel et al., 2009; Uhlhaas and Singer, 2010) or by failed modulation with WM task difficulty (Basar-Eroglu et al., 2007). The deficits related to impaired gamma and working memory in SZ were demonstrated to arise from deficits in the function of the dorsolateral prefrontal cortex (DLPFC) (Barch et al., 2001; Cho et al., 2006; Lewis et al., 2008; Lewis and Gonzalez-Burgos, 2008; Senkowski and Gallinat, 2015) and might be explained by abnormalities in the GABAergic system (Gonzalez- Burgos and Lewis, 2008; Lewis et al., 2008; Haenschel et al., 2009; Uhlhaas and Singer, 2010; Gonzalez-Burgos et al., 2011).

While many studies focus on high frequency gamma band oscillations because of their role in information transfer between brain regions (Williams and Boksa, 2010; Sun et al., 2013; Tan et al., 2013; Uhlhaas and Singer, 2013), an increasing body of evidence supports the view that gamma oscillation abnormalities in schizophrenia often occur in the context of oscillation abnormalities at lower frequencies (White et al., 2010; Allen et al., 2011; Moran and Hong, 2011; Kirihara et al., 2012; Sun et al., 2013). Abnormali- ties in oscillatory activity in SZ at low and high frequency ranges were reported not only in relation to stimulus/task conditions but also at rest. Both reduced and increased resting state gamma activity has been previously reported (Kissler et al., 2000; Rutter et al., 2009; Kikuchi et al., 2011; Spencer, 2011).

Although varying with respect to the frequency band of interest, task or resting condition, the existing literature seems to agree upon the observation that brain oscillations are a suitable dependent metric when studying cognition. Accordingly, changes in cognitive performance are expected to manifest in changes in oscillatory activity. The next section provides a summary of studies targeting changes in cognitive performance in schizophrenia.

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Changing cognitive deficits in schizophrenia 14

1.5 Changing cognitive deficits in schizophrenia

In the middle of the last century the predominant opinion among neuroscientists was that the brain is aplastic from early childhood onward (Merzenich et al., 2013). Experi- ments in the field of physiological psychology contradicted this view and supported the thesis that the brain on the contrary is continuously plastic (Merzenich et al., 2013;

Vinogradov et al., 2013; Merzenich et al., 2014; Leung et al., 2015). Today there is a consensus that the central nervous system possesses an adaptive capacity and thus is far from being hardwired after the critical periods of maturation and development. In the following sections the concept of neuroplasticity as well as findings from neuroplasticity- based training applications in the field of schizophrenia will be presented.

The concept of neuroplasticity

Neuroplasticity is the ability of the brain to change and adapt in response to changes in inputs (McCullumsmith, 2015). That is, changes in the brain occur through experience or in response to external behavioral training as a consequence of the effort to adapt to a changing environment. Thereby the brain undergoes physical modifications, including structural changes in neuronal quantity or functional formation or reorganization, strengthening or weakening of neuronal circuits or other functional mechanisms as yet unknown (Elbert and Rockstroh, 2004; Merzenich et al., 2013).

Neuroplasticity is how the brain converts experience and the need for adaptation into the learning of new behavioral patterns. Conforming to the Hebbian rule that states that

“cells that fire together wire together”, activated neurons in the cortex strengthen their connections with their nearest neighbors (Hebb, 1949), thus increasing the neuronal response coordination during overt behavior. The greater the coordination of neurons in lower levels of the network that feeds “higher” system levels, the greater their response selectivity and computational power in the processing of information to drive dynamic remodeling at higher processing levels (Nahum et al., 2013; Merzenich et al., 2014). Dysfunctions in distributed neural systems in the prefrontal cortex that underlie perception, cognition, social interactions, emotion regulation and motivation are found in neuropsychiatric disorders such as schizophrenia (Vinogradov et al., 2012).

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Evidence for functional neuroplasticity in schizophrenia 15

It is further suggested that the high degree of learning-dependent plasticity in these regions combined with a very specific training program (e.g. with focus on perceptual, cognitive or socio-affective functions) with advanced computerized technology could drive meaningful and enduring improvements in impaired neural systems relevant to neuropsychiatric illness (Vinogradov et al., 2012).

1.6 Evidence for functional neuroplasticity in schizophrenia

In schizophrenia the durable cognitive deficits (Penn et al., 1997a; Penn et al., 1997b;

Penn et al., 2008; Fett et al., 2011; Keefe and Harvey, 2012; Mehta et al., 2013; Bliksted et al., 2014) associated with poor functional, social and occupational outcome (Green et al., 2000; Green et al., 2004; Green and Leitman, 2008; Hofer et al., 2009; Hoe et al., 2012) are hardly modified by standard antipsychotic medication treatments (Goldberg et al., 2007; Keefe et al., 2007; Goff et al., 2011), motivating the development of alterna- tive approaches to target remediation (Klingberg, 2010; Merzenich et al., 2013; Park and Bischof, 2013). Computerized neuroplasticity-based auditory training (targeted cognitive training, TCT, known also as Brain Fitness Program, BFP) consisting of exercises in auditory perception, discrimination, working-memory and verbal processing was repeatedly applied for remediation of basic auditory processing and higher-order cognitive functions, like verbal learning or executive functions in schizophrenia (Fisher et al., 2009; Popov et al., 2011b; Dale et al., 2016). Encouraging results showing significant improvement in cognitive domains and brain measures (e.g. M100, sensory gating ratio) as well as their relationship were reported (Adcock et al., 2009; Popov et al., 2011b;

Dale et al., 2016).

Social cognition as a core deficit in chronic, first-onset and prodromal period schizophrenia patients is related to cognitive deficits and functional outcome (Sachs et al., 2004; Barkl et al., 2014) and is regarded as a target component for treatment (Barkl et al., 2014). The combination of computerized auditory neuroplasticity-based training with additional computerized social cognition training was found to induce not only beneficial effects on brain activity and decrease in positive symptomatic but also as an increase in higher order cognitive abilities like working memory performance or emotion perception, reflected in several social cognition measures in schizophrenia (Hooker and Park, 2002; Sacks et al., 2013; Subramaniam et al., 2014). Nevertheless, since the

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Evidence for functional neuroplasticity in schizophrenia 16

aforementioned social-cognition trainings are applied additively and not in separated groups, it becomes difficult to investigate the specificity of effects depending on training focus (auditory-verbal vs. social-emotional).

In summary, four interim conclusions can be made in the field of cognitive training- based approaches to schizophrenia. First, computerized neuroplasticity-based trainings have the potential to initiate beneficial changes in brain activity and behavior (Merzenich et al., 2013; Vinogradov et al., 2013; Merzenich et al., 2014). Second, such intervention strategies seem to be generalizable beyond trained skills, observed in various neurophysiological measures of brain activity under multiple task conditions by ap- plying the same training procedure (Subramaniam et al., 2012; Hooker et al., 2013;

Sacks et al., 2013; Subramaniam et al., 2014). Third, the beneficial modification of brain activity after training seems to correlate with behavioral performance, pointing to adjustable brain-behavior interactions (Popov et al., 2011b; Popov et al., 2011a;

Subramaniam et al., 2012; Hooker et al., 2013; Subramaniam et al., 2014; Popov et al., 2015). Furthermore, the improvements are demonstrated in both chronic and first- episode patients with schizophrenia (Fisher et al., 2013b; Carolus et al., 2014; Nahum et al., 2014; Fisher et al., 2015; Popov et al., 2015). And last, but perhaps of greatest importance, the improvement in neurocognitive and social-cognitive functions can be generalized into durable benefits in global functioning and functional outcome 6 months later (Fisher et al., 2010; Subramaniam et al., 2012; Subramaniam et al., 2014).

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Open Questions 17

1.7 Open Questions

The neural mechanisms leading to the beneficial effects of cognitive trainings fostering neuroplasticity are not well understood. It has been demonstrated that cognitive training interventions have beneficial and generalizable effects on brain and behavior. It is an open question to what extent specific targeted training (e.g. auditory-verbal vs.

visual-emotional) relies on specific processing mechanisms mediating brain and behavioral changes. This question is addressed in Study 1 of the present thesis.

A neural mechanism that has been shown to be impaired in schizophrenia (alpha synchronization during dynamic facial affective processing) was probed with two different cognitive trainings targeting different processing modalities.

Second, changes in cognitive performance are expected to also manifest in changes in intrinsic dynamics and functional organization of the brain. However, it is often not clear which part of the recorded signal (e.g. multiple frequencies observed over multiple sensors and multiple time points) is of particular relevance for relating spontaneous brain activity to cognitive performance. Study 2 focused on the description and characterization of power spectra, oscillatory interaction mechanisms and related cerebral network organization in a large sample of schizophrenia patients and healthy controls.

Finally, Study 3 was concerned with the question whether effects of training on oscillatory brain activity observed during task conditions are generalized to spontaneous brain activity during rest. The effects of the trainings on sponta- neous brain activity were evaluated with a particular focus on the metric derived in Study 2.

Adjusting deviant oscillatory dynamics in Schizophrenia via computerized-cognitive training