Time-varying functional connectivity and dynamic neurofeedback with MEG: methods and applications to visual perception

Cognitive function involves the interplay of functionally-separate regions of the human brain. Of critical importance to neuroscience research is to accurately measure the activity and communication between these regions. The MEG imaging modality is well-suited to capturing functional cortical communication due to its high temporal resolution, on the millisecond scale. However, localizing the sources of cortical activity from the sensor measurements is an ill-posed problem, where different solutions trade-off between spatial accuracy, correcting for linear mixing of cortical signals, and computation time. Linear mixing, in particular, affects the reliability of many connectivity measures. We present a MATLAB-based pipeline that we developed to correct for linear mixing and compute time-varying connectivity (phase synchrony, Granger Causality) between cortically-defined regions interfacing with established toolboxes for MEG data processing (Minimum Norm Estimation Toolbox, Brainstorm, Fieldtrip). In Chapter 1, we present a new method for localizing cortical activation while controlling cross-talk on the cortex. In Chapter 2, we apply a nonparametric statistical test for measuring phase locking in the presence of cross-talk. Chapters 3 and 4 describe the application of the pipeline to MEG data collected from subjects performing a visual object motion detection task. Chapter 5 focuses on real-time MEG (rt-MEG) neurofeedback which is the real-time measurement of brain activity and its self-regulation through feedback. Typically neurofeedback modulates directly brain activation for the purpose of training sensory, motor, emotional or cognitive functions. Direct measures, however, are not suited to training dynamic measures of brain activity, such as the speed of switching between tasks, for example. We developed a novel rt-MEG neurofeedback method called state-based neurofeedback, where brain activity states related to subject behavior are decoded in real-time from the MEG sensor measurements. The timing related to maintaining or transitioning between decoded states is then presented as feedback to the subject. In a group of healthy subjects we applied the state-based neurofeedback method for training the time required for switching spatial attention from one side of the visual field to the other (e.g. left side to right side) following a brief presentation of a visual cue. In Chapter 6, we used our pipeline to investigate training-related changes in cortical activation and network connectivity in each subject. Our results suggested that the rt-MEG neurofeedback training resulted in strengthened beta-band connectivity prior to the switch of spatial attention, and strengthened gamma-band connectivity during the switch. There were two goals of this dissertation: First was the development of the MATLAB-based pipeline for computing time-evolving functional connectivity analysis in MEG and its application to visual motion perception. The second goal was the development of a real-time MEG neurofeedback method to train the dynamics of brain states and its application to a group of healthy subjects.2019-11-02T00:00:00

Investigating large-scale brain dynamics using field potential recordings: Analysis and interpretation

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (EEG, MEG, ECoG and LFP) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide best-practice recommendations for the analyses and interpretations using a forward model and an inverse model. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems

Oscillatory Network Dynamics in Perceptual Decision-Making

Synchronized oscillations of ensembles of neurons in the brain underlie human cognition and behaviors. Neuronal network oscillations can be described by the physics of coupled dynamical systems. This dissertation examines the dynamic network activities in two distinct neurocognitive networks, the salience network (SN) and the ventral temporal cortex-dorsolateral prefrontal cortex (VTC-DLPFC) network, during perceptual decision-making (PDM). The key nodes of the SN include the right anterior insula (rAI), left anterior insula (lAI), and dorsal anterior cingulate cortex (dACC) in the brain. When and how a sensory signal enters and organizes within the SN before reaching the central executive network including the prefrontal cortex has been a mystery. Second, prior studies also report that perception of visual objects (face and house) involves a network of the VTC—the fusiform face area (FFA) and para-hippocampal place area (PPA)—and the DLPFC. How sensory information enters and organizes within the VTC-DLPFC network is not well understood, in milliseconds time-scale of human’s perception and decision-making. We used clear and noisy face/house image categorization tasks and scalp electroencephalography (EEG) recordings to study the dynamics of these networks. We demonstrated that beta (13–30 Hz) oscillation bound the SN, became most active around 100 ms after the stimulus onset, the rAI acted as a main outflow hub within the SN, and the SN activities were negatively correlated with the difficult tasks. We also uncovered that the VTC-DLPFC network activities were mediated by beta (13-30 Hz) and gamma (30-100 Hz) oscillations. Beta activities were enhanced in the time frame 125-250 ms after stimulus onset, the VTC acted as main outflow hub, and network activities were negatively correlated with the difficult tasks. In contrast, gamma activities were elevated in the time frame 0-125 ms, the DLPFC acted as a main outflow hub, and network activities—specifically the FFA-PPA pair—were positively correlated with the difficult tasks. These findings significantly enhance our understanding of how sensory information enters and organizes within the SN and the VTC-DLPFC network, respectively in PDM

Using Brain–Computer Interfaces and Brain-State Dependent Stimulation as Tools in Cognitive Neuroscience

Large efforts are currently being made to develop and improve online analysis of brain activity which can be used, e.g., for brain–computer interfacing (BCI). A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for aiding the disabled and for augmenting human performance. While technical developments obviously are important, we will here argue that new insight gained from cognitive neuroscience can be used to identify signatures of neural activation which reliably can be modulated by the subject at will. This review will focus mainly on oscillatory activity in the alpha band which is strongly modulated by changes in covert attention. Besides developing BCIs for their traditional purpose, they might also be used as a research tool for cognitive neuroscience. There is currently a strong interest in how brain-state fluctuations impact cognition. These state fluctuations are partly reflected by ongoing oscillatory activity. The functional role of the brain state can be investigated by introducing stimuli in real-time to subjects depending on the actual state of the brain. This principle of brain-state dependent stimulation may also be used as a practical tool for augmenting human behavior. In conclusion, new approaches based on online analysis of ongoing brain activity are currently in rapid development. These approaches are amongst others informed by new insight gained from electroencephalography/magnetoencephalography studies in cognitive neuroscience and hold the promise of providing new ways for investigating the brain at work

Information Processing in the Orbitofrontal Cortex and the Ventral Striatum in Rats Performing an Economic Decision-Making Task

University of Minnesota Ph.D. dissertation. August 2015. Major: Neuroscience. Advisor: David Redish. 1 computer file (PDF); vi, 144 pages.The orbitofrontal cortex (OFC) and ventral striatum (vStr) are key brain structures that represent information about value during decision-making tasks. Despite their very different anatomical properties, numerous studies have found similar patterns of value-related signaling in these structures. In particular, both structures are intimately involved in delay-discounting tasks, which involve a tradeoff between reward magnitude and delay to reward. However, the overlapping activity profiles of these brain regions makes it difficult to tease apart their specific contributions to delay-discounting behavior, and to economic decision-making more generally. In order to better understand the contributions of these two regions to value-based choice, we made simultaneous recordings in the OFC and vStr in rats performing a spatial variant of a traditional delay-discounting task. This allowed us to compare OFC and vStr activity directly in the same subjects while they engaged in a prototypical economic decision-making task, and additionally it allowed us to leverage the tools of spatial decoding analysis to measure non-local reward signaling. Chapter 1 provides an introduction to current theories of OFC and vStr function within the decision-making literature, in particular contrasting the concepts of neuroeconomics with the multiple decision-making systems framework. Chapter 2 describes the methods used in this thesis, including the design of the spatial delay-discounting task and the analysis of the neural data. Chapter 3 presents the results of single-unit and Bayesian decoding analyses from this dataset. We found that activity in the OFC and vStr was quite similar at the single-unit level, and inconsistent with the neuroeconomic account of value signaling in a common currency. Instead, when we looked specifically at moments of deliberative decision-making (as emphasized by the multiple systems account), we found important differences between the OFC and vStr. Both the OFC and the vStr showed covert reward signaling during deliberative, vicarious trial-and-error (VTE) behaviors. But vStr signals emerged earlier, before the moment of choice, while covert reward coding in the OFC appeared after the rats had committed to their decision. These analyses were extended to the level of local field potentials (LFPs), recorded from the same dataset. Local field potentials are a useful tool for studying local processing and interactions between brain regions. Chapter 4 describes the LFP results. Important among these was the finding that the vStr led the OFC at the LFP level (again showing temporal precedence), and furthermore, that the vStr was a stronger driver of OFC activity than vice versa, particularly during VTE. The implications of these results, along with those from the single-unit and Bayesian decoding analyses, are discussed in Chapter 5. Emphasis is placed on our emerging understanding of the role of the vStr in flexible behavior, and how the OFC and the vStr might cooperate to influence value-based choice

Information Processing in the Orbitofrontal Cortex and the Ventral Striatum in Rats Performing an Economic Decision-Making Task

University of Minnesota Ph.D. dissertation. August 2015. Major: Neuroscience. Advisor: David Redish. 1 computer file (PDF); vi, 144 pages.The orbitofrontal cortex (OFC) and ventral striatum (vStr) are key brain structures that represent information about value during decision-making tasks. Despite their very different anatomical properties, numerous studies have found similar patterns of value-related signaling in these structures. In particular, both structures are intimately involved in delay-discounting tasks, which involve a tradeoff between reward magnitude and delay to reward. However, the overlapping activity profiles of these brain regions makes it difficult to tease apart their specific contributions to delay-discounting behavior, and to economic decision-making more generally. In order to better understand the contributions of these two regions to value-based choice, we made simultaneous recordings in the OFC and vStr in rats performing a spatial variant of a traditional delay-discounting task. This allowed us to compare OFC and vStr activity directly in the same subjects while they engaged in a prototypical economic decision-making task, and additionally it allowed us to leverage the tools of spatial decoding analysis to measure non-local reward signaling. Chapter 1 provides an introduction to current theories of OFC and vStr function within the decision-making literature, in particular contrasting the concepts of neuroeconomics with the multiple decision-making systems framework. Chapter 2 describes the methods used in this thesis, including the design of the spatial delay-discounting task and the analysis of the neural data. Chapter 3 presents the results of single-unit and Bayesian decoding analyses from this dataset. We found that activity in the OFC and vStr was quite similar at the single-unit level, and inconsistent with the neuroeconomic account of value signaling in a common currency. Instead, when we looked specifically at moments of deliberative decision-making (as emphasized by the multiple systems account), we found important differences between the OFC and vStr. Both the OFC and the vStr showed covert reward signaling during deliberative, vicarious trial-and-error (VTE) behaviors. But vStr signals emerged earlier, before the moment of choice, while covert reward coding in the OFC appeared after the rats had committed to their decision. These analyses were extended to the level of local field potentials (LFPs), recorded from the same dataset. Local field potentials are a useful tool for studying local processing and interactions between brain regions. Chapter 4 describes the LFP results. Important among these was the finding that the vStr led the OFC at the LFP level (again showing temporal precedence), and furthermore, that the vStr was a stronger driver of OFC activity than vice versa, particularly during VTE. The implications of these results, along with those from the single-unit and Bayesian decoding analyses, are discussed in Chapter 5. Emphasis is placed on our emerging understanding of the role of the vStr in flexible behavior, and how the OFC and the vStr might cooperate to influence value-based choice

From sequences to cognitive structures : neurocomputational mechanisms

Ph. D. Thesis.Understanding how the brain forms representations of structured information distributed in time is a challenging neuroscientific endeavour, necessitating computationally and neurobiologically informed study. Human neuroimaging evidence demonstrates engagement of a fronto-temporal network, including ventrolateral prefrontal cortex (vlPFC), during language comprehension. Corresponding regions are engaged when processing dependencies between word-like items in Artificial Grammar (AG) paradigms. However, the neurocomputations supporting dependency processing and sequential structure-building are poorly understood. This work aimed to clarify these processes in humans, integrating behavioural, electrophysiological and computational evidence. I devised a novel auditory AG task to assess simultaneous learning of dependencies between adjacent and non-adjacent items, incorporating learning aids including prosody, feedback, delineated sequence boundaries, staged pre-exposure, and variable intervening items. Behavioural data obtained in 50 healthy adults revealed strongly bimodal performance despite these cues. Notably, however, reaction times revealed sensitivity to the grammar even in low performers. Behavioural and intracranial electrode data was subsequently obtained in 12 neurosurgical patients performing this task. Despite chance behavioural performance, time- and time-frequency domain electrophysiological analysis revealed selective responsiveness to sequence grammaticality in regions including vlPFC. I developed a novel neurocomputational model (VS-BIND: “Vector-symbolic Sequencing of Binding INstantiating Dependencies”), triangulating evidence to clarify putative mechanisms in the fronto-temporal language network. I then undertook multivariate analyses on the AG task neural data, revealing responses compatible with the presence of ordinal codes in vlPFC, consistent with VS-BIND. I also developed a novel method of causal analysis on multivariate patterns, representational Granger causality, capable of detecting flow of distinct representations within the brain. This alluded to top-down transmission of syntactic predictions during the AG task, from vlPFC to auditory cortex, largely in the opposite direction to stimulus encodings, consistent with predictive coding accounts. It finally suggested roles for the temporoparietal junction and frontal operculum during grammaticality processing, congruent with prior literature. This work provides novel insights into the neurocomputational basis of cognitive structure-building, generating hypotheses for future study, and potentially contributing to AI and translational efforts.Wellcome Trust, European Research Counci

Novel methods to evaluate blindsight and develop rehabilitation strategies for patients with cortical blindness

20 à 57 % des victimes d'un accident vasculaire cérébral (AVC) sont diagnostiqués aves des déficits visuels qui réduisent considérablement leur qualité de vie. Parmi les cas extrêmes de déficits visuels, nous retrouvons les cécités corticales (CC) qui se manifestent lorsque la région visuelle primaire (V1) est atteinte. Jusqu'à présent, il n'existe aucune approche permettant d'induire la restauration visuelle des fonctions et, dans la plupart des cas, la plasticité est insuffisante pour permettre une récupération spontanée. Par conséquent, alors que la perte de la vue est considérée comme permanente, des fonctions inconscientes mais importantes, connues sous le nom de vision aveugle (blindsight), pourraient être utiles pour les stratégies de réhabilitation visuelle, ce qui suscite un vif intérêt dans le domaine des neurosciences cognitives. La vision aveugle est un phénomène rare qui dépeint une dissociation entre la performance et la conscience, principalement étudiée dans des études de cas. Dans le premier chapitre de cette thèse, nous avons abordé plusieurs questions concernant notre compréhension de la vision aveugle. Comme nous le soutenons, une telle compréhension pourrait avoir une influence significative sur la réhabilitation clinique des patients souffrant de CC. Par conséquent, nous proposons une stratégie unique pour la réhabilitation visuelle qui utilise les principes du jeu vidéo pour cibler et potentialiser les mécanismes neuronaux dans le cadre de l'espace de travail neuronal global, qui est expliqué théoriquement dans l'étude 1 et décrit méthodologiquement dans l'étude 5. En d'autres termes, nous proposons que les études de cas, en conjonction avec des critères méthodologiques améliorés, puissent identifier les substrats neuronaux qui soutiennent la vision aveugle et inconsciente. Ainsi, le travail de cette thèse a fourni trois expériences empiriques (études 2, 3 et 4) en utilisant de nouveaux standards dans l'analyse électrophysiologique qui décrivent les cas de patients SJ présentant une cécité pour les scènes complexes naturelles affectives et ML présentant une cécité pour les stimuli de mouvement. Dans les études 2 et 3, nous avons donc sondé les substrats neuronaux sous-corticaux et corticaux soutenant la cécité affective de SJ en utilisant la MEG et nous avons comparé ces corrélats à sa perception consciente. L’étude 4 nous a permis de caractériser les substrats de la détection automatique des changements en l'absence de conscience visuelle, mesurée par la négativité de discordance (en anglais visual mismatch negativity : vMMN) chez ML et dans un groupe neurotypique. Nous concluons en proposant la vMMN comme biomarqueur neuronal du traitement inconscient dans la vision normale et altérée indépendante des évaluations comportementales. Grâce à ces procédures, nous avons pu aborder certains débats ouverts dans la littérature sur la vision aveugle et sonder l'existence de voies neurales secondaires soutenant le comportement inconscient. En conclusion, cette thèse propose de combiner les perspectives empiriques et cliniques en utilisant des avancées méthodologiques et de nouvelles méthodes pour comprendre et cibler les substrats neurophysiologiques sous-jacents à la vision aveugle. Il est important de noter que le cadre offert par cette thèse de doctorat pourrait aider les études futures à construire des outils thérapeutiques ciblés efficaces et des stratégies de réhabilitation multimodale.20 to 57% of victims of a cerebrovascular accident (CVA) develop visual deficits that considerably reduce their quality of life. Among the extreme cases of visual deficits, we find cortical blindness (CC) which manifests when the primary visual region (V1) is affected. Until now, there is no approach that induces restoration of visual function and in most cases, plasticity is insufficient to allow spontaneous recovery. Therefore, while sight loss is considered permanent, unconscious yet important functions, known as blindsight, could be of use for visual rehabilitation strategies raising strong interest in cognitive neurosciences. Blindsight is a rare phenomenon that portrays a dissociation between performance and consciousness mainly investigated in case reports. In the first chapter of this thesis, we’ve addressed multiple issues about our comprehension of blindsight and conscious perception. As we argue, such understanding might have a significant influence on clinical rehabilitation patients suffering from CB. Therefore, we propose a unique strategy for visual rehabilitation that uses video game principles to target and potentiate neural mechanisms within the global neuronal workspace framework, which is theoretically explained in study 1 and methodologically described in study 5. In other words, we propose that case reports, in conjunction with improved methodological criteria, might identify the neural substrates that support blindsight and unconscious processing. Thus, the work in this Ph.D. work provided three empirical experiments (studies 2, 3, and 4) that used new standards in electrophysiological analyses as they describe the cases of patients SJ presenting blindsight for affective natural complex scenes and ML presenting blindsight for motion stimuli. In studies 2 and 3, we probed the subcortical and cortical neural substrates supporting SJ’s affective blindsight using MEG as we compared these unconscious correlates to his conscious perception. Study 4 characterizes the substrates of automatic detection of changes in the absence of visual awareness as measured by the visual mismatch negativity (vMMN) in ML and a neurotypical group. We conclude by proposing the vMMN as a neural biomarker of unconscious processing in normal and altered vision independent of behavioral assessments. As a result of these procedures, we were able to address certain open debates in the blindsight literature and probe the existence of secondary neural pathways supporting unconscious behavior. In conclusion, this thesis proposes to combine empirical and clinical perspectives by using methodological advances and novel methods to understand and target the neurophysiological substrates underlying blindsight. Importantly, the framework offered by this doctoral dissertation might help future studies build efficient targeted therapeutic tools and multimodal rehabilitation training

Statistical causality in the EEG for the study of cognitive functions in healthy and pathological brains

Understanding brain functions requires not only information about the spatial localization of neural activity, but also about the dynamic functional links between the involved groups of neurons, which do not work in an isolated way, but rather interact together through ingoing and outgoing connections. The work carried on during the three years of PhD course returns a methodological framework for the estimation of the causal brain connectivity and its validation on simulated and real datasets (EEG and pseudo-EEG) at scalp and source level. Important open issues like the selection of the best algorithms for the source reconstruction and for time-varying estimates were addressed. Moreover, after the application of such approaches on real datasets recorded from healthy subjects and post-stroke patients, we extracted neurophysiological indices describing in a stable and reliable way the properties of the brain circuits underlying different cognitive states in humans (attention, memory). More in detail: I defined and implemented a toolbox (SEED-G toolbox) able to provide a useful validation instrument addressed to researchers who conduct their activity in the field of brain connectivity estimation. It may have strong implication, especially in methodological advancements. It allows to test the ability of different estimators in increasingly less ideal conditions: low number of available samples and trials, high inter-trial variability (very realistic situations when patients are involved in protocols) or, again, time varying connectivity patterns to be estimate (where stationary hypothesis in wide sense failed). A first simulation study demonstrated the robustness and the accuracy of the PDC with respect to the inter-trials variability under a large range of conditions usually encountered in practice. The simulations carried on the time-varying algorithms allowed to highlight the performance of the existing methodologies in different conditions of signals amount and number of available trials. Moreover, the adaptation of the Kalman based algorithm (GLKF) I implemented, with the introduction of the preliminary estimation of the initial conditions for the algorithm, lead to significantly better performance. Another simulation study allowed to identify a tool combining source localization approaches and brain connectivity estimation able to provide accurate and reliable estimates as less as possible affected to the presence of spurious links due to the head volume conduction. The developed and tested methodologies were successfully applied on three real datasets. The first one was recorded from a group of healthy subjects performing an attention task that allowed to describe the brain circuit at scalp and source level related with three important attention functions: alerting, orienting and executive control. The second EEG dataset come from a group of healthy subjects performing a memory task. Also in this case, the approaches under investigation allowed to identify synthetic connectivity-based descriptors able to characterize the three main memory phases (encoding, storage and retrieval). For the last analysis I recorded EEG data from a group of stroke patients performing the same memory task before and after one month of cognitive rehabilitation. The promising results of this preliminary study showed the possibility to follow the changes observed at behavioural level by means of the introduced neurophysiological indices

Decoding Electrophysiological Correlates of Selective Attention by Means of Circular Data

Sustaining our attention to a relevant sensory input in a complex listening environment, is of great importance for a successful auditory communication. To avoid the overload of the auditory system, the importance of the stimuli is estimated in the higher levels of the auditory system. Based on these information, the attention is drifted away from the irrelevant and unimportant stimuli. Long-term habituation, a gradual process independent from sensory adaptation, plays a major role in drifting away our attention from irrelevant stimuli. A better understanding of attention-modulated neural activity is important for shedding light on the encoding process of auditory streams. For instance, these information can have a direct impact on developing smarter hearing aid devices in which more accurate objective measures can be used to re ect the hearing capabilities of patients with hearing pathologies. As an example, an objective measures of long-term habituation with respect to di erent level of sound stimuli can be used more accurately for adjustment of hearing aid devices in comparison to verbal reports. The main goal of this thesis is to analyze the neural decoding signatures of long-term habituation and neural modulations of selective attention by exploiting circular regularities in electrophysiological (EEG) data, in which we can objectively measure the level of attentional-binding to di erent stimuli. We study, in particular, the modulations of the instantaneous phase (IP) in event related potentials (ERPs) over trials for di erent experimental settings. This is in contrast to the common approach where the ERP component of interest is computed through averaging a su ciently large number of ERP trials. It is hypothesized that a high attentional binding to a stimulus is related to a high level of IP cluster. As the attention binding reduces, IP is spread more uniformly on a unit circle. This work is divided into three main parts. In the initial part, we investigate the dynamics of long-term habituation with di erent acoustical stimuli (soft vs. loud) over ERP trials. The underlying temporal dynamics in IP and the level of phase cluster of the ERPs are assessed by tting circular probability functions (pdf) over data segments. To increase the temporal resolution of detecting times at which a signi cant change in IP occurs, an abrupt change point model at di erent pure-tone stimulations is used. In a second study, we improve upon the results and methodology by relaxing some of the constrains in order to integrate the gradual process of long-term habituation into the model. For this means, a Bayesian state-space model is proposed. In all of the aforementioned studies, we successfully classi ed between di erent stimulation levels, using solely the IP of ERPs over trials. In the second part of the thesis, the experimental setting is expanded to contain longer and more complex auditory stimuli as in real-world scenarios. Thereby, we study the neural-correlates of attention in spontaneous modulations of EEG (ongoing activity) which uses the complete temporal resolution of the signal. We show a mapping between the ERP results and the ongoing EEG activity based on IP. A Markov-based model is developed for removing spurious variations that can occur in ongoing signals. We believe the proposed method can be incorporated as an important preprocessing step for a more reliable estimation of objective measures of the level of selective attention. The proposed model is used to pre-process and classify between attending and un-attending states in a seminal dichotic tone detection experiment. In the last part of this thesis, we investigate the possibility of measuring a mapping between the neural activities of the cortical laminae with the auditory evoked potentials (AEP) in vitro. We show a strong correlation between the IP of AEPs and the neural activities at the granular layer, using mutual information.Die Aufmerksamkeit auf ein relevantes auditorisches Signal in einer komplexen H orumgebung zu lenken ist von gro er Bedeutung f ur eine erfolgreiche akustische Kommunikation. Um eine Uberlastung des H orsystems zu vermeiden, wird die Bedeutung der Reize in den h oheren Ebenen des auditorischen Systems bewertet. Basierend auf diesen Informationen wird die Aufmerksamkeit von den irrelevanten und unwichtigen Reizen abgelenkt. Dabei spielt die sog. Langzeit- Habituation, die einen graduellen Prozess darstellt der unabh angig von der sensorischen Adaptierung ist, eine wichtige Rolle. Ein besseres Verst andnis der aufmerksamkeits-modulierten neuronalen Aktivit at ist wichtig, um den Kodierungsprozess von sog. auditory streams zu beleuchten. Zum Beispiel k onnen diese Informationen einen direkten Ein uss auf die Entwicklung intelligenter H orsysteme haben bei denen genauere, objektive Messungen verwendet werden k onnen, um die H orf ahigkeiten von Patienten mit H orpathologien widerzuspiegeln. So kann beispielsweise ein objektives Ma f ur die Langzeit- Habituation an unterschiedliche Schallreize genutzt werden um - im Vergleich zu subjektiven Selbsteinsch atzungen - eine genauere Anpassung der H orsysteme zu erreichen. Das Hauptziel dieser Dissertation ist die Analyse neuronaler Dekodierungssignaturen der Langzeit- Habituation und neuronaler Modulationen der selektiver Aufmerksamkeit durch Nutzung zirkul arer Regularit aten in elektroenzephalogra schen Daten, in denen wir objektiv den Grad der Aufmerksamkeitsbindung an verschiedene Reize messen k onnen. Wir untersuchen insbesondere die Modulation der Momentanphase (engl. Instantaneous phase, IP) in ereigniskorrelierten Potenzialen (EKPs) in verschiedenen experimentellen Settings. Dies steht im Gegensatz zu dem traditionellen Ansatz, bei dem die interessierenden EKP-Komponenten durch Mittelung einer ausreichend gro en Anzahl von Einzelantworten im Zeitbereich ermittelt werden. Es wird vermutet, dass eine hohe Aufmerksamkeitsbindung an einen Stimulus mit einem hohen Grad an IP-Clustern verbunden ist. Nimmt die Aufmerksamkeitsbindung hingegen ab, so ist die Momentanphase uniform auf dem Einheitskreis verteilt. Diese Arbeit gliedert sich in drei Teile. Im ersten Teil untersuchen wir die Dynamik der Langzeit-Habituation mit verschiedenen akustischen Reizen (leise vs. laut) in EKP-Studien. Die zugrundeliegende zeitliche Dynamik der Momentanphase und die Ebene des Phasenclusters der EKPs werden durch die Anpassung von zirkul aren Wahrscheinlichkeitsfunktionen (engl. probability density function, pdf) uber Datensegmente bewertet. Mithilfe eines sog. abrupt change-point Modells wurde die zeitliche Au osung der Daten erh oht, sodass signi kante Anderungen in der Momentanphase bei verschiedenen Reintonstimulationen detektierbar sind. In einer zweiten Studie verbessern wir die Ergebnisse und die Methodik, indem wir einige der Einschr ankungen lockern, um den gradualen Prozess der Langzeit-Habituation in das abrupt changepoint Modell zu integrieren. Dazu wird ein bayes`sches Zustands-Raum-Modell vorgeschlagen. In den zuvor genannten Studien konnte erfolgreich mithilfe der Momentanphase zwischen verschiedenen Stimulationspegeln unterschieden werden. Im zweiten Teil der Arbeit wird der experimentelle Rahmen erweitert, um komplexere auditorische Reize wie in realen H orsituationen untersuchen zu k onnen. Dabei analysieren wir die neuronalen Korrelate der Aufmerksamkeit anhand spontaner Modulationen der kontinuierlichen EEG-Aktivit at, die eine zeitliche Au osung erm oglicht. Wir zeigen eine Abbildung zwischen den EKP-Ergebnissen und der kontinuierlichen EEG-Aktivit at auf Basis der Momentanphase. Ein Markov-basiertes Modell wird entwickelt, um st orende Variationen zu entfernen, die in kontinuierlichen EEG-Signalen auftreten k onnen. Wir glauben, dass die vorgeschlagene Methode als wichtiger Vorverarbeitungsschritt zur soliden objektiven Absch atzung des Aufmerksamkeitsgrades mithilfe von EEG-Daten verwendet werden kann. In einem dichotischen Tonerkennungsexperiment wird das vorgeschlagene Modell zur Vorverarbeitung der EEG-Daten und zur Klassi zierung zwischen gerichteten und ungerichteten Aufmerksamkeitszust anden erfolgreich verwendet. Im letzten Teil dieser Arbeit untersuchen wir den Zusammenhang zwischen den neuronalen Aktivit aten der kortikalen Laminae und auditorisch evozierten Potentialen (AEP) in vitro im Tiermodell. Wir zeigen eine starke Korrelation zwischen der Momentanphase der AEPs und den neuronalen Aktivit aten in der Granularschicht unter Verwendung der Transinformation