The brain is a complex dynamic and self-organizing system. Normal brain function emerges from synchronized neuronal firing between local neurons which are integrated into large scale networks via white matter tracts. Normal brain function and consciousness arise from the continual integration and dissolution of neuronal networks, and this fluctuation in synchronization is termed variability. Brain electrical activity is recorded as local field potentials using electroencephalography (EEG). The phase synchrony and variability of EEG waveforms can be quantified. The healthy brain exhibits a relatively low degree of phase synchrony and a high degree of variability. Clinicians are interested in using a complex system approach to brain function to provide dynamic information on neuronal physiology and pathology not available by other evaluation methods. A common challenge in paediatric critical care is evaluation of the comatose child post brain injury. Coma and medical interventions confound the clinical examination making monitoring and prognostication of outcome difficult. Brain cells and white matter tracts are disrupted post injury altering the phase synchrony between neuronal networks. It is proposed in this thesis that the estimation of the variability in EEG phase synchrony can evaluate paediatric brain function. The EEG recordings of normal children and patients in coma post brain injury are used, in a series of studies, to test the main hypothesis that slow EEG wave brain states associated with brain injury have higher magnitudes of EEG phase synchrony and lower variability values than those of EEG waves associated with consciousness. Further, the effects of age, brain development brain and the effect of a conscious slow wave EEG state (hyperventilation) on phase synchrony and variability are evaluated. Results of the studies showed that EEG phase synchrony is increased in all slow wave states and is highest in comatose children with poor neurological outcome. Younger children’s brains have higher phase synchrony than older children. The variability of the EEG phase synchrony differentiates between the awake (higher values) and unconscious states (lower values). Physiologic models underlying EEG phase synchrony are discussed. The EEG phase synchrony and variability measures provide new insight into paediatric brain function.

The brain is a complex dynamic and self-organizing system. Normal brain function emerges from synchronized neuronal firing between local neurons which are integrated into large scale networks via white matter tracts. Normal brain function and consciousness arise from the continual integration and dissolution of neuronal networks, and this fluctuation in synchronization is termed variability. Brain electrical activity is recorded as local field potentials using electroencephalography (EEG). The phase synchrony and variability of EEG waveforms can be quantified. The healthy brain exhibits a relatively low degree of phase synchrony and a high degree of variability. Clinicians are interested in using a complex system approach to brain function to provide dynamic information on neuronal physiology and pathology not available by other evaluation methods. A common challenge in paediatric critical care is evaluation of the comatose child post brain injury. Coma and medical interventions confound the clinical examination making monitoring and prognostication of outcome difficult. Brain cells and white matter tracts are disrupted post injury altering the phase synchrony between neuronal networks. It is proposed in this thesis that the estimation of the variability in EEG phase synchrony can evaluate paediatric brain function. The EEG recordings of normal children and patients in coma post brain injury are used, in a series of studies, to test the main hypothesis that slow EEG wave brain states associated with brain injury have higher magnitudes of EEG phase synchrony and lower variability values than those of EEG waves associated with consciousness. Further, the effects of age, brain development brain and the effect of a conscious slow wave EEG state (hyperventilation) on phase synchrony and variability are evaluated. Results of the studies showed that EEG phase synchrony is increased in all slow wave states and is highest in comatose children with poor neurological outcome. Younger children’s brains have higher phase synchrony than older children. The variability of the EEG phase synchrony differentiates between the awake (higher values) and unconscious states (lower values). Physiologic models underlying EEG phase synchrony are discussed. The EEG phase synchrony and variability measures provide new insight into paediatric brain function.

Over the last decades sleep research has focused on epidemiological studies of how different factors affect sleep, and how sleep influences other physiologic and cognitive functions. However, the complex dynamics of sleep stage transitions and arousals which occur at time scales of seconds to minutes during healthy sleep and constitute the sleep micro-architecture are not yet understood. I analyze long-term continuous EEG recordings in rats and human, and dissect emergent signatures of criticality in the dynamics of cortical rhythm bursts in relation to their correlation properties and reciprocal coupling. I show that active states durations follow a power-law distribution while the quiet states durations follow an exponential-like behavior. Such emerging bursting activity in the brain rhythm dynamics described by power-laws and exhibiting long-range spatio-temporal correlations has been proposed as an indication of self-organized criticality (SOC). To have a deeper understanding of SOC in cortical rhythm bursting dynamics, it is essential to study the dynamical evolution of an entire network of physiologic interactions in the context of different physiologic states and pathologic conditions. The human organism comprises various physiological systems, each with its own structural organization and dynamic complexity, leading to transient, fluctuating and nonlinear signals. Understanding integrated physiologic function as emergent phenomena from complex interactions among diverse organ systems is the main focus of a new field, Network Physiology. I apply Network Physiology approach and the novel concept of time delay stability (TDS), and I demonstrate their utility to study transient synchronous bursts in systems dynamics as a fundamental form of physiologic network communications. My results demonstrate that during a given physiological state, the physiological network is characterized by a specific topology and coupling strength between systems. Probing physiological network connectivity and the stability of physiological coupling across physiological states provide new insights on integrated physiological function. / 2023-03-04T00:00:00Z

abstract: Interictal spikes, together with seizures, have been recognized as the two hallmarks of epilepsy, a brain disorder that 1% of the world's population suffers from. Even though the presence of spikes in brain's electromagnetic activity has diagnostic value, their dynamics are still elusive. It was an objective of this dissertation to formulate a mathematical framework within which the dynamics of interictal spikes could be thoroughly investigated. A new epileptic spike detection algorithm was developed by employing data adaptive morphological filters. The performance of the spike detection algorithm was favorably compared with others in the literature. A novel spike spatial synchronization measure was developed and tested on coupled spiking neuron models. Application of this measure to individual epileptic spikes in EEG from patients with temporal lobe epilepsy revealed long-term trends of increase in synchronization between pairs of brain sites before seizures and desynchronization after seizures, in the same patient as well as across patients, thus supporting the hypothesis that seizures may occur to break (reset) the abnormal spike synchronization in the brain network. Furthermore, based on these results, a separate spatial analysis of spike rates was conducted that shed light onto conflicting results in the literature about variability of spike rate before and after seizure. The ability to automatically classify seizures into clinical and subclinical was a result of the above findings. A novel method for epileptogenic focus localization from interictal periods based on spike occurrences was also devised, combining concepts from graph theory, like eigenvector centrality, and the developed spike synchronization measure, and tested very favorably against the utilized gold rule in clinical practice for focus localization from seizures onset. Finally, in another application of resetting of brain dynamics at seizures, it was shown that it is possible to differentiate with a high accuracy between patients with epileptic seizures (ES) and patients with psychogenic nonepileptic seizures (PNES). The above studies of spike dynamics have elucidated many unknown aspects of ictogenesis and it is expected to significantly contribute to further understanding of the basic mechanisms that lead to seizures, the diagnosis and treatment of epilepsy. / Dissertation/Thesis / Ph.D. Electrical Engineering 2012

Spielt Ihr Gehirn Ihre letzten Lebenserfahrungen ab, während Sie sich ausruhen? Eine offene Frage in den Neurowissenschaften ist, welche Ereignisse unser Gehirn wiederholt und gibt es eine Korrelation zwischen der Wiederholung und der Dauer des Ereignisses? In dieser Studie habe ich versucht, dieser Frage nachzugehen, indem ich Magnetenzephalographie-Daten aus einem Experiment zum aktiven Hören verwendet habe. Die Magnetenzephalographie (MEG) ist ein nicht-invasives Neuroimaging-Verfahren, das verwendet wird, um die Gehirnaktivität zu untersuchen und die Gehirndynamik bei Wahrnehmungs- und kognitiven Aufgaben insbesondere in den Bereichen Sprache und Hören zu verstehen. Es zeichnet das in unserem Gehirn erzeugte Magnetfeld auf, um die Gehirnaktivität zu erkennen. Ich baue eine Pipeline für maschinelles Lernen, die einen Teil der Experimentdaten verwendet, um die Klangmuster zu lernen und dann das Vorhandensein von Geräuschen im späteren Teil der Aufnahmen vorhersagt, in denen die Teilnehmer untätig sitzen mussten und kein Ton zugeführt wurde. Das Ziel der Untersuchung der Testwiedergabe von gelernten Klangsequenzen in der Nachhörphase. Ich habe ein Klassifikationsschema verwendet, um Muster zu identifizieren, wenn MEG auf verschiedene Tonsequenzen in der Zeit nach der Aufgabe reagiert. Die Studie kam zu dem Schluss, dass die Lautfolgen über dem theoretischen Zufallsniveau identifiziert und unterschieden werden können und bewies damit die Gültigkeit unseres Klassifikators. Darüber hinaus könnte der Klassifikator die Geräuschsequenzen in der Nachhörzeit mit sehr hoher Wahrscheinlichkeit vorhersagen, aber um die Modellergebnisse über die Nachhörzeit zu validieren, sind mehr Beweise erforderlich. / Does your brain replay your recent life experiences while you are resting? An open question in neuroscience is which events does our brain replay and is there any correlation between the replay and duration of the event? In this study I tried to investigate this question by using Magnetoencephalography data from an active listening experiment. Magnetoencephalography (MEG) is a non-invasive neuroimaging technique used to study the brain activity and understand brain dynamics in perception and cognitive tasks particularly in the fields of speech and hearing. It records the magnetic field generated in our brains to detect the brain activity. I build a machine learning pipeline which uses part of the experiment data to learn the sound patterns and then predicts the presence of sound in the later part of the recordings in which the participants were made to sit idle and no sound was fed. The aim of the study of test replay of learned sound sequences in the post listening period. I have used classification scheme to identify patterns if MEG responses to different sound sequences in the post task period. The study concluded that the sound sequences can be identified and distinguished above theoretical chance level and hence proved the validity of our classifier. Further, the classifier could predict the sound sequences in the post-listening period with very high probability but in order to validate the model results on post listening period, more evidence is needed.

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Menschliche Hirnsignale von der Kopfhaut bieten einen Einblick in die neuronalen Prozesse, denen Wahrnehmung, Denken und Verhalten zugrunde liegen. Rhythmen, die historisch den Grundstein für die Erforschung großflächiger Hirnsignale legten, sind ein häufiges Zeichen neuronaler Koordination, und damit von weitem Interesse für die kognitiven, systemischen und komputationalen Neurowissenschaften. Typischen Messungen von Rhythmizität fehlt es jedoch an Details, z. B. wann und wie lange Rhythmen auftreten. Darüber hinaus weisen neuronale Zeitreihen zahlreiche dynamische Muster auf, von denen nur einige rhythmisch erscheinen. Obwohl aperiodischen Beiträgen traditionell der Status irrelevanten „Rauschens“ zugeschrieben wird, attestieren neuere Erkenntnisse ihnen ebenfalls eine Signalrolle in Bezug auf latente Hirndynamik. Diese kumulative Dissertation fasst Projekte zusammen, die darauf abzielen, rhythmische und aperiodische Beiträge zum menschlichen Elektroenzephalogramm (EEG) methodisch zu dissoziieren, und ihre Relevanz für die flexible Wahrnehmung zu untersuchen. Projekt 1 ermittelt insbesondere die Notwendigkeit und Durchführbarkeit der Trennung rhythmischer von aperiodischer Aktivität in kontinuierlichen Signalen. Projekt 2 kehrt diese Perspektive um und prüft Multiscale Entropy als Index für die Unregelmäßigkeit von Zeitreihen. Diese Arbeit weist auf methodische Probleme in der klassischen Messung zeitlicher Unregelmäßigkeit hin, und schlägt Lösungen für zukünftige Anwendungen vor. Abschließend untersucht Projekt 3 die neurokognitive Relevanz rhythmischer und aperiodischer Zustände. Anhand eines parallelen multimodalen EEG-fMRT-Designs mit gleichzeitiger Pupillenmessung liefert dieses Projekt erste Hinweise dafür, dass erhöhte kognitive Anforderungen Hirnsignale von einem rhythmischen zu einem unregelmäßigen Regime verschieben und impliziert gleichzeitige Neuromodulation und thalamische Aktivierung in diesem Regimewechsel. / Non-invasive signals recorded from the human scalp provide a window on the neural dynamics that shape perception, cognition and action. Historically motivating the assessment of large-scale network dynamics, rhythms are a ubiquitous sign of neural coordination, and a major signal of interest in the cognitive, systems, and computational neurosciences. However, typical descriptions of rhythmicity lack detail, e.g., failing to indicate when and for how long rhythms occur. Moreover, neural times series exhibit a wealth of dynamic patterns, only some of which appear rhythmic. While aperiodic contributions are traditionally relegated to the status of irrelevant ‘noise’, they may be informative of latent processing regimes in their own right. This cumulative dissertation summarizes and discusses work that (a) aims to methodologically dissociate rhythmic and aperiodic contributions to human electroencephalogram (EEG) signals, and (b) probes their relevance for flexible cognition. Specifically, Project 1 highlights the necessity, feasibility and limitations of dissociating rhythmic from aperiodic activity at the single-trial level. Project 2 inverts this perspective, and examines the utility of multi-scale entropy as an index for the irregularity of brain dynamics, with a focus on the relation to rhythmic and aperiodic descriptions. By highlighting prior biases and proposing solutions, this work indicates future directions for measurements of temporal irregularity. Finally, Project 3 examines the neurocognitive relevance of rhythmic and aperiodic regimes with regard to the neurophysiological context in which they may be engaged. Using a parallel multi-modal EEG-fMRI design with concurrent pupillometry, this project provides initial evidence that elevated demands shift cortical dynamics from a rhythmic to an irregular regime; and implicates concurrent phasic neuromodulation and subcortical thalamic engagement in these regime shifts.