Cerebral Synchrony Assessment Tutorial: A General Review on Cerebral Signals' Synchronization Estimation Concepts and Methods

The human brain is ultimately responsible for all thoughts and movements that the body produces. This allows humans to successfully interact with their environment. If the brain is not functioning properly many abilities of human can be damaged. The goal of cerebral signal analysis is to learn about brain function. The idea that distinct areas of the brain are responsible for specific tasks, the functional segregation, is a key aspect of brain function. Functional integration is an important feature of brain function, it is the concordance of multiple segregated brain areas to produce a unified response. There is an amplified feedback mechanism in the brain called reentry which requires specific timing relations. This specific timing requires neurons within an assembly to synchronize their firing rates. This has led to increased interest and use of phase variables, particularly their synchronization, to measure connectivity in cerebral signals. Herein, we propose a comprehensive review on concepts and methods previously presented for assessing cerebral synchrony, with focus on phase synchronization, as a tool for brain connectivity evaluation

Information-theoretic approach for the characterization of interactions in nonlinear dynamical systems

Symbolic time series analysis provides us a solid and broadly used toolkit for the characterization of interactions between nonlinear dynamical systems. In this thesis, information-theoretic measures are evaluated with respect to their capability to characterize interactions between dynamical systems. We investigate several important limitations of these measures which may appear when experimental data exhibit strong correlations. It is demonstrated that a high degree of static and/or long-term temporal correlations can, in general, lead to the incorrect inference of directionality of interactions between underlying dynamical systems. In this thesis, we propose two complementary information-theoretic measures which can provide a better characterization of the directionality of interactions in cases where the influence of such correlations in data cannot be neglected. First, the proposed information-theoretic measures are applied to characterize interactions between dynamical model systems with known equations of motion. Finally, they are applied to characterize interactions between multi-channel electroencephalographic recordings from epilepsy patients undergoing the presurgical diagnostics.Informationstheoretischer Ansatz zur Charakterisierung von Interaktionen in nichtlinearen dynamischen Systemen Mit Hilfe der Zeitreihenanalyse können Interaktionen zwischen natürlichen dynamischen Systemen anhand experimenteller Daten charakterisiert werden. In den letzten Jahren wurde eine Reihe von Maßen vorgestellt, die darauf abzielen, neben der Interaktionsrichtung auch die Interaktionsstärke zu bestimmen. Die zur Charakterisierung von Interaktionsrichtungen konzipierte Transferentropie zeichnet sich gerade durch eine besonders hohe Rauschtoleranz gegenüber anderen Maßen aus. Ziel der vorliegenden Arbeit ist es, zwei Limitationen, die die Interpretierbarkeit der Charakterisierungen mit der bisher vorgeschlagenen Transferentropie einschränken, zu untersuchen und auszuräumen. Zum einen wird ein Verfahren entwickelt und implementiert, mit dem langreichweitige Korrelationen besser beobachtet werden können, zum anderen werden Korrekturen vorgeschlagen, die den Einfluss so genannter statischer Korrelationen berücksichtigen. Bei Charakterisierungen von Interaktionsrichtungen mit Hilfe der Transferentropie konnten langreichweitige Korrelationen nur durch die Abschätzung von hochdimensionalen Wahrscheinlichkeitsräumen berücksichtigt werden. Für diese Abschätzung sind sehr viele Datenpunkte innerhalb des Beobachtungsintervalls notwendig, was bei Felddaten, gemessen an unbekannten Systemen, mit der Annahme der Stationarität in einem Beobachtungsintervall konkurriert. Um diese Beschränkung zu umgehen, wird in dieser Dissertation eine Verallgemeinerung des Konzepts der Entropie im Sinne von Lempel-Ziv auf das Maß der Transferentropie übertragen. Hierdurch können langreichweitige Korrelationen ohne die Abschätzung eines hochdimensionalen Wahrscheinlichkeitsraums bestimmt werden. Zeitgleiche Korrelationen der zugrunde liegenden Signale - so genannte statische Korrelationen - können die Interpretierbarkeit der Charakterisierung einschränken. Zur Berücksichtigung statistischer Korrelationen mit den bisher vorgestellten Maßen war ebenfalls eine mit einem großen Rechenaufwand verbundene Abschätzung hochdimensionaler Wahrscheinlichkeiten notwendig. In der vorliegenden Dissertation wird eine Korrektur der Transferentropie zur Abschätzung der statischen Korrelationen vorgeschlagen, ohne höherdimensionale Terme berechnen zu müssen. Durch die in dieser Arbeit vorgestellten Maße und Korrekturen kann die Charakterisierung der Interaktionsrichtung verbessert werden. Dabei wird anhand prototypischer Modellsysteme mit chaotischen Dynamiken demonstriert, dass die Charakterisierungen mit Hilfe der vorgeschlagenen Maße und Korrekturen gerade bei Systemen, die ohne Zeitversatz interagieren, besser interpretierbar sind. Weiterhin wurden Interaktionsstärke und Interaktionsrichtung an Zeitreihen hirnelektrischer Aktivität von Epilepsiepatienten bestimmt und mit Charakterisierungen der Transferentropie verglichen. Hierbei lässt sich zusammenfassen, dass sich mit den in dieser Arbeit vorgestellten Maßen Kontraste unterschiedlicher Interaktionsrichtungen besser auflösen lassen

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks

EEG and ERP biomarkers of Alzheimer's disease: a critical review.

Here we critically review studies that used electroencephalography (EEG) or event-related potential (ERP) indices as a biomarker of Alzheimer's disease. In the first part we overview studies that relied on visual inspection of EEG traces and spectral characteristics of EEG. Second, we survey analysis methods motivated by dynamical systems theory (DST) as well as more recent network connectivity approaches. In the third part we review studies of sleep.  Next, we compare the utility of early and late ERP components in dementia research. In the section on mismatch negativity (MMN) studies we summarize their results and limitations and outline the emerging field of computational neurology. In the following we overview the use of EEG in the differential diagnosis of the most common neurocognitive disorders. Finally, we provide a summary of the state of the field and conclude that several promising EEG/ERP indices of synaptic neurotransmission are worth considering as potential biomarkers. Furthermore, we highlight some practical issues and discuss future challenges as well

EARLY DETECTION OF DEMENTIA USING THE HUMAN ELECTROENCEPHALOGRAM

Improved life expectancy has led to a significant increase in the number of people in the high-risk age groups that will develop Alzheimer's disease and other dementia. Efforts are being made to develop treatments that slow the progress of these diseases. However, unless a sufferer is diagnosed in the early stages the treatments cannot give the maximum benefit. Therefore, there is an urgent need for a practical, decision support tool that will enable the earliest possible detection of dementia within the large at-risk population. Current techniques such as Magnetic Resonance Imaging (MRI) that are used to diagnose and assess neurological disorders require specialist equipment and expert clinicians to interpret results. Such techniques are inappropriate as a method of detecting individual subjects with early dementia within the large at-risk population, because everyone within the at-risk group would need to be tested regularly and this would carry a very high cost. Therefore, it is desirable to develop a low cost method of assessment. This thesis describes research into the use of automated EEG analysis to provide the required testing for dementia. The research begins with a review of previous automated EEG analysis, particularly fractal dimension measures. Initial investigation into the nature of the fractal dimension of the EEG are conducted, including problems encountered when applying fractal measures in affine space. More appropriate fractal methods were evaluated and the most promising of these methods was blind tested using an independent clinical data set. This method was estimated to achieve 67% sensitivity to probable early Alzheimer's disease and 17% sensitivity to vascular dementia (as confirmed by a clinical neurophysiologist from the EEG) with a specificity of 99.9%.Department of Neurophysiology, Derriford Hospital, Plymout