Investigation of Brain Functional Connectivity from Electroencephalogram Data

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About this Research Topic

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Background

For many years, it was a common idea that each human brain function was associated by a specific brain area. New neuroscientific studies had disproved this hypothesis based on functional magnetic resonance imaging (fMRI) evidence that most of the brain functions are a result of coordinated activity between distinct separated brain regions. Functional connectivity (FC) is the study of how spatially distant regions of the brain are interconnected. Improving our knowledge about these connections is fundamental for better understanding how the brain works and how some diseases could be justified by some FC alterations. For example, neurologic disorders like Alzheimer’s disease (AD) or dementia, autism spectrum disorder (ASD) and post-traumatic stress disorder (PTSD) are very different illnesses that share substantial differences in FC with respect to healthy subjects.

This Research Topic will focus on deepening the study of functional connectivity (FC) using Electroencephalogram (EEG). To assess FC, different bivariate metrics could be used, such as coherence, phase synchronization, phase-slope index and Granger causality. Particular consideration will be given to the discussion of the feasibility of the application of the metrics to avoid erroneous conclusions due to incorrect hypotheses (e.g. affected by noise interference, common input problems or volume conduction). The introduction of new metrics would also be appreciated as an innovative way to assess FC.

We encourage the submission of work covering the following subtopics:

• Research based on experimental EEG from subjects affected by neurological diseases, to assess functional connectivity (FC) differences with respect to healthy subjects.

• Innovative descriptors that significantly improve or describe in a different way the functional connection between different regions of the brain.

• Innovative protocols to study FC, validated using affirmed descriptors (e.g., coherence, phase locking value or phase slope index), possibly post-processed to extract indexes (e.g. from graph theory) characterizing emerging overall properties.

• Reviews of the current methods and protocols highlighting the relevant future directions of the field

Keywords: Computational Physiology, Bivariate Metrics, Electroencephalogram, Functional Connectivity, Neurological Disease

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