Automated network analysis to measure brain effective connectivity estimated from EEG data of patients with alcoholism

OBJECTIVE

Detection and diagnosis based on extracting features and classification using electroencephalography (EEG) signals are being studied vigorously. A network analysis of time series EEG signal data is one of many techniques that could help study brain functions. In this study, we analyze EEG to diagnose alcoholism.

APPROACH

We propose a novel methodology to estimate the differences in the status of the brain based on EEG data of normal subjects and data from alcoholics by computing many parameters stemming from effective network using Granger causality.

MAIN RESULTS

Among many parameters, only ten parameters were chosen as final candidates. By the combination of ten graph-based parameters, our results demonstrate predictable differences between alcoholics and normal subjects. A support vector machine classifier with best performance had 90% accuracy with sensitivity of 95.3%, and specificity of 82.4% for differentiating between the two groups.

Applied strategies towards EEG/MEG biomarker identification in clinical and cognitive research

As the underlying causes of several neuronal disorders and neurodegenerative diseases still remain, to some extent, unknown and no accurate diagnostic tests are available, the identification of prognostic and predictive neurophysiological biomarkers has attracted tremendous interest. The continuous advancement of neuroscience methods applied in EEG and magnetoencephalography has been successful in capturing brain processes and identifying persistent cognitive deficits. In this article, the most promising approaches of this rapidly evolving field, along with some indicative clinical applications in major neuropathophysiological disorders, are reviewed. Such strategies for biomarker identification will lead the way to future clinical applications even if, currently, EEG biomarkers are in a premature state.

BrainNetVis: an open-access tool to effectively quantify and visualize brain networks

This paper presents BrainNetVis, a tool which serves brain network modelling and visualization, by providing both quantitative and qualitative network measures of brain interconnectivity. It emphasizes the needs that led to the creation of this tool by presenting similar works in the field and by describing how our tool contributes to the existing scenery. It also describes the methods used for the calculation of the graph metrics (global network metrics and vertex metrics), which carry the brain network information. To make the methods clear and understandable, we use an exemplar dataset throughout the paper, on which the calculations and the visualizations are performed. This dataset consists of an alcoholic and a control group of subjects.