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Biomarkers Based on Comprehensive Hierarchical EEG Coherence Analysis: Example Application to Social Competence in
Mo Modarres1, David Cochran2,3, David N Kennedy2
1The Eunice Kennedy Shriver Center Department of Psychiatry, University of Massachusetts Medical School, 55 Lake Avenue North, Room S3-312, Worcester, MA, 01655, USA. mo.modarres@umassmed.edu.
Neuroinformatics
|March 30, 2021
Summary
This study introduces a standardized hierarchical electroencephalography (EEG) coherence analysis to improve brain network connectivity research. The method aids in identifying biomarkers for brain dysfunction, particularly in Autism Spectral Disorder.
Area of Science:
- Neuroscience
- Biomarkers
- Brain Network Analysis
Background:
- Electroencephalography (EEG) coherence analysis measures synchronous neuronal oscillations to assess functional brain connectivity.
- Variability in EEG coherence study designs (time, frequency, regions, paradigms) hinders cross-study comparisons and understanding of brain disorders.
Purpose of the Study:
- To present a comprehensive hierarchical EEG coherence analysis structure to standardize methods.
- To enable formal inclusion of analysis duration, frequency band, cortical region, and experimental condition.
- To describe the derivation of biomarkers for brain (dys)function and abnormalities.
Main Methods:
- Developed a hierarchical EEG coherence analysis framework.
- Integrated analysis duration, EEG frequency band, cortical region, and experimental condition.
- Applied the method to EEG and behavioral data from a social synchrony paradigm.
Main Results:
- Demonstrated the utility of the hierarchical approach in analyzing EEG and behavioral data.
- Applied the method to a cohort of adolescents with and without Autism Spectral Disorder.
- The approach facilitates biomarker discovery for brain abnormalities.
Conclusions:
- The hierarchical EEG coherence analysis provides a standardized framework for brain connectivity research.
- This method can identify reliable biomarkers for neurological and psychiatric disorders.
- The approach is valuable for comparing findings across studies and advancing understanding of brain function.
