EvaluatingEEGcomplexity metrics as biomarkers for depression

Author:

Lord Brian1ORCID,Allen John J. B.1

Affiliation:

1. Department of Psychology University of Arizona Tucson Arizona USA

Abstract

AbstractNonlinear EEG analysis offers the potential for both increased diagnostic accuracy and deeper mechanistic understanding of psychopathology. EEG complexity measures have previously been shown to positively correlate with clinical depression. In this study, resting state EEG recordings were taken across multiple sessions and days with both eyes open and eyes closed conditions from a total of 306 subjects, 62 of which were in a current depressive episode, and 81 of which had a history of diagnosed depression but were not currently depressed. Three different EEG montages (mastoids, average, and Laplacian) were also computed. Higuchi fractal dimension (HFD) and sample entropy (SampEn) were calculated for each unique condition. The complexity metrics showed high internal consistency within session and high stability across days. Higher complexity was found in open‐eye recordings compared to closed eyes. The predicted correlation between complexity and depression was not found. However, an unexpected sex effect was observed, in which males and females exhibited different topographic patterns of complexity.

Funder

National Institutes of Health

Publisher

Wiley

Subject

Experimental and Cognitive Psychology,Neuropsychology and Physiological Psychology,Biological Psychiatry,Cognitive Neuroscience,Developmental Neuroscience,Endocrine and Autonomic Systems,Neurology,Experimental and Cognitive Psychology,Neuropsychology and Physiological Psychology,General Neuroscience

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