An Electroencephalography connectome predictive model of major depressive disorder severity

TL;DR

This study developed a machine learning model using EEG resting-state connectivity to predict depression severity, demonstrating significant correlations across multiple datasets and highlighting the default mode network's role.

Contribution

The paper introduces a novel EEG-based connectome model that accurately predicts depression severity, validated across three independent datasets.

Findings

EEG alpha band connectivity predicts depression severity (r=0.61).

Model validated externally with high correlations (r=0.49, r=0.37).

Default mode network regions are key contributors.

Abstract

Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N=328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r=0.61, p=4 x 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1=53, N2=154). Results showed high significant correlations between the predicted and the measured depression scale scores (r1= 0.49, r2=0.37, p<0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.