Graph Convolutional Neural Network based Depression Detection using Brain Functional Connectivity Measures

AI Summary

Proposes a novel GCNN with a unique EEG graph representation combining brain region domain knowledge and data-driven functional relationships without relying on spatial geometry.
Extensive evaluation on two scalp EEG datasets achieved AUROC 93% (dataset 1) and 71% (dataset 2), showing strong MDD classification performance.
Identifies consistent biomarkers: dominant left prefrontal, left frontoparietal and right prefrontal activity, mainly in delta, theta and alpha bands; lower beta minimal.

TL;DR (AI Generated)

The structural and functional connectivity of the brain network is a combination of complex connections and interconnections among neurons of different brain regions.

The dynamic complexities in the functional connectivity of the brain and its intricate interconnections have posed significant challenges in effectively utilizing EEG data and machine learning techniques to extract meaningful information for accurate MDD diagnosis.

This paper makes three key contributions: it proposes a novel GCNN model with a unique graph representation for EEG data that integrates domain knowledge of brain regions with data-driven functional relationships without relying on explicit spatial geometry; it conducts extensive evaluation using two distinct scalp- EEG datasets and identifies effective frequency bands and brain region markers associated with MDD.

Basic summary

IEEE J Biomed Health Inform. 2026 Jun 29;PP. doi: 10.1109/JBHI.2026.3708311. Online ahead of print.

ABSTRACT

The structural and functional connectivity of the brain network is a combination of complex connections and interconnections among neurons of different brain regions. Analysis of these connectivity patterns provides a representation of the mental health of an individual, including major depressive disorder (MDD). MDD analysis with Electroencephalogram (EEG) has increasingly focused on characterizing alterations in brain connectivity patterns. The dynamic complexities in the functional connectivity of the brain and its intricate interconnections have posed significant challenges in effectively utilizing EEG data and machine learning techniques to extract meaningful information for accurate MDD diagnosis. This paper proposes the use of a graph convolutional neural network (GCNN) for MDD classification and analysis while discovering the dynamic functional connectivity involved through scalp EEG recordings. The connectivity graph was obtained using spectral coherence of resting-state and task-based EEG recordings. This paper makes three key contributions: it proposes a novel GCNN model with a unique graph representation for EEG data that integrates domain knowledge of brain regions with data-driven functional relationships without relying on explicit spatial geometry; it conducts extensive evaluation using two distinct scalp- EEG datasets and identifies effective frequency bands and brain region markers associated with MDD. The proposed method demonstrates strong performance in classifying MDD patients and healthy individuals, achieving an AUROC of 93% for dataset 1 and 71% for dataset 2. The study found consistent identification of neurophysiologically relevant biomarkers across both datasets, including dominant activity in the left prefrontal, left frontoparietal and right prefrontal regions, along with contributions from delta, theta, and alpha frequency bands. In contrast, the lower beta band has only a minimal influence compared to the others.

PMID:42371895 | DOI:10.1109/JBHI.2026.3708311

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