Assessing the Predictive Utility of Quantitative Electroencephalography Coherence in Adolescent Major Depressive...

Area of Science:

• Neuroscience
• Psychiatry
• Biomarkers

Background:

• Early diagnosis of major depressive disorder (MDD) in children is crucial.
• Quantitative electroencephalogram (qEEG) offers a noninvasive method for identifying MDD biomarkers.
• Previous research indicated reduced resting-state connectivity in adolescents with MDD.

Purpose of the Study:

• To investigate qEEG coherence as a predictive biomarker for MDD diagnosis in adolescents.
• To analyze resting-state qEEG coherence in medication-free adolescents with MDD compared to healthy controls (HCs).

Main Methods:

• Recruited 28 adolescents with MDD and 27 HCs (ages 14-17).
• Recorded baseline resting 32-channel EEG and calculated brain-wide coherence across frequency bands.
• Utilized random forest classifiers with cross-validation to predict MDD status.

Main Results:

• Random forest models showed a trend towards significant prediction of MDD status (mean AUC-ROC = 0.65, p = 0.08).
• Adolescent MDD was associated with lower coherence in specific electrode pairs (e.g., T7-P7) and higher coherence in others (e.g., P4-O2).
• Aberrant coherence patterns were observed within the default mode and cognitive control networks.

Conclusions:

• Multivariate qEEG coherence patterns show preliminary potential for diagnosing adolescent MDD.
• Specific patterns of altered brain connectivity characterize adolescent MDD.
• Further validation in larger cohorts is warranted to confirm these findings.