Neurophysiological correlates of neuroanatomical dimensions in major depressive disorder: EEG markers of brain function and treatment outcome
Bibliographic record
Abstract
<title>Abstract</title> Major depressive disorder (MDD) is a heterogeneous disorder with variable treatment responses and no established biomarkers for identification or predictors of treatment response. In the COORDINATE-MDD consortium, a data-driven classification identified two neuroanatomic-based dimensions: Dimension 1 (D1), with preserved grey and white matter volumes, and Dimension 2 (D2), with widespread reductions. Here, we investigated whether resting-state electroencephalography (EEG) features differ between these dimensions and whether such features predict treatment response. Participants were 237 MDD (155 women; mean age 37.47 ± 13.36 year) from two clinical trials: CAN-BIND (escitalopram) and EMBARC (randomized to sertraline or placebo). All were medication-free at baseline, in a current depressive episode of at least moderate severity. Resting-state, eyes-closed EEG was recorded at baseline. EEG features included spectral power, frontal alpha asymmetry (FAA), multiscale sample entropy (MSE), and inter-site phase clustering (ISPC). Analyses examined effects of neuroanatomical dimension (D1, D2) and clinical outcome (responder, non-responder), using ANCOVA and threshold-free cluster enhancement (TFCE). D1 participants showed greater absolute and log-transformed theta, alpha, and beta power, and lower relative delta power compared to D2, particularly in frontal and central regions. Among responders, D1 showed higher alpha and theta power and greater MSE at coarse time scales. Individuals with preserved neuroanatomy in D1 exhibit electrophysiological markers of more flexible and integrated brain function, possibly reflecting efficient top-down regulation and adaptive neural dynamics. In contrast, the D2 dimension, marked by lower complexity and elevated delta activity, may reflect disrupted network integrity, reduced cortical arousal, and impaired information processing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".