Altered coordination between frontal delta and parietal alpha networks underlies anhedonia and depressive rumination in major depressive disorder
Bibliographic record
Abstract
BACKGROUND: A hyperactive default mode network (DMN) has been observed in people with major depressive disorder (MDD), and weak DMN suppression has been linked to depressive symptoms. However, whether dysregulation of the DMN contributes to blunted positive emotional experience in people with MDD is unclear. METHODS: We recorded 128-channel electroencephalograms (EEGs) from 24 participants with MDD and 31 healthy controls in a resting state (RS) and an emotion-induction state (ES), in which participants engaged with emotionally positive pictures. We combined Granger causality analysis and data-driven decomposition to extract latent brain networks shared among states and groups, and we further evaluated their interactions across individuals. RESULTS: We extracted 2 subnetworks. Subnetwork 1 represented a delta (δ)-band (1~4 Hz) frontal network that was activated more in the ES than the RS (i.e., task-positive). Subnetwork 2 represented an alpha (α)-band (8~13 Hz) parietal network that was suppressed more in the ES than the RS (i.e., task-negative). These subnetworks were anticorrelated in both the healthy control and MDD groups, but with different sensitivities: for participants with MDD to achieve the same level of task-positive (subnetwork 1) activation as healthy controls, more suppression of task-negative (subnetwork 2) activation was necessary. Furthermore, the anticorrelation strength in participants with MDD correlated with the severity of 2 core MDD symptoms: anhedonia and rumination. LIMITATIONS: The sample size was small. CONCLUSION: Our findings revealed altered coordination between 2 functional networks in MDD and suggest that weak suppression of the task-negative α-band parietal network contributes to blunted positive emotional responses in adults with depression. The subnetworks identified here could be used for diagnosis or targeted for treatment in the future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".