Abnormally slow dynamics in occipital cortex of depression
Bibliographic record
Abstract
AIM: Major depressive disorder (MDD) is characterized by altered activity in various higher-order regions like the anterior cingulate and prefrontal cortex. While some findings also show changes in lower-order sensory regions like the occipital cortex in MDD, the latter's exact neural and temporal, e.g., dynamic characterization and symptom severity remains yet unclear. METHODS: We conducted resting state fMRI in MDD (N = 49) and healthy controls to investigate the global activity representation of the brain's spontaneous activity in occipital cortex including lower-order (V1) and higher-order (hMT+) regions in the hierarchy of the visual cortex. We further explored (i) these regions' functional connectivity to higher-order prefrontal and subcortical regions, (ii) global signal correlation differences between MDD and controls in different frequency bands, and (iii) their power spectrum's correlation (using median frequency/MF) with symptom severity. RESULTS: Our findings in MDD show: (i) abnormally high functional connectivity of the occipital cortex to both subcortical and higher-order cortical regions; (ii) occipital global signal correlation is reduced mainly in the faster infraslow frequency range (slow 3: 0.073 to 0.198 Hz) as distinguished from the slower ones (slow 5 and 4: 0.01 to 0.027 Hz, and 0.027 to 0.073 Hz); (iii) the reduced neural dynamics in occipital cortex (MF) correlate with the severity of both overall depressive symptoms and psychomotor retardation scores. CONCLUSIONS: MDD shows reduced global activity with abnormally slow neural dynamics in occipital cortex that is functionally connected with higher-order regions like the anterior cingulate cortex. The slow dynamics in occipital cortex relates to overall symptom severity and psychomotor retardation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".