Resting state default‐mode network connectivity in early depression using a seed region‐of‐interest analysis: Decreased connectivity with caudate nucleus
Bibliographic record
Abstract
AIM: Reports on resting brain activity in healthy controls have described a default-mode network (DMN) and important differences in DMN connectivity have emerged for several psychiatric conditions. No study to date, however, has investigated resting-state DMN in relatively early depression before years of medication treatment. The objective of the present study was, therefore, to investigate the DMN in patients seeking help from specialized mental health services for the first time for symptoms of depression. METHODS: Fourteen depressed subjects and 15 matched controls were scanned using 4-T functional magnetic resonance imaging while resting with eyes closed. All but one subject was medication free. A precuneus/posterior cingulate cortex (P/PCC) seed-region connectivity analysis was used to identify the DMN and compare study groups in regions of relevance to depression. RESULTS: The P/PCC analysis identified the DMN well in both study groups, consistent with prior literature. Direct comparison showed significantly reduced correlation between the P/PCC and the bilateral caudate in depression compared with controls and no areas of increased connectivity in the depressed group. CONCLUSIONS: The present study is the first to investigate resting-state DMN in the early stages of treatment-seeking for depression. Depressed subjects had decreased connectivity between the P/PCC and the bilateral caudate, regions known to be involved in motivation and reward processing. Deficits in DMN connectivity with the caudate may be an early manifestation of major depressive disorder.
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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 | 0.001 | 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) | 0.001 | 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".