The characteristics of the functional connectivity in subjects with cognitive vulnerability to depression while processing negative information
Bibliographic record
Abstract
Objective To examine the patterns of the functional connectivity between the amygdala and other brain regions in subjects with cognitive vulnerability and subjects with major depressive disorder while processing negative information. Methods Using the cognitive style questionnaire (CSQ) and some psychological scales,subjects with cognitive vulnerability (CV,26 subjects) and healthy control subjects (HC,31 subjects) were recruited.Besides,first-episode treatment-free subjects with major depressive disorder (MDD,29 subjects) from outpatients in the psychology clinic were included.All subjects were scanned by functional magnetic resonance imaging (f MRI) while performing an emotional matching task.The functional connectivity patterns between the amygdala and other brain regions were examined for each group. Results The right middle frontal gyrus showed less connectivity with the amygdala in subjects with CV than in HC subjects[Montreal Neurological Institute (MNI) :26,40,-12; t=3.88,P<0.001,k=10].The left inferior frontal gyrus showed decreased connectivity with amygdala in MDD patients compared with HC subjects (MNI:-46,24,-12;t=4.20,P<0.001,k=16). Conclusion When viewing negative stimuli,the functional connectivity between the amygdala and the prefrontal cortex is decreased in individuals with CV and MDD. Key words: Depressive disorder; Magnetic resonance imaging; Amygdala; Negative cognitive processing; Functional connectivity; Cognitive vulnerability
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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) | 0.001 | 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".