The neural basis of hot and cold cognition in depressed patients, unaffected relatives, and low-risk healthy controls: An fMRI investigation
Bibliographic record
Abstract
BACKGROUND: Modern cognitive neuropsychological models of depression posit that negatively biased emotional ("hot") processing confers risk for depression, while preserved executive function ("cold") cognition promotes resilience. METHODS: We compared neural responses during hot and cold cognitive tasks in 99 individuals: those at familial risk for depression (N = 30 unaffected first-degree relatives of depressed individuals) and those currently experiencing a major depressive episode (N = 39 unmedicated depressed patients) with low-risk healthy controls (N = 30). Primary analyses assessed neural activation on two functional magnetic resonance imaging tasks previously associated with depression: dorsolateral prefrontal cortex (DLPFC) responsivity during the n-back working memory task; and amygdala and subgenual anterior cingulate cortex (sgACC) responsivity during incidental emotional face processing. RESULTS: Depressed patients exhibited significantly attenuated working memory-related DLPFC activation, compared to low-risk controls and unaffected relatives; unaffected relatives did not differ from low-risk controls. We did not observe a complementary pattern during emotion processing. However, we found preliminary support that greater DLPFC activation was associated with lower amygdala response during emotion processing. LIMITATIONS: These findings require confirmation in a longitudinal study to observe each individual's risk of developing depression; without this, we cannot identify the true risk level of the first-degree relative or low-risk control group. CONCLUSIONS: These findings have implications for understanding the neural mechanisms of risk and resilience in depression: they are consistent with the suggestion that preserved executive function might confer resilience to developing depression in first-degree relatives of depressed patients.
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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".