The neural correlates of regulating positive and negative emotions in medication-free major depression
Bibliographic record
Abstract
Depressive cognitive schemas play an important role in the emergence and persistence of major depressive disorder (MDD). The current study adapted emotion regulation techniques to reflect elements of cognitive behavioural therapy (CBT) and related psychotherapies to delineate neurocognitive abnormalities associated with modulating the negative cognitive style in MDD. Nineteen non-medicated patients with MDD and 19 matched controls reduced negative or enhanced positive feelings elicited by emotional scenes while undergoing functional magnetic resonance imaging. Although both groups showed significant emotion regulation success as measured by subjective ratings of affect, the controls were significantly better at modulating both negative and positive emotion. Both groups recruited regions of dorsolateral prefrontal cortex and ventrolateral prefrontal cortex (VLPFC) when regulating negative emotions. Only in controls was this accompanied by reduced activity in sensory cortices and amygdala. Similarly, both groups showed enhanced activity in VLPFC and ventral striatum when enhancing positive affect; however, only in controls was ventral striatum activity correlated with regulation efficacy. The results suggest that depression is associated with both a reduced capacity to achieve relief from negative affect despite recruitment of ventral and dorsal prefrontal cortical regions implicated in emotion regulation, coupled with a disconnect between activity in reward-related regions and subjective positive affect.
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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) | 0.000 | 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 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".