Corticostriatothalamic reward prediction error signals and executive control in late-life depression
Bibliographic record
Abstract
BACKGROUND: Altered corticostriatothalamic encoding of reinforcement is a core feature of depression. Here we examine reinforcement learning in late-life depression in the theoretical framework of the vascular depression hypothesis. This hypothesis attributes the co-occurrence of late-life depression and poor executive control to prefrontal/cingulate disconnection by vascular lesions. METHOD: Our fMRI study compared 31 patients aged ⩾60 years with major depression to 16 controls. Using a computational model, we estimated neural and behavioral responses to reinforcement in an uncertain, changing environment (probabilistic reversal learning). RESULTS: Poor executive control and depression each explained distinct variance in corticostriatothalamic response to unexpected rewards. Depression, but not poor executive control, predicted disrupted functional connectivity between the striatum and prefrontal cortex. White-matter hyperintensities predicted diminished corticostriatothalamic responses to reinforcement, but did not mediate effects of depression or executive control. In two independent samples, poor executive control predicted a failure to persist with rewarded actions, an effect distinct from depressive oversensitivity to punishment. The findings were unchanged in a subsample of participants with vascular disease. Results were robust to effects of confounders including psychiatric comorbidities, physical illness, depressive severity, and psychotropic exposure. CONCLUSIONS: Contrary to the predictions of the vascular depression hypothesis, altered encoding of rewards in late-life depression is dissociable from impaired contingency learning associated with poor executive control. Functional connectivity and behavioral analyses point to a disruption of ascending mesostriatocortical reward signals in late-life depression and a failure of cortical contingency encoding in elderly with poor executive control.
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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".