Towards a neuroimaging biomarker of depression vulnerability
Bibliographic record
Abstract
Abstract Major depressive disorder (MDD) is a pervasive and debilitating illness, with a recurrent course and chronic prognosis. Although effective treatments for MDD exist, there is a pressing need to characterize relapse vulnerability in order to design effective prophylactic care. To date, heterogeneity within depression neuroimaging research has made it difficult to establish a reliable biomarker of disorder susceptibility. In this paper, we review neuroimaging evidence for the assessment of MDD vulnerability, theorizing that current findings can be broadly distinguished between those indicating the presence of depressive episodes and those indicating MDD vulnerability during symptom remission. We argue that unlike the amygdala hyperactivity and prefrontal hypoactivity observed during MDD episodes, prefrontal hyperactivity may be a characteristic of dysphoric cognition during symptom remission that indicates MDD vulnerability and relapse risk. Drawing on current research of normative emotion regulation, we describe a potential test of MDD vulnerability, employing emotional challenge paradigms that induce cognitive reactivity — the increased endorsement of negative self-descriptions during a transient dysphoric mood. Relative to a normative model of prefrontal function, the neuroimaging assessment of cognitive reactivity may provide a reliable indicator of MDD vulnerability, advancing the field of biomarker research as well as the delivery of preventative treatment on an individual basis.
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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 | 0.004 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".