Socioeconomic Status Is Associated With Reward Processing, Interleukin 1β, Striatal Connectivity, and Antidepressant Outcomes in Individuals With Major Depressive Disorder: A CAN-BIND-1 Report
Bibliographic record
Abstract
Background: Major depressive disorder (MDD) is a common condition with heterogeneous risk factors. Socioeconomic status (SES) is one such risk factor, which is negatively linked to MDD treatment outcomes and symptom severity. SES is associated with altered resting-state functional connectivity (RSFC) in reward-processing circuitry and elevated proinflammatory cytokine levels in individuals without depression. However, how the negative consequences of low SES exacerbate MDD psychopathology is poorly understood. Methods: Data on SES (household income and education), depression severity, self-reported reward processing, serum proinflammatory cytokine levels, and neuroimaging for 323 adult participants (211 patients with MDD receiving open-label escitalopram, 112 control participants without depression; 63.4% female) were obtained from the CAN-BIND-1 (Canadian Biomarker Integration in Depression Study-1) dataset. General linear models assessed the effects of MDD diagnosis and SES on self-reported reward processing and proinflammatory cytokine levels. Whole-brain seed-to-voxel RSFC analyses were performed for the dorsal and ventral striatum leveraging 249 participants (150 patients with MDD, 99 control participants; 62.2% female). We also assessed the impact of SES on response to open-label escitalopram. Results: > 2.3 threshold, MDD household income correlated with striatal RSFC with the dorsolateral prefrontal and posterior cingulate cortices. Conclusions: Our results elucidate the role of SES and its negative consequences in altering reward processing and antidepressant treatment efficacy in MDD, corroborating previous literature suggesting that SES significantly impacts health outcomes. Better characterizing the relationship between SES and MDD psychopathology may inform future treatment approaches and intervention development.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".