Polygenic risk for depression and resting-state functional connectivity of subgenual anterior cingulate cortex in young adults
Bibliographic record
Abstract
BACKGROUND: Genetic variants may confer risk for depression by modulating brain structure and function; evidence has underscored the key role of the subgenual anterior cingulate cortex (sgACC) in depression. We sought to examine how the resting-state functional connectivity (rsFC) of the sgACC was associated with polygenic risk for depression in a subclinical population. METHODS: Following published protocols, we computed seed-based whole-brain sgACC rsFC and calculated polygenic risk scores (PRS) using data from healthy young adults from the Human Connectome Project. We performed whole-brain regression against PRS and severity of depression symptoms in a single model for all participants and by sex, controlling for age, sex, race or ethnicity, alcohol use severity, and household income. We evaluated the results at a corrected threshold. RESULTS: We included data for 717 healthy young adults. We found lower rsFC between the sgACC and the default mode network and frontal regions in association with PRS and lower sgACC-cerebellar rsFC in association with depression severity. We also noted differences by sex in the connectivity correlates of PRS and depression severity. In an additional set of analyses, we observed a significant correlation between PRS and somatic complaints, as well as altered sgACC-somatosensory cortical connectivity in association with the severity of somatic complaints. LIMITATIONS: The current findings should be considered specific to subclinical depression and may not generalize to patients with depressive disorders. CONCLUSION: Our findings highlight the pivotal role of distinct sgACC-based networks in the genetic predisposition for depression and the manifestation of depression among young adults with subclinical depression. Distinguishing the risk from severity markers of depression may have implications in developing early and effective treatments for people at risk for depression.
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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 | 0.002 |
| 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) | 0.002 | 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".