Subgenual anterior cingulate cortex functional connectivity abnormalities in depression: insights from brain imaging big data and precision-guided personalized intervention via transcranial magnetic stimulation
Bibliographic record
Abstract
The subgenual anterior cingulate cortex (sgACC) plays a central role in the pathophysiology of major depressive disorder (MDD). Its functional interactive profile with the left dorsal lateral prefrontal cortex (DLPFC) is associated with transcranial magnetic stimulation (TMS) treatment outcomes. Previous research on sgACC functional connectivity (FC) in MDD has yielded inconsistent results, partly due to small sample sizes and limited statistical power. Furthermore, calculating sgACC-FC to target TMS individually is challenging. We used a large multi-site cross-sectional sample (1660 patients with MDD vs. 1341 healthy controls) from Phase II of the Depression Imaging REsearch ConsorTium (DIRECT) to systematically delineate case-control difference maps of sgACC-FC. We explored the potential impact of group-level abnormality profiles on TMS target localization and clinical efficacy. Next, we developed an MDD big data-guided, individualized TMS targeting algorithm to integrate group-level statistical maps with individual-level brain activity to individually localize TMS targets. We found enhanced sgACC-DLPFC FC in patients with MDD compared with healthy controls (HC). These group differences altered the position of the sgACC anti-correlation peak in the left DLPFC. We showed that the magnitude of case-control differences in the sgACC-FC was related to clinical improvement in two independent clinical samples. This targeting algorithm may generate targets demonstrating stronger associations with clinical efficiency than group-level targets. We reliably delineated MDD-related abnormalities of sgACC-FC profiles in a large, independently ascertained sample and demonstrated the potential impact of such case-control differences on FC-guided localization of TMS targets.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".