An Analysis of Clinical Outcome and Tractography following Bilateral Anterior Capsulotomy for Depression
Bibliographic record
Abstract
INTRODUCTION: Bilateral anterior capsulotomy (BAC) is an effective surgical procedure for patients with treatment-resistant major depression (TRMD). In this work, we analyze the connectivity of the BAC lesions to identify connectivity "fingerprints" associated with clinical outcomes in patients with TRMD. METHODS: We performed a retrospective study of ten patients following BAC surgery. These patients were divided into "responders" and "non-responders" based on the relative change in the Beck depression inventory (BDI) score after surgery. We generated the dorsolateral prefrontal associative (DLPFC) pathways and the ventromedial prefrontal limbic (vmPFC) pathways going through the anterior limb of the internal capsule and analyzed if the overlap of the BAC lesions with these pathways was associated with either outcome. Finally, we used the BAC lesions of our patients to generate group-averaged connectivity "fingerprints" associated with either outcome. RESULTS: Six patients were responders (≥50% improvement in BDI), four patients were non-responders (<50% improvement). No significant impairments were found in most neuropsychological tests after surgery. The overlap analysis showed that in the responder group, there was less involvement of the DLPFC pathways than the vmPFC pathways (p = 0.001). Conversely, in the non-responder group, there was no significant difference between the involvement of both pathways (p = 0.157). The responder and non-responder connectivity fingerprint showed significant connections with the vmPFC limbic areas. However, the non-responder connectivity fingerprint also showed stronger connectivity to associative areas including the DLPFC and lateral orbitofrontal cortices. CONCLUSIONS: The optimum outcome following BAC surgery in this cohort was associated with interruption of vmPFC pathways and the relative preservation of DLPFC pathways.
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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 | 0.001 | 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".