An Analysis of Clinical Outcome and Tractography following Bilateral Anterior Capsulotomy for Depression
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it