Tractography-Guided Anterior Capsulotomy for Major Depression and Obsessive-Compulsive Disorder: Targeting the Emotion Network
Bibliographic record
Abstract
BACKGROUND: Bilateral anterior capsulotomy (BAC) is an effective surgical option for patients with treatment-resistant major depression (TRMD) and treatment-resistant obsessive-compulsive disorder (TROCD). The size of the lesion and its precise dorsal-ventral location within the anterior limb of the internal capsule (ALIC) remain undefined. OBJECTIVE: To present a method to identify the trajectories of the associative and limbic white matter pathways within the ALIC for targeting in BAC surgery. METHODS: Using high-definition tractography, we prospectively tested the feasibility of this method in 2 patients with TRMD and TROCD to tailor the capsulotomy lesion to their limbic pathway. RESULTS: The trajectories of the associative and limbic pathways were identified in the ALIC of both patients and we targeted the limbic pathways by defining the dorsal limit of the lesion in a way to minimize the damage to the associative pathways. The final lesions were smaller than those that have been previously published. This individualized procedure was associated with long-term benefit in both patients. CONCLUSION: Tractography-guided capsulotomy is feasible and was associated with long-term benefit in patients with TRMD and TROCD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".