The History and Future of Ablative Neurosurgery for Major Depressive Disorder
Bibliographic record
Abstract
BACKGROUND: There is an urgent need to develop safe and effective treatments for patients with treatment-resistant depression (TRD). Several neurosurgical procedures have been developed to treat the dysfunctional brain circuits implicated in major depression. OBJECTIVES: This review describes the most common ablative procedures used to treat major depressive disorder: anterior cingulotomy, subcaudate tractotomy, limbic leucotomy, and anterior capsulotomy. The efficacy and safety of each are discussed and compared with other current and emerging modalities, including deep brain stimulation (DBS) and MR-guided focused ultrasound (MRgFUS). METHODS: The PubMed and MEDLINE electronic databases were used in this study, through July 2016. Keywords, including "treatment resistant depression," and "ablative neurosurgery," etc. were used to generate reference hits. RESULTS: Approximately a third to half of patients who underwent ablative procedures achieved a treatment response and/or remission. The efficacy and safety profiles corresponding to both ablative procedures and DBS were very similar. CONCLUSIONS: The longitudinal experience with ablative procedures shows that there remains an important role for accurate, discrete lesions in disrupting affective circuitry in the treatment of TRD. New modalities, such as MRgFUS, have the potential to further improve the accuracy of ablative procedures, while enhancing safety by obviating the need for open brain surgery.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".