Magnetic Resonance-Guided Focused Ultrasound Capsulotomy for Treatment-Resistant Psychiatric Disorders
Bibliographic record
Abstract
BACKGROUND: Psychiatric surgery is an important domain of functional neurosurgery and involves deep brain stimulation (DBS) or lesional procedures performed for treatment-resistant psychiatric illness. It has recently become possible to use magnetic-guided focused ultrasound (MRgFUS) to perform bilateral capsulotomy, a lesional technique commonly carried out with surgical radiofrequency ablation or stereotactic radiosurgery. MRgFUS offers several advantages, including improved safety and real-time imaging of the lesions. OBJECTIVE: To describe the clinical and technical aspects of performing bilateral MRgFUS capsulotomy in patients with severe refractory depression and obsessive-compulsive disorder. METHODS: We describe the clinical and technical considerations of performing MRgFUS capsulotomy. Topics discussed include patient selection, headframe application, targeting, sonication strategies, and follow-up procedures. RESULTS: MRgFUS capsulotomy was performed in 16 patients without serious clinical or radiographic adverse events. CONCLUSION: MRgFUS allows for a safe, less invasive technique for performing a well-studied psychiatric surgery procedure-the anterior capsulotomy.
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 | 0.001 |
| 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) | 0.002 | 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".