Double-blind optimization of subcallosal cingulate deep brain stimulation for treatment-resistant depression: a pilot study
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) is reported to be a safe and effective new treatment for treatment-resistant depression (TRD). However, the optimal electrical stimulation parameters are unknown and generally selected by trial and error. This pilot study investigated the relationship between stimulus parameters and clinical effects in SCC-DBS treatment for TRD. METHODS: Four patients with TRD underwent SCC-DBS surgery. In a double-blind stimulus optimization phase, frequency and pulse widths were randomly altered weekly, and corresponding changes in mood and depression were evaluated using a visual analogue scale (VAS) and the 17-item Hamilton Rating Scale for Depression (HAM-D-17). In the open-label postoptimization phase, depressive symptoms were evaluated biweekly for 6 months to determine long-term clinical outcomes. RESULTS: Longer pulse widths (270-450 μs) were associated with reductions in HAM-D-17 scores in 3 patients and maximal happy mood VAS responses in all 4 patients. Only 1 patient showed acute clinical or mood effects from changing the stimulation frequency. After 6 months of open-label therapy, 2 patients responded and 1 patient partially responded. LIMITATIONS: Limitations include small sample size, weekly changes in stimulus parameters, and fixed-order and carry-forward effects. CONCLUSION: Longer pulse width stimulation may have a role in stimulus optimization for SCC-DBS in TRD. Longer pulse durations produce larger apparent current spread, suggesting that we do not yet know the optimal target or stimulus parameters for this therapy. Investigations using different stimulus parameters are required before embarking on large-scale randomized sham-controlled trials.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".