Tract-based analysis of target engagement by subcallosal cingulate deep brain stimulation for treatment resistant depression
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) of subcallosal cingulate cortex (SCC) is a promising investigational therapy for treatment-resistant depression (TRD). However, outcomes vary, likely due to suboptimal DBS placement. Ideal placement is proposed to stimulate 4 SCC white matter bundles; however, no quantitative data have linked activation of these target tracts to response. OBJECTIVE: Here we used the volume of tissue activated (VTA) and probabilistic diffusion tensor imaging (DTI) to quantify tract activation relating to response. METHODS: DTI was performed in 19 TRD patients who received SCC-DBS. We defined clinical response as >48% reduction from baseline in the Hamilton Depression Rating Scale. Bilateral VTAs were generated based on subject-specific stimulation parameters. Patient-specific tract maps emanating from the VTAs were calculated using whole-brain probabilistic DTI. The four target tracts were isolated using tract-specific quantification and examined for overlap with DBS activated tissue. RESULTS: Medial frontal and temporal projections were stimulated in all responders at 6 and 12 months. Individual tract-based generalized linear mixed model analysis revealed a significant tract-by-response interaction at both 6 (F(1,135) = 3.828, p = 0.001) and 12 (F(1,135) = 5.688, p < 0.001) months, with post hoc tests revealing a response-related increase in cingulum activation at 6 months (t(135) = 2.418, p = 0.017) and decrease in forceps minor activation at 12 months (t(135) = -2.802, p = 0.006). CONCLUSIONS: A wider profile of white matter tracts, particularly to the medial frontal, was associated with DBS response. Cingulum bundle stimulation may promote early response and excess stimulation of the forceps minor might be detrimental. Our work supports prospective patient-specific targeting to inform personalized DBS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".