Connectomic Predictive Modeling Guides Selective Perturbation of Tracts in the Subcallosal Cingulate White Matter
Bibliographic record
Abstract
Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) is an emerging experimental therapy for treatment-resistant depression. Successful outcomes are critically dependent on placing the active contact within the confluence of white matter adjacent to the SCC using patient-specific connectomic guidance, but the relative contribution of each putative target fiber bundle to the clinical response is still unknown. This study's goal was to assess the feasibility of selective target activation of two candidate SCC targets, forceps minor and the cingulum bundle, using biophysical modeling to guide parameter selection in individual patients. We tested two complementary use cases, isolated (or preferential) activation of each target, and activation of the same targets as the clinical setting but with minimal theoretical battery depletion. One selective setting per fiber bundle and one energy-efficient settings per lead were selected from 774 settings, and cortical responses to model settings were evaluated with the left lead at 2 Hz using high-density EEG. Optimal settings differed by patient, hemisphere, and use case. Isolated activation of the left cingulum bundle generated an ipsilateral cortical response with peak activity near 16 ms, whereas preferential activation of forceps minor produced a relatively slower bilateral response in the frontal sensors. A key feature of concomitant target activation was a midline sweep from 40-90 ms. Efficient setting generated a topographically similar response as the monopolar clinical setting but with some differences in the spatial extent of frontal polar activity. The results demonstrate the feasibility of selective perturbation of SCC white matter with model-based guidance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".