Deep Brain Stimulation for Epilepsy: Optimal Targeting and Clinical Outcomes
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) has been investigated for patients with drug-resistant epilepsy who are not candidates for resective surgery. Because different types of epilepsy involve different brain networks, numerous DBS targets have been explored. METHODS: To provide a comprehensive overview of this expanding literature, we conducted a systematic review of studies for DBS in epilepsy, collecting data on surgical targets, individual disease characteristics, outcomes, and precise electrode placements. DBS electrode coordinates were gathered into a common template space and related to clinical outcomes. FINDINGS: We included 124 studies, corresponding to 1,210 patients and 20 distinct surgical targets. While the anterior (ANT) and centromedian (CM) nuclei of the thalamus remain the most studied, we also review less commonly used targets that show promise for specific forms of epilepsy and may warrant further investigation. Substantial variability in targeting strategies and electrode placement was observed within each of the target regions. Importantly, significant relationships between stimulation location and outcomes were identified for ANT- and CM-DBS. For ANT-DBS, shorter distance to the mammillothalamic tract junction was associated with greater seizure reduction on both study- and patient-level analyses (r=-0.55, p<0.001 and r=-0.51, p<0.001, respectively). For CM-DBS, localization effects may be dependent on the form of epilepsy, with stimulation of the parvocellular CM being associated with better outcomes in generalized epilepsy. INTERPRETATION: Our results emphasize the importance of accurate targeting in DBS for epilepsy. Our database and atlas of DBS targets are made publicly available, potentially serving further meta-analytical work.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".