Deep brain stimulation targets in epilepsy: Systematic review and meta‐analysis of anterior and centromedian thalamic nuclei and hippocampus
Bibliographic record
Abstract
Deep brain stimulation (DBS) is a neuromodulatory treatment used in patients with drug-resistant epilepsy (DRE). The primary goal of this systematic review and meta-analysis is to describe recent advancements in the field of DBS for epilepsy, to compare the results of published trials, and to clarify the clinical utility of DBS in DRE. A systematic literature search was performed by two independent authors. Forty-four articles were included in the meta-analysis (23 for anterior thalamic nucleus [ANT], 8 for centromedian thalamic nucleus [CMT], and 13 for hippocampus) with a total of 527 patients. The mean seizure reduction after stimulation of the ANT, CMT, and hippocampus in our meta-analysis was 60.8%, 73.4%, and 67.8%, respectively. DBS is an effective and safe therapy in patients with DRE. Based on the results of randomized controlled trials and larger clinical series, the best evidence exists for DBS of the anterior thalamic nucleus. Further randomized trials are required to clarify the role of CMT and hippocampal stimulation. Our analysis suggests more efficient deep brain stimulation of ANT for focal seizures, wider use of CMT for generalized seizures, and hippocampal DBS for temporal lobe seizures. Factors associated with clinical outcome after DBS for epilepsy are electrode location, stimulation parameters, type of epilepsy, and longer time of stimulation. Recent advancements in anatomical targeting, functional neuroimaging, responsive neurostimulation, and sensing of local field potentials could potentially lead to improved outcomes after DBS for epilepsy and reduced sudden, unexpected death of patients with epilepsy. Biomarkers are needed for successful patient selection, targeting of electrodes and optimization of stimulation parameters.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.025 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".