Targeting the centromedian nucleus of the thalamus for epilepsy
Bibliographic record
Abstract
Approximately one-third of the 50 million patients with epilepsy worldwide are resistant to pharmacological treatments and may require aggressive interventions, such as surgery. However, many patients do not benefit from surgery due to anatomical challenges or multifocal epileptogenic origins. Deep brain stimulation (DBS) is a promising alternative for these patients. DBS modulates neurotransmitter activity to prevent seizure propagation and has already been approved for the treatment of Parkinson’s disease and essential tremors. Although the anterior nucleus of the thalamus is the only DBS target approved for drug resistant epilepsy in Europe and Canada, the centromedian nucleus (CM) has emerged as a promising target, particularly for generalized and frontal lobe seizures. The CM is challenging to target because of its small size and complex connections, and it cannot be easily visualized using conventional imaging. This study focused on advanced methods for CM identification, including specialized magnetic resonance imaging sequences, intraoperative neurophysiological recordings, and diffusion tensor imaging tractography. These techniques are crucial for precise DBS targeting and for improving seizure control in affected patients. Our findings indicate that combining these advanced imaging and neurophysiological methods enhances the accuracy of DBS, potentially expanding its therapeutic applications in epilepsy. By optimizing CM-DBS electrode placement, these approaches can improve clinical outcomes in drug resistant epilepsy, making them vital for effective treatment strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.004 | 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".