Targeting the centromedian thalamic nucleus for deep brain stimulation
Bibliographic record
Abstract
ABSTRACT Objectives Deep brain stimulation (DBS) of the centromedian thalamic nucleus (CM) is an emerging treatment for multiple brain diseases, including the drug-resistant epilepsy Lennox-Gastaut syndrome (LGS). We aimed to improve neurosurgical targeting of the CM by (i) developing a structural MRI approach for CM visualisation, (ii) identifying the CM’s neurophysiological characteristics, and (iii) mapping connectivity from CM-DBS sites using functional MRI (fMRI). Methods Nineteen patients with LGS (mean age=28 years) underwent pre-surgical 3 tesla MRI using magnetisation-prepared 2 rapid acquisition gradient echoes (MP 2 RAGE) and fMRI sequences; 16 proceeded to bilateral CM-DBS implantation and intraoperative microelectrode recordings (MERs) from the thalamus. CM visualisation was achieved by highlighting intrathalamic borders on MP 2 RAGE using Sobel edge-detection. Mixed-effects analysis compared two MER features (spike firing rate, background noise) between ventrolateral, CM, and parafasicular nuclei. Resting-state fMRI connectivity was assessed using implanted CM-DBS electrode positions as regions-of-interest. Results The CM appeared as a hyperintense region bordering the comparatively hypointense pulvinar, mediodorsal, and parafasicular nuclei. At the group-level, reduced spike firing and background noise distinguished CM from the ventrolateral nucleus; however, these trends were not found in 20-25% of individual MER trajectories. Areas of fMRI connectivity included basal ganglia, brainstem, cerebellum, sensorimotor/premotor and limbic cortex. Conclusions In the largest clinical trial cohort of LGS patients undergoing CM-DBS reported to date, we show that accurate targeting of the CM is achievable using 3 tesla MP 2 RAGE MRI. MERs may provide additional localising features in some cases, however their utility is limited by inter-patient variability. Therapeutic effects of CM-DBS may be mediated via connectivity with brain networks that support diverse arousal, cognitive, and sensorimotor processes.
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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) | 0.002 | 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".