Optimal location of thalamotomy lesions for tremor associated with Parkinson disease: a probabilistic analysis based on postoperative magnetic resonance imaging and an integrated digital atlas
Bibliographic record
Abstract
OBJECT: Renewed interest in stereotactic neurosurgery for movement disorders has led to numerous reports of clinical outcomes associated with different treatment strategies. Nevertheless, there is a paucity of autopsy and imaging data that can be used to describe the optimal size and location of lesions or the location of implantable stimulators. In this study the authors correlated the clinical efficacy of stereotactic thalamotomy for tremor with precise anatomical localization by using postoperative magnetic resonance (MR) imaging and an integrated deformable digital atlas of subcortical structures. METHODS: Thirty-one lesions were created by stereotactic thalamotomy in 25 patients with tremor-dominant Parkinson disease. Lesion volume and configuration were evaluated by reviewing early postoperative MR images and were correlated with excellent, good, or fair tremor outcome categories. To allow valid comparisons of configurations of lesions with respect to cytoarchitectonic thalamic boundaries, the MR image obtained in each patient was nonlinearly deformed into a standardized MR imaging space, which included an integrated atlas of the basal ganglia and thalamus. The volume and precise location of lesions associated with different clinical outcomes were compared using nonparametric statistical methods. Probabilistic maps of lesions in each tremor outcome category were generated and compared. Statistically significant differences in lesion location between excellent and good. and excellent and fair outcome categories were demonstrated. On average, lesions associated with excellent outcomes involved thalamic areas located more posteriorly than sites affected by lesions in the other two outcome groups. Subtraction analysis revealed that lesions correlated with excellent outcomes necessarily involved the interface of the nucleus ventralis intermedius (Vim; also known as the ventral lateral posterior nucleus [VLp]) and the nucleus ventrocaudalis (Vc; also known as the ventral posterior [VP] nucleus). Differences in lesion volume among outcome groups did not achieve statistical significance. CONCLUSIONS: Anatomical evaluation of lesions within a standardized MR image-atlas integrated reference space is a useful method for determining optimal lesion localization. The results of an analysis of probabilistic maps indicates that optimal relief of tremor is associated with lesions involving the Vim (VLp) and the anterior Vc (VP).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".