Cerebral Gray Matter Volume Losses in Essential Tremor: A Case-Control Study Using High Resolution Tissue Probability Maps
Bibliographic record
Abstract
Abstract Introduction Essential tremor (ET) is increasingly recognized as a multi-dimensional disorder with both motor and non-motor features. For this reason, imaging studies are more broadly examining regions outside the cerebellar motor loop. Reliable detection of cerebral gray matter (GM) atrophy requires optimized processing, adapted to high-resolution magnetic resonance imaging (MRI). We investigated cerebral GM volume loss in ET cases using automated segmentation of MRI T1-weighted images. Methods MRI was acquired on 47 ET cases and 36 controls. Automated segmentation and voxel-wise comparisons of volume were performed using Statistical Parametric Mapping (SPM) software. To improve upon standard protocols, the high-resolution International Consortium for Brain Mapping (ICBM) 2009a atlas and tissue probability maps were used to process each subject image. Group comparisons were performed: all ET vs. Controls, ET with head tremor (ETH) vs. Controls, and severe ET vs. Controls. An analysis of variance (ANOVA) was performed between ET with and without head tremor and controls. Age, sex, and Montreal Cognitive Assessment (MoCA) score were regressed out from each comparison. Results We were able to consistently identify regions of cerebral GM volume loss in ET and in ET subgroups in the posterior insula, superior temporal gyri, cingulate cortex, inferior frontal gyri and other occipital and parietal regions. There were no significant increases in GM volume in ET in any comparisons with controls. Conclusion This study, which uses improved methodologies, provides evidence that GM volume loss in ET is present beyond the cerebellum, and in fact, is widespread throughout the cerebrum as well.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".