Characterizing Thalamo-Cortical Structural Connectivity in Essential Tremor with Diffusional Kurtosis Imaging Tractography
Bibliographic record
Abstract
Background: Neuromodulation of the cerebello-thalamo-cortical (CTC) circuit via thalamic stimulation is an effective therapy for essential tremor (ET). In order to develop non-invasive neuromodulation approaches, clinically relevant thalamo-cortical connections must be elucidated. Methods: Twenty-eight subjects (18 ET patients and 10 controls) underwent MRI diffusional kurtosis imaging (DKI). A deterministic fiber-tracking algorithm based on DKI was used, with a seeding region placed at the ventral intermediate nucleus (Vim—located based on intraoperative physiology) to the ending regions at the supplementary motor area (SMA), pre-SMA, or primary motor cortex. One-tailed t-tests were performed to compare groups, and associations with tremor severity were determined by Pearson correlations. All p-values were adjusted for multiple comparisons using Bonferroni correction. Results: There was a decrease in the mean diffusivity (MD) in patients compared to controls in all three tracts: Vim-M1 (ET 0.87, control 0.96, p < 0.01), Vim-SMA (ET 0.86, control 0.96, p < 0.05), and Vim-pre-SMA (ET 0.87, control 0.95, p < 0.05). There was a significant positive correlation between Tremor Rating Scale score and MK (r = 0.471, p = 0.033) and mean FA (r = 0.438, p = 0.045) for the Vim-SMA tract, and no significant correlation for the Vim-pre-SMA or Vim-M1 tracts was found. Discussion: Patients with ET demonstrated a reinforcement of Vim-cortical connectivity, with higher Vim-SMA connectivity being associated with greater tremor severity. This finding suggests that the Vim-SMA connection is relevant to the underlying pathophysiology of ET, and inhibition of the SMA may be an effective therapeutic approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".