Characterizing Thalamo-Cortical Structural Connectivity in Essential Tremor with Diffusional Kurtosis Imaging Tractography
Bibliographic record
Abstract
<strong>Background:</strong> 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. <strong>Methods:</strong> 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 <em>t</em>-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. <strong>Results:</strong> 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 (<em>r</em> = 0.471, p = 0.033) and mean FA (<em>r</em> = 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. <strong>Discussion:</strong> 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".