Cortical and subcortical atrophy in individuals with Huntington's disease and Huntington-like disease
Bibliographic record
Abstract
Background: Huntington-like (HL) syndrome represents a group of diseases clinically similar to Huntington disease (HD) with different genetic etiology. Here, we aimed to compare clinical and neuroimaging features between HL and HD. Methods: We assessed 12 patients with HL (6 men; 53.66±13.02 years old) and 12 with HD (genetically confirmed, 6 men; 52.58±11.64 years old). All patients were followed at UNICAMP and were matched to sex, age, age at onset and duration of disease. They underwent 3T MRI scans, detailed neurological examination, the unified Huntington’s disease rating scale (UHDRS), the Montreal cognitive assessment (MOCA), Beck depression inventory (BDI), and the scale for the evaluation of rating ataxia (SARA). We APPLIED voxel-based morphometry technique (SPM12/CAT12/MATLAB software) to assess differences in the gray and white matters between groups and matched controls. Results: Groups were clinically similar, but the VBM study revealed widespread cortical (bilateral) and subcortical atrophy in HD (bilateral globi pallidi, amygdala, hippocampi, caudate and putamen), with a more restricted cortical (left temporal lobe) subcortical atrophy in HL (bilateral thalami, putamen and left hippocampus). Cortical atrophy in HL concentrated in the bilateral putamen. The left hippocampus were atrophic in both groups. Conclusion: Despite similar clinical presentation, patients with HL and HD have distinctive patterns of atrophy subcortical structures, mainly in the thalami. These results may raise insights into the underlying disease mechanisms in HL and HD and could be useful as biomarkers of disease progression in future therapy trials.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".