Aberrant corticospinal tract characteristics in prodromal PD: A diffusion tensor imaging study
Bibliographic record
Abstract
Introduction: Parkinson's disease (PD) is typically diagnosed when motor symptoms first occur. However, PD-related non-motor symptoms may appear several years before diagnosis. REM sleep behaviour disorder (RBD) and olfactory deficits (hyposmia) are risk factors, but they are not specific for predicting progression towards PD. Other PD-related markers, for example brain imaging markers, may help to identify preclinical PD in hyposmic RBD patients. Studies have reported abnormal structural characteristics in the corticospinal tract (CST) of PD patients, but it is unclear whether hyposmic RBD patients have similar abnormalities that may help to predict PD in these individuals. This study examined whether CST abnormalities may be a potential marker of PD risk by using diffusion tensor imaging (DTI) measures. Methods: Twenty hyposmic RBD patients, 31 PD patients, and 29 healthy controls (HCs) were studied. DTI data were collected on a 1.5 T MRI scanner and CST characteristics (FA, MD, AD, and RD) were evaluated using probabilistic tractography (with seed regions in the bilateral primary motor cortex and mediolateral cerebral peduncles). Olfactory function was assessed with the University of Pennsylvania Smell Identification Test (UPSIT). Results: Hyposmic RBD patients showed significantly higher mean diffusivity (MD) values of the right CST compared to HCs but did not differ from PD patients. PD patients showed a trend of higher MD values compared to HCs. Conclusions: Altered diffusivity in the CST seems to be associated with RBD. The combination of RBD, hyposmia, and CST alterations may be related to later development of PD with comorbid RBD.
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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) | 0.001 | 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".