A Prodromal Brain‐Clinical Pattern of Cognition in Synucleinopathies
Bibliographic record
Abstract
OBJECTIVE: Isolated (or idiopathic) rapid eye movement sleep behavior disorder (iRBD) is associated with dementia with Lewy bodies (DLB) and Parkinson's disease (PD). Biomarkers are lacking to predict conversion to a dementia or a motor-first phenotype. Here, we aimed at identifying a brain-clinical signature that predicts dementia in iRBD. METHODS: A brain-clinical signature was identified in 48 patients with polysomnography-confirmed iRBD using partial least squares between brain deformation and 27 clinical variables. The resulting variable was applied to 78 patients with iRBD followed longitudinally to predict conversion to a synucleinopathy, specifically DLB. The deformation scores from patients with iRBD were compared with 207 patients with PD, DLB, or prodromal DLB to assess if scores were higher in DLB compared to PD. RESULTS: One latent variable explained 31% of the brain-clinical covariance in iRBD, combining cortical and subcortical deformation and subarachnoid/ventricular expansion to cognitive and motor variables. The deformation score of this signature predicted conversion to a synucleinopathy in iRBD (p = 0.036, odds ratio [OR] = 2.249; 95% confidence interval [CI] = 1.053-4.803), specifically to DLB (OR = 4.754; 95% CI = 1.283-17.618, p = 0.020) and not PD (p = 0.286). Patients with iRBD who developed dementia had scores similar to clinical and prodromal patients with DLB but higher scores compared with patients with PD. The deformation score also predicted cognitive performance over 1, 2, and 4 years in patients with PD. INTERPRETATION: We identified a brain-clinical signature that predicts conversion in iRBD to more severe/dementing forms of synucleinopathy. This pattern may serve as a new biomarker to optimize patient care, target risk reduction strategies, and administer neuroprotective trials. ANN NEUROL 2021;89:341-357.
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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".