Longitudinal network changes and phenoconversion risk in isolated REM sleep behavior disorder
Bibliographic record
Abstract
Isolated rapid eye movement sleep behavior disorder is a prodrome of α-synucleinopathies. Using positron emission tomography, we assessed changes in Parkinson’s disease-related motor and cognitive metabolic networks and caudate/putamen dopaminergic input in a 4-year longitudinal imaging study of 13 male subjects with this disorder. We also correlated times to phenoconversion with baseline network expression in an independent validation sample. Expression values of both Parkinson’s disease-related networks increased over time while dopaminergic input gradually declined in the longitudinal cohort. While abnormal functional connections were identified at baseline in both networks, others bridging these networks appeared later. These changes resulted in compromised information flow through the networks years before phenoconversion. We noted an inverse correlation between baseline network expression and times to phenoconversion to Parkinson’s disease or dementia with Lewy bodies in the validation sample. Here, we show that the rate of network progression is a useful outcome measure in disease modification trials. In isolated REM sleep behavior disorder, a prodromal syndrome often leading to parkinsonism, expression levels for an abnormal disease network increase steadily before diagnosis. The measure can help evaluate phenoconversion risk in these patients.
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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 | 0.002 |
| 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) | 0.001 | 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".