Altered network efficiency in isolated REM sleep behavior disorder: A multicentric study
Bibliographic record
Abstract
INTRODUCTION: Isolated rapid eye movement (REM) sleep behavior disorder (iRBD), characterized by abnormal movements during REM sleep, is a prodromal stage of dementia with Lewy bodies (DLB) and Parkinson's disease (PD). While iRBD shows emerging brain changes, their impact on structural connectivity and network efficiency, and their predictive value, remain poorly characterized. METHODS: In this international prospective study, 198 polysomnography-confirmed iRBD patients and 174 controls underwent diffusion magnetic resonance imaging and were analyzed. Cutting-edge diffusion tractography and network-based statistics were applied to reconstruct individual connectomes and assess network properties predicting DLB or PD. RESULTS: Structural architecture was already disrupted in iRBD, with both reduced and compensatory increased connections. Global efficiency was decreased. Local efficiency in motor regions was altered and associated with early clinical symptoms. Altered local efficiency in the supramarginal gyrus predicted DLB only. DISCUSSION: Early disruption of brain architecture in iRBD predicts progression to synucleinopathy-related dementia, offering a novel potential prognostic biomarker. HIGHLIGHTS: Isolated rapid eye movement sleep behavior disorder (iRBD) patients show significant alterations in inter-regional structural connectivity. Global efficiency is reduced in iRBD compared to controls. Areas with increased local efficiency contribute to decreased global efficiency. Altered network efficiency is associated with emerging Parkinsonian features. Higher supramarginal efficiency predicts dementia with Lewy bodies in iRBD.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".