Association of DTI-ALPS Glymphatic Index With Differential Phenoconversion in Isolated REM Sleep Behavior Disorder
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Isolated REM sleep behavior disorder (iRBD) is the strongest prodromal marker of synucleinopathies, including Parkinson disease (PD) and dementia with Lewy bodies (DLB). Identifying brain biomarkers that predict progression and distinguish phenoconversion trajectories remains a challenge. The glymphatic system is involved in interstitial waste clearance, and its dysfunction has been associated with pathologic protein accumulation and neurodegeneration. Diffusion tensor imaging along the perivascular space (DTI-ALPS) has been proposed as a noninvasive proxy for glymphatic function. The aim of this study was to determine whether patients with iRBD show a reduced DTI-ALPS index compared with controls and whether a lower DTI-ALPS index predicts future phenoconversion to PD or DLB. METHODS: We conducted a longitudinal, multicenter cohort study using brain MRI scans from patients with polysomnography-confirmed iRBD and healthy controls recruited across 5 international centers. All participants underwent T1-weighted and diffusion-weighted MRI. DTI-ALPS indices were computed from diffusivity along projection and associative fibers adjacent to the lateral ventricles. The primary outcome was time to phenoconversion to synucleinopathy. Linear models assessed baseline group differences and clinical correlates, and Cox proportional hazard models assessed the predictive value of DTI-ALPS for time to phenoconversion. RESULTS: = 0.012). Other diffusion metrics inside periventricular masks, namely fractional anisotropy, diffusivity metrics, and free water, did not differ between groups. DISCUSSION: Patients with iRBD exhibit a reduced DTI-ALPS index, suggesting altered glymphatic function. This reduction was associated with future phenoconversion to PD, supporting the DTI-ALPS index as a potential prognostic MRI biomarker of progression in prodromal synucleinopathies.
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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 | 0.001 | 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 | 0.001 |
| 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".