Glymphatic System Assessment Using DTI-ALPS in Age-Dependent Neurodegenerative Diseases
Bibliographic record
Abstract
Introduction. Dysfunction of the glymphatic system glymphatic system of the brain is considered a pathogenetic factor in some age-dependent neurodegenerative diseases, including Alzheimer's disease (AD), dementia with Lewy bodies (DLB), Parkinson's disease (PD), and normal pressure hydrocephalus (NPH). The innovative method for calculating DTI-ALPS (Diffusion Tensor Image Analysis ALong the Perivascular Space) allows non-invasive assessment of the glymphatic system status using magnetic resonance imaging (MRI). The aim of the study is to compare DTI-ALPS in patients with AD, DLB, PD, and NPH and to evaluate its potential use as a biomarker of the glymphatic system status in these diseases. Materials and methods. The study included 116 subjects: 32 patients with AD, 15 patients with DLB, 31 patients with PD, 11 patients with NPH, and 27 healthy volunteers. Cognitive testing was performed for patients in the main groups using the Montreal Cognitive Assessment (MoCA) score. All subjects underwent diffusion tensor imaging (DTI) of the brain. DTI-ALPS was then calculated. Results. DTI-ALPS index significantly differed across groups (p 0.001). Patients with AD, DLB, and NPH had a significantly lower DTI-ALPS index on both sides compared to the PD group and healthy volunteers (p 0.01). Analysis of the entire sample showed a direct correlation between MoCA score and DTI-ALPS index (p 0.05). Conclusion. This is the first comparison of DTI-ALPS across such a broad range of age-dependent neurodegenerative diseases. Since our DTI-ALPS results were comparable to previously reported data, we believe that this parameter can be used as an indirect marker of the glymphatic system status.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".