A Novel Diffusion Tensor Image Analysis Along the Perivascular Space Method to Evaluate Glymphatic Alterations in Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer 's disease (AD) is the most prevalent neurodegenerative disorder worldwide. The glymphatic system is considered to be associated with the pathogenesis of AD. However, the alterations of glymphatic system along the AD continuum are still unknown. In this study, we used a novel DTI analysis method, diffusion tensor image analysis along the perivascular space (DTI-ALPS), to evaluate the difference in the activity of the glymphatic system among normal control (NC) subjects, mild cognitive impairment (MCI) and AD patients. The data utilized in the study was obtained from Tongji Hospital in Shanghai, China, including 65 NCs, 58 MCIs and 36 ADs. First, we calculated the ALPS-index to evaluate the activity of the glymphatic system. Then, analysis of variance (ANOVA) was used to find the differences of ALPS-index among different groups, and to explore the correlation between ALPS-index and the three clinical scales: Minimum Mental State Examination (MMSE), Montreal Cognitive Assessment-Basic (MoCA-B) and Instrumental Activity of Daily Living (IADL). Receiver operating characteristic curve (ROC) analysis was used to evaluate the role of the ALPS-index in disease classification. The findings indicated a significant difference in the ALPS-index between the groups of participants with normal cognition, MCI, and AD. In addition, we found that ALPS-index was significantly correlated with the scores of the three clinical scales (with MoCA-B: r=0.233, p=0.001). Furthermore, with ALPS-index, Fractional Anisotropy (FA) values achieved best classification results (AUC=0.8899). Cognitive dysfunction is closely associated with the activity of the glymphatic system, and ALPS-index can be used as a biomarker for alterations along the AD continuum.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".