Diffusion Tensor Imaging Along the Perivascular Space Is a Promising Imaging Method in Parkinson's Disease: A Systematic Review and Meta‐Analysis Study
Bibliographic record
Abstract
ABSTRACT Introduction Parkinson's disease (PD) is a chronic, progressive neurodegenerative disorder that primarily affects motor functions. Recently, a diffusion tensor imaging technique called DTI along the perivascular space (DTI‐ALPS) has gained attention as a noninvasive biomarker for glymphatic function. This systematic review and meta‐analysis aimed to evaluate the potential and implications of the DTI‐ALPS index for diagnosing PD. Methods This study followed the PRISMA 2020 statement. Eligible cohort and cross‐sectional studies measured the ALPS index in PD patients versus non‐PD participants. Web of Science, Medline, Scopus, Embase, Cochrane, PROSPERO, and ICTRP databases were explored until November 14, 2024. Two researchers independently screened studies, extracted data, and assessed the risk of bias using the Newcastle–Ottawa Scale (NOS). The meta‐analysis used a random effects model (REM), assessing heterogeneity ( I 2 , Q‐test) and publication bias (Egger's test, trim&fill plot). The certainty of the evidence was evaluated using the GRADE approach. Results This meta‐analysis of 11 studies, involving 1462 patients (855 PD, 607 non‐PD of both genders), yielded significant findings. The overall ALPS index differed substantially between PD and non‐PD groups (SMD: −0.61, 95% CI: −0.72, −0.50, p < 0.001). Additionally, a significant negative correlation emerged between the ALPS index and Unified PD Rating Scale III (UPDRS III) (r = −0.40, (95% CI: −0.59, −0.18, I 2 : 89.81, p < 0.001)), indicating glymphatic dysfunction's impact on cognitive decline. However, a weak and statistically non‐significant correlation was observed between the ALPS index and Montreal Cognitive Assessment (MoCA) ( r = 0.24, 95% CI: −0.32 to 0.68), with high heterogeneity across studies ( I 2 = 87.37, p < 0.001 for heterogeneity). Publication bias risk was low for the overall ALPS index. Conclusion These findings highlight the potential of DTI‐ALPS as a noninvasive biomarker for PD diagnosis and progression monitoring. Further studies are warranted to explore its applicability in differentiating PD from other neurodegenerative disorders.
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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 | 0.024 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".