Diffusion Tensor Imaging Along the Perivascular Space (DTI-ALPS) in Ischemic Stroke: A Systematic Review of Diagnostic and Prognostic Performance for Post-Stroke Cognitive Impairment
Bibliographic record
Abstract
Background/Objectives: Post-stroke cognitive impairment (PSCI) affects ~40% of survivors. Diffusion Tensor Imaging Analysis Along the Perivascular Space (DTI-ALPS) is a fast, contrast-free surrogate of perivascular (glymphatic-aligned) diffusivity that may stratify PSCI risk. We systematically synthesized evidence on the diagnostic and prognostic performance of ALPS in ischemic stroke. Methods: Following PRISMA 2020, we searched PubMed/MEDLINE, Scopus, and Web of Science from inception to August 2025 for human ischemic stroke studies reporting ALPS and cognitive or functional outcomes. Eligible designs were cohort or case–control. Outcomes included group differences, associations with cognition (Montreal Cognitive Assessment [MoCA]/Mini-Mental State Examination [MMSE]), prognostic accuracy for PSCI/functional outcome, and longitudinal change. Risk of bias was appraised with QUADAS-2 (diagnostic) and QUIPS (prognostic). The protocol was registered on OSF. Heterogeneity among studies precluded a meta-analysis. Results: Five single-center cohorts (n per cohort 29–120) from Asia, Europe, and the USA used 3T DTI with ventricular-level ALPS ROIs. Across studies, ALPS was lower after stroke, with early ipsilesional depression and partial recovery over weeks to months. ALPS correlated with cognition (MoCA r ≈ 0.43–0.56) and discriminated early cognitive impairment (AUC 0.868; sensitivity 96%, specificity 66%). Follow-up ALPS predicted poor 6-month outcome (AUC 0.786). In lacunar stroke with small-vessel disease, higher baseline ALPS related to better cognitive trajectories and lower incident dementia risk (HR ≈ 0.33), though associations attenuated after adjustment for diffusion microstructural covariates (PSMD/MD). Reporting of acquisition parameters and ROI methods varied; overall risk of bias was moderate. Conclusions: DTI-ALPS shows consistent post-stroke reductions, recovery-sensitive trajectories, and promising—though context-dependent—prognostic value for PSCI and longer-term outcomes. Clinical translation will require standardized acquisition/analysis, multimodal adjustment, prespecified cut-offs, and prospective multicenter validation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".