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Record W7117308653 · doi:10.1002/alz70856_104492

Impaired glymphatic clearance measured from DTI‐ALPS in neurodegenerative and cerebrovascular disease

2025· article· en· W7117308653 on OpenAlexaffabout
Daniela Andriuta, Joel Ramirez, Lauren Abby Woods, Min Su Kang, Stephanie Berberian, Fuqiang Gao, Christopher J.M. Scott, Dana N Broberg, Robert Bartha, Richard H. Swartz, Mario Masellis, Sandra E. Black, ONDRI Investigators

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsHealth Sciences CentreRobarts Clinical TrialsWestern UniversitySunnybrook Health Science CentreOntario Brain Institute
Fundersnot available
KeywordsGlymphatic systemAtrophyInflammationCohortCognitive declineDiseaseCentral nervous system diseasePerivascular spaceDegenerative disease

Abstract

fetched live from OpenAlex

BACKGROUND: Impaired glymphatic clearance has been associated with neurodegenerative diseases causing dementia. The diffusion tensor image analysis along the perivascular space (DTI-ALPS) index has been proposed as a non-invasive measure to assess glymphatic function. This preliminary analysis aimed to examine correlations with DTI-ALPS and changes in brain atrophy in the Ontario Neurodegenerative Disease Research Initiative (ONDRI) cohort. METHODS: The ONDRI study participants (n = 125; age = 54.9+/-7.5 years; 76% male) in this analysis included clinically diagnosed patients with Alzheimer's disease/mild cognitive impairment (ADMCI; n = 44), cerebrovascular disease (CVD; n = 68), and frontotemporal dementia (FTD; n = 13). DTI images were preprocessed with the FSL Diffusion Toolbox, and the ALPS indices (mean, left and right) were calculated using diffusion data from the projection and association fiber regions. Lower DTI-ALPS indices having been linked to reduced glymphatic function. Fractional perivascular space (PVS) and white matter hyperintensity (WMH) volumes were extracted from MRI. Whole brain atrophy was assessed using the brain parenchymal fraction (BPF), including one-year change in BPF. ANOVA, Pearson correlation, and linear regression models were used to analyze the data. Demographics (age, sex, education, handedness) and vascular risk factors (hypertension, diabetes, hypercholesterolemia, smoking, waist-to-hip ratio) were also collected. RESULTS: Mean DTI-ALPS was highest in ADMCI and lowest in CVD (ADMCI=1.49; FTD=1.44, CVD=1.37; contrasts: ADMCI vs. CVD, p <0.05, others n.s.). At baseline, partial correlations controlling for demographics, clinical diagnosis, and vascular risk factors showed mean DTI-ALPS was significantly correlated with baseline BPF (r=.204, p = 0.028) and baseline WMH (r=-0.287, p = 0.002), but not with baseline PVS. Baseline right DTI-ALPS was significantly associated with delta BPF (β=0.209, p = 0.019), independent of demographics, clinical diagnosis, vascular risk factors, and baseline WMH. CONCLUSION: As a possible measure of glymphatic clearance functioning along the perivascular space, our preliminary results showed that baseline DTI-ALPS measures were associated with one-year change in whole brain atrophy in patients with neurodegenerative and cerebrovascular disease. This finding was independent of demographics, clinical diagnosis, and vascular risk factors. Future planned analysis of the ONDRI cohort will examine DTI-ALPS associations with cognitive decline in the presence of white matter changes and elevated plasma glial fibrillary acidic protein as an inflammation marker.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.260
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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