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Record W7117117253 · doi:10.1002/alz70856_100920

Glymphatic perivascular clearance and extracellular vesicles: neuroimaging & blood‐based biomarkers in mild cognitive impairment

2025· article· en· W7117117253 on OpenAlexaff
Ramirez Joel, Lauren Abby Woods, Jeng‐liang Wu, Stephanie Berberian, Austyn D. Roseborough, Min Su Kang, Erin Gibson, Daniela Andriuta, Sandra E. Black, Manuel Montero‐Odasso, Shawn N. Whitehead

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsHealth Sciences CentreOntario Brain InstituteWestern UniversitySunnybrook Health Science Centre
Fundersnot available
KeywordsGlymphatic systemBiomarkerPerivascular spaceNeuroimagingExecutive dysfunctionCognitive impairmentImaging biomarker

Abstract

fetched live from OpenAlex

BACKGROUND: Poor glymphatic clearance is associated with various neurodegenerative disorders. Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) has been suggested as a non-invasive technique to assess glymphatic function, with lower DTI-ALPS indices having been linked to reduced glymphatic function. We evaluated the relationships between the DTI-ALPS index, plasma circulating extracellular vesicles (EV), whole brain atrophy and executive function in mild cognitive impairment (MCI) and cognitively normal (CN) individuals. METHODS: Study participants (CN=35, MCI=33) were recruited from the Gait & Brain Cohort Study at Western University. DTI data was preprocessed using FSL Diffusion Toolbox commands, then FA and diffusivity maps were registered to a template. DTI-ALPS indices were calculated by extracting diffusion data from designated regions of interest. EV blood-based biomarkers were also collected: Total EV, TMEM119, pTau181, GFAP, GAL3, TMEM119/pTau181, TMEM119/GAL3, GFAP/GAL3. T-tests were used to compare CN vs. MCI and regression models were used to evaluate associations between DTI-ALPS, perivascular spaces (PVS), atrophy via the brain parenchymal fraction (BPF), and executive function (Trail-making-tests B-A). Age, sex, education, body mass index, white matter hyperintensities, and systolic blood pressure were included as covariates. RESULTS: The mean and left DTI-ALPS indices were significantly lower in the MCI group compared to CN (left: p <0.02, mean p = 0.05). Regression models revealed that within the CN cohort, only age was negatively associated with BPF (β=0.704, p <0.001), however in the MCI cohort the mean DTI-ALPS index was strongly associated with BPF (β=0.786, p <0.001). In both CN and MCI, GFAP/GAL3 was associated with PVS (CN: β=-0.739, p = 0.013; MCI: β=1.089, p = 0.003). Furthermore, regression models revealed a negative association between Left DTI-ALPS index and executive function in both the CN (β=-0.395 p = 0.025) and MCI cohorts (β=-0.447, p = 0.02). CONCLUSION: These preliminary results suggest that glymphatic dysfunction estimated by the DTI-ALPS index is associated with brain atrophy, where the left hemisphere DTI-ALPS is associated with executive dysfunction in both CN and MCI. Additionally, GFAP/GAL3 may be a potential useful astrocytic EV biomarker to indicate dysfunction of perivascular space clearance. Future work will examine mediation models with these imaging and EV biomarkers in neurodegenerative clinical populations.

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)0.000
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
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 head0.275
Teacher spread0.249 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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