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Record W3110986702 · doi:10.1002/alz.045432

Perivascular space dilation in white matter mediates a significant relationship between white matter hyperintensity burden and venous collagenosis

2020· article· en· W3110986702 on OpenAlexaff
David Lahna, Daniel L. Schwartz, Randy Woltjer, Sandra E. Black, Natalie Roese, Hiroko H. Dodge, Erin L. Boespflug, Julia Keith, Fuqiang Gao, Joel Ramirez, Lisa C. Silbert

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsSunnybrook HospitalUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineHyperintensityPerivascular spacePathologyWhite matterAnatomyMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Abstract Background Periventricular T2 MRI white matter hyperintensities (WMH) are commonly observed in older individuals and are associated with cognitive and motor decline. While likely vascular in origin, the specific etiology of WMH remains unknown. Vascular insufficiency due to venous collagenosis has been implicated. Collagenosis may also interfere with perivascular fluid flow leading to dilation of perivascular spaces (PVS). PVS are thus an unexplored potential mediating factor between collagenosis and periventricular WMH. Method Brain tissue from 25 Oregon Alzheimer’s Disease Center subjects was selected based on availability of in vivo 1.5 Tesla MRI (Table 1), which was used to quantify whole brain WMH burden. Three paraffin embedded 6μm thick coronal blocks of tissue per subject from anterior, middle and posterior white matter abutting the ventricle were stained with Masson’s Trichrome and Smooth Muscle Actin (SMA). Slides were scanned and an automated hue based algorithm identified 547 vessels and segmented them into blue collagen vessel walls, lumen holes within them and perivascular spaces outside them (Figure 1). Pearson correlation coefficients were calculated to assess relationships between PVS, periventricular WMH and collagenosis. Coregistered SMA images were used to classify vessels into veins (n=163) and arteries (n=251). Multiple linear regressions accounting for sex, age at death, and MRI to death interval followed by a Sobel test of mediation were calculated to determine the mediating effect of PVS on the relationship between collagenosis and periventricular WMH. Result Periventricular WMH volume, collagenosis and PVS were correlated with each other (p<.001) (Figure 1). In separate regression models, PVS and collagenosis in veins were significant predictors of WMH volume (p=.005, p=.007) but not in arteries (p=.256, p=.244). A Sobel test of the mediating effect of PVS on the relationship between collagenosis in veins and WMH burden was significant (p=.027) and the remaining direct effect of collagenosis on WMH was not significant (p=.21) (Figure 2). Conclusion Perivascular space dilation is an under recognized mechanism which may mediate the relationship between the development of venous collagenosis and periventricular WMH in an aged population.

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)0.004
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.043
GPT teacher head0.250
Teacher spread0.207 · 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
Published2020
Admission routes1
Has abstractyes

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