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Record W4200332029 · doi:10.3389/fimmu.2021.803504

Heterogeneity of White Matter Hyperintensities in Cognitively Impaired Patients With Cerebral Small Vessel Disease

2021· article· en· W4200332029 on OpenAlexaboutno aff
Tingting Wang, Aoming Jin, Ying Fu, Zaiqiang Zhang, Shaowu Li, David Wang, Yilong Wang

Bibliographic record

VenueFrontiers in Immunology · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsHyperintensityWhite matterMedicineDiseaseCardiologyNeuroscienceMagnetic resonance imagingPsychologyInternal medicineRadiology

Abstract

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Objective: Similar white matter hyperintensities (WMH) might have different impact on the cognitive outcomes in patients with cerebral small vessel disease (CSVD). This study is to assess the possible factors related to the heterogeneity of WMH in cognitively impaired patients with CVSD. Methods: We analyzed data from a cohort of patients with CVSD who were recruited consecutively from the Beijing Tiantan Hospital from 2015 to 2020. WMH, lacunes, enlarged perivascular space (ePVS), microbleeds and lacunar infarcts were rated on brain MRI. A score of <26 on the Montreal Cognitive Assessment (MoCA) indicated cognitive impairment. A mismatch was defined as the severity of WMH not matching the severity of cognitive dysfunction. Type-1 mismatch was defined as a mild WMH (Fazekas score = 0-1) associated with cognitive impairment, and type-2 mismatch was defined as a severe WMH (Fazekas score = 5-6) associated with normal cognitive function. Ultrasmall superparamagnetic iron oxide (USPIO)-enhanced SWI on 3-Tesla MRI was used to image the penetrating arteries in basal ganglia to explore the underlying mechanism of this mismatch. Multivariable logistic regression was used to analyze the association between the imaging features and cognitive impairment. Results: In 156 patients, 118 (75.6%) had cognitive impairment and 37 (23.7%) showed mismatch. Twenty five (16.0%) had type-1 mismatch and 12 (7.7%) had type-2 mismatch. Regression analysis found that WMH, lacunes, microbleeds and total CSVD scores were associated with cognitive impairment and were independent of vascular risk factors. However, lacunes, microbleeds and total CSVD scores were related to the mismatch between WMH and cognitive impairment (p=0.006, 0.005 and 0.0001, respectively). Specially, age and ePVS in basal ganglia were related to type-1 mismatch (p=0.04 and 0.02, respectively); microbleeds and total CSVD scores were related to type-2 mismatch (p=0.01 and 0.03, respectively). Although the severity of WMH was similar, the injury scores of penetrating arteries were significantly different between those with and without cognitive impairment (p=0.04). Conclusions: Heterogeneity of WMH was present in cognitively impaired patients with CSVD. Conventional imaging features and injury of penetrating arteries may account for such heterogeneity, which can be a hallmark for early identification and prevention of cognitive impairment.

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)0.003
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.015
GPT teacher head0.206
Teacher spread0.191 · 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

Citations32
Published2021
Admission routes1
Has abstractyes

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