MétaCan
Menu
← Back to cohort
Record W7117148479 · doi:10.1002/alz70856_102637

White Matter Hyperintensities and Their Spatial Relationship to Mild Behavioral Impairment: Insights from Cluster‐Based Analysis

2025· article· en· W7117148479 on OpenAlexaboutno aff
Zdenek Linha, Rafael Doležal, Matej Seifert, Tejasvi Ravi, Pavla Brennan Kearns

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsHyperintensityLesionWhite matterSpatial relationshipAssociation (psychology)DiseaseBrain mapping

Abstract

fetched live from OpenAlex

BACKGROUND: Mild behavioral impairment (MBI) is defined by the presence of neuropsychiatric symptoms in cognitively healthy individuals and often predicts the occurrence of dementia. We hypothesize that white matter hyperintensities (WMH), which are a key manifestation of cerebral small vessel disease and are associated with the risk of dementia, may contribute to the development of MBI. This study aims to investigate whether specific WMH lesion locations are associated with MBI. METHOD: In our preliminary analysis, a total of 125 WMH lesions from 27 cognitively healthy participants with present WMH lesions (mean age 75 years, 56% females) of the Alzheimer's Disease Neuroimaging Initiative were analyzed (Table 1). The MBI score was derived from Neuropsychiatric Inventory Questionnaire, the MBI syndrome was characterized as having at least one positive symptom of MBI domain persisting for a minimum of six months. WMH lesions from T2-FLAIR images were automatically segmented with Lesion Segmentation Tool at a 0.95 probability threshold, and their centroids were spatially clustered (Figure 1, 2). Multiple linear mixed-effects models were employed to predict WMH volumes, lesion spatial distribution or clustered WMH centroids (dependent variables) based on MBI syndrome or MBI scores (independent variables) adjusting for age, sex, education, Mini-Mental State Examination score, and total intracranial volume. RESULT: The analysis revealed a significant association between the MBI syndrome (p < 0.001), MBI score (p < 0.017), and total WMH lesion volume. Additionally, a negative correlation was observed between the Montreal Neurological Institute (MNI) z- and y-coordinates (p < 0.0001), indicating that WMH lesions in MBI-positive individuals were more likely to localize in posterior superior brain regions. Notably, a cluster including the right lingual gyrus, right calcarine gyrus, corpus callosum, and the inferior occipitofrontal fasciculus demonstrated a significant link with the MBI syndrome (p < 0.020). CONCLUSION: The association of MBI syndrome with increased WMH lesion volume suggests that cerebral small vessel disease may be involved in the emergence of MBI. The distinct lesion localization in posterior superior brain regions, areas involved in memory recollection, high-emotion processing, and goal-oriented behavior, emphasize the importance of spatially resolved WMH analyses in clarifying MBI's neurobiological basis.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.307
Teacher spread0.279 · 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 routes1
Has abstractyes

Explore more

Same venueAlzheimer s & Dementia→Same topicDementia and Cognitive Impairment Research→French-language works237,207→