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Record W7117062403 · doi:10.1002/alz70862_110131

Microstructural differences in white matter tracts in Alzheimer’s disease, cerebrovascular disease, and Parkinson's disease

2025· article· en· W7117062403 on OpenAlexaffabout
Dana N Broberg, Sandra E. Black, Richard H. Swartz, Anthony E Lang, Angela C. Roberts, Robert Bartha

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsToronto Western HospitalSunnybrook HospitalUniversity of TorontoRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsWhite matterDiseaseDiffusion MRIMagnetic resonance imagingWhite (mutation)

Abstract

fetched live from OpenAlex

Abstract Background Though previous research supports diffusion tensor imaging (DTI) as a biomarker for white matter integrity in diseases such as Alzheimer’s disease/mild cognitive impairment (ADMCI), Parkinson’s disease (PD), and cerebrovascular disease (CVD), few studies have compared white matter microstructure between different neurodegenerative aetiologies. This study aimed to characterize and compare the microstructure of white matter tracts in ADMCI, PD, and CVD patients using DTI data from the Ontario Neurodegenerative Disease Research Initiative (ONDRI). Method The ONDRI study included 119 ADMCI, 149 CVD, and 137 PD patients with usable DTI and T 1 MRI data. FreeSurfer’s TRACULA [ https://dmri.mgh.harvard.edu/tract‐atlas/ ] was used to reconstruct 39 white matter pathways and calculate average DTI metrics (fractional anisotropy, FA; mean diffusivity, MD; axial diffusivity, AxD; and radial diffusivity, RD) in each pathway. Patients with severe cerebral infarcts, hypointensities, or ventricular dilation that compromised tract reconstruction were excluded from further analyses. The final dataset included 96 ADMCI [n(%) female =50(52%); median(range) age =71(53‐87)], 107 CVD [n(%) female =35(33%); median(range) age =69(55‐85)], and 117 PD [n(%) female =28(24%); median(range) age =68(55‐84)] participants. Multivariate general linear models evaluated the effect of diagnosis on FA, MD, AxD, and RD in all 39 tracts. Result 3D segmentations of the 39 white matter tracts in a representative subject are shown in Figure 1. In the multivariate analyses, diagnosis had a strong, significant effect on all four DTI metrics (FA: p = 0.002, ηp 2 =0.180; MD: p = 0.001, ηp 2 =0.183; AxD: p = 0.003, ηp 2 =0.178; RD: p = 0.002, ηp 2 =0.181). Figures 2 and 3 display the post hoc pairwise comparisons of FA and MD, respectively, in only the tracts for which diagnosis had a significant ( p <0.05) effect in the univariate analyses. On average, PD participants had higher FA (2.3%, ηp 2 =0.030) and lower MD (1.7%; ηp 2 =0.032), AxD (1.3%; ηp 2 =0.036), and RD (2.3%; ηp 2 =0.027) within these tracts compared to ADMCI and CVD participants. There were very few significant differences between ADMCI and CVD participants. Conclusion PD participants had slightly more preserved white matter tract microstructural integrity than ADMCI or CVD participants who had lower FA and higher RD and MD values. Higher‐quality, advanced diffusion imaging may be necessary to detect the more subtle changes in white matter microstructure present in Parkinson’s disease patients.

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)0.002
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 score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.034
GPT teacher head0.305
Teacher spread0.271 · 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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