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Record W7120987569 · doi:10.1002/alz70856_106745

Gray and white matter pathology progression in Parkinson's disease patients that develop mild cognitive impairment

2025· article· en· W7120987569 on OpenAlexaff
Roqaie Moqadam, Houman Azizi, Yashar Zeighami, Mahsa Dadar

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsMontreal Neurological Institute and HospitalUniversité de MontréalInstitut Universitaire de Gériatrie de MontréalMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsGray (unit)White matterDiseaseCognitive impairmentVoxel-based morphometryNeuroimagingGrey matter

Abstract

fetched live from OpenAlex

BACKGROUND: Cognitive impairment is a common non-motor symptom in Parkinson's disease (PD) that can potentially occur at any disease stage (Aarsland et al., 2021). Compared to cognitively normal patients, PD patients that develop mild cognitive impairment (MCI) show greater levels of atrophy (Mak et al., 2015). Greater baseline white matter hyperintensity (WMH) burden is also linked to more severe future cognitive deficits in PD patients (Dadar et al., 2018). Here, we assess the longitudinal changes in gray matter (GM) atrophy and WMH burden in PD patients that develop MCI compared to those that remain cognitively normal. METHODS: Imaging and clinical data were obtained from the Parkinson's Progression Markers Initiative (PPMI) study (Marek et al., 2018). Deformation-based morphometry (DBM) maps and WMH segmentations were extracted using T1w images in 312 PD (559 timepoints) using an in-house pipeline (Lajoie et al. 2025) and BISON (Dadar et al., 2021), respectively. A series of linear mixed-effects models were used to assess the differences in the longitudinal trajectories of the brain measures (DBM and WMHs) between the patients that developed MCI compared to those that remained stable using an interaction term between conversion_status and time from baseline visit as the variable of interest. The models included MoCA score at baseline, sex, and age at the baseline as covariates. False Discovery Rate (FDR) method was applied for multiple comparisons correction (Benjamini and Hochberg, 1995). RESULTS: All included PD patients were cognitively normal at baseline and had 6 years of follow-up assessments available for cognitive status. During these 6 years, 47 patients developed MCI, while 265 remained cognitively normal. Figures 1A-B show the t-statistic maps of the regions that showed significantly greater rates of longitudinal atrophy, ventricular expansion, and WMH progression in patients that developed MCI compared against those that remained cognitively healthy following FDR correction. The observed differences for both atrophy and WMHs were the strongest in bilateral frontal regions as well as the accumbens areas (Table 1). CONCLUSIONS: Our results suggest that PD patients that develop MCI experience greater levels of pathology in both gray and white matter brain regions in the frontal lobes.

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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.016
GPT teacher head0.281
Teacher spread0.266 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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