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Record W4406869327 · doi:10.1101/2025.01.24.634687

Neuroimaging and plasma biomarker differences and commonalities in Lewy body dementia subtypes

2025· preprint· en· W4406869327 on OpenAlexaff
Naomi Hannaway, Angeliki Zarkali, Rohan Bhome, Ivelina Dobreva, George E. Thomas, Elena Veleva, Irene Gorostiaga Belio, Katie Tucker, Amanda Heslegrave, Henrik Zetterberg, Rimona S. Weil

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldMedicine
TopicNeurological and metabolic disorders
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsNeuroimagingLewy bodyBiomarkerDementiaNeuroscienceDementia with Lewy bodiesMedicinePsychologyPathologyBiologyDiseaseGenetics

Abstract

fetched live from OpenAlex

Abstract INTRODUCTION Despite ongoing debate about whether Parkinson’s disease dementia (PDD) and dementia with Lewy bodies (DLB) are separable diseases or a single Lewy body dementia (LBD) spectrum, there are limited neuroimaging investigations of differences between these conditions. METHODS We used fixel-based diffusion MRI and plasma measures to examine white matter integrity and burden of amyloid pathology (using tau phosphorylated at theonine-217 (p-tau217) in 47 patients with DLB, 21 PDD, 29 PD and 23 age-matched controls. RESULTS We show reduced fibre cross-section in LBD versus PD, and increased concentrations of plasma neurofilament light chain and p-tau217; with p-tau217 and fibre cross-section associated with cognition. Fibre density was reduced in PDD versus DLB, but neither plasma measures nor fibre cross-section differed between LBD subtypes. DISCUSSION Our findings suggest differences in white matter integrity between DLB and PDD that are driven by distinct processes from those causing changes in white matter integrity in LBD compared with PD.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.238
Teacher spread0.217 · 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 teacher head, not a consensus.

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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