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Record W4391261560 · doi:10.1101/2024.01.24.576996

Joint signatures of morphological and microstructural inter-individual variation in the Alzheimer’s spectrum

2024· preprint· en· W4391261560 on OpenAlexafffund
Aurélie Bussy, Raihaan Patel, Olivier Parent, Alyssa Salaciak, Saashi A. Bedford, Sarah Farzin, Stéphanie Tullo, Cynthia Picard, Sylvia Villeneuve, Judes Poirier, John C.S. Breitner, Gabriel A. Devenyi, Christine Tardif, M. Mallar Chakravarty

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMontreal Neurological Institute and HospitalMcGill UniversityDouglas Mental Health University Institute
FundersCanadian Institutes of Health ResearchCanada First Research Excellence FundNatural Sciences and Engineering Research Council of CanadaAlzheimer SocietyWeston Brain Institute
KeywordsHippocampusAtrophyCortex (anatomy)DementiaTemporal cortexPsychologyEntorhinal cortexNeuroscienceTemporal lobeCognitive declineDiseaseMedicinePathology

Abstract

fetched live from OpenAlex

Abstract Alzheimer’s disease (AD) is primarily characterized by the accumulation of amyloid and tau pathologies. However, alterations in the detailed organization and composition of neural tissue also contribute to the disease’s early stages. Here, we sought to explore whether hippocampal and cortical microstructural changes, such as myelin alterations and inflammation-mediated increases in iron, could serve as indices of AD-related pathophysiology. In this study, we included 158 participants across the AD spectrum: from individuals without cognitive impairment, at high risk for AD, in the prodromal phase with mild cognitive impairment, and suffering from clinical dementia. We measured atrophy using structural magnetic resonance imaging (MRI) and estimated myelin and iron content using quantitative MRI (qMRI) metrics derived from T1 and T2* relaxation, times respectively. We integrated these contrasts to estimate a joint multivariate signature of tissue alterations across the cortex and hippocampus using non-negative matrix factorization. The relevance of these signatures to AD-spectrum measures of medical history, lifestyle, and cognition were further explored using partial least squares correlation. Our results reveal lower disease-related cortical thickness over large areas of the cortex while T2* provided specific variation across the brain (lower in dorsomedial and superior temporal areas, superior frontal cortex, and premotor cortex, and higher in the occipital lobe). Additionally, we observed longer T1 and T2* times in the hippocampus associated with specific lifestyle risk factors like past smoking, high blood pressure, high cholesterol levels, and higher anxiety. These patterns were significantly related to older age, associated with AD progression, being female, and being an APOE-□4 carrier. Taken together, our results suggest that qMRI metrics could serve as a valuable non-invasive tool for exploring the role of myelin and inflammation in AD-related pathophysiology and could be sensitive to modifiable risk factors related to lifestyle and medical history. Future studies may use these signatures to investigate their relationship in investigations related to lifestyle interventions or novel therapeutics.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.033
GPT teacher head0.276
Teacher spread0.242 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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