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Record W4390198567 · doi:10.1002/alz.082032

The structural integrity of anterior‐temporal and posterior‐medial brain regions in Alzheimer’s disease and Parkinson’s disease: Effects of APOEε4, p‐tau181 and Aβ42

2023· article· en· W4390198567 on OpenAlexaffabout
Gillian Coughlan, Peter Zhukovsky, Erlan Sanchez, Paula McLaughlin, Cheryl L. Grady, Sandra E. Black, Douglas P. Munoz, Rachel F. Buckley, Mario Masellis

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsSunnybrook Health Science CentreQueen's UniversityBaycrest HospitalUniversity of TorontoSunnybrook Hospital
Fundersnot available
KeywordsPrecuneusDementiaParahippocampal gyrusApolipoprotein EClinical Dementia RatingFusiform gyrusBiomarkerAmygdalaNeuroscienceEpisodic memoryMedicineLingual gyrusNeuroimagingImaging biomarkerCognitive declinePsychologyTemporal lobePosterior cingulateDiseaseCognitionPathologyMagnetic resonance imagingBiology

Abstract

fetched live from OpenAlex

Abstract Background Alzheimer’s disease (AD) and Parkinson’s disease (PD) are two distinct neurodegenerative brain conditions associated with varying degrees of cognitive impairment, including an overlapping amnestic phenotype. At autopsy, APOEe4 and AD proteinopathies are associated with dementia severity in both AD and PD. The extent to which in‐vivo AD biomarkers are associated with memory and underlying regional brain volume in AD and PD patients is under‐investigation. Here, we focused on plasma biomarker associations with anterior‐temporal and posterior‐medial regions that are known to accumulate tau and Aß in AD. Method Neuroimaging, biomarker, genetic and cognitive data were collected from 244 patients through the Ontario Neurodegenerative Disease Research Initiative (MeanAge = 68.96; 41% women; 35%APOE‐e4‐carriers). Based on clinical diagnostic criteria, three groups were formed: i)AD with mild cognitive impairment or dementia(ADMCI/ADD;N = 120), ii)PD with MCI or dementia(PDMCI/PDD;N = 76) and iii)PD with normal cognition(PD‐NC;N = 48). Linear regressions assessed clinical diagnosis differences in the volumetric integrity of a priori regions: anterior‐temporal(ATN; including amygdala, fusiform gyrus, and ITG) and posterior‐medial(PMN; including PCC, parahippocampal cortex and precuneus;Fig1). Differences on related item recognition memory and associative memory was examined via the FaceName Association Task. APOE(e4‐/e4+), p‐tau181‐UGOT and Aß42 associations between ATN/PMN and item recognition/associative memory outcomes were estimated. Diagnostic*APOEe4*ptau181 interactions were tested. Age, sex, education and ICV were covaried. Result Background characteristics showed that the ADMCI/ADD group exhibited the oldest age and the highest frequency of female sex and e4‐carriership. ADMCI/ADD and PDMCI/PDD, but not PD‐NC, exhibited elevated ptau181 and Aß42 relative to a healthy control group. Both ADMCI/ADD and PDMCI/PDD groups exhibited PMN and ATN degradation, as well as memory impairment, relative to the PD‐NC(Fig2A‐B). Plasma p‐tau181 was associated with ATN and PMN, as well as associative memory(Fig3A). APOEe4 was associated with PMN only, as well as item recognition(Fig3B). Diagnostic*APOEe4*ptau181 interactions were not significant. Post‐hoc analysis showed significant p‐tau181xAPOEe4 interactions on item recognition and total white matter hyperintensity(WMH;Fig3C). Aß42 was not associated with outcomes. Conclusion Volumes of the ATN and PMN are lower in cognitively impaired AD and PD patients with higher p‐tau and/or APOEe4‐carriership. P‐tau and APOEe4 may work synergistically to promote domain‐specific memory impairment and WMH burden in both AD and 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 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)0.000
metaresearch head score (Gemma)0.001
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 score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.026
GPT teacher head0.314
Teacher spread0.288 · 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
Published2023
Admission routes2
Has abstractyes

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