MétaCan
Menu
Back to cohort
Record W4223652742 · doi:10.1093/brain/awac138

Hippocampal-subfield microstructures and their relation to plasma biomarkers in Alzheimer’s disease

2022· article· en· W4223652742 on OpenAlexaboutno aff
Syed Salman Shahid, Qiuting Wen, Shannon L. Risacher, Martin R. Farlow, Frederick W. Unverzagt, Liana G. Apostolova, Tatiana M. Foroud, Henrik Zetterberg, Kaj Blennow, Andrew J. Saykin, Yu Chien Wu

Bibliographic record

VenueBrain · 2022
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeEuropean Research CouncilStiftelsen för Gamla TjänarinnorFamiljen Erling-Perssons StiftelseSchool of Medicine, Indiana UniversityVetenskapsrådetUK Dementia Research InstituteNational Institutes of HealthOlav Thon StiftelsenHjärnfondenEuropean CommissionAlzheimer's Drug Discovery FoundationNational Institute on AgingAlzheimer's Association
KeywordsHippocampal formationAlzheimer's diseasePsychologyDementiaNeurosciencePathologyDentate gyrusMedicineDisease

Abstract

fetched live from OpenAlex

Hippocampal subfields exhibit differential vulnerabilities to Alzheimer's disease-associated pathology including abnormal accumulation of amyloid-β deposition and neurofibrillary tangles. These pathological processes extensively impact on the structural and functional interconnectivities of the subfields and may explain the association between hippocampal dysfunction and cognitive deficits. In this study, we investigated the degree of alterations in the microstructure of hippocampal subfields across the clinical continuum of Alzheimer's disease. We applied a grey matter-specific multi-compartment diffusion model (Cortical-Neurite orientation dispersion and density imaging) to understand the differential effects of Alzheimer's disease pathology on the hippocampal subfield microstructure. A total of 119 participants were included in this cross-sectional study. Participants were stratified into three categories, cognitively normal (n = 47), mild cognitive impairment (n = 52), and Alzheimer's disease (n = 19). Diffusion MRI, plasma biomarkers and neuropsychological test scores were used to determine the association between the microstructural integrity and Alzheimer's disease-associated molecular indicators and cognition. For Alzheimer's disease-related plasma biomarkers, we studied amyloid-β, total tau and neurofilament light; for Alzheimer's disease-related neuropsychological tests, we included the Trail Making Test, Rey Auditory Verbal Learning Test, Digit Span and Montreal Cognitive Assessment. Comparisons between cognitively normal subjects and those with mild cognitive impairment showed significant microstructural alterations in the hippocampal cornu ammonis (CA) 4 and dentate gyrus region, whereas CA 1-3 was the most sensitive region for the later stages in the Alzheimer's disease clinical continuum. Among imaging metrics for microstructures, the volume fraction of isotropic diffusion for interstitial free water demonstrated the largest effect size in between-group comparisons. Regarding the plasma biomarkers, neurofilament light appeared to be the most sensitive biomarker for associations with microstructural imaging findings in CA4-dentate gyrus. CA 1-3 was the subfield which had stronger correlations between cognitive performance and microstructural metrics. Particularly, poor performance on the Rey Auditory Verbal Learning Test and Montreal Cognitive Assessment was associated with decreased intracellular volume fraction. Overall, our findings support the value of tissue-specific microstructural imaging for providing pathologically relevant information manifesting in the plasma biomarkers and neuropsychological outcomes across various stages of Alzheimer's disease.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

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

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 head0.044
GPT teacher head0.325
Teacher spread0.281 · 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.

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

Citations61
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBrainSame topicAdvanced Neuroimaging Techniques and ApplicationsFrench-language works237,207