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Record W4390193664 · doi:10.1002/alz.079029

Further along the Alzheimer’s disease continuum: associations between mild behavioral impairment with hippocampal and entorhinal volume and incident cognitive decline

2023· article· en· W4390193664 on OpenAlexaff
Dylan X. Guan, Tanaeem Rehman, Santhosh Nathan, Romella Durrani, Olivier Potvin, Simon Duchesne, G. Bruce Pike, Eric E. Smith, Zahinoor Ismail

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHotchkiss Brain InstituteUniversité LavalUniversity of Calgary
Fundersnot available
KeywordsEntorhinal cortexGrey matterDementiaCognitionPsychologyHazard ratioProportional hazards modelCognitive reserveCognitive declineAudiologyHippocampusClinical psychologyMedicineCognitive impairmentDiseaseGerontologyPsychiatryNeuroscienceConfidence intervalInternal medicineMagnetic resonance imagingWhite matterRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: Mild behavioral impairment (MBI) identifies a high-risk group for Alzheimer's disease and related dementias (ADRD), leveraging the risk associated with later-life emergent and persistent neuropsychiatric symptoms (NPS). MBI may act as a complementary behavioral analog to later-life emergent cognitive symptoms (mild cognitive impairment, MCI). These constructs are not mutually exclusive and can co-occur. We characterized the relationship between MBI, grey matter volume in the hippocampus and entorhinal cortex, and incident cognitive decline in older adults with normal cognition (NC) and MCI. METHOD: Seven-hundred-forty-two participants were included from the National Alzheimer's Coordinating Center Uniform Dataset. The Normative Morphometry Image Statistics tool was used to generate estimates of grey matter volume for the bilateral hippocampi and entorhinal cortices, normalized by age, sex, total intracranial volume, and image quality. NPS were evaluated using the informant-rated Neuropsychiatric Inventory Questionnaire, from which MBI scores were derived based on a published algorithm. Associations between MBI status and the regions-of-interest were modelled separately in NC and MCI using multivariable linear regressions, adjusting for education and clinical-MRI visit interval time. Associations between MBI status and incident cognitive decline were modelled using Cox proportional hazards regressions. RESULT: Participant characteristics are summarized in Table 1. As per Figures 1 and 2, NC participants with MBI had lower grey matter volume in the bilateral hippocampi (B = -0.40, 95%CI:[-0.66, -0.15], p = .004), and a greater hazard of incident MCI or dementia, than those without MBI (HR = 3.34, 95%CI:[2.04, 5.48), p<.001). NC MBI+ participants also had lower grey matter volume in the bilateral entorhinal cortices compared to NC MBI- participants, although not statistically significant (B = -0.13, 95%CI:[-0.35, -0.08], p = .22). In MCI, participants with MBI had lower grey matter volume bilaterally in both the hippocampus (B = -0.46, 95%CI:[-0.80, -0.12], p = .01) and entorhinal cortex (B = -0.42, 95%CI:[-0.76, -0.12], p = .009), and had greater progression-rates to dementia (HR = 4.13, 95%CI:[2.45, 6.94), p<.001), than those without MBI. CONCLUSION: Our findings suggest that evaluating dementia-free older adults for MBI status in conjunction with cognitive status identifies a group further along the ADRD continuum than those without MBI, who have greater baseline atrophy in key ADRD regions and greater progression rates to later cognitive stages.

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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.338
Teacher spread0.296 · 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 routes1
Has abstractyes

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