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Record W4390200153 · doi:10.1002/alz.078281

Associations between accelerated long‐term forgetting of Complex Figure Drawing, cerebral amyloid deposition, brain atrophy and serum neurofilament light in 73‐year‐olds

2023· article· en· W4390200153 on OpenAlexfundno aff
Kirsty Lu, Ashvini Keshavan, John Baker, Jennifer M. Nicholas, Rebecca E Street, Sarah E Keuss, William Coath, Sarah‐Naomi James, Philip S.J. Weston, Heidi Murray‐Smith, David M. Cash, Ian B. Malone, Andrew Wong, Nick C. Fox, Marcus Richards, Sebastian J. Crutch, Jonathan M. Schott

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersMedical Research CouncilAvid RadiopharmaceuticalsCalifornia State University, BakersfieldDementias Platform UKRosetrees TrustBritish Heart FoundationUniversity College LondonUK Dementia Research InstituteNational Institute for Health and Care ResearchBrain Research TrustAlzheimer's SocietyWeston Brain InstituteEngineering and Physical Sciences Research CouncilEli Lilly and CompanyUK Research and InnovationAlzheimer's Association
KeywordsForgettingAtrophyRecallCohortNeuroimagingMedicinePsychologyAudiologyInternal medicinePathologyPediatricsNeuroscienceCognitive psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Accelerated Long-term Forgetting (ALF) is the phenomenon whereby material is retained normally over short intervals (minutes or hours) but forgotten abnormally rapidly over longer periods (days or weeks). ALF may be an early marker of cognitive decline, but little is known about its relationships with preclinical Alzheimer's disease pathology in older adults, and how memory selectivity may influence which material is forgotten. METHOD: Participants in 'Insight 46', a sub-study of the MRC National Survey of Health and Development (British 1946 birth cohort), completed cognitive and neuroimaging assessments at two time-points (baseline at age ∼70; follow-up ∼2.4 years later). At follow-up, we assessed Complex Figure Drawing (copy; immediate recall; 30-minute recall; 7-day recall). Complex Figure items were categorized as 'outline' or 'detail' (Fig1), to test the hypothesis that forgetting the outline of the structure would be more sensitive to the effect of brain pathologies. ALF scores were calculated as the proportion of material retained after 7 days, relative to 30 minutes. Rates of cerebral atrophy between baseline and follow-up were quantified from T1-weighted MRI using the Brain Boundary Shift Integral (BBSI). β-amyloid status (positive/negative) was determined from 18F-Florbetapir-PET. Baseline serum neurofilament light (NfL) was quantified (Quanterix Simoa assay). Multivariable regression models were used to investigate the effects (mutually adjusted) of β-amyloid status, BBSI and NfL on ALF in n = 316 clinically-normal individuals (50% female; 22% β-amyloid positive; 30% APOE-ε4 carriers), and to explore interactions between these predictors, adjusting for potential confounders including prospectively-collected childhood cognitive ability and education. RESULT: 'Outline' items were better retained than 'detail' (Fig1). β-amyloid-positive participants had poorer ALF scores for 'outline' (but not 'detail') items (Fig1C; Table 1). Unexpectedly, higher NfL was associated with scores for 'outline' items (Table 1). Greater rate of cerebral atrophy predicted poorer retention among participants with elevated β-amyloid and higher NfL (Table 1; Fig2). CONCLUSION: These results provide evidence of associations between biomarkers of brain pathologies and ALF in 73-year-olds. Interactions between different biomarkers merit further exploration. ALF may be a sensitive outcome measure for therapeutic trials in preclinical AD. Better retention of 'outline' (vs. 'detail') items illustrates the strategic role of memory selectivity.

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)0.002
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 score0.036
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.047
GPT teacher head0.328
Teacher spread0.282 · 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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