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Record W3117637287 · doi:10.1101/2020.12.18.423245

Memory precision of object-location binding is unimpaired in <i>APOE</i> ε4-carriers with spatial navigation deficits

2020· preprint· en· W3117637287 on OpenAlexafffund
Helena M. Gellersen, Gillian Coughlan, Michael Hornberger, Jon S. Simons

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsBaycrest Hospital
FundersBiotechnology and Biological Sciences Research CouncilCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchWellcome TrustMedical Research CouncilDirectorate for Biological SciencesJames S. McDonnell Foundation
KeywordsMnemonicObject (grammar)Episodic memoryCognitive psychologyCognitionPsychologySpatial memoryComputer scienceBayesian probabilityArtificial intelligenceWorking memoryNeuroscience

Abstract

fetched live from OpenAlex

Abstract Research suggests that tests of memory fidelity, feature binding and spatial navigation are promising for early detection of subtle behavioural changes related to Alzheimer’s disease (AD). In the absence of longitudinal data, one way of testing the early detection potential of cognitive tasks is through the comparison of individuals at different genetic risk for AD. Most studies have done so using samples aged 70 years or older. Here, we tested whether memory fidelity of long-term object-location binding may be a sensitive marker even among cognitively healthy individuals in their mid-60s by comparing participants at low and higher risk based on presence of the ε4-allele of the apolipoprotein gene ( n =26 ε3ε3, n =20 ε3ε4 carriers). We used a continuous report paradigm in a visual memory task that required participants to recreate the spatial position of objects in a scene. We employed mixture modelling to estimate the two distinct memory processes that underpin the trial-by-trial variation in localisation errors: retrieval success which indexes the proportion of trials where participants recalled any information about an object’s position and the precision with which participants retrieved this information. Prior work has shown that these memory paradigms that separate retrieval success from precision are capable of detecting subtle differences in mnemonic fidelity even when retrieval success could not. Nonetheless, a Bayesian analysis found good evidence that ε3ε4 carriers did not remember fewer object locations ( F (1, 42)=.450, p =.506, BF 01 =3.02), nor was their precision for the spatial position of objects reduced compared to ε3ε3 carriers ( F (1, 42)=.12, p =.726, BF 01 =3.19). Because the participants in the sample presented here were a subset of a study on APOE effects on spatial navigation in the Sea Hero Quest game (Coughlan et al., 2019. PNAS, 116 (9)), we obtained these data to contrast APOE effects on the two tasks within the same sample ( n =33). Despite the smaller sample size, wayfinding deficits among ε3ε4 could be replicated ( F (1, 33) =5.60, p =.024, BF 10 =3.44). Object-location memory metrics and spatial navigation scores were not correlated (all r <.25, p >.1, 0 10 <3). These findings show spared object-location binding in the presence of a detrimental APOE ε4 effect on spatial navigation. This suggests that the sensitivity of memory fidelity and binding tasks may not extend to individuals with one ε4-allele in their early to mid-60s. The results provide further support to prior proposals that spatial navigation may be a sensitive marker for the earliest AD-dependent cognitive changes, even before episodic memory.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.018
GPT teacher head0.258
Teacher spread0.240 · 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
Published2020
Admission routes2
Has abstractyes

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