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Record W4380883406 · doi:10.1002/alz.065936

A longitudinal study of the task‐related activation trajectory in people with mild cognitive impairment and subjective cognitive decline

2023· article· en· W4380883406 on OpenAlexaff
Kenia S. Correa-Jaraba, Samira Mellah, Isaora Zefania Dialahy, CIMA‐Q Group, Sylvie Belleville

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsCognitionDementiaTrajectoryCohortPsychologyCognitive declineNeuroimagingHippocampusLongitudinal studyAudiologyNeuroscienceCognitive psychologyMedicineInternal medicineDiseasePathology

Abstract

fetched live from OpenAlex

Abstract Background Brain hyperactivation — defined as higher level of activation compared to controls — was suggested as a very early signature of prodromal Alzheimer’s disease (AD), which would gradually decrease with progression to dementia. Longitudinal studies with people who have mild cognitive impairment (MCI) and subjective cognitive decline (SCD) can be used to capture the temporal dynamics and inter‐individual differences of these very early activation changes. Here, we aimed to identify the temporal trajectory of task‐related activation in participants with SCD and MCI from the CIMA‐Q cohort, which has data collected at multiple time‐points. We thus identified subgroups based on their common activation trajectory and characterized subgroups defined from the activation trajectory. Method The study included 53 older participants (40 SCD; 13 MCI) from the CIMA‐Q cohort with neuroimaging data collected over at least two time‐points (66‐85 years old; 36 women, 17 men). An fMRI examination was done every two years (2‐4 time‐points; average follow‐up: 3.2 years). Task‐related activation was measured during an associative memory encoding task. Group‐based trajectory models were estimated to identify homogeneous groups of participants based on activation trajectories in the hippocampus and in regions from the cortical signature of AD. Groups defined based on activation trajectories were then compared using Apolipoprotein‐ε4 (ApoE4), baseline cognition and hippocampal volume. Result Two different trajectories of activation were identified: Trajectory 1 was found in several cortical regions and was characterized by a high level of initial activation, which decreased over time. Trajectory 2 was characterized by a lower activation level, which remained stable over time or increased slightly on time‐point 4. Smaller hippocampal volume and ApoE4 were associated with Trajectory 1 for the left angular gyrus, and left hippocampal and right middle temporal gyrus activation, respectively. Conclusion An inverted U‐shape trajectory was found with high activation followed by gradually decreasing activation in AD‐signature regions. This trajectory was associated with smaller hippocampal volume and/or the presence of ApoE4 allele, both of which are biomarkers that increase the likelihood of developing AD. This finding supports the hypothesis that the inverted U‐shape trajectory of hyperactivation could be an index of prodromal AD.

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 score0.009
Threshold uncertainty score0.018

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 communication0.0000.000
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 head0.030
GPT teacher head0.318
Teacher spread0.287 · 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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