Mind Over Matter: Understanding the Relationship Between Memory Self‐Efficacy, Cognition and Brain Health in Older Adults with Probable Mild Cognitive Impairment
Bibliographic record
Abstract
Background In our aging population, cognitive decline and brain health are critical areas of concern for healthy aging. Evidence has shown that personality factors such as self‐efficacy, one's personal perceived ability to perform a specific task, directly impacts components of healthy aging, including total brain volume. However, it is unknown whether memory self‐efficacy, specifically, might also be associated with brain function and structure. Methods A cross‐sectional pilot study of community dwelling older women with probable Mild Cognitive Impairment (Montreal Cognitive Assessment score <26) were asked to evaluate their global memory self‐efficacy using two questionnaires: Memory Self‐Efficacy Questionnaire (MSEQ‐4) and the Multifactorial Memory Questionnaire (MMQ). Participants were asked to complete various standardized cognitive tests: Alzheimer's Dementia Assessment Scale – Cognition (ADAS‐cog), Digit Span, Auditory Verbal Learning Test, Stroop and Trail Making Test. Participants also performed an associative memory task during an fMRI scan. High resolution T1 weighted structural imaging was obtained from a 3T SIEMENS scanner. Multivariate linear regression models were constructed for cognitive and brain health measures in relation to the memory self‐efficacy measures. Covariates of the models included age and current physical activity level. Results We report that the MMQ subscale of Mistakes and Ability (MMQ‐A) was the strongest measure in accounting for variance after including covariates. The final model for ADAS‐cog accounted for 65% of the variance, with the MMQ‐A score accounting for 44%. For structural brain measures, total brain volume, white matter and grey matter volumes, the final model accounted for 70%, 98% and 13% for each of the listed measures, with MMQ‐A accounting for 52%, 63% and 9% respectively. Other measures of global memory self‐efficacy, MSEQ‐4 and MMQ subscale of feelings of contentment (MMQ‐C), were also seen to have correlations to ADAS scores and structural brain measures, but could not account for the same level of variance as the MMQ‐A. Conclusion Based on the results collected it appears that one's perceived self‐efficacy of memory mistakes and ability is associated with measures of cognition and brain health. Based on this data our research has the potential to progress into a longitudinal study of observing the relationship between changes in memory self‐efficacy and brain health and cognition, as well as progression to collaborative clinical studies in memory self‐efficacy modification for healthy aging. Support or Funding Information Funding: Natural Sciences and Engineering Research Council of Canada This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".