Functional <scp>MRI</scp> on executive functioning in aging and dementia: A scoping review of cognitive tasks
Bibliographic record
Abstract
Cognitive decline with aging and dementia is especially poignant with regard to the executive functioning that is necessary for activities of daily independent living. The relationship between age-related neurodegeneration in the prefrontal cortex and executive functioning has been uniquely investigated using task-phase functional magnetic resonance imaging (fMRI) to detect brain activity in response to stimuli; however, a comprehensive list of task designs that have been implemented to task-phase fMRI is absent in the literature. The purpose of this review was to recognize what methods have been used to study executive functions with aging and dementia in fMRI tasks, and to describe and categorize them. The following cognitive subdomains were emphasized: cognitive flexibility, planning and decision-making, working memory, cognitive control/inhibition, semantic processing, attention and concentration, emotional functioning, and multitasking. Over 30 different task-phase fMRI designs were found to have been implemented in the literature, all adopted from standard neuropsychological assessments. Cognitive set-shifting and decision-making tasks were particularly well studied in regard to age-related neurodegeneration, while emotional functioning and multitasking designs were found to be the least utilized. Summarizing the information on which tasks have shown the greatest usability will assist in the future design and implementation of effective fMRI experiments targeting executive functioning.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".