Using Cognitive-Motor Dual-Tasks and Functional Near-Infrared Spectroscopy to Characterize Older Adults with and without Subjective Cognitive Decline
Bibliographic record
Abstract
BACKGROUND: Subjective cognitive decline (SCD) refers to individuals who report persistent cognitive deficits but perform normally on neuropsychological tests. Performance may be facilitated by increased prefrontal cortex activation, known as neural compensation, and could be used to differentiate between older adults with and without SCD. OBJECTIVE: This cross-sectional pilot study measured changes in the hemodynamic response (ΔHbO2) using functional near-infrared spectroscopy (fNIRS) as well as cognitive and motor performance during fine and gross motor dual-tasks in older adults with and without SCD. METHODS: Twenty older adults over 60 years old with (n = 10) and without (n = 10) SCD were recruited. Two experiments were conducted using 1) gross motor walking and 2) fine motor finger tapping tasks that were paired with an n-back working memory task. Participants also completed neuropsychological assessments and questionnaires on everyday functioning. RESULTS: Repeated measures ANOVAs demonstrated slower response times during dual-task gait compared to the single task (p = 0.032) and in the non-SCD group, slower gait speed was also observed in the dual compared to single task (p = 0.044). Response times during dual-task finger tapping were slower than the single task (p = 0.049) and greater ΔHbO2 was observed overall in the SCD compared to non-SCD group (p = 0.002). CONCLUSIONS: Examining neural and performance outcomes revealed differences between SCD and non-SCD groups and single and dual-tasks. Greater brain activation during dual-task finger tapping may reflect neural compensation, which should be examined in a larger sample and longitudinally to better characterize SCD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".