Assessment of Brain Function in Patients With Cognitive Impairment Based on fNIRS and Gait Analysis
Bibliographic record
Abstract
Background: Early detection of mild cognitive impairment is crucial in the prevention of Alzheimer's disease (AD). This study aims to explore the changes in gait and brain co-functional connectivity between cognitively healthy and cognitively impaired groups under dual-task walking through the functional near-infrared spectroscopy (fNIRS) and gait analysis devices. Method: This study used fNIRS device and gait analysis devices to collect the data of 54 older adults. According to the Mini-mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) scales, the older adults were cognitively healthy (control group) and cognitively impaired (experimental group), of which 38 were in the control group and 16 were in the experimental group. The experiment was divided into a total of three sets of task experiments: a walking-only experiment, a dual-task walking-easy (DTW-easy) experiment, and a dual-task walking-difficult (DTW-difficult) experiment. Main Result: For the cognitively impaired and cognitively healthy populations, there were no significant differences in overall functional connectivity, region of interest (ROI) connection strength, and gait performance during single-task walking between the two groups.Whereas the performances of DTW differed significantly from the single-task walking in terms of between-group variability of functional connectivity strength change values, and ROI connection strength change values in relation to the dual-task cost of gait. Finally, the cognitively impaired group was significantly more affected by DTW-difficult tasks than the cognitively healthy group. Conclusion: This study provides a new approach to assist in the diagnosis of people with cognitive impairment and provides a new research pathway for the identification of cognitive impairment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".