Neural Signature of Complex Daily Function in Early Dementia Risk: Brain Region and Spectral‐Specific Insights during Dual‐Task Walking
Bibliographic record
Abstract
BACKGROUND: Difficulties in performing complex everyday activities are a key component of diagnosing dementia syndromes. Subtle limitations in these functions have been observed even before a diagnosis of mild cognitive impairment. However, the neural correlates underlying functional decline, particularly during the early stages of dementia, remain poorly understood. METHOD: To address this gap, we utilized a dual-task walking paradigm, portable electroencephalography (EEG), and 3D body tracking to record brain activity synchronized with cognitive and gait events in 36 individuals aged 65 and older. Participants were divided into lower-risk (Montreal Cognitive Assessment [MoCA] ≥ 27, n = 18) and higher-risk (MoCA ≤ 26, n = 18) groups for cognitive impairment (CI) using a median split. We assessed gait-related brain activity in the 8-28 Hz range over the pre/postcentral gyrus, as a marker of sensorimotor activation, and in the 3-7 Hz range over the frontomedial cortex, as a marker of motor control. We hypothesized that higher CI risk would be associated with poorer performance and distinct fronto-parietal activation patterns during dual-task walking. RESULT: To address this gap, we utilized a dual-task walking paradigm, portable electroencephalography (EEG), and 3D body tracking to record brain activity synchronized with cognitive and gait events in 36 individuals aged 65 and older. Participants were divided into lower-risk (Montreal Cognitive Assessment [MoCA] ≥ 27, n = 18) and higher-risk (MoCA ≤ 26, n = 18) groups for cognitive impairment (CI) using a median split. We assessed gait-related brain activity in the 8-28 Hz range over the pre/postcentral gyrus, as a marker of sensorimotor activation, and in the 3-7 Hz range over the frontomedial cortex, as a marker of motor control. We hypothesized that higher CI risk would be associated with poorer performance and distinct fronto-parietal activation patterns during dual-task walking. CONCLUSION: By leveraging the high spatiotemporal resolution of EEG and 3D motion tracking to align brain activity with gait and cognitive events, this approach enables task-specific, brain regional, and spectral insights into complex daily functions. This method holds significant potential to improve the prediction and identification of non-invasive brain stimulation targets for interventions in early-stage dementia syndromes.
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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) | 0.000 | 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".