Visuomotor Impairments in Older Adults at Increased Alzheimer's Disease Risk
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Recent evidence suggests that visuomotor behaviors may be disrupted in the very early stages of Alzheimer's disease (AD). Here we propose that using kinematic measures under conditions that place demands on visual-spatial and cognitive-motor processing may provide an effective behavioral means to detect subtle changes associated with AD risk. METHODS: To this end, we have tested 22 young adults (mean age = 26.4 ± 4.1) and 22 older adults (mean age = 64.3 ± 10.1) at low AD, and 22 older adults (mean age = 67.7 ± 11.3) at high AD risk (i.e., strong family history or diagnosis of mild cognitive impairment). Kinematic measures were acquired on four visuomotor transformation tasks (standard, feedback reversal, plane dissociated, and plane dissociated + feedback reversal) using a dual-touchscreen tablet. RESULTS: Comparing participants at increased AD risk with both young and old healthy control groups revealed significant performance disruptions in at-risk individuals as task demands increased. Furthermore, we were able to discriminate between individuals at high and low AD risk with a classification accuracy of 86.4% (sensitivity: 81.8%, specificity: 90.9%). CONCLUSION: We suggest that the impairments observed in individuals at increased AD risk may reflect inherent brain alteration and/or early neuropathology disrupting the reciprocal communication between hippocampal, parietal, and frontal brain regions required to successfully prepare and update complex reaching movements. Such impairment has the potential to affect activities of daily living, and may serve as a sensitive measure of functional ability in at-risk adults.
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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 | 0.001 | 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".