Diffusion Tensor Imaging Correlates of Cognitive-Motor Decline in Normal Aging and Increased Alzheimer's Disease Risk
Bibliographic record
Abstract
Alzheimer's disease (AD) is typically associated with impairments in memory and other aspects of cognition, while deficits in complex movements are commonly observed later in the course of the disease. Recent studies, however, have indicated that subtle deteriorations in visuomotor control under cognitively demanding conditions may in fact be an early identifying feature of AD. Our previous work has shown that the ability to perform visuomotor tasks that rely on visual-spatial and rule-based transformations is disrupted in prodromal and preclinical AD. Here, in a sample of 30 female participants (10 young: mean age = 26.6 ± 2.7, 10 low AD risk: mean age = 58.7 ± 5.6, and 10 high AD risk: mean age = 58.5 ± 6.9), we test the hypothesis that these cognitive-motor impairments are associated with early AD-related brain alterations. Using diffusion-weighted magnetic resonance imaging, we examined changes in white matter (WM) integrity associated with normal aging and increased AD risk, and assessed the relationship between these underlying WM alterations and cognitive-motor performance. Our whole-brain analysis revealed significant age-related declines in WM integrity, which were more widespread in high relative to low AD risk participants. Furthermore, analysis of mean diffusivity measures within isolated WM clusters revealed a stepwise decline in WM integrity across young, low AD risk, and high AD risk groups. In support of our hypothesis, we also observed that lower WM integrity was associated with poorer cognitive-motor performance. These results are the first to demonstrate a relationship between AD-related WM alterations and impaired cognitive-motor control. The application of these findings may provide a novel clinical strategy for the early detection of individuals at increased AD risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".