Resistance training improves white matter structural connectivity in older adults at‐risk for cognitive decline
Bibliographic record
Abstract
Abstract Background Diabetes is a global health concern that impacts 415 million people worldwide. Individuals who are at‐risk for diabetes (characterized by high blood glucose and/or being overweight) have white matter atrophy, decreased cognitive function, and an increased risk of Alzheimer’s disease (AD). Recently, resistance training (RT) has been shown to lower white matter atrophy and white matter lesion volume. However, investigating changes in white matter tracts is complex, hence previous findings remain inconclusive. Diffusion tensor imaging (DTI) serves as a highly sensitive tool that enables visualization and characterization of white matter tracts and has the potential to combat this complexity. The study aimed to measure the effects of RT on structural connectivity in older adults at‐risk for cognitive decline using DTI. Method We conducted a 6‐month, thrice‐weekly randomized controlled trial. Twenty‐four participants (aged 60‐80 years, sedentary; body mass index ≥25) were randomized into one of two groups: 1) progressive resistance (weight) training (RT), or 2) balance and tone (BAT; control group). High resolution DTI images were obtained using a 3T Siemens MRI scanner at both baseline and endpoint for 17 (RT:11, BAT:6) participants. Images were analyzed using FSL’s tract based spatial statistics (TBSS) to evaluate structural connectivity between groups based on fractional anisotropy (FA), a measure reflecting fiber density, axonal diameter, and myelination in white matter tracts. Results Six months of RT led to higher FA values in the splenium of the corpus callosum, right posterior thalamic radiation, right and left superior corona radiata (+0.56%, +0.90%, +0.64% and +0.66%, respectively) compared to BAT (+0.16%, ‐0.72%, ‐0.28% and ‐0.24%). Conclusion These findings suggest that RT is associated with improvements in WM fiber microstructural integrity related to motor functions and visual short term memory capacity (vSTM). Thus, resistance training may be a promising intervention for patients with AD as the literature suggests that motor function deficits and vSTM dysfunction are early markers of AD pathology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".