The effect of computerized cognitive training and exercise on white matter integrity: a secondary analysis of a randomized controlled trial
Bibliographic record
Abstract
Abstract Background As a consequence of aging, the structure of the brain can be negatively impacted, and consequently, cognitive function. Although aerobic exercise and computerized cognitive training (CCT) are promising lifestyle interventions to prevent or delay cognitive decline, the underlying mechanisms of the effect of CCT, with or without exercise, on cognitive function remain poorly understood. Diffusion tensor imaging (DTI) is a neuroimaging technique for detecting changes in white matter (WM) tissue. Common DTI metrics such as fractional anisotropy (FA) can be summarized as a measure of overall WM integrity. The aim of this analysis was to explore the effect of CCT, with or without exercise, on WM integrity in healthy older adults. Methods This sub‐analysis included 65 community‐dwelling older adults aged 65‐85 years. Participants were enrolled in an 8‐week proof‐of‐concept randomized controlled trial and were assigned to either: 1) Group‐based CTT (Fit Brains; FBT; n = 19) 3x/week for 1hr plus 3x/week home‐based training; 2) Group‐based CCT preceded by exercise (ExFBT; n = 22) 3x/week for 1hr plus 3x/week home‐based training or; 3) Group‐based balanced‐and‐toned (BAT; n = 24) classes 3x/week for 1hr (control). DTI scans were acquired at baseline and at trial completion. Analysis of covariance (ANCOVA) evaluated treatment effects on FA at trial completion controlling for baseline age, MoCA, BMI, and FA. We conducted planned contrasts of FBT vs. BAT and ExFBT vs. BAT. In addition, we included an exploratory comparison between ExFBT vs. FBT. Results We found significant group differences in fornix and superior longitudinal fasciculus (SLF) FA at trial completion. Planned contrasts revealed significantly greater fornix FA in BAT vs. FBT (mean difference = 0.015, 95%CI = 0.002‐0.029). Also, fornix FA was significantly greater in ExFBT vs. FBT (mean difference = 0.017, 95%CI = 0.003‐0.031). In addition, SLF FA was significantly greater in ExFBT vs. FBT (mean difference = 0.008, 95%CI = 0.001‐0.014) at trial completion. Conclusion These findings suggest CCT alone may be detrimental to WM integrity, potentially due to its sedentary nature.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".