Multimodal Neuromarkers Predict Successful Physical Activity Engagement in Older Adults with Vascular Disease
Bibliographic record
Abstract
Abstract Background Physical inactivity is a significant risk factor for cognitive decline, particularly in vulnerable populations including those with cardiovascular risk‐factors1. To better understand the mechanisms that support positive physical activity engagement, the current study aimed to evaluate whether neuromarkers can predict future change in physical activity among older adults with a newly diagnosed cardiovascular risk‐factor. Method We analyzed baseline resting‐state functional and structural magnetic resonance imaging (MRI) data from the UK‐Biobank, a large population longitudinal cohort (n = 365; mean age = 62.10 years ± 6.5; cognitively normal). Brain imaging was obtained at baseline, and physical activity data was obtained at baseline and follow‐up after 5 years. Inclusion criteria were a new diagnosis of a cardiovascular risk factor (hypertension, type‐II diabetes‐mellitus, dyslipidemia, cardiac angina or myocardial infarction) between baseline and follow‐up; and did not meet the World Health Organization recommended 150 minutes/week of moderate or 75 minutes/week of vigorous physical activity at baseline. Demographic variables including age, sex, and education were included as covariates of non‐interest. To assess whether baseline resting‐state brain imaging predicts future change in physical activity behaviour, we performed a kernel‐ridge‐regression model with 5‐fold nested‐cross‐validation. Preprocessed neuroimaging data was used as input for the analysis, and the machine‐learning pipeline included feature reduction using grid‐search, hyperparameter selection, and model‐building: the model predicts whether test subject successfully increased physical activity engagement at follow up (reported above 150 min/week moderate or 75 min/week vigorous physical activity). Permutation‐testing (100 times) was performed before evaluating prediction metrics using accuracy and Receiver Operating Characteristic curves. Result Our prediction model delivered an accurate performance with average accuracy of 0.861 ± 3.8, and 0.883 ± 4.2 area under the curve (AUC) in predicting future behaviour change from physically inactive at baseline to physically active in follow up. Conclusion These results show baseline brain imaging can accurately predict physical activity behavior change in older adults with new cardiovascular disease. Leveraging machine‐learning methods to predict future lifestyle engagement will help characterize the neural mechanisms that support successful lifestyle change after a new cardiovascular diagnosis. 1. Gallen et al. Trends in Cog Sci. 2019
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".