Relationship between chronological age and Cognitionfluid (a) and predictive performance of prediction models using Brain MRI from different sets of MRI features to predict chronological age (b) and Cognitionfluid (c).

Note we only provided the scatter plots between observed and predicted values in the test sets from the best prediction models for each target here. See Supplementary Figures 1 and 2 for the scatter plots from other prediction models.

Feature importance of prediction models based on each of the 18 sets of features.

We calculated feature importance by, first, standardising Elastic Net weights across brain features of each set of features from each test fold. We then plotted the averaged weights across the five test folds for each of the set of features. For functional connectivity (FC), we, first, multiplied the absolute PCA scores (extracted from the ‘components_’ attribute of ‘sklearn.decomposition.PCA’) with Elastic Net weights and, then, summed the multiplied values across the 75 components, leaving 71,631 ROI-pair indices. Thereafter, we standardised these indices from each test fold and averaged them across the five test folds. Finally, given that the 71,631 ROI-pair indices were based on correlations among 379 ROIs, we averaged the ROI-pair indices from each ROI and plotted them. Accordingly, our FC plots showed the contribution of each seeding area.

Feature importance of the eight stacked prediction models.

Here we plotted the Elastic weights, standardised across predicted values from different sets of features and averaged across the five test folds.

Simple regression: using each Brain Age index or Brain Cognition to explain Cognitionfluid.

4a shows variation in Cognitionfluid explained by each Brain Age index as a function of the predictive performance of age-prediction models. 4b plots variation in Cognitionfluid explained by Brain Age indices and Brain Cognition.

Commonality analysis of a multiple regression model, having both chronological age and each Brain Age index as the regressors for Cognitionfluid.

Commonality analysis of a multiple regression model, having chronological age and each Brain Age index and Brain Cognition as the regressors for Cognitionfluid.

The scatter plots between observed and predicted values in the test sets from age-prediction models.

The scatter plots between observed and predicted values in the test sets from cognition-prediction models