Predictive models, predicting cognitive abilities from mental-health features via Partial Least Square (PLS).

a) predictive performance of the models, indicated by scatter plots between observed vs predicted cognitive abilities based on mental health. All data points are from test sets. r is the average Pearson’s r across 21 test sites, and a value in the parenthesis is the standard deviation of Pearson’s r across sites. UPPS-P Impulsive and Behaviour Scale and the Behavioural Inhibition System/Behavioural Activation System (BIS/BAS) were used for child temperaments, conceptualised as risk factors for mental issues. Mental health includes features from CBCL and child temperaments. b) Feature importance of mental health, predicting cognitive abilities. The features were ordered based on the loading of the first PLS component. Univariate correlations were Pearson’s r between each mental-health feature and cognitive abilities. Error bars reflect 95%CIs of the correlations. CBCL = Child Behavioural Checklist, reflecting children’s emotional and behavioural problems; UPPS-P = Urgency, Premeditation, Perseverance, Sensation seeking and Positive urgency Impulsive Behaviour Scale; BAS = Behavioural Activation System.

Predictive models predicting cognitive abilities from neuroimaging via opportunistic stacking and polygenic scores via Elastic Net.

a) Scatter plots between observed vs predicted cognitive abilities based on neuroimaging and polygenic scores. a) All data points are from test sets. r is the average Pearson’s r across 21 test sites, and a value in the parenthesis is the standard deviation of Pearson’s r across sites. b) Feature importance of the stacking layer of neuroimaging, predicting cognitive abilities via Random Forest. For the stacking layer of neuroimaging, the feature importance was based on the absolute value of SHAP, averaged across test sites. A higher absolute value of SHAP indicates a higher contribution to the prediction. Error bars reflect standard deviations across sites. c) Feature the importance of polygenic scores in predicting cognitive abilities via Elastic Net. For polygenic scores, the feature importance was based on the Elastic Net coefficients, averaged across test sites. We also plotted Pearson’s correlations between each polygenic score and cognitive abilities computed from the full data. Error bars reflect 95%CIs of these correlations.

Feature importance of each set of neuroimaging features, predicting cognitive abilities in the baseline data.

The feature importance was based on the Elastic Net coefficients, averaged across test sites. We did not order these sets of neuroimaging features according to their feature importance (see Figure 2). MID = Monetary Incentive Delay task; SST = Stop Signal Task; DTI = Diffusion Tensor Imaging; FC = functional connectivity.

Predictive models, predicting cognitive abilities from socio-demographics, lifestyles and developmental adverse events via Partial Least Square (PLS).

a) Scatter plots between observed vs predicted cognitive abilities based on socio-demographics, lifestyles and developmental adverse events. All data points are from test sets. r is the average Pearson’s r across 21 test sites, and a value in the parenthesis is the standard deviation of Pearson’s r across sites. b) Feature importance of socio-demographics, lifestyles and developmental adverse events, predicting cognitive abilities via Partial Least Square. The features were ordered based on the loading of the first component. Univariate correlations were Pearson’s correlation between each feature and cognitive abilities. Error bars reflect 95%CIs of the correlations.

Venn diagrams showing common and unique effects of proxy measures of cognitive abilities based on mental health, neuroimaging, polygenic scores and/or socio-demographics, lifestyles and developmental adverse events in explaining cognitive abilities across test sites.

We computed the common and unique effects in % based on the marginal of four sets of linear-mixed models.