Relationship between cognitive abilities and mental health as represented by cognitive abilities at the neural and genetic levels of analysis
- Department of Psychology, University of Otago, New Zealand
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine and Wolfson Centre for Young People's Mental Health, Cardiff University, United Kingdom
Figures
Figure 1
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. Cognitive abilities are based on the second-order latent variable, the g-factor, based on a confirmatory factor analysis of six cognitive tasks. All data points are from test sets. r is the average Pearson’s r across 21 test sites. The parentheses following the r indicate bootstrapped 95% CIs, calculated based on observed vs predicted cognitive abilities from all test sites combined. 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 via PLS. 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 (in green), reflecting children’s emotional and behavioural problems; UPPS-P = Urgency, Premeditation, Perseverance, Sensation seeking, and Positive urgency Impulsive Behaviour Scale; BAS = Behavioural Activation System (in orange).
Figure 2 with 2 supplements
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. Cognitive abilities are based on the second-order latent variable, the g-factor, based on a confirmatory factor analysis of six cognitive tasks. The parentheses following the r indicate the bootstrapped 95% CIs, calculated based on observed vs predicted cognitive abilities from all test sites combined. All data points are from test sets. r is the average Pearson’s r across 21 test sites. The parentheses following the r indicate bootstrapped 95% CIs, calculated based on observed vs predicted cognitive abilities from all test sites combined. (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 SHapley Additive exPlanations (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. Different sets of neuroimaging features were filled with different colours: pink for dMRI, orange for fMRI, purple for resting-state functional MRI (rsMRI), and green for structural MRI (sMRI). (c) Feature importance of polygenic scores, 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.
Figure 2—figure supplement 1
Scatter plots between observed vs predicted cognitive abilities based on each set of 45 neuroimaging features in the baseline data.
All data points are from test sets. r is the average Pearson’s r across 21 test sites, and the parenthesis is the standard deviation of Pearson’s r across sites.
Figure 2—figure supplement 2
Scatter plots between observed vs predicted cognitive abilities based on each set of 45 neuroimaging features in the follow-up data.
All data points are from test sets. r is the average Pearson’s r across 21 test sites, and the parenthesis is the standard deviation of Pearson’s r across sites.
Figure 3 with 11 supplements
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 contribution to the stacking layer (see Figure 2). Larger versions of the feature importance for each set of neuroimaging features can be found in Figure 3—figure supplements 1–11. MID = Monetary Incentive Delay task; SST = Stop Signal Task; DTI = Diffusion Tensor Imaging; FC = functional connectivity.
Figure 3—figure supplement 1
Feature importance of each set of neuroimaging features, predicting cognitive abilities in the follow-up data via Elastic Net.
The feature importance was based on the Elastic Net coefficient, 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. The brain plots were created via the ggseg and ggsegExtra packages (1).
Figure 3—figure supplement 2
Feature importance of Nback task-fMRI features, predicting cognitive abilities in the baseline data via Elastic Net.
The feature importance was based on the Elastic Net coefficient, averaged across test sites. We did not order these sets of neuroimaging features according to their feature importance (see Figure 2). The brain plots were created via the ggseg and ggsegExtra packages (1).
Figure 3—figure supplement 3
Feature importance of MID task-fMRI features, predicting cognitive abilities in the baseline data via Elastic Net.
The feature importance was based on the Elastic Net coefficient, 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. The brain plots were created via the ggseg and ggsegExtra packages (1).
Figure 3—figure supplement 4
Feature importance of SST task-fMRI features, predicting cognitive abilities in the baseline data via Elastic Net.
The feature importance was based on the Elastic Net coefficient, averaged across test sites. We did not order these sets of neuroimaging features according to their feature importance (see Figure 2). SST = Stop Signal Task. The brain plots were created via the ggseg and ggsegExtra packages (1).
Figure 3—figure supplement 5
Feature importance of resting-state functional MRI (rs-fMRI) features, predicting cognitive abilities in the baseline data via Elastic Net.
The feature importance was based on the Elastic Net coefficient, averaged across test sites. We did not order these sets of neuroimaging features according to their feature importance (see Figure 2). FC = functional connectivity. The brain plots were created via the ggseg and ggsegExtra packages (1).
Figure 3—figure supplement 6
Feature importance of structural MRI (sMRI) and dMRI features, predicting cognitive abilities in the baseline data via Elastic Net.
The feature importance was based on the Elastic Net coefficient, averaged across test sites. We did not order these sets of neuroimaging features according to their feature importance (see Figure 2). DTI = Diffusion Tensor Imaging. The brain plots were created via the ggseg and ggsegExtra packages (1).
Figure 3—figure supplement 7
Feature importance of Nback task-fMRI features, predicting cognitive abilities in the follow-up data via Elastic Net.
The feature importance was based on the Elastic Net coefficient, averaged across test sites. We did not order these sets of neuroimaging features according to their feature importance (see Figure 2). The brain plots were created via the ggseg and ggsegExtra packages (1).
Figure 3—figure supplement 8
Feature importance of monetary incentive delay (MID) task-fMRI features, predicting cognitive abilities in the follow-up data via Elastic Net.
The feature importance was based on the Elastic Net coefficient, 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. The brain plots were created via the ggseg and ggsegExtra packages (1).
Figure 3—figure supplement 9
Feature importance of SST task-fMRI features, predicting cognitive abilities in the follow-up data via Elastic Net.
The feature importance was based on the Elastic Net coefficient, averaged across test sites. We did not order these sets of neuroimaging features according to their feature importance (see Figure 2). SST = Stop Signal Task. The brain plots were created via the ggseg and ggsegExtra packages (1).
Figure 3—figure supplement 10
Feature importance of resting-state functional MRI (rs-fMRI) features, predicting cognitive abilities in the follow-up data via Elastic Net.
The feature importance was based on the Elastic Net coefficient, averaged across test sites. We did not order these sets of neuroimaging features according to their feature importance (see Figure 2). FC = functional connectivity. The brain plots were created via the ggseg and ggsegExtra packages (1).
Figure 3—figure supplement 11
Feature importance of structural MRI (sMRI) and dMRI features, predicting cognitive abilities in the follow-up data via Elastic Net.
The feature importance was based on the Elastic Net coefficient, averaged across test sites. We did not order these sets of neuroimaging features according to their feature importance (see Figure 2). DTI = Diffusion Tensor Imaging. The brain plots were created via the ggseg and ggsegExtra packages (1).
Figure 4
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. Cognitive abilities are based on the second-order latent variable, the g-factor, based on a confirmatory factor analysis of six cognitive tasks. All data points are from test sets. r is the average Pearson’s r across 21 test sites. The parentheses following the r indicate bootstrapped 95% CIs, calculated based on observed vs predicted cognitive abilities from all test sites combined. (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. Different types of environmental factors were filled with different colours: orange for socio-demographics, purple for developmental adverse events and green for lifestyle. A dashed horizontal line in the follow-up feature importance figure distinguishes whether the variables were collected at baseline or follow-up.
Figure 5 with 1 supplement
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.
Figure 5—figure supplement 1
Stacked bar plots showing common and unique effects of proxy measures of cognitive abilities based on each set of neuroimaging features in explaining cognitive abilities across test sites.
We computed the common and unique effects in % based on the marginal of linear-mixed models.
Figure 6 with 1 supplement
Flow diagram of participants’ inclusion and exclusion criteria.
Here, we show the criteria for cognitive abilities and mental health across the two time points.
Figure 6—figure supplement 1
Flow diagram of participants’ inclusion and exclusion criteria.
Here, we show the criteria for polygenic scores and social demographics, lifestyle, and developmental adverse events across the two time points.
Figure 7
Standardised weights of the second-order ‘g-factor’ model.
These weights were derived from confirmatory factor analysis, fitted on cognitive abilities across six cognitive tasks from the entire baseline dataset. The actual weights used for predictive modelling were slightly different, as the predictive modelling was based on leave-one-site-out cross-validation, which trained on data from all but one site.
Figure 8
Predictive performance of leave one site out cross-validation vs 10-fold cross validation.
Figure 9
Illustration of data missingness (black) versus presence (grey) across different sets of neuroimaging features.
This figure compares the number of observations in the analysis. Opportunist stacking (referred to as stacking here) requires only at least one neuroimaging feature to be present, thus allowing the inclusion of more neuroimaging features compared to listwise deletion.
Tables
Table 1
Performance metrics for predictive models, predicting cognitive abilities from mental health, neuroimaging, polygenic scores, and socio-demographics, lifestyles, and developments.
The metrics were averaged across test sites with standard deviations in parentheses.
| Features | Correlation | R2 | MAE | RMSE |
|---|---|---|---|---|
| Baseline | ||||
| Mental Health | 0.353 (0.051) | 0.124 (0.038) | 0.736 (0.019) | 0.934 (0.02) |
| CBCL | 0.272 (0.048) | 0.074 (0.028) | 0.758 (0.014) | 0.961 (0.015) |
| Child personality | 0.268 (0.058) | 0.071 (0.034) | 0.759 (0.019) | 0.962 (0.017) |
| Neuroimaging | 0.539 (0.073) | 0.291 (0.082) | 0.658 (0.039) | 0.839 (0.05) |
| Polygenic scores | 0.252 (0.056) | 0.02 (0.075) | 0.696 (0.055) | 0.884 (0.066) |
| Socio-demo Life Dev Adv | 0.486 (0.081) | 0.239 (0.084) | 0.686 (0.041) | 0.87 (0.049) |
| Follow-up | ||||
| Mental Health | 0.36 (0.07) | 0.116 (0.061) | 0.715 (0.043) | 0.903 (0.051) |
| CBCL | 0.24 (0.056) | 0.043 (0.034) | 0.746 (0.045) | 0.94 (0.053) |
| Child personality | 0.311 (0.076) | 0.084 (0.059) | 0.728 (0.046) | 0.919 (0.051) |
| Neuroimaging | 0.524 (0.097) | 0.266 (0.112) | 0.645 (0.038) | 0.818 (0.053) |
| Polygenic scores | 0.25 (0.075) | 0.031 (0.068) | 0.672 (0.053) | 0.854 (0.068) |
| Socio-demo Life Dev Adv | 0.488 (0.093) | 0.226 (0.096) | 0.664 (0.044) | 0.843 (0.05) |
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R2=coefficient of determination; MAE = mean-absolute error; RMSE = root mean square error.
Table 2
Performance metrics for predictive models, predicting cognitive abilities from the 45 sets of neuroimaging features in the baseline data.
The metrics were averaged across test sites with standard deviations in parentheses.
| Features | Correlation | MAE | RMSE | |
|---|---|---|---|---|
| Neuroimaging | 0.539 (0.073) | 0.291 (0.082) | 0.658 (0.039) | 0.839 (0.05) |
| ENback 2back vs 0back | 0.393 (0.048) | 0.147 (0.042) | 0.661 (0.038) | 0.841 (0.045) |
| ENback 2back | 0.367 (0.06) | 0.128 (0.048) | 0.667 (0.036) | 0.848 (0.043) |
| rsfMRI temporal variance | 0.3 (0.094) | 0.09 (0.054) | 0.728 (0.04) | 0.921 (0.045) |
| rsfMRI cortical FC | 0.299 (0.055) | 0.088 (0.034) | 0.734 (0.027) | 0.929 (0.032) |
| ENback emotion | 0.277 (0.06) | 0.07 (0.041) | 0.689 (0.031) | 0.876 (0.035) |
| Cortical thickness | 0.265 (0.1) | 0.072 (0.055) | 0.756 (0.026) | 0.96 (0.03) |
| T2 gray matter avg intensity | 0.264 (0.106) | 0.069 (0.064) | 0.752 (0.032) | 0.953 (0.035) |
| T1 gray matter avg intensity | 0.263 (0.103) | 0.063 (0.071) | 0.761 (0.033) | 0.965 (0.039) |
| ENback 0back | 0.261 (0.058) | 0.061 (0.038) | 0.688 (0.031) | 0.878 (0.035) |
| T1 white matter avg intensity | 0.26 (0.103) | 0.067 (0.063) | 0.76 (0.029) | 0.963 (0.035) |
| rsfMRI subcortical-network FC | 0.258 (0.083) | 0.066 (0.043) | 0.743 (0.033) | 0.94 (0.035) |
| ENback place | 0.239 (0.065) | 0.049 (0.041) | 0.695 (0.032) | 0.886 (0.038) |
| T2 white matter avg intensity | 0.238 (0.103) | 0.056 (0.056) | 0.756 (0.03) | 0.96 (0.031) |
| T2 normalised intensity | 0.236 (0.082) | 0.057 (0.041) | 0.755 (0.021) | 0.96 (0.024) |
| DTI | 0.23 (0.074) | 0.042 (0.048) | 0.762 (0.027) | 0.967 (0.029) |
| Cortical volume | 0.228 (0.095) | 0.053 (0.044) | 0.767 (0.02) | 0.971 (0.024) |
| MID Small Rew vs Neu anticipation | 0.223 (0.049) | 0.048 (0.022) | 0.743 (0.017) | 0.938 (0.02) |
| Cortical area | 0.218 (0.101) | 0.049 (0.046) | 0.768 (0.021) | 0.973 (0.025) |
| T1 normalised intensity | 0.215 (0.109) | 0.047 (0.049) | 0.769 (0.022) | 0.974 (0.028) |
| MID Reward vs Neutral anticipation | 0.214 (0.062) | 0.043 (0.028) | 0.745 (0.022) | 0.944 (0.024) |
| MID Loss vs Neutral anticipation | 0.214 (0.075) | 0.043 (0.034) | 0.745 (0.025) | 0.944 (0.028) |
| MID Small Loss vs Neu anticipation | 0.203 (0.073) | 0.038 (0.03) | 0.747 (0.026) | 0.945 (0.026) |
| MID Pos vs Neg Punishment Feedback | 0.202 (0.066) | 0.037 (0.027) | 0.745 (0.021) | 0.945 (0.026) |
| T1 subcortical avg intensity | 0.2 (0.087) | 0.037 (0.043) | 0.773 (0.023) | 0.979 (0.026) |
| MID Large Rew vs Neu anticipation | 0.2 (0.072) | 0.037 (0.03) | 0.747 (0.021) | 0.946 (0.024) |
| MID Pos vs Neg Reward Feedback | 0.198 (0.05) | 0.036 (0.02) | 0.748 (0.022) | 0.945 (0.028) |
| T1 summations | 0.196 (0.08) | 0.009 (0.059) | 0.784 (0.029) | 0.992 (0.033) |
| Sulcal depth | 0.18 (0.095) | 0.032 (0.039) | 0.777 (0.02) | 0.984 (0.026) |
| MID Large Loss vs Neu anticipation | 0.173 (0.066) | 0.026 (0.026) | 0.749 (0.022) | 0.95 (0.025) |
| subcortical volume | 0.17 (0.078) | 0.028 (0.029) | 0.775 (0.018) | 0.982 (0.021) |
| SST Any Stop vs Correct Go | 0.164 (0.065) | 0.022 (0.025) | 0.736 (0.038) | 0.935 (0.043) |
| T2 subcortical avg intensity | 0.158 (0.057) | 0.023 (0.023) | 0.77 (0.018) | 0.977 (0.02) |
| ENback Face vs Place | 0.148 (0.076) | 0.014 (0.028) | 0.712 (0.027) | 0.904 (0.034) |
| SST Incorrect Stop vs Correct Go | 0.147 (0.059) | 0.017 (0.02) | 0.738 (0.035) | 0.937 (0.04) |
| SST Correct Stop vs Correct Go | 0.145 (0.056) | 0.017 (0.018) | 0.739 (0.033) | 0.936 (0.038) |
| SST Correct Go vs Fixation | 0.145 (0.053) | 0.017 (0.017) | 0.74 (0.033) | 0.938 (0.036) |
| MID Large Rew vs Small anticipation | 0.133 (0.05) | 0.015 (0.014) | 0.757 (0.022) | 0.956 (0.025) |
| T2 summations | 0.114 (0.053) | 0.008 (0.022) | 0.777 (0.018) | 0.984 (0.016) |
| SST Incorrect Go vs Correct Go | 0.11 (0.061) | 0.008 (0.015) | 0.744 (0.034) | 0.94 (0.038) |
| SST Correct Stop vs Incorrect Stop | 0.096 (0.068) | 0.005 (0.018) | 0.744 (0.033) | 0.943 (0.036) |
| MID Large vs Small Loss anticipation | 0.093 (0.063) | 0.006 (0.014) | 0.756 (0.024) | 0.96 (0.026) |
| SST Incorrect Go vs Incorrect Stop | 0.061 (0.039) | 0 (0.008) | 0.744 (0.032) | 0.943 (0.036) |
| ENback Positive vs Neutral Face | 0.024 (0.06) | –0.007 (0.012) | 0.716 (0.027) | 0.908 (0.034) |
| ENback Emotion vs Neutral Face | 0.019 (0.058) | –0.007 (0.01) | 0.716 (0.026) | 0.908 (0.033) |
| ENback Negative vs Neutral Face | 0.002 (0.058) | –0.007 (0.009) | 0.718 (0.024) | 0.911 (0.03) |
-
R2=coefficient of determination; MAE = mean-absolute error; RMSE = root mean square error.
Table 3
Performance metrics for predictive models, predicting cognitive abilities from the 45 sets of neuroimaging features in the follow-up data.
| Features | Correlation | MAE | RMSE | |
|---|---|---|---|---|
| Neuroimaging | 0.524 (0.097) | 0.266 (0.112) | 0.645 (0.038) | 0.818 (0.053) |
| ENback 2back vs 0back | 0.402 (0.092) | 0.15 (0.075) | 0.671 (0.032) | 0.844 (0.041) |
| ENback 2back | 0.39 (0.083) | 0.14 (0.071) | 0.676 (0.036) | 0.848 (0.045) |
| ENback place | 0.32 (0.073) | 0.089 (0.049) | 0.695 (0.038) | 0.874 (0.047) |
| ENback emotion | 0.319 (0.076) | 0.089 (0.05) | 0.696 (0.04) | 0.876 (0.047) |
| rsfMRI cortical FC | 0.309 (0.093) | 0.081 (0.071) | 0.718 (0.037) | 0.908 (0.046) |
| ENback 0back | 0.299 (0.078) | 0.077 (0.057) | 0.7 (0.045) | 0.881 (0.052) |
| rsfMRI temporal variance | 0.297 (0.111) | 0.077 (0.071) | 0.718 (0.045) | 0.903 (0.052) |
| rsfMRI subcortical-network FC | 0.265 (0.092) | 0.056 (0.059) | 0.732 (0.039) | 0.92 (0.048) |
| Cortical thickness | 0.259 (0.106) | 0.055 (0.062) | 0.738 (0.034) | 0.932 (0.041) |
| Cortical volume | 0.243 (0.091) | 0.046 (0.049) | 0.744 (0.034) | 0.936 (0.039) |
| T1 white matter avg intensity | 0.243 (0.09) | 0.044 (0.057) | 0.742 (0.035) | 0.937 (0.042) |
| T1 gray matter avg intensity | 0.241 (0.105) | 0.04 (0.069) | 0.742 (0.039) | 0.939 (0.047) |
| Cortical area | 0.233 (0.092) | 0.041 (0.05) | 0.746 (0.032) | 0.939 (0.04) |
| T2 gray matter avg intensity | 0.226 (0.112) | 0.04 (0.064) | 0.743 (0.037) | 0.939 (0.049) |
| DTI | 0.218 (0.065) | 0.022 (0.052) | 0.747 (0.034) | 0.944 (0.041) |
| T2 white matter avg intensity | 0.213 (0.099) | 0.033 (0.057) | 0.747 (0.036) | 0.942 (0.045) |
| T1 summations | 0.213 (0.062) | 0.011 (0.046) | 0.756 (0.039) | 0.954 (0.044) |
| MID Pos vs Neg Punish Feedback | 0.208 (0.058) | 0.025 (0.033) | 0.743 (0.044) | 0.933 (0.049) |
| MID Pos vs Neg Reward Feedback | 0.196 (0.071) | 0.021 (0.042) | 0.742 (0.038) | 0.933 (0.042) |
| T2 normalised intensity | 0.195 (0.077) | 0.025 (0.035) | 0.749 (0.039) | 0.946 (0.045) |
| T1 subcortical avg intensity | 0.191 (0.094) | 0.002 (0.083) | 0.759 (0.039) | 0.957 (0.046) |
| sulcal depth | 0.185 (0.087) | 0.018 (0.048) | 0.756 (0.034) | 0.95 (0.043) |
| MID Reward vs Neutral anticipation | 0.185 (0.078) | 0.016 (0.039) | 0.746 (0.037) | 0.937 (0.04) |
| SST Any Stop vs Correct Go | 0.184 (0.079) | 0.018 (0.034) | 0.745 (0.047) | 0.934 (0.054) |
| T1 normalised intensity | 0.181 (0.077) | 0.018 (0.036) | 0.752 (0.038) | 0.95 (0.045) |
| ENback Face vs Place | 0.179 (0.075) | 0.019 (0.03) | 0.721 (0.039) | 0.907 (0.044) |
| subcortical volume | 0.178 (0.062) | 0.016 (0.032) | 0.752 (0.036) | 0.949 (0.041) |
| SST Correct Stop vs Correct Go | 0.175 (0.062) | 0.015 (0.026) | 0.746 (0.048) | 0.936 (0.053) |
| MID Large Rew vs Neu anticipation | 0.172 (0.055) | 0.012 (0.028) | 0.747 (0.04) | 0.939 (0.044) |
| SST Incorrect Stop vs Correct Go | 0.17 (0.085) | 0.015 (0.032) | 0.746 (0.051) | 0.936 (0.059) |
| T2 subcortical avg intensity | 0.157 (0.085) | 0.011 (0.033) | 0.755 (0.039) | 0.952 (0.043) |
| MID Small Rew vs Neu anticipation | 0.154 (0.086) | 0.007 (0.04) | 0.75 (0.04) | 0.941 (0.044) |
| MID Loss vs Neutral anticipation | 0.147 (0.07) | 0.004 (0.024) | 0.75 (0.04) | 0.942 (0.043) |
| SST Correct Go vs Fixation | 0.138 (0.065) | 0.005 (0.026) | 0.749 (0.046) | 0.938 (0.054) |
| SST Incorrect Go vs Correct Go | 0.122 (0.072) | 0.001 (0.03) | 0.752 (0.053) | 0.944 (0.059) |
| MID Large Loss vs Neu anticipation | 0.121 (0.074) | –0.004 (0.03) | 0.752 (0.04) | 0.942 (0.044) |
| T2 summations | 0.116 (0.07) | –0.003 (0.029) | 0.763 (0.041) | 0.96 (0.048) |
| MID Small Loss vs Neu Anticipation | 0.106 (0.071) | –0.005 (0.021) | 0.755 (0.041) | 0.948 (0.044) |
| SST Correct Stop vs Incorrect Stop | 0.09 (0.086) | –0.006 (0.023) | 0.754 (0.049) | 0.947 (0.057) |
| MID Large vs Small Loss Anticipation | 0.064 (0.07) | –0.012 (0.025) | 0.756 (0.043) | 0.948 (0.048) |
| MID Large vs Small Rew anticipation | 0.063 (0.059) | –0.012 (0.018) | 0.759 (0.042) | 0.952 (0.046) |
| SST Incorrect Go vs Incorrect Stop | 0.038 (0.067) | –0.014 (0.019) | 0.756 (0.052) | 0.95 (0.059) |
| ENback Positive vs Neutral Face | 0.006 (0.069) | –0.013 (0.018) | 0.732 (0.037) | 0.919 (0.044) |
| ENback Negative vs Neutral Face | –0.012 (0.031) | –0.012 (0.015) | 0.735 (0.039) | 0.923 (0.043) |
| ENback Emotion vs Neutral Face | –0.027 (0.067) | –0.014 (0.016) | 0.733 (0.038) | 0.921 (0.045) |
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The metrics were averaged across test sites with standard deviations in parentheses. R2=coefficient of determination; MAE = mean-absolute error; RMSE = root mean square error.
Table 4
Results of linear-mixed models using proxy measures of cognitive abilities based on mental health and/or neuroimaging as regressors to explain cognitive abilities across test sites in the baseline.
| Response | Cognitive abilities | Cognitive abilities | Cognitive abilities | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Regressors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.02 | –0.00–0.03 | 0.058 | 0.02 | –0.00–0.04 | 0.057 | 0.02 | –0.00–0.03 | 0.067 |
| mental savg | 0.00 | –0.02–0.02 | 0.895 | 0.00 | –0.02–0.02 | 0.985 | |||
| mental cws | 0.19 | 0.17–0.20 | <0.001 | 0.31 | 0.29–0.33 | <0.001 | |||
| neuroimaging savg | –0.01 | –0.02–0.01 | 0.507 | –0.01 | –0.02–0.01 | 0.523 | |||
| neuroimaging cws | 0.43 | 0.41–0.44 | <0.001 | 0.48 | 0.47–0.50 | <0.001 | |||
| Random Effects | |||||||||
| σ2 | 0.55 | 0.54 | 0.57 | ||||||
| τ00 | 0.17 SITE_ID_L:REL_FAMILY_ID | 0.35 SITE_ID_L:REL_FAMILY_ID | 0.18 SITE_ID_L:REL_FAMILY_ID | ||||||
| ICC | 0.24 | 0.39 | 0.24 | ||||||
| N | 21 SITE_ID_L | 21 SITE_ID_L | 21 SITE_ID_L | ||||||
| 9001 REL_FAMILY_ID | 9001 REL_FAMILY_ID | 9001 REL_FAMILY_ID | |||||||
| Observations | 10728 | 10728 | 10728 | ||||||
| Marginal R2 | 0.272 | 0.098 | 0.238 | ||||||
| Conditional R2 | 0.444 | 0.452 | 0.423 | ||||||
-
cws = values centred within each site; savg = values averaged within each site.
Table 5
Results of linear-mixed models using proxy measures of cognitive abilities based on mental health and/or neuroimaging as regressors to explain cognitive abilities across test sites in the follow-up.
| Response | Cognitive abilities | Cognitive abilities | Cognitive abilities | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Regressors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.82 | 0.80–0.84 | <0.001 | 0.82 | 0.80–0.85 | <0.001 | 0.82 | 0.80–0.84 | <0.001 |
| mental savg | 0.02 | 0.00–0.04 | 0.047 | 0.02 | 0.00–0.05 | 0.037 | |||
| mental cws | 0.19 | 0.17–0.21 | <0.001 | 0.31 | 0.29–0.33 | <0.001 | |||
| neuroimaging savg | 0.02 | 0.00–0.05 | 0.021 | 0.03 | 0.01–0.05 | 0.012 | |||
| neuroimaging cws | 0.42 | 0.40–0.44 | <0.001 | 0.47 | 0.45–0.49 | <0.001 | |||
| Random Effects | |||||||||
| σ2 | 0.41 | 0.45 | 0.42 | ||||||
| τ00 | 0.24 SITE_ID_L:REL_FAMILY_ID | 0.37 SITE_ID_L:REL_FAMILY_ID | 0.27 SITE_ID_L:REL_FAMILY_ID | ||||||
| ICC | 0.37 | 0.46 | 0.40 | ||||||
| N | 21 SITE_ID_L | 21 SITE_ID_L | 21 SITE_ID_L | ||||||
| 5434 REL_FAMILY_ID | 5434 REL_FAMILY_ID | 5434 REL_FAMILY_ID | |||||||
| Observations | 6315 | 6315 | 6315 | ||||||
| Marginal R2 | 0.286 | 0.104 | 0.245 | ||||||
| Conditional R2 | 0.552 | 0.513 | 0.545 | ||||||
-
cws = values centred within each site; savg = values averaged within each site.
Table 6
Results of linear-mixed models using proxy measures of cognitive abilities based on mental health and/or polygenic scores as regressors to explain cognitive abilities across test sites in the baseline.
| Response | Cognitive abilities | Cognitive abilities | Cognitive abilities | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Regressors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.23 | 0.21–0.26 | <0.001 | 0.23 | 0.21–0.25 | <0.001 | 0.23 | 0.21–0.26 | <0.001 |
| mental savg | 0.06 | 0.02–0.09 | 0.004 | 0.13 | 0.10–0.15 | <0.001 | |||
| mental cws | 0.25 | 0.23–0.27 | <0.001 | 0.25 | 0.23–0.27 | <0.001 | |||
| PGS savg favg | –0.08 | –0.12 to –0.05 | <0.001 | –0.13 | –0.15 to –0.10 | <0.001 | |||
| PGS cws cwf | 0.05 | 0.03–0.07 | <0.001 | 0.06 | 0.04–0.08 | <0.001 | |||
| Random Effects | |||||||||
| σ2 | 0.51 | 0.52 | 0.53 | ||||||
| τ00 | 0.27 SITE_ID_L:REL_FAMILY_ID | 0.26 SITE_ID_L:REL_FAMILY_ID | 0.32 SITE_ID_L:REL_FAMILY_ID | ||||||
| ICC | 0.34 | 0.33 | 0.38 | ||||||
| N | 21 SITE_ID_L | 21 SITE_ID_L | 21 SITE_ID_L | ||||||
| 4734 REL_FAMILY_ID | 4734 REL_FAMILY_ID | 4734 REL_FAMILY_ID | |||||||
| Observations | 5766 | 5766 | 5766 | ||||||
| Marginal R2 | 0.098 | 0.092 | 0.026 | ||||||
| Conditional R2 | 0.408 | 0.394 | 0.394 | ||||||
-
cws = values centred within each site; savg = values averaged within each site; cws,cwf = values centred within each family first and then within each site; savg,favg = values averaged within each family first and then within each site. PGS = polygenic scores.
Table 7
Results of linear-mixed models using proxy measures of cognitive abilities based on mental health and/or polygenic scores as regressors to explain cognitive abilities across test sites in the follow-up.
| Response | Cognitive abilities | Cognitive abilities | Cognitive abilities | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 1.06 | 1.03–1.09 | <0.001 | 1.06 | 1.03–1.09 | <0.001 | 1.06 | 1.03–1.09 | <0.001 |
| mental savg | 0.03 | –0.00–0.07 | 0.063 | 0.07 | 0.05–0.10 | <0.001 | |||
| mental cws | 0.22 | 0.19–0.25 | <0.001 | 0.22 | 0.20–0.25 | <0.001 | |||
| PGS savg favg | –0.07 | –0.10 to –0.04 | <0.001 | –0.09 | –0.12 to –0.06 | <0.001 | |||
| PGS cws cwf | 0.04 | 0.02–0.06 | <0.001 | 0.05 | 0.03–0.07 | <0.001 | |||
| Random Effects | |||||||||
| σ2 | 0.42 | 0.43 | 0.43 | ||||||
| τ00 | 0.32 SITE_ID_L:REL_FAMILY_ID | 0.31 SITE_ID_L:REL_FAMILY_ID | 0.37 SITE_ID_L:REL_FAMILY_ID | ||||||
| ICC | 0.43 | 0.42 | 0.46 | ||||||
| N | 21 SITE_ID_L | 21 SITE_ID_L | 21 SITE_ID_L | ||||||
| 3370 REL_FAMILY_ID | 3370 REL_FAMILY_ID | 3370 REL_FAMILY_ID | |||||||
| Observations | 4036 | 4036 | 4036 | ||||||
| Marginal R2 | 0.075 | 0.068 | 0.013 | ||||||
| Conditional R2 | 0.470 | 0.460 | 0.469 | ||||||
-
cws = values centred within each site; savg = values averaged within each site; cws,cwf = values centred within each family first and then within each site; savg,favg = values averaged within each family first and then within each site. PGS = polygenic scores.
Table 8
Results of linear-mixed models using proxy measures of cognitive abilities based on mental health and/or socio-demographics, lifestyles, and developmental adverse events as regressors to explain cognitive abilities across test sites in the baseline.
| Response | Cognitive abilities | Cognitive abilities | Cognitive abilities | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Regressors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.01 | –0.01–0.02 | 0.525 | 0.01 | –0.01–0.03 | 0.385 | 0.01 | –0.01–0.02 | 0.558 |
| mental savg | –0.00 | –0.02–0.02 | 0.917 | –0.00 | –0.02–0.02 | 0.930 | |||
| mental cws | 0.20 | 0.18–0.22 | <0.001 | 0.31 | 0.29–0.33 | <0.001 | |||
| sdl savg | 0.00 | –0.02–0.02 | 0.819 | 0.00 | –0.01–0.02 | 0.792 | |||
| sdl cws | 0.40 | 0.38–0.41 | <0.001 | 0.46 | 0.44–0.48 | <0.001 | |||
| Random Effects | |||||||||
| σ2 | 0.52 | 0.53 | 0.54 | ||||||
| τ00 | 0.22 SITE_ID_L:REL_FAMILY_ID | 0.35 SITE_ID_L:REL_FAMILY_ID | 0.24 SITE_ID_L:REL_FAMILY_ID | ||||||
| ICC | 0.30 | 0.40 | 0.31 | ||||||
| N | 21 SITE_ID_L | 21 SITE_ID_L | 21 SITE_ID_L | ||||||
| 9390 REL_FAMILY_ID | 9390 REL_FAMILY_ID | 9390 REL_FAMILY_ID | |||||||
| Observations | 11294 | 11294 | 11294 | ||||||
| Marginal R2 | 0.249 | 0.098 | 0.213 | ||||||
| Conditional R2 | 0.474 | 0.458 | 0.456 | ||||||
-
cws = values centred within each site; savg = values averaged within each site; sdl = socio-demographics, lifestyles and developmental adverse events.
Table 9
Results of linear-mixed models using proxy measures of cognitive abilities based on mental health and/or socio-demographics, lifestyles and developmental adverse events as regressors to explain cognitive abilities across test sites in the follow-up.
| Response | Cognitive abilities | Cognitive abilities | Cognitive abilities | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Regressors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.83 | 0.81–0.85 | <0.001 | 0.83 | 0.81–0.86 | <0.001 | 0.83 | 0.81–0.85 | <0.001 |
| mental savg | 0.01 | –0.01–0.03 | 0.185 | 0.01 | –0.01–0.04 | 0.198 | |||
| mental cws | 0.20 | 0.18–0.22 | <0.001 | 0.30 | 0.28–0.32 | <0.001 | |||
| sdl savg | 0.00 | –0.02–0.02 | 0.957 | 0.00 | –0.02–0.02 | 0.757 | |||
| sdl cws | 0.39 | 0.37–0.41 | <0.001 | 0.44 | 0.42–0.47 | <0.001 | |||
| Random Effects | |||||||||
| σ2 | 0.42 | 0.45 | 0.43 | ||||||
| τ00 | 0.27 SITE_ID_L:REL_FAMILY_ID | 0.37 SITE_ID_L:REL_FAMILY_ID | 0.30 SITE_ID_L:REL_FAMILY_ID | ||||||
| ICC | 0.39 | 0.45 | 0.41 | ||||||
| N | 21 SITE_ID_L | 21 SITE_ID_L | 21 SITE_ID_L | ||||||
| 6217 REL_FAMILY_ID | 6217 REL_FAMILY_ID | 6217 REL_FAMILY_ID | |||||||
| Observations | 7382 | 7382 | 7382 | ||||||
| Marginal R2 | 0.256 | 0.099 | 0.213 | ||||||
| Conditional R2 | 0.543 | 0.508 | 0.535 | ||||||
-
cws = values centred within each site; savg = values averaged within each site; sdl = socio-demographics, lifestyles and developmental adverse events.
Table 10
Results of linear-mixed models using proxy measures of cognitive abilities based on mental health, neuroimaging, polygenic scores and/or socio-demographics, lifestyles and developmental adverse events as regressors to explain cognitive abilities across test sites in the baseline.
| Response | Cognitive abilities | Cognitive abilities | ||||
|---|---|---|---|---|---|---|
| Regressors | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.24 | 0.21–0.26 | <0.001 | 0.24 | 0.21–0.26 | <0.001 |
| mental savg | 0.00 | –0.05–0.05 | 0.975 | 0.09 | 0.05–0.12 | <0.001 |
| mental cws | 0.14 | 0.11–0.16 | <0.001 | 0.18 | 0.15–0.20 | <0.001 |
| neuroimaging savg | 0.01 | –0.03–0.05 | 0.533 | 0.05 | 0.01–0.09 | 0.006 |
| neuroimaging cws | 0.26 | 0.24–0.29 | <0.001 | 0.31 | 0.28–0.33 | <0.001 |
| PGS savg favg | –0.04 | –0.08–0.00 | 0.070 | |||
| PGS cws cwf | 0.05 | 0.03–0.07 | <0.001 | |||
| sdl savg | 0.09 | 0.03–0.16 | 0.006 | |||
| sdl cws | 0.18 | 0.16–0.21 | <0.001 | |||
| σ2 | 0.50 | 0.52 | ||||
| τ00 | 0.15 SITE_ID_L:REL_FAMILY_ID | 0.17 SITE_ID_L:REL_FAMILY_ID | ||||
| ICC | 0.23 | 0.25 | ||||
| N | 21 SITE_ID_L | 21 SITE_ID_L | ||||
| Observations | 5520 | 5520 | ||||
| Marginal R2 | 0.241 | 0.197 | ||||
| Conditional R2 | 0.416 | 0.395 | ||||
| Regressors | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.24 | 0.21–0.26 | <0.001 | 0.24 | 0.21–0.26 | <0.001 |
| mental savg | 0.06 | 0.03–0.10 | 0.001 | 0.00 | –0.04–0.05 | 0.890 |
| mental cws | 0.24 | 0.22–0.27 | <0.001 | 0.19 | 0.16–0.21 | <0.001 |
| neuroimaging savg | ||||||
| neuroimaging cws | ||||||
| PGS savg favg | –0.08 | –0.12 to –0.05 | <0.001 | |||
| PGS cws cwf | 0.06 | 0.04–0.08 | <0.001 | |||
| sdl savg | 0.14 | 0.09–0.19 | <0.001 | |||
| sdl cws | 0.25 | 0.22–0.27 | <0.001 | |||
| σ2 | 0.51 | 0.52 | ||||
| τ00 | 0.27 SITE_ID_L:REL_FAMILY_ID | 0.20 SITE_ID_L:REL_FAMILY_ID | ||||
| ICC | 0.34 | 0.28 | ||||
| N | 21 SITE_ID_L | 21 SITE_ID_L | ||||
| 4571 REL_FAMILY_ID | 4571 REL_FAMILY_ID | |||||
| Observations | 5520 | 5520 | ||||
| Marginal R2 | 0.097 | 0.163 | ||||
| Conditional R2 | 0.408 | 0.395 | ||||
-
cws = values centred within each site; savg = values averaged within each site; cws,cwf = values centred within each family first and then within each site; savg,favg = values averaged within each family first and then within each site; PGS = polygenic scores; sdl = socio-demographics, lifestyles and developmental adverse events.
Table 11
Results of linear-mixed models using proxy measures of cognitive abilities based on mental health, neuroimaging, polygenic scores and/or socio-demographics, lifestyles, and developmental adverse events as regressors to explain cognitive abilities across test sites in the follow-up.
| Response | Cognitive abilities | Cognitive abilities | ||||
|---|---|---|---|---|---|---|
| Regressors | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 1.05 | 1.02–1.08 | <0.001 | 1.05 | 1.02–1.08 | <0.001 |
| mental savg | 0.05 | –0.01–0.10 | 0.100 | 0.06 | 0.03–0.10 | <0.001 |
| mental cws | 0.13 | 0.11–0.16 | <0.001 | 0.17 | 0.14–0.20 | <0.001 |
| neuroimaging savg | 0.00 | –0.06–0.06 | 0.935 | 0.03 | –0.01–0.06 | 0.146 |
| neuroimaging cws | 0.27 | 0.24–0.30 | <0.001 | 0.31 | 0.28–0.33 | <0.001 |
| PGS savg favg | 0.00 | –0.03–0.04 | 0.833 | |||
| PGS cws cwf | 0.04 | 0.02–0.06 | <0.001 | |||
| sdl savg | 0.04 | –0.04–0.12 | 0.349 | |||
| sdl cws | 0.20 | 0.17–0.23 | <0.001 | |||
| σ2 | 0.38 | 0.40 | ||||
| τ00 | 0.23 SITE_ID_L:REL_FAMILY_ID | 0.25 SITE_ID_L:REL_FAMILY_ID | ||||
| ICC | 0.38 | 0.39 | ||||
| N | 21 SITE_ID_L | 21 SITE_ID_L | ||||
| 2930 REL_FAMILY_ID | 2930 REL_FAMILY_ID | |||||
| Observations | 3423 | 3423 | ||||
| Marginal R2 | 0.242 | 0.190 | ||||
| Conditional R2 | 0.527 | 0.506 | ||||
| Regressors | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 1.05 | 1.02–1.08 | <0.001 | 1.05 | 1.02–1.08 | <0.001 |
| mental savg | 0.08 | 0.04–0.11 | <0.001 | 0.05 | –0.00–0.10 | 0.074 |
| mental cws | 0.23 | 0.20–0.26 | <0.001 | 0.18 | 0.15–0.21 | <0.001 |
| neuroimaging savg | ||||||
| neuroimaging cws | ||||||
| PGS savg favg | 0.00 | - 0.03–0.04 | 0.844 | |||
| PGS cws cwf | 0.05 | 0.03–0.07 | <0.001 | |||
| PGS savg favg | 0.00 | - 0.03–0.04 | 0.844 | |||
| PGS cws cwf | 0.05 | 0.03–0.07 | <0.001 | |||
| sdl savg | 0.04 | –0.01–0.09 | 0.092 | |||
| sdl cws | 0.25 | 0.22–0.28 | <0.001 | |||
| σ2 | 0.41 | 0.42 | ||||
| τ00 | 0.33 SITE_ID_L:REL_FAMILY_ID | 0.27 SITE_ID_L:REL_FAMILY_ID | ||||
| ICC | 0.45 | 0.39 | ||||
| N | 21 SITE_ID_L | 21 SITE_ID_L | ||||
| 2930 REL_FAMILY_ID | 2930 REL_FAMILY_ID | |||||
| Observations | 3423 | 3423 | ||||
| Marginal R2 | 0.076 | 0.153 | ||||
| Conditional R2 | 0.491 | 0.486 | ||||
-
cws = values centred within each site; savg = values averaged within each site; cws,cwf = values centred within each family first and then within each site; savg,favg = values averaged within each family first and then within each site; PGS = polygenic scores; sdl = socio-demographics, lifestyles and developmental adverse events.
Table 12
The differences in social demographics, lifestyles, and developmental adverse events between participants who provided cognitive scores in the follow-up.
We used social demographics, lifestyles, and developmental adverse events collected at baseline.
| Variable names | Having cognitive scores in the follow-up. | Not having cognitive scores in the follow-up. | Test statistics |
|---|---|---|---|
| Age in months | Mean (sd): 119.3 (7.5) | Mean (sd): 118.3 (7.6) | Yuen’s t(3783)=6.05, p < 0.001, Cohen’s d = 0.092 |
| Sex | Male = 3918 (52.4%) Female = 3564 (47.6%) Intersex-Male=1 (0.0%) Intersex-female=0 (0.0%) Do not know = 0 (0.0%) | Male = 1776 (53.2%) Female = 1563 (46.8%) Intersex-Male=2 (0.1%) Intersex-female=0(0.0%) Do not know = 0 (0.0%) | (X2 = 4, N = 10824)=6, p=0.199 |
| Body Mass Index | Mean (sd): 18.7 (4.1) | Mean (sd): 18.9 (4.4) | Yuen’s t (3658)=1.605, p=0.109, Cohen’s d=0.023 |
| Race | White = 4190 (56.0%) Black = 918 (12.3%) Hispanic = 1441 (19.3%) Asian = 157(2.1%) Other = 777 (10.4%) | White = 1611 (48.2%) Black = 612 (18.3%) Hispanic = 689 (20.6%) Asian = 68(2.0%) Other = 360 (10.8%) | X2(16, N=10823)=20, p = 0.22 |
| Bilingual Use | Mean (sd): 1 (1.7) | Mean (sd): 1 (1.7) | Yuen’s t(3776)=0.696, p=0.486, Cohen’s d=0.011 |
| Parent Marital Status | Married = 5239 (70.5%) Widowed = 59(0.8%) Divorced = 684 (9.2%) Separated = 264 (3.6%) NeverMarried = 806(10.8%) LivingWithPartner = 381 (5.1%) | Married = 2194 (66.0%) Widowed = 29(0.9%) Divorced = 290 (8.7%) Separated = 135 (4.1%) NeverMarried = 460(13.8%) LivingWithPartner = 214 (6.4%) | X2(25, N=10755)=30, p=0.224 |
| Parents' Education | Mean (sd): 16.6 (2.6) | Mean (sd): 16.3 (2.8) | Yuen’s t(3262)=4.175, p<0.001, Cohen’s d=0.068 |
| Parents' Income | Mean (sd): 7.4 (2.3) | Mean (sd): 7.2 (2.5) | Yuen’s t(2854)=2.243, p=0.025, Cohen’s d=0.034 |
| Household Size | Mean (sd): 4.7 (1.5) | Mean (sd): 4.7 (1.6) | Yuen’s t(3718)=0.39, p=0.697, Cohen’s d=0.007 |
| Economics Insecurities | Mean (sd): 0.4 (1.1) | Mean (sd): 0.5 (1.1) | Yuen’s t(1982)=2.65, p=0.008, Cohen’s d=0.033 |
| Area Deprivation Index | Mean (sd): 94.6 (20.7) | Mean (sd): 94.9 (21.2) | Yuen’s t(3297)=1.686, p=0.092, Cohen’s d=0.029 |
| Lead Risk | Mean (sd): 5 (3.1) | Mean (sd): 5.1 (3.1) | Yuen’s t(3374)=1.797, p=0.072, Cohen’s d=0.027 |
| Uniform Crime Reports | Mean (sd): 12.1 (5.5) | Mean (sd): 12 (6.1) | Yuen’s t(3370)=0.873, p=0.383, Cohen’s d=0.014 |
| Parent reported Neighbourhood Safety | Mean (sd): 11.8 (2.9) | Mean (sd): 11.6 (3) | Yuen’s t(3382)=1.799, p=0.072, Cohen’s d=0.025 |
| Child reported Neighbourhood Safety | Mean (sd): 4.1 (1.1) | Mean (sd): 4 (1.1) | Yuen’s t(3786)=2.258, p=0.024, Cohen’s d=0.036 |
| School Environment | Mean (sd): 20 (2.8) | Mean (sd): 19.8 (2.9) | Yuen’s t(3787)=1.763, p=0.078, Cohen’s d=0.029 |
| School Involvement | Mean (sd): 13.1 (2.3) | Mean (sd): 12.9 (2.4) | Yuen’s t(3790)=3.203, p=0.001, Cohen’s d=0.05 |
| School Disengagement | Mean (sd): 3.7 (1.4) | Mean (sd): 3.8 (1.5) | Yuen’s t(3800)=2.171, p=0.03, Cohen’s d=0.035 |
| Lack of Sleep | Mean (sd): 1.7 (0.8) | Mean (sd): 1.7 (0.8) | Yuen’s t(3860)=3.084, p=0.002, Cohen’s d=0.05 |
| Sleep Disturbance | Mean (sd): 1.9 (Abramovitch et al., 2021) | Mean (sd): 1.9 (Abramovitch et al., 2021) | Yuen’s t(3877)=1.567, p=0.117, Cohen’s d=0.025 |
| Sleep Initiating Maintaining | Mean (sd): 11.7 (3.7) | Mean (sd): 11.9 (3.8) | Yuen’s t(3862)=2.481, p=0.013, Cohen’s d=0.038 |
| Sleep Breathing Disorders | Mean (sd): 3.7 (1.2) | Mean (sd): 3.8 (1.3) | Yuen’s t(3834)=1.43, p=0.153, Cohen’s d=0.022 |
| Sleep Arousal Disorders | Mean (sd): 3.4 (0.9) | Mean (sd): 3.4 (Abramovitch et al., 2021) | Yuen’s t(3885)=0.966, p=0.334, Cohen’s d=0.013 |
| Sleep Wake Transition Disorders | Mean (sd): 8.2 (2.6) | Mean (sd): 8.1 (2.6) | Yuen’s t(3828)=1.198, p=0.231, Cohen’s d=0.022 |
| Sleep Excessive Somnolence | Mean (sd): 6.9 (2.4) | Mean (sd): 7 (2.5) | Yuen’s t(3836)=0.131, p=0.896, Cohen’s d=0.007 |
| Sleep Hyperhidrosis | Mean (sd): 2.4 (1.2) | Mean (sd): 2.5 (1.2) | Yuen’s t(4375)=1.755, p=0.079, Cohen’s d=0.029 |
| Individual Physical Extracurricular Activities | Mean (sd): 5 (5.7) | Mean (sd): 4.7 (5.4) | Yuen’s t(4173)=2.933, p=0.003, Cohen’s d=0.044 |
| Team Physical Extracurricular Activities | Mean (sd): 8.4 (7.7) | Mean (sd): 7.8 (7.4) | Yuen’s t(4007)=3.604, p<0.001, Cohen’s d=0.055 |
| Non Physical Extracurricular Activities | Mean (sd): 5.1 (6.3) | Mean (sd): 4.8 (6.1) | Yuen’s t(4075)=2.961, p=0.003, Cohen’s d=0.047 |
| Physically Active | Mean (sd): 3.5 (2.3) | Mean (sd): 3.4 (2.3) | Yuen’s t(3838)=2.094, p=0.036, Cohen’s d=0.033 |
| Mature Video Games Play | Mean (sd): 0.5 (0.8) | Mean (sd): 0.6 (0.9) | Yuen’s t(3816)=1.396, p=0.163, Cohen’s d=0.022 |
| Mature Movies Watch | Mean (sd): 0.4 (0.6) | Mean (sd): 0.4 (0.7) | Yuen’s t(3728)=4.038, p<0.001, Cohen’s d=0.065 |
| Weekday Screen Use | Mean (sd): 3.3 (3) | Mean (sd): 3.6 (3.3) | Yuen’s t(3220)=4.161,p<0.001, Cohen’s d=0.069 |
| Weekend Screen Use | Mean (sd): 4.5 (3.5) | Mean (sd): 4.8 (3.7) | Yuen’s t(3521)=3.218, p=0.001, Cohen’s d=0.053 |
| Tobacco Before Pregnant | No = 6328 (86.7%) Yes = 974 (13.3%) | No = 2838 (86.7%) Yes = 436 (13.3%) | X2(1,=10576)=0, p=1 |
| Tobacco After Pregnant | No = 6968 (95.2%) Yes = 351 (4.8%) | No = 3081 (94.2%) Yes = 190 (5.8%) | X2(1,=10590)=0, p=1 |
| Alcohol Before Pregnant | No = 5174 (73.4%) Yes = 1871 (26.6%) | No = 2380 (75.4%) Yes = 775 (24.6%) | X2(1,=10200)=0, p=1 |
| Alcohol After Pregnant | No = 7096 (97.1%) Yes = 210 (2.9%) | No = 3175 (97.4%) Yes = 85 (2.6%) | X2(1,=10566)=0, p=1 |
| Marijuana Before Pregnant | No = 6874 (94.5%) Yes = 399 (5.5%) | No = 3044 (93.9%) Yes = 199 (6.1%) | X2(1,=10516)=0, p=1 |
| Marijuana After Pregnant | No = 7182 (98.2%) Yes = 130 (1.8%) | No = 3191 (97.7%) Yes = 74 (2.3%) | X2(1,=10577)=0, p=1 |
| Developmental Prematurity | No = 5945 (80.3%) Yes = 1458 (19.7%) | No = 2735 (83.0%) Yes = 561 (17.0%) | X2(1, N=10699)=0, p=1 |
| Birth Complications | Mean (sd): 0.4 (0.8) | Mean (sd): 0.4 (0.7) | Yuen’s t(3591)=0.121, p=0.904, Cohen’s d=0.007 |
| Pregnancy Complications | Mean (sd): 0.6 (Abramovitch et al., 2021) | Mean (sd): 0.6 (Abramovitch et al., 2021) | Yuen’s t(3543)=1.19, p=0.234, Cohen’s d=0.018 |
| Parental Monitoring | Mean (sd): 4.4 (0.5) | Mean (sd): 4.4 (0.5) | Yuen’s t(3810)=0.451, p=0.652, Cohen’s d=0.009 |
| Parent-reported Family Conflict | Mean (sd): 2.5 (1.9) | Mean (sd): 2.6 (2) | Yuen’s t(3805)=1.404, p=0.16, Cohen’s d=0.023 |
| Child report Family Conflict | Mean (sd): 2 (1.9) | Mean (sd): 2.1 (2) | Yuen’s t(3809)=1.751, p=0.08, Cohen’s d=0.026 |
| Parent reported Prosocial | Mean (sd): 1.8 (0.4) | Mean (sd): 1.8 (0.4) | Yuen’s t(3817)=0.288, p=0.774, Cohen’s d=0.007 |
| Child reported Prosocial | Mean (sd): 1.7 (0.4) | Mean (sd): 1.7 (0.4) | Yuen’s t(3849)=2.529, p=0.011, Cohen’s d=0.041 |
Table 13
Exclusion criteria for neuroimaging features in the baseline.
| Neuroimaging features | Data provided | Did not pass quality control | Had vision problems | From site 22 | Had any missing feature | Flagged as outliers | Observations kept |
|---|---|---|---|---|---|---|---|
| ENback 0back | 11771 | 3996 | 38 | 21 | 8 | 292 | 7416 |
| ENback 2back | 11771 | 3996 | 38 | 21 | 12 | 281 | 7423 |
| ENback 2back vs 0back | 11771 | 3996 | 38 | 21 | 10 | 397 | 7309 |
| ENback emotion | 11771 | 3996 | 38 | 21 | 10 | 303 | 7403 |
| ENback Emotion vs Neutral Face | 11771 | 3996 | 38 | 21 | 11 | 480 | 7225 |
| ENback Face vs Place | 11771 | 3996 | 38 | 21 | 10 | 391 | 7315 |
| ENback Negative vs Neutral Face | 11771 | 3996 | 38 | 21 | 11 | 454 | 7251 |
| ENback Positive vs Neutral Face | 11771 | 3996 | 38 | 21 | 10 | 500 | 7206 |
| ENback place | 11771 | 3996 | 38 | 21 | 11 | 331 | 7374 |
| MID Reward vs Neutral anticipation | 11771 | 2596 | 51 | 22 | 11 | 250 | 8841 |
| MID Loss vs Neutral anticipation | 11771 | 2596 | 51 | 22 | 11 | 245 | 8846 |
| MID Positive vs Negative Reward Feedback | 11771 | 2596 | 51 | 22 | 12 | 338 | 8752 |
| MID Positive vs Negative Punishment Feedback | 11771 | 2596 | 51 | 22 | 10 | 334 | 8758 |
| MID Large Reward vs Neutral anticipation | 11771 | 2596 | 51 | 22 | 12 | 241 | 8849 |
| MID Small Reward vs Neutral anticipation | 11771 | 2596 | 51 | 22 | 10 | 270 | 8822 |
| MID Large Reward vs Small Reward anticipation | 11771 | 2596 | 51 | 22 | 13 | 266 | 8823 |
| MID Large Loss vs Neutral anticipation | 11771 | 2596 | 51 | 22 | 11 | 250 | 8841 |
| MID Small Loss vs Neutral anticipation | 11771 | 2596 | 51 | 22 | 11 | 282 | 8809 |
| MID Large Loss vs Small Loss anticipation | 11771 | 2596 | 51 | 22 | 12 | 307 | 8783 |
| SST Any Stop vs Correct Go | 11771 | 3672 | 45 | 20 | 14 | 227 | 7793 |
| SST Correct Go vs Fixation | 11771 | 3672 | 45 | 20 | 13 | 262 | 7759 |
| SST Correct Stop vs Correct Go | 11771 | 3672 | 45 | 20 | 13 | 236 | 7785 |
| SST Correct Stop vs Incorrect Stop | 11771 | 3672 | 45 | 20 | 14 | 292 | 7728 |
| SST Incorrect Go vs Correct Go | 11771 | 3672 | 45 | 20 | 15 | 481 | 7538 |
| SST Incorrect Go vs Incorrect Stop | 11771 | 3672 | 45 | 20 | 14 | 366 | 7654 |
| SST Incorrect Stop vs Correct Go | 11771 | 3672 | 45 | 20 | 13 | 246 | 7775 |
| rsfMRI temporal variance | 11771 | 2397 | 62 | 25 | 14 | 682 | 8591 |
| rsfMRI subcortical-network FC | 11771 | 2397 | 62 | 25 | 14 | 1 | 9272 |
| rsfMRI cortical FC | 11771 | 2397 | 62 | 25 | 14 | 3 | 9270 |
| T1 subcortical avg intensity | 11771 | 501 | 66 | 27 | 0 | 60 | 11117 |
| T1 white matter avg intensity | 11771 | 501 | 66 | 27 | 12 | 13 | 11152 |
| T1 gray matter avg intensity | 11771 | 501 | 66 | 27 | 12 | 11 | 11154 |
| T1 normalised intensity | 11771 | 501 | 66 | 27 | 12 | 2 | 11163 |
| T1 summations | 11771 | 501 | 66 | 27 | 12 | 34 | 11131 |
| cortical thickness | 11771 | 501 | 66 | 27 | 12 | 2 | 11163 |
| cortical area | 11771 | 501 | 66 | 27 | 12 | 1 | 11164 |
| cortical volume | 11771 | 501 | 66 | 27 | 12 | 0 | 11165 |
| subcortical volume | 11771 | 501 | 66 | 27 | 0 | 215 | 10962 |
| sulcal depth | 11771 | 501 | 66 | 27 | 12 | 1106 | 10059 |
| T2 subcortical avg intensity | 11771 | 1217 | 58 | 25 | 0 | 67 | 10404 |
| T2 white matter avg intensity | 11771 | 1217 | 58 | 25 | 10 | 56 | 10405 |
| T2 gray matter avg intensity | 11771 | 1217 | 58 | 25 | 10 | 55 | 10406 |
| T2 normalised intensity | 11771 | 1217 | 58 | 25 | 10 | 12 | 10449 |
| T2 summations | 11771 | 1217 | 58 | 25 | 10 | 14 | 10447 |
| DTI | 11771 | 1577 | 57 | 13 | 0 | 24 | 10100 |
Table 14
Exclusion criteria for neuroimaging features in the follow-up.
| Neuroimaging features | Data provided | Did not pass quality control | Had vision problems | Had any missing feature | Flagged as outliers | Observations kept |
|---|---|---|---|---|---|---|
| ENback 0back | 8123 | 1804 | 35 | 11 | 216 | 6057 |
| ENback 2back | 8123 | 1804 | 35 | 13 | 186 | 6085 |
| ENback 2back vs 0back | 8123 | 1804 | 35 | 14 | 294 | 5976 |
| ENback emotion | 8123 | 1804 | 35 | 13 | 202 | 6069 |
| ENback Emotion vs Neutral Face | 8123 | 1804 | 35 | 13 | 347 | 5924 |
| ENback Face vs Place | 8123 | 1804 | 35 | 13 | 295 | 5976 |
| ENback Negative vs Neutral Face | 8123 | 1804 | 35 | 11 | 355 | 5918 |
| ENback Positive vs Neutral Face | 8123 | 1804 | 35 | 13 | 342 | 5929 |
| ENback place | 8123 | 1804 | 35 | 12 | 234 | 6038 |
| MID Reward vs Neutral anticipation | 8123 | 1379 | 40 | 8 | 153 | 6543 |
| MID Loss vs Neutral anticipation | 8123 | 1379 | 40 | 8 | 154 | 6542 |
| MID Positive vs Negative Reward Feedback | 8123 | 1379 | 40 | 9 | 192 | 6503 |
| MID Positive vs Negative Punishment Feedback | 8123 | 1379 | 40 | 9 | 197 | 6498 |
| MID Large Reward vs Neutral anticipation | 8123 | 1379 | 40 | 8 | 142 | 6554 |
| MID Small Reward vs Neutral anticipation | 8123 | 1379 | 40 | 8 | 163 | 6533 |
| MID Large Reward vs Small Reward anticipation | 8123 | 1379 | 40 | 8 | 155 | 6541 |
| MID Large Loss vs Neutral anticipation | 8123 | 1379 | 40 | 8 | 150 | 6546 |
| MID Smal Loss vs Neutral anticipation | 8123 | 1379 | 40 | 9 | 179 | 6516 |
| MID Large Loss vs Small Loss anticipation | 8123 | 1379 | 40 | 9 | 173 | 6522 |
| SST Any Stop vs Correct Go | 8123 | 2036 | 33 | 7 | 123 | 5924 |
| SST Correct Go vs Fixation | 8123 | 2036 | 33 | 7 | 173 | 5874 |
| SST Correct Stop vs Correct Go | 8123 | 2036 | 33 | 7 | 163 | 5884 |
| SST Correct Stop vs Incorrect Stop | 8123 | 2036 | 33 | 7 | 187 | 5860 |
| SST Incorrect Go vs Correct Go | 8123 | 2036 | 33 | 7 | 345 | 5702 |
| SST Incorrect Go vs Incorrect Stop | 8123 | 2036 | 33 | 7 | 267 | 5780 |
| SST Incorrect Stop vs Correct Go | 8123 | 2036 | 33 | 7 | 131 | 5916 |
| rsfMRI temporal variance | 8123 | 1152 | 49 | 14 | 512 | 6396 |
| rsfMRI subcortical-network FC | 8123 | 1152 | 49 | 14 | 3 | 6905 |
| rsfMRI cortical FC | 8123 | 1152 | 49 | 14 | 3 | 6905 |
| T1 subcortical avg intensity | 8123 | 227 | 51 | 0 | 32 | 7813 |
| T1 white matter avg intensity | 8123 | 227 | 51 | 10 | 8 | 7827 |
| T1 gray matter avg intensity | 8123 | 227 | 51 | 10 | 9 | 7826 |
| T1 normalised intensity | 8123 | 227 | 51 | 10 | 0 | 7835 |
| T1 summations | 8123 | 227 | 51 | 10 | 19 | 7816 |
| cortical thickness | 8123 | 227 | 51 | 10 | 2 | 7833 |
| cortical area | 8123 | 227 | 51 | 10 | 0 | 7835 |
| cortical volume | 8123 | 227 | 51 | 10 | 0 | 7835 |
| subcortical volume | 8123 | 227 | 51 | 0 | 112 | 7733 |
| sulcal depth | 8123 | 227 | 51 | 10 | 890 | 6945 |
| T2 subcortical avg intensity | 8123 | 600 | 50 | 0 | 39 | 7434 |
| T2 white matter avg intensity | 8123 | 600 | 50 | 10 | 47 | 7416 |
| T2 gray matter avg intensity | 8123 | 600 | 50 | 10 | 49 | 7414 |
| T2 normalised intensity | 8123 | 600 | 50 | 10 | 5 | 7458 |
| T2 summations | 8123 | 600 | 50 | 10 | 14 | 7449 |
| DTI | 8123 | 638 | 47 | 0 | 15 | 7423 |
Table 15
Medication reports in the baseline and follow-up.
This report is derived from the su_y_plus table and utilises the Anatomical Therapeutic Chemical (ATC) Classification System to group medications according to their functionality.
| Functionality | Baseline | Follow-up |
|---|---|---|
| Alimentary tract and metabolism | 144 | 145 |
| Blood and blood forming organs | 12 | 22 |
| Cardiovascular system | 124 | 142 |
| Dermatologicals | 108 | 64 |
| Genitourinary system and sex hormones | 72 | 76 |
| Systemic hormonal preparations, excl. sex hormones and insulins | 26 | 24 |
| Anti-infectives for systemic use | 56 | 35 |
| Antineoplastic and immunomodulating agents | 5 | 5 |
| Musculo-skeletal system | 145 | 183 |
| Nervous system | 710 | 729 |
| Antiparasitic products, insecticides and repellents | 5 | 4 |
| Respiratory system | 721 | 538 |
| Sensory organs | 42 | 42 |
| Various | 1 | 3 |
Additional files
-
Supplementary file 1
Summary statistics of the measures of mental health in the baseline.
Med = Median IQR = interquartile range; CV = Coefficient of variation; 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. Under the variable names, there are information about the method to compute these variables and the original variables names in ABCD data dictionary.
- https://cdn.elifesciences.org/articles/105537/elife-105537-supp1-v1.docx
-
Supplementary file 2
Summary statistics of the measures of mental health in the follow-up.
Med = Median IQR = interquartile range; CV = Coefficient of variation; 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. Under the variable names, there are information about the method to compute these variables and the original variables names in ABCD data dictionary.
- https://cdn.elifesciences.org/articles/105537/elife-105537-supp2-v1.docx
-
Supplementary file 3
Summary statistics of the measures of socio-demographics, lifestyles and developmental adverse events in the baseline.
Med = Median IQR = interquartile range; CV = Coefficient of variation. Under the variable names, there are information about the method to compute these variables and the original variables names in ABCD data dictionary.
- https://cdn.elifesciences.org/articles/105537/elife-105537-supp3-v1.docx
-
Supplementary file 4
Summary statistics of the measures of socio-demographics, lifestyles and developmental adverse events in the follow-up.
We only provided variables that were repeatedly correction in the follow-up here. Med = Median IQR = interquartile range; CV = Coefficient of variation. Under the variable names, there are information about the method to compute these variables and the original variables names in ABCD data dictionary.
- https://cdn.elifesciences.org/articles/105537/elife-105537-supp4-v1.docx
-
MDAR checklist
- https://cdn.elifesciences.org/articles/105537/elife-105537-mdarchecklist1-v1.pdf
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Relationship between cognitive abilities and mental health as represented by cognitive abilities at the neural and genetic levels of analysis
eLife 14:RP105537.
https://doi.org/10.7554/eLife.105537.3
