Cognitive abilities and mental health are linked through neural and genetic factors.
Poster No:
1607
Submission Type:
Abstract Submission
Authors:
Yue Wang1, Richard Anney2, Narun Pat1
Institutions:
1University of Otago, Dunedin, Otago, 2Cardiff University, Cardiff, Cardiff
First Author:
Co-Author(s):
Introduction:
Cognitive abilities such as attention, memory, and cognitive control are often impaired in psychiatric disorders. These impairments also correlate with mental health in normative populations, as seen in the ABCD dataset, where children's cognitive performance linked to emotional and behavioral problems (Pat et al., 2022). This study explored how environmental factors and neurobiological measures (MRI, polygenic scores) contribute to cognition-mental health relationships in 9–10-year-olds. Neuroscientific models and GWAS show improved predictions, with socio-demographics and lifestyle factors also playing a significant role.
Methods:
We used data from the ABCD Study (Release 5.1; DOI:10.15154/z563-zd24) at baseline (11,868 children, aged 9–10) and two-year follow-up (10,908 children), excluding Site 22 and 69 children with vision impairments. Cognitive abilities were assessed with six tasks, modeled into a latent g-factor using second-order confirmatory factor analysis (CFA), showing good fit (CFI: .994, RMSEA: .031, SRMR: .013, OmegaL2: .78).
Mental health was assessed using the Child Behavior Checklist (CBCL) for emotional and behavioral problems and the UPPS-P and BIS/BAS scales for temperament.
Neuroimaging features were derived from preprocessed MRI data, including task-fMRI, resting-state fMRI (rs-fMRI), structural MRI (sMRI), and diffusion tensor imaging (DTI), standardized using ComBat.
Polygenic scores (PGS) (Savage et al., 2018) were computed from three GWAS meta-analyses on cognitive abilities.
Environmental factors were measured using 44 features, including socio-demographics, social interactions, sleep, physical activity, screen time, parental substance use, and developmental adverse events.
Predictive models for cognitive abilities used features from mental health, neuroimaging, polygenic scores, and socio-demographics. Models were evaluated with RMSE, Pearson's correlation, and R². Commonality analyses examined how these factors explained variance in cognitive abilities.
Mental health was assessed using the Child Behavior Checklist (CBCL) for emotional and behavioral problems and the UPPS-P and BIS/BAS scales for temperament.
Neuroimaging features were derived from preprocessed MRI data, including task-fMRI, resting-state fMRI (rs-fMRI), structural MRI (sMRI), and diffusion tensor imaging (DTI), standardized using ComBat.
Polygenic scores (PGS) (Savage et al., 2018) were computed from three GWAS meta-analyses on cognitive abilities.
Environmental factors were measured using 44 features, including socio-demographics, social interactions, sleep, physical activity, screen time, parental substance use, and developmental adverse events.
Predictive models for cognitive abilities used features from mental health, neuroimaging, polygenic scores, and socio-demographics. Models were evaluated with RMSE, Pearson's correlation, and R². Commonality analyses examined how these factors explained variance in cognitive abilities.
·Fig 1Performance of Mental Health, Neuroimaging Measures, Polygenic Scores and Socio-Demographics, Lifestyle and developmental Adverse in Predicting Cognitive abilities
Results:
Predictive Modeling (Fig1):
- Mental Health: Combining emotional and behavioral problems (CBCL) and temperament (BIS/BAS, UPPS-P) improved cognitive ability prediction (r = 0.36). The first Partial Least Squares (PLS) component explained 22.3-25.7% of variance, influenced by attention, rule-breaking, and aggression.
- Neuroimaging: Combining 45 neuroimaging features (task-fMRI, rs-fMRI, sMRI, DTI) improved prediction to r = 0.54, with ENBack task contrasts, rs-fMRI, and cortical thickness as key predictors.
- Polygenic Scores (PGS): PGSs explained 25% of variance at both time points (r = 0.25), with Savage GWAS contributing the most.
- Socio-demographics and Lifestyle: Using 44 features, PLS models explained r = 0.49, with positive influences like parental income and education, and negative influences such as screen time and economic insecurities.
Commonality Analyses (Fig2):
- Mental Health and Neuroimaging: Explained 27% of variance in cognitive abilities, with 66% of the relationship between cognitive abilities and mental health shared with neuroimaging features.
- Mental Health and PGSs: Explained 11.8% of variance, with 21% of the relationship between cognitive abilities and mental health shared with PGSs.
- Mental Health and Environmental Factors: Explained 24.9% of variance, with 63% of the relationship between cognitive abilities and mental health shared with environmental factors.
- All Factors Combined: Explained 24.2% of the variance in cognitive abilities. 79% of the relationship between cognitive abilities and mental health was shared with neuroimaging, PGSs, and environmental factors. Neuroimaging accounted for 58%, PGSs for 21%, and environmental factors for the remainder.
- Mental Health: Combining emotional and behavioral problems (CBCL) and temperament (BIS/BAS, UPPS-P) improved cognitive ability prediction (r = 0.36). The first Partial Least Squares (PLS) component explained 22.3-25.7% of variance, influenced by attention, rule-breaking, and aggression.
- Neuroimaging: Combining 45 neuroimaging features (task-fMRI, rs-fMRI, sMRI, DTI) improved prediction to r = 0.54, with ENBack task contrasts, rs-fMRI, and cortical thickness as key predictors.
- Polygenic Scores (PGS): PGSs explained 25% of variance at both time points (r = 0.25), with Savage GWAS contributing the most.
- Socio-demographics and Lifestyle: Using 44 features, PLS models explained r = 0.49, with positive influences like parental income and education, and negative influences such as screen time and economic insecurities.
Commonality Analyses (Fig2):
- Mental Health and Neuroimaging: Explained 27% of variance in cognitive abilities, with 66% of the relationship between cognitive abilities and mental health shared with neuroimaging features.
- Mental Health and PGSs: Explained 11.8% of variance, with 21% of the relationship between cognitive abilities and mental health shared with PGSs.
- Mental Health and Environmental Factors: Explained 24.9% of variance, with 63% of the relationship between cognitive abilities and mental health shared with environmental factors.
- All Factors Combined: Explained 24.2% of the variance in cognitive abilities. 79% of the relationship between cognitive abilities and mental health was shared with neuroimaging, PGSs, and environmental factors. Neuroimaging accounted for 58%, PGSs for 21%, and environmental factors for the remainder.
·Fig 2 Venn diagrams showing common and unique effects of all proxy measures of cognitive abilities based on in explaining cognitive abilities across test sites.
Conclusions:
Results showed the significance of neurobiological analysis for cognitive abilities, measured by neuroimaging and PGS, in understanding a) the relationship between cognitive abilities and mental health, and b) variance with environmental factors.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Multivariate Approaches 1
Keywords:
Cognition
Data analysis
Machine Learning
1|2Indicates the priority used for review
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Provide references using APA citation style.
Pat, N., Wang, Y., Anney, R., Riglin, L., Thapar, A., & Stringaris, A. (2022). Longitudinally stable, brain-based predictive models mediate the relationships between childhood cognition and socio-demographic, psychological and genetic factors. Human Brain Mapping, 43(18), 5520–5542. https://doi.org/10.1002/hbm.26027
Pat, N., Wang, Y., Bartonicek, A., Candia, J., & Stringaris, A. (2023). Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition. Cerebral Cortex, 33(6), 2682–2703. https://doi.org/10.1093/cercor/bhac235
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