Relationship between cognitive abilities and mental health as represented by cognitive abilities at the neural and genetic levels of analysis

Cognitive abilities across various domains, such as attention, working memory, declarative memory, verbal fluency, and cognitive control, are often altered in several psychiatric disorders (Millan et al., 2012). This is evident in recent meta-analyses of case-control studies involving patients with mood and anxiety disorders, obsessive-compulsive disorder, posttraumatic stress disorder, and attention-deficit/hyperactivity disorder (ADHD), among others (Abramovitch et al., 2021; East-Richard et al., 2020). Beyond typical case-control studies, the association between cognitive abilities and mental health is also observed when mental health varies from normal to abnormal in normative samples (Morris et al., 2022). For instance, our study Pat et al., 2022a found an association between cognitive abilities and mental health in a relatively large, non-referred sample of 9–10 year-old children from the ABCD study (Casey et al., 2018). In this study, we measured cognitive abilities using behavioural performance across cognitive tasks (Luciana et al., 2018) while measuring mental health using a broad range of emotional and behavioural problems (Achenbach et al., 2017). Thus, cognitive abilities are frequently considered crucial for understanding mental health issues throughout life, beginning in childhood (Abramovitch et al., 2021; Hankin et al., 2016; Morris and Cuthbert, 2012).

According to the National Institute of Mental Health’s Research Domain Criteria (RDoC) framework (Insel et al., 2010), cognitive abilities should be investigated not only behaviourally but also neurobiologically, from the brain to genes. It remains unclear to what extent the relationship between cognitive abilities and mental health is represented in part by different neurobiological units of analysis -- such as neural and genetic levels measured by multimodal neuroimaging and polygenic scores (PGS). To fully comprehend the role of neurobiology in the relationship between cognitive abilities and mental health, we must also consider how these neurobiological units capture variations due to environmental factors, such as socio-demographics, lifestyles, and childhood developmental adverse events (Morris et al., 2022). Our study investigated the extent to which (a) environmental factors explain the relationship between cognitive abilities and mental health, and (b) cognitive abilities at the neural and genetic levels capture these associations due to environmental factors. Specifically, we conducted these investigations in a large normative group of children from the ABCD study (Casey et al., 2018). We chose to examine children because, while their emotional and behavioural problems might not meet full diagnostic criteria (Kessler et al., 2007), issues at a young age often forecast adult psychopathology (Reef et al., 2010; Roza et al., 2003). Moreover, the associations among different emotional and behavioural problems in children reflect transdiagnostic dimensions of psychopathology (Michelini et al., 2019; Pat et al., 2022a), making children an appropriate population to study the transdiagnostic aetiology of mental health, especially within a framework that emphasises normative variation from normal to abnormal, such as the RDoC (Morris et al., 2022).

Recently, several neuroscientists have developed predictive models using neuroimaging data from brain magnetic resonance imaging (MRI) of various modalities in the so-called Brain-Wide Association Studies (BWAS) (Marek et al., 2022; Sui et al., 2020). BWAS aims to create models from MRI data that can accurately predict behavioural phenotypes in participants not included in the model-building process (Dadi et al., 2021). In one of the most extensive BWAS benchmarks to date, Marek et al., 2022 concluded, ‘More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires), and multivariate methods (versus univariate).’ This benchmark has significant implications for using neuroimaging as a neural unit of analysis for cognitive abilities. First, while current BWAS may not be robust enough to predict mental health directly, it is more suitable for predicting cognitive abilities (see Zhi et al., 2024 for a similar conclusion). This aligns with the Research Domain Criteria (RDoC) framework, which emphasises neurobiological units of analysis for functional domains, such as cognitive abilities, rather than mental health itself (Cuthbert and Insel, 2013). RDoC’s functional domains capture basic human functioning and include cognitive abilities along with negative/positive valence, arousal, and regulation, and social and sensory processes (Morris et al., 2022). Accordingly, the current study conducted BWAS to capture cognitive abilities rather than mental health.

The second implication of Marek et al., 2022 benchmark is the support it provides for using multivariate algorithms, which draw MRI information simultaneously across regions/voxels, over massively univariate algorithms that draw data from one area/voxel at a time. Similar to Marek et al., 2022 study, which focused on resting-state functional MRI (rs-fMRI), our recent study on task-fMRI also found that multivariate algorithms performed superiorly, up to several folds, in predicting cognitive abilities compared to massively univariate algorithms (Pat et al., 2023). The third implication is that the performance of neuroimaging in predicting cognitive abilities depends on MRI modalities. Previous research has used brain MRI data of different modalities to predict cognitive abilities (Vieira et al., 2022). For instance, many studies have used rs-fMRI, which reflects functional connectivity between regions during rest (Dubois et al., 2018; Keller et al., 2023; Rasero et al., 2021; Sripada et al., 2020; Sripada et al., 2021). Others have utilised structural MRI (sMRI), which reflects anatomical morphology based on thickness, area, and volume in cortical/subcortical areas, and diffusion tensor imaging (DTI), which reflects diffusion distribution within white matter tracts (Mihalik et al., 2019; Rasero et al., 2021). While less common, task-fMRI, which reflects blood-oxygen-level-dependent (BOLD) activity relevant to each task condition, shows relatively good predictive performance, especially from specific contrasts, such as the 2-Back vs 0-Back from the N-Back working-memory task (Barch et al., 2013) nor (Makowski et al., 2024; Pat et al., 2023; Pat et al., 2022b; Sripada et al., 2020; Tetereva et al., 2022; Zhao et al., 2023). A recent meta-analysis estimated the performance of multivariate methods in predicting cognitive abilities from MRI of different modalities at around an out-of-sample r of 0.42 (Vieira et al., 2022). However, we and others found that this predictive performance could be further boosted by drawing information across different MRI modalities, rather than relying on only one modality (Pat et al., 2022b; Rasero et al., 2021; Tetereva et al., 2022; Tetereva and Pat, 2024). Therefore, the current study used opportunistic stacking (Engemann et al., 2020; Pat et al., 2022b). This multivariate modelling technique allowed us to combine information across MRI modalities with the added benefit of handling missing values. With opportunistic stacking, we created a ‘proxy’ measure of cognitive abilities (i.e. predicted value from the model) at the neural unit of analysis using multimodal neuroimaging.

Geneticists, like neuroscientists, have conducted Genome-Wide Association Studies (GWAS) to explore the links between single-nucleotide polymorphisms (SNPs) and various behavioural phenotypes (Bogdan et al., 2018). Similar to BWAS, GWAS can develop predictive models from genetic profiles, resulting in polygenic scores (PGS) that predict behavioural phenotypes in participants not included in the model-building process (Choi et al., 2020). Several large-scale GWAS on cognitive abilities have been conducted, with some studies involving over 250,000 participants (Davies et al., 2018; Lee et al., 2018; Savage et al., 2018). Recently, researchers have used these large-scale GWAS to compute PGS for cognitive abilities and applied these scores to predict cognitive abilities in children (Allegrini et al., 2019; Pat et al., 2022b). For example, Allegrini et al., 2019 found that PGS based on Savage et al.’s (2018) GWAS accounted for approximately 5.3% of the variance in cognitive abilities among 12-year-old children. The current study adopted this approach with children of a similar age in the ABCD study, creating a proxy measure of cognitive abilities at the genetic unit of analysis using PGS.

Environmental factors, broadly defined, significantly influence cognitive abilities (Duyme et al., 1999; Pietschnig and Voracek, 2015). A classic example is the Flynn Effect (Flynn, 1984; Flynn, 2009; Rundquist, 1936; Williams, 2013), which describes the observed rise in cognitive abilities, as measured by various cognitive tasks, across generations in the general population over time, particularly in high-income countries during the 20th century (Pietschnig and Voracek, 2015; Trahan et al., 2014; Wongupparaj et al., 2017). Experts attribute the Flynn Effect to environmental factors such as improved living standards and better education (Baker et al., 2015; Rindermann et al., 2017). Recently, researchers have used multivariate algorithms to create proxy measures of cognitive abilities in children based on environmental factors, similar to approaches used in neuroimaging and polygenic scores (PGS) (Kirlic et al., 2021; Pat et al., 2022b). These environmental factors often include socio-demographic variables (e.g., parental income/education, area deprivation index, parental marital status), lifestyle factors (e.g. screen/video game use, extracurricular activities), and developmental adverse events (e.g. parental use of alcohol/tobacco before and after pregnancy, birth complications). Studies, including ours, Kirlic et al., 2021; Pat et al., 2022b have applied multivariate algorithms to predict cognitive abilities from various environmental factors in the ABCD study (Casey et al., 2018). In these predictive models, parental income/education, area deprivation index, and extracurricular activities are particularly important predictors of cognitive abilities (Kirlic et al., 2021; Pat et al., 2022b). Following this approach, the current study created another proxy measure of cognitive abilities based on socio-demographics, lifestyles, and developmental adverse events.

In this study, inspired by RDoC (Insel et al., 2010), we (a) focused on cognitive abilities as a functional domain, (b) created predictive models to capture the continuous individual variation (as opposed to distinct categories) in cognitive abilities, (c) computed two neurobiological units of analysis of cognitive abilities: multimodal neuroimaging and PGS, and (d) investigated the potential contributions of environmental factors. To operationalise cognitive abilities, we estimated a latent variable representing behavioural performance across various cognitive tasks, commonly referred to as general cognitive ability or the g-factor (Deary, 2012). The g-factor was computed from various cognitive tasks pertinent to RDoC constructs, including attention, working memory, declarative memory, language, and cognitive control. However, using the g-factor to operationalise cognitive abilities caused this study to diverge from the original conceptualisation of RDoC, which emphasises studying separate constructs within cognitive abilities (Morris et al., 2022; Morris and Cuthbert, 2012). Recent studies suggest that including a general factor, such as the g-factor, in the model, rather than treating each construct separately, improved model fit (Beam et al., 2021; Quah et al., 2025). The g-factor in children is also longitudinally stable and can forecast future health outcomes (Calvin et al., 2017; Deary et al., 2013). Notably, our previous research found that neuroimaging predicts the g-factor more accurately than predicting performance from separate individual cognitive tasks (Pat et al., 2023). Accordingly, we decided to conduct predictive models on the g-factor while keeping the RDoC’s holistic, neurobiological, and basic-functioning characteristics.

Using the ABCD study (Casey et al., 2018), we first developed predictive models to estimate the cognitive abilities of unseen children based on their mental health. These models enabled us to quantify the relationship between cognitive abilities and mental health, thereby creating a proxy measure of cognitive abilities derived from mental health data. The mental health variables included children’s emotional and behavioural problems (Achenbach et al., 2017) and temperaments, such as behavioural inhibition/activation (Carver and White, 1994) and impulsivity (Zapolski et al., 2010). These temperaments are linked to externalising and internalising aspects of mental health and are associated with disorders like depression, anxiety, and substance use (Carver and Johnson, 2018; Johnson et al., 2003). Next, we built predictive models of cognitive abilities using neuroimaging, polygenic scores (PGS), and socio-demographic, lifestyle, and developmental adverse event data, resulting in various proxy measures of cognitive abilities. For neuroimaging, we included 45 types of brain MRI data from task-fMRI, rs-fMRI, sMRI, and DTI. For PGS, we used three definitions of cognitive abilities based on previous large-scale GWAS (Davies et al., 2018; Lee et al., 2018; Savage et al., 2018). For socio-demographic, lifestyle, and developmental adverse events, we included 44 features, covering variables such as parental income/education, screen use, and birth/pregnancy complications. Finally, we conducted a series of commonality analyses (Nimon et al., 2008) using these proxy measures of cognitive abilities to address three specific questions. First, we examined the extent to which the relationship between cognitive abilities and mental health was represented in part by cognitive abilities at the neural and genetic levels, as measured by multimodal neuroimaging and PGS, respectively. Second, we assessed the extent to which this relationship was partly explained by environmental factors, as measured by socio-demographic, lifestyle, and developmental adverse events. Third, we tested whether the two neurobiological units of analysis for cognitive abilities, measured by multimodal neuroimaging and PGS, could account for the variance due to environmental factors. To ensure the stability of our results, we repeated the analyses at two time points (ages 9–10 and 11–12).

We aim to understand the extent to which the relationship between cognitive abilities and mental health is represented in part by cognitive abilities at the neural and genetic levels of analysis. We began by quantifying the relationship between cognitive abilities and mental health, finding a medium-sized out-of-sample correlation of approximately r=0.36. This relationship was shared with neuroimaging (66% at baseline) and PGS (21% at baseline), based on two separate sets of commonality analyses. This suggests the significant roles of these two neurobiological units of analysis in shaping the relationship between cognitive abilities and mental health (Morris and Cuthbert, 2012). We also found that the relationship between cognitive abilities and mental health was partly shared with environmental factors, as measured by socio-demographics, lifestyles, and developmental adverse events (63% at baseline). In another set of commonality analysis, this variance due to socio-demographics, lifestyles, and developmental adverse events was explained by neuroimaging and PGS at 58% and 21%, respectively, at baseline. Accordingly, the neurobiological units of analysis for cognitive abilities captured the environmental factors, consistent with RDoC’s viewpoint (Morris et al., 2022). Notably, this pattern of results remained stable over two years in early adolescence.

Our predictive modelling revealed a medium-sized predictive relationship between cognitive abilities and mental health. This finding aligns with recent meta-analyses of case-control studies that link cognitive abilities and mental disorders across various psychiatric conditions (Abramovitch et al., 2021; East-Richard et al., 2020). Unlike previous studies, we estimated the predictive, out-of-sample relationship between cognitive abilities and mental disorders in a large normative sample of children. Although our predictive models, like other cross-sectional models, cannot determine the directionality of the effects, the strength of the relationship between cognitive abilities and mental health estimated here should be more robust than when calculated using the same sample as the model itself, known as in-sample prediction/association (Marek et al., 2022; Yarkoni and Westfall, 2017). Examining the PLS loadings of our predictive models revealed that the relationship was driven by various aspects of mental health, including thought and externalising symptoms, as well as motivation. This suggests that there are multiple pathways—encompassing a broad range of emotional and behavioural problems and temperaments—through which cognitive abilities and mental health are linked.

Our predictive modelling created proxy measures of cognitive abilities based on two neurobiological units of analysis: neuroimaging and PGS (Morris and Cuthbert, 2012). For neuroimaging, inspired by recent BWAS benchmarks (Engemann et al., 2020; Marek et al., 2022), we used a multivariate modelling technique called opportunistic stacking, which integrates information across various MRI features and modalities. Combining 45 sets of neuroimaging features resulted in relatively high predictive performance (out-of-sample r=0.54 at baseline), compared to using any single set. This finding aligns with previous research that pooled multiple neuroimaging modalities (Engemann et al., 2020; Rasero et al., 2021; Tetereva et al., 2022). This level of predictive performance is numerically higher than that found in a recent meta-analysis, which mainly included studies using only one set of neuroimaging features, with an r of 0.42 (Vieira et al., 2022). Moreover, this performance level in predicting cognitive abilities is nearly the same as our previous attempt using a similar stacking technique to integrate MRI modalities in young adult samples from the Human Connectome Project (HCP) (Van Essen et al., 2013), which achieved an out-of-sample r=0.57 (Tetereva et al., 2022). Similarly, in the current study, the top contributing set of neuroimaging features, the 2-Back vs. 0-Back task fMRI, was consistent with previous studies using the HCP (Sripada et al., 2020; Tetereva et al., 2022). Altogether, this demonstrates the robustness of our proxy measure of cognitive abilities based on multimodal neuroimaging. In addition to predictive performance, opportunistic stacking offers the added benefit of handling missing values (Engemann et al., 2020; Pat et al., 2022b), allowing us to retain data from 10,754 participants who completed the cognitive tasks at baseline and has at least one set of neuroimaging features. Consequently, with opportunistic stacking, we were more likely to retain MRI data from participants with higher fMRI noise, such as those with socioeconomic disadvantages (Cosgrove et al., 2022). More importantly, we demonstrated that the proxy measure based on multimodal neuroimaging explained the majority of the variance in the relationship between cognitive abilities and mental health, underscoring its significant role as a neurobiological unit of analysis for cognitive abilities (Morris and Cuthbert, 2012).

For PGS, we created a proxy measure based on three large-scale GWAS on cognitive abilities (Davies et al., 2018; Lee et al., 2018; Savage et al., 2018). Using PGS resulted in a numerically weaker predictive performance (out-of-sample r=0.25 at baseline) compared to multimodal neuroimaging. However, this predictive strength is still comparable to previous research. For instance, Allegrini et al., 2019 used a different cohort of children and found R²=0.053 when applying PGS based on Savage et al., 2018 to predict the cognitive abilities of 12-year-old children. Given that PGS based on Savage et al., 2018 also drove the prediction in the current study, as seen in its feature importance, this similar level of predictive performance between Allegrini et al., 2019 and our study suggests consistency in the predictive performance of PGS. Despite this level of performance, PGS was able to explain some variance (21% at baseline) in the relationship between cognitive abilities and mental health, indicating some capacity of PGS as a neurobiological unit of analysis for cognitive abilities.

There are multiple potential reasons why PGS performed much poorer than multimodal neuroimaging. First, unlike genes, the brain changes throughout development and lifespan (Bethlehem et al., 2022), and so do cognitive abilities (Hartshorne and Germine, 2015). This dynamic nature might make multimodal neuroimaging a better tool for tracing cognitive abilities. Second, there might be a mismatch in the age of participants between the original GWAS (Davies et al., 2018; Lee et al., 2018; Savage et al., 2018) and the current study. While the original GWAS conducted meta-analyses pooling data from participants aged 5–102, these studies might draw more heavily from older cohorts with large participant numbers, such as the UK Biobank (Sudlow et al., 2015). Allegrini et al., 2019 also demonstrated that PGS performs better in predicting cognitive abilities in older children (aged 16) compared to younger ones (aged 12). Therefore, a more child-specific PGS might be needed to explain more variance in children. Thirdly, the PGS used here included only common SNPs and not rare variants. Recent studies using whole-genome sequence data have found that rare variants contribute to the heritability of complex traits, such as height and body mass index (Wainschtein et al., 2022). Given that cognitive abilities are also complex traits, future studies might need to examine if including rare variants can improve the predictive performance of PGS.

Similarly, our predictive modelling created proxy measures of cognitive abilities for environmental factors based on socio-demographics, lifestyles, and developmental adverse events. In line with previous work (Kirlic et al., 2021; Pat et al., 2022b), we could predict unseen children’s cognitive abilities based on their socio-demographics, lifestyles, and developmental adverse events with a medium-to-high out-of-sample r=0.49 (at baseline). This prediction was driven more strongly by socio-demographics (e.g. parent’s income and education, neighbourhood safety, area deprivation, single parenting), somewhat weaker by lifestyles (e.g. extracurricular activities, sleep, screen time, video gaming, mature movie watching, and parental monitoring), and much weaker by developmental adverse events (e.g. pregnancy complications). Importantly, proxy measures based on socio-demographics, lifestyles, and developmental adverse events captured a large proportion of the relationship between cognitive abilities and mental health. Furthermore, this variance captured by socio-demographics, lifestyles, and developmental adverse events overlapped mainly with the neurobiological proxy measures. This reiterates RDoC’s central tenet that understanding the neurobiology of a functional domain, such as cognitive abilities, could help us understand the extent to which environments influence mental health (Cuthbert and Insel, 2013; Insel et al., 2010). More importantly, all the results regarding neuroimaging, PGS, and socio-demographics, lifestyles, and developmental adverse events were reliable across two years during a sensitive period for adolescents.

This study has several limitations that might affect its generalisability. Firstly, the range of mental health variables was not exhaustive. While we covered various emotional and behavioural problems (Achenbach et al., 2017) and temperaments, including behavioural inhibition/activation (Carver and White, 1994) and impulsivity (Zapolski et al., 2010), we may still miss other critical mental health variables, such as psychotic-like experiences, eating disorder symptoms, and mania. Similarly, our ABCD samples were young and community-based, likely limiting the severity of their psychopathological issues (Kessler et al., 2007). Future work needs to test if the results found here are generalisable to adults and participants with stronger severity. Next, for cognitive abilities, while the six cognitive tasks (Luciana et al., 2018; Thompson et al., 2019) covered most of the RDoC cognitive abilities/systems constructs, we still missed variability in some domains, such as perception (Morris and Cuthbert, 2012). Additionally, several children (3274) did not complete all six cognitive tasks at follow-up, which might create a discrepancy between baseline and follow-up samples. However, the differences in social demographics, lifestyles, and developmental adverse events between participants who provided cognitive scores in the follow-up were minimal (Cohen’s d ranging from 0.007 to 0.092, see Table 12). Moreover, given that we found a similar pattern of predictive performance across the two time points, we believe excluding the children who did not complete the cognitive tasks at follow-up should not alter our conclusions.

Furthermore, while we used comprehensive multimodal MRI from 45 sets of features for neuroimaging, three fMRI tasks were not chosen based on their relevance to cognitive abilities (Casey et al., 2018). It is possible to obtain higher predictive performance based on other fMRI tasks. For all analyses involving PGS, we limited our participants to children of European ancestry due to the lack of summary statistics from well-powered GWAS for cognitive abilities in non-European participants. This prevented us from fully leveraging the diverse samples in the ABCD study (Garavan et al., 2018). Future GWAS work with more diverse samples is needed to ensure equity and fairness in developing neurobiological units of analysis for cognitive abilities. Lastly, we relied on 44 variables of socio-demographics, lifestyles, and developmental adverse events included in the study, which might have missed some variables relevant to cognitive abilities (e.g. nutrition). The ABCD study (Casey et al., 2018) is ongoing, and future data might address some of these limitations.

Overall, aligning with the RDoC perspective (Morris and Cuthbert, 2012), our findings support the use of neurobiological units of analysis for cognitive abilities, as assessed through multimodal neuroimaging and Polygenic Scores (PGS). These measures explain (a) the relationship between cognitive abilities and mental health and (b) the variance in this cognitive-ability-and-mental-health relationship attributable to environmental factors. Our results emphasise the importance of considering both neurobiology and environmental factors, such as socio-demographics, lifestyles, and adverse childhood events, to gain a comprehensive understanding of the aetiology of mental health (Insel et al., 2010; Morris et al., 2022).

We used publicly available ABCD 5.1 data (DOI: 10.15154/z563-zd24) provided by the ABCD study, held in the NIMH Data Archive. We uploaded the R analysis script and detailed outputs here (https://github.com/HAM-lab-Otago-University/Commonality-Analysis-ABCD5.1, copy archived at HAM-lab-Otago-University, 2025).

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