Longitudinally stable, brain‐based predictive models mediate the relationships between childhood cognition and socio‐demographic, psychological and genetic factors. Issue 18 (28th July 2022)

• Record Type: Journal Article
• Authors: Pat, Narun; Wang, Yue; Anney, Richard; Riglin, Lucy; Thapar, Anita; Stringaris, Argyris
• Is Part Of: Human brain mapping. Volume 43:Issue 18(2022)
• Page Start: 5520
• Page End: 5542
• Publication Date: 2022-07-28
• Subjects: adolescent brain cognitive development -- general cognition -- longitudinal large‐scale data -- machine learning -- multimodal MRI -- polygenic score -- research domain criteria; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.26027
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Cognitive abilities are one of the major transdiagnostic domains in the National Institute of Mental Health's Research Domain Criteria (RDoC). Following RDoC's integrative approach, we aimed to develop brain‐based predictive models for cognitive abilities that (a) are developmentally stable over years during adolescence and (b) account for the relationships between cognitive abilities and socio‐demographic, psychological and genetic factors. For this, we leveraged the unique power of the large‐scale, longitudinal data from the Adolescent Brain Cognitive Development (ABCD) study ( n ~ 11 k) and combined MRI data across modalities (task‐fMRI from three tasks: resting‐state fMRI, structural MRI and DTI) using machine‐learning. Our brain‐based, predictive models for cognitive abilities were stable across 2 years during young adolescence and generalisable to different sites, partially predicting childhood cognition at around 20% of the variance. Moreover, our use of 'opportunistic stacking' allowed the model to handle missing values, reducing the exclusion from around 80% to around 5% of the data. We found fronto‐parietal networks during a working‐memory task to drive childhood‐cognition prediction. The brain‐based, predictive models significantly, albeit partially, accounted for variance in childhood cognition due to (1) key socio‐demographic and psychological factors (proportion mediated = 18.65% [17.29%–20.12%]) and (2) genetic variation, as reflected by the polygenic score of cognition (proportion mediated = 15.6% [11%–20.7%]). Thus, our brain‐based predictive models for cognitive abilities facilitate the development of a robust, transdiagnostic research tool for cognition at the neural level in keeping with the RDoC's integrative framework. Abstract : (1) Using opportunistic stacking and multimodal MRI, we developed brain‐based predictive models for children′s cognitive abilities that were longitudinally stable, generalisable to different sites and robust against missing data. (2) Our brain‐based models were able to partially mediate the relationships of childhood cognitive abilities with the socio‐demographic, psychological and genetic factors. (3) Our approach should pave the way for future researchers to employ multimodal MRI as a tool for the brain‐based indicator of cognitive abilities, according to the integrative RDoC framework.