Biology subjects

Pornpattananangkul, N.

Publications and source records attributed to Pornpattananangkul, N..

3 recordsLinked to original sources

bioRxiv2021

Multimodal-Neural Predictive Models of Children's General Intelligence That Are Stable Across Two Years of Development

Cognitive abilities are one of the major transdiagnostic domains in the National Institute of Mental Healths Research Domain Criteria (RDoC). Following RDoCs 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 [~]11k) and combined MRI data across modalities (task-fMRI from three tasks, resting-state fMRI, structural MRI, DTI) using machine-learning. Our brain-based, predictive models for cognitive abilities were stable across two 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 RDoCs integrative framework. Key PointsO_LIUsing opportunistic stacking and multimodal MRI, we developed brain-based predictive models for childrens cognitive abilities that were longitudinally stable, generalisable to different sites and robust against missing data. C_LIO_LIOur brain-based models were able to partially mediate the relationships of childhood cognitive abilities with the socio-demographic, psychological and genetic factors. C_LIO_LIOur 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. C_LI

neuroscience↗

bioRxiv2020

An Omics-Inspired Elastic Net Approach Drastically Improves Out-of-Sample Prediction and Regional Inference of Task-Based fMRI

Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based fMRI. Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n=3,989). We compared nine predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and OLS multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than non-linear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects and Friedmans H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study (Sripada et al. 2020) and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.

neuroscience↗

bioRxiv2020

Great Expectations: A Critical Review of and Recommendations for the study of Reward Processing as a Cause and Predictor of Depression

Both human and animal studies support the relationship between depression and reward processing abnormalities, giving rise to the expectation that neural signals of these processes may serve as biomarkers or mechanistic treatment targets. Given the great promise of this research line, we scrutinize those findings and the theoretical claims that underlie them. To achieve this, we apply the framework provided by classical work on causality as well as contemporary approaches to prediction. We identify a number of conceptual, practical, and analytical challenges to this line of research, and use a pre-registered meta-analysis to quantify the longitudinal associations between reward processing aberrations and depression. We also investigate the impact of measurement error on reported data. We find that reward processing abnormalities do not reach levels that would be useful for clinical prediction, yet the evidence thus far does not exclude their possible causal role in depression.

neuroscience↗