Leveraging the Adolescent Brain Cognitive Development Study to improve behavioral prediction from neuroimaging in smaller replication samples

Psychiatric neuroimaging at a crossroads: Insights from psychiatric genetics

Thanks to methodological advances, large-scale data collections, and longitudinal designs, psychiatric neuroimaging is better equipped than ever to identify the neurobiological underpinnings of youth mental health problems. However, the complexity of such endeavors has become increasingly evident, as the field has been confronted by limited clinical relevance, inconsistent results, and small effect sizes. Some of these challenges parallel those historically encountered by psychiatric genetics. In past genetic research, robust findings were historically undermined by oversimplified biological hypotheses, mistaken assumptions about expectable effect sizes, replication problems, confounding by population structure, and shared biological patterns across disorders. Overcoming these challenges has contributed to current successes in the field. Drawing parallels across psychiatric genetics and neuroimaging, we identify key shared challenges as well as pinpoint relevant insights that could be gained in psychiatric neuroimaging from the transition that occurred from the candidate gene to (post) genome-wide "eras" of psychiatric genetics. Finally, we discuss the prominent developmental component of psychiatric neuroimaging and how that might be informed by epidemiological and omics approaches. The evolution of psychiatric genetic research offers valuable insights that may expedite the resolution of key challenges in psychiatric neuroimaging, thus potentially moving our understanding of psychiatric pathophysiology forward.

Multivariate analysis of multimodal brain structure predicts individual differences in risk and intertemporal preference

Large changes to brain structure (e.g., from damage or disease) can explain alterations in behavior. It is therefore plausible that smaller structural differences in healthy samples can be used to better understand and predict individual differences in behavior. Despite the brain's multivariate and distributed structure-to-function mapping, most studies have used univariate analyses of individual structural brain measures. Here we used a multivariate approach in a multimodal data set composed of volumetric, surface-based, diffusion-based, and functional resting-state MRI measures to predict reliable individual differences in risk and intertemporal preferences. We show that combining twelve brain structure measures led to better predictions across tasks than using any individual measure, and by examining model coefficients, we visualize the relative contribution of different brain measures from different brain regions. Using a multivariate approach to brain structure-to-function mapping that combines across many brain structure properties, along with reliably measured behavior phenotypes, may increase out-of-sample prediction accuracies and insight into neural underpinnings. Furthermore, this methodological approach may be useful to improve predictions and neural insight across basic, translational, and clinical research fields.

Brain imaging studies of emotional well-being: a scoping review

This scoping review provides an overview of previous empirical studies that used brain imaging techniques to investigate the neural correlates of emotional well-being (EWB). We compiled evidence on this topic into one accessible and usable document as a foundation for future research into the relationship between EWB and the brain. PRISMA 2020 guidelines were followed. We located relevant articles by searching five electronic databases with 95 studies meeting our inclusion criteria. We explored EWB measures, brain imaging modalities, research designs, populations studied, and approaches that are currently in use to characterize and understand EWB across the literature. Of the key concepts related to EWB, the vast majority of studies investigated positive affect and life satisfaction, followed by sense of meaning, goal pursuit, and quality of life. The majority of studies used functional MRI, followed by EEG and event-related potential-based EEG to study the neural basis of EWB (predominantly experienced affect, affective perception, reward, and emotion regulation). It is notable that positive affect and life satisfaction have been studied significantly more often than the other three aspects of EWB (i.e., sense of meaning, goal pursuit, and quality of life). Our findings suggest that future studies should investigate EWB in more diverse samples, especially in children, individuals with clinical disorders, and individuals from various geographic locations. Future directions and theoretical implications are discussed, including the need for more longitudinal studies with ecologically valid measures that incorporate multi-level approaches allowing researchers to better investigate and evaluate the relationships among behavioral, environmental, and neural factors.

Systematic review registration

https://osf.io/t9cf6/.

Power and reproducibility in the external validation of brain-phenotype predictions

Identifying reproducible and generalizable brain-phenotype associations is a central goal of neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype models in unseen data. Most prediction studies train and evaluate a model in the same dataset. However, external validation, or the evaluation of a model in an external dataset, provides a better assessment of robustness and generalizability. Despite the promise of external validation and calls for its usage, the statistical power of such studies has yet to be investigated. In this work, we ran over 60 million simulations across several datasets, phenotypes, and sample sizes to better understand how the sizes of the training and external datasets affect statistical power. We found that prior external validation studies used sample sizes prone to low power, which may lead to false negatives and effect size inflation. Furthermore, increases in the external sample size led to increased simulated power directly following theoretical power curves, whereas changes in the training dataset size offset the simulated power curves. Finally, we compared the performance of a model within a dataset to the external performance. The within-dataset performance was typically within r=0.2 of the cross-dataset performance, which could help decide how to power future external validation studies. Overall, our results illustrate the importance of considering the sample sizes of both the training and external datasets when performing external validation.