Figure 1.

Broad and deep neuroimaging studies. The neuroimaging community has seen the rise of studies with increasingly large sample sizes (broad studies) and increasingly intensive sampling (deep studies). Broad studies (left) typically involve cross-sectional sampling of a specific population. Multivariate statistics and deep neural-network classifiers are two examples of methods that are well-suited to identify high-dimensional patterns between brain network, behavioral, clinical, and genetic phenotypes. Ultimately, such models might lead to the automated classification of neuropsychiatric illness based on neurobehavioral phenotypes, along with predictions of responses to various treatment options. Deep studies (right) typically involve intensive, repeated sampling of a small number of individuals longitudinally over days, months, or years. Multilayer network models can capture how the interaction between different components of brain activity, transcriptomes, metabolomes, or behavior changes over time. Here (middle right), each node represents a component and the time-varying edges represent their time-dependent interactions. Such models could prove particularly powerful for delineating how variability in individual networks over time affects treatment response.