In the past decade, imaging designs began to shift from analyzing specific regions of interest to studying whole‐brain networks. Network approaches can be used to examine structural connectivity, using diffusion weighted imaging (DTI; Piras, Piras, Caltagirone, & Spalletta, 2013; Radua et al., 2014) as well as structural covariance (Yun et al., 2020), and functional connectivity at rest (Gursel, Avram, Sorg, Brandl, & Koch, 2018) and during task performance (e.g., Douw et al., 2019). As with other neuroimaging studies, a key challenge has been the reproducibility of research findings, as most single center imaging studies have limited statistical power to detect effects of the disorder, while adequately controlling for multiple comparisons and heterogeneity in demographic and clinical characteristics. Although each individual study differs in the choice of paradigms, a structural MRI scan is typically part of every study design, enabling meta‐analyses of data across many samples, or mega‐analyses of pooled raw MRI scans or MRI‐extracted measures. Although technically more challenging, meta‐analyses and mega‐analyses of resting state fMRI (rsfMRI), and even task‐based fMRI, are also possible (Adhikari et al., 2018; Adhikari et al., 2019; Yan et al., 2019).
