graynet: single-subject morphometric networks for neuroscience connectivity applications

Connectivity, and network-level features in general, have proven to be valuable tools in several aspects of neuroscience research. Although network analysis is rooted in analysis of functional MRI data, it has recently gained traction in the analyses of morphometric features such as cortical thickness Such networks of anatomical covariance (derived based on distributions of features across a group of subjects) provided insight into changes caused by various brain disorders. When we individualize this approach to enable extraction of single-subject network features, they further enriched insights into abnormalities due to disease (Tijms, Seris, Willshaw, & Lawrie, 2012,Raamana et al. (2015),Palaniyappan, Park, Balain, Dangi, & Liddle (2015),Xu et al. (2017)). Moreover, these network-level features demonstrated potential for prognostic applications (Raamana et al., 2015,Raamana et al. (2014)), in addition to being robust to changes in scale and edge weight metrics (Raamana & Strother, 2017a).