Box 1.

What Are Regions and Connections, and How Are They Used?.

All functional MRI (fMRI) analyses seek to characterize meaningful patterns of brain activity across space and time, but approaches to study these patterns vary. Specifically, after defining functionally coherent regions (which, critically, can be based on anatomy, activation, or even connectivity with other regions), one can study properties of those regions (i.e., region-based analyses) or relationships among them (i.e., connection-based analyses). That is, one can study a discrete region, around which boundaries can be drawn on the brain, or the interactions between regions. These approaches can be alternatively framed as segregationism and integrationism (Friston 2011; Genon and others 2018), and while many tend to conceptualize the brain in one way or the other, they are not mutually exclusive. Take, for example, the popular application of independent component analysis (ICA) to fMRI data. In its most common application, spatial ICA decomposes the BOLD signal into spatial components that are maximally independent, each with a corresponding time course of activity (Calhoun and others 2009; McKeown and others 2003). In region-based analyses, these components, themselves, can be studied, for example, by comparing voxel loadings to a given IC across patient groups (e.g., Zhu and others 2012; Fig. 6a); such analyses shed light on group differences in within-network connectivity. Connection-based analyses can also be performed using ICA, by studying similarity between time courses from ICA components (e.g., (Calhoun and de Lacy 2017; Fig. 6b).

Network neuroscience offers additional examples of the complementary nature of region- and connection-based analyses. Nodal measures—for example, degree, or the number of connections incident to a given node (Rubinov and Sporns 2010)—while based on connections, summarize a property of the region of interest. Conversely, characteristic path length, or the average shortest distance between all pairs of regions, is not specific to any area, but rather is an explicitly connection-based measure that reflects brain integration (Rubinov and Sporns 2010).