FIGURE 3.

Basic principles of rsfMRI Analysis. (A) Independent Component Analysis divides the BOLD signal of all brain-voxels in different spatially confined independent sources, or components. Each ICA component consists of brain regions with correlated BOLD time courses. In other words, voxels of one component represent regions that are functionally connected. (B) In voxel-based analyses, the mean BOLD signal time course of a specific seed region is extracted from a series of EPI images. This time course is compared to the time course of all other voxels in the brain, resulting in a functional connectivity map (voxel based processing). (C) In an ROI based approach, the mean BOLD signal time courses of multiple brain regions are compared, resulting in FC matrices showing the strength of connectivity between each pair of brain regions (warmer colors indicate stronger functional connectivity, colder colors represent anti-correlation). These matrices can then be used to visualize brain networks as nodes (brain regions) and edges (connections). Moreover, the brain network can be divided into modules that represent brain circuits where similar time courses are displayed by different colors (graph approach). (Adopted from Jonckers et al., 2013b).