Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest.

Cortical functional connectivity, as indicated by the concurrent spontaneous activity of spatially segregated regions, is being studied increasingly because it may determine the reaction of the brain to external stimuli and task requirements and it is reportedly altered in many neurological and psychiatric disorders. In functional magnetic resonance imaging (fMRI), such functional connectivity is investigated commonly by correlating the time course of a chosen "seed voxel" with the remaining voxel time courses in a voxel-by-voxel manner. This approach is biased by the actual choice of the seed voxel, however, because it only shows functional connectivity for the chosen brain region while ignoring other potentially interesting patterns of coactivation. We used spatial independent component analysis (sICA) to assess cortical functional connectivity maps from resting state data. SICA does not depend on any chosen temporal profile of local brain activity. We hypothesized that sICA would be able to find functionally connected brain regions within sensory and motor regions in the absence of task-related brain activity. We also investigated functional connectivity patterns of several parietal regions including the superior parietal cortex and the posterior cingulate gyrus. The components of interest were selected in an automated fashion using predefined anatomical volumes of interest. SICA yielded connectivity maps of bilateral auditory, motor and visual cortices. Moreover, it showed that prefrontal and parietal areas are also functionally connected within and between hemispheres during the resting state. These connectivity maps showed an extremely high degree of consistency in spatial, temporal, and frequency parameters within and between subjects. These results are discussed in the context of the recent debate on the functional relevance of fluctuations of neural activity in the resting state.