One useful approach for studying brain activity in the absence of cognitive or emotional tasks is independent component analysis (ICA), which resolves data signals into maximally-independent sources. This approach is similar to that used to resolve the voices of multiple individuals, as well as random noise, in a tape-recorded conversation. When applied to functional MRI (fMRI) data, ICA can detect signal changes due to motion or other artifacts, as well as neuronal activity (van de Ven et al., 2004). ICA produces spatial maps (components) within which voxels with stronger contributions to the component have increasingly similar blood oxygen level dependent (BOLD) signal timecourses. Application of ICA to fMRI datasets readily detects the DMN (Greicius et al., 2003), as well as several other neuronal networks (visual association, auditory, and sensory-motor) with characteristic low-frequency BOLD signal fluctuations (Beckmann et al., 2005). Detection of these biologically meaningful networks is particularly compelling because ICA examines BOLD signal coherence without prior assumptions about brain function. Studies using region of interest based approaches have confirmed and extended these findings in humans and primates, and indicate that there is a rich landscape of ongoing brain activity even “at rest” (Vincent et al., 2007).
