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Large scale brain networks are collections of widespread brain regions showing functional connectivity by statistical analysis of the fMRI BOLD signal or other signal fluctuations. Functional connectivity may be measured as long-range synchronization of the EEG, MEG, or other dynamic brain signals. Synchronized brain regions may also be identified using spatial independent component analysis. The set of identified brain areas that are linked together in a large-scale network varies with cognitive function. When the cognitive state is not explicit (i.e., the subject is at "rest"), the large scale brain network is a resting state network (RSN). As a physical system with graph-like properties, a large scale brain network has both nodes and edges, and cannot be identified simply by the co-activation of brain areas. Large scale brain networks are identified by their function, and provide a coherent framework for understanding cognition by offering a neural model of how different cognitive functions emerge when different sets of brain regions join together as self-organized coalitions.
Networks
The following four networks have been identified by at least three studies.
Several other brain networks have also been identified: auditory, motor, right executive, posterior default mode, left fronto-parietal, cerebellar, ventral attention, spatial attention, language, left executive, and sensorimotor. There are also models suggesting that “components of memory representation are distributed widely across different parts of the brain as mediated by multiple neocortical circuits”.