Large-scale brain networks are collections of widespread brain regions showing functional connectivity by statistical analysis of the fMRIBOLD signal or other recording methods such as EEG, PET and MEG. An emerging paradigm in neuroscience is that cognitive tasks are performed not by individual brain regions working in isolation but by networks consisting of several discrete brain regions that are said to be "functionally connected" due to tightly coupled activity. 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, the large-scale brain network is a resting state network. 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. In recent decades, the analysis of brain networks was made feasible by advances in imaging techniques as well as new tools from graph theory and dynamical systems. 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. The identification of the coalitions will vary with different parameters used to run the ICA algorithm, which can result in a different number of networks. In one model, there is only the default mode network and the task-positive network, but most current analyses show several networks, which are enumerated below. The property called functional network flexibility a brain region with strong functional connections within a brain network suddenly establish many connections to a different network. Disruptions in activity in various networks have been implicated in neuropsychiatric disorders such as depression, Alzheimer's, autism spectrum disorder, schizophrenia and bipolar disorder.
Networks
The following six networks have been identified by at least three studies.
Default mode
The default mode network is active when an individual is awake and at rest. It preferentially activates when individuals focus on internally-oriented tasks such as daydreaming, envisioning the future, retrieving memories, and theory of mind. It is negatively correlated with brain systems that focus on external visual signals. It is the most widely researched network.
Three areas of the brain are active in this network, and they include the visual cortex, temporoparietal junction, and the ventral frontal cortex. These areas respond when behaviorally relevant stimuli occur unexpectedly. The ventral attention network may also become inhibited during focused attention in which top down processing is being used, such as when one is visually searching for something. This response may prevent goal driven attention from being distracted by non-relevant stimuli. It becomes active again when the target, or relevant information about the target is found.
Salience
The salience network consists of several structures, including the anterior insula, dorsal anterior cingulate cortex, and three subcortical structures which are the ventral striatum, substantia nigra/ventral tegmental region. It plays the key role of monitoring the salience of external inputs and internal brain events. Specifically it aids in directing attention by identifying important biological and cognitive events.
This network is important in complex emotional stimuli.
Several other brain networks have also been identified: auditory, motor, right executive, posterior default mode, left fronto-parietal, cerebellar, spatial attention, attention, language, left executive, sensorimotor network, somatomotor, visual, temporal, limbic, visual perception, and visual imagery.
