Collections of brain regions
Large-scale brain networks (also known as intrinsic brain networks) are collections of widespread brain regions showing functional connectivity by statistical analysis of the fMRI BOLD 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". Functional connectivity networks may be found using algorithms such as cluster analysis, spatial independent component analysis (ICA), seed based, and others. Synchronized brain regions may also be identified using long-range synchronization of the EEG, MEG, or other dynamic brain signals.
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. 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.
The Organization for Human Brain Mapping has created the Workgroup for HArmonized Taxonomy of NETworks (WHATNET) group to work towards a consensus regarding network nomenclature. WHATNET conducted a survey in 2021 which showed a large degree of agreement about the name and topography of three networks: the "somato network", the "default network" and the "visual network", while other networks had less agreement. Several issues make the work of creating a common atlas for networks difficult: some of these issues are the variability of spatial and time scales, variability across individuals, and the dynamic nature of some networks.
Some 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 number and composition of the coalitions will vary with the algorithm and parameters used to identify them. In one model, there is only the default mode network and the task-positive network, but most current analyses show several networks, from a small handful to 17. The most common and stable networks are enumerated below. The regions participating in a functional network may be dynamically reconfigured.
Disruptions in activity in various networks have been implicated in neuropsychiatric disorders such as depression, Alzheimer's, autism spectrum disorder, schizophrenia, ADHD and bipolar disorder.
Commonly identified networks.
Because brain networks can be identified at various different resolutions and with various different neurobiological properties, there is currently no universal atlas of brain networks that fits all circumstances. Uddin, Yeo, and Spreng proposed in 2019 that the following six networks should be defined as core networks based on converging evidences from multiple studies to facilitate communication between researchers.
Other networks.
Different methods and data have identified several other brain networks, many of which greatly overlap or are subsets of more well-characterized core networks.
References.
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