Large-scale brain networks

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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[1] or other recording methods such as EEG,[2] PET[3] and MEG.[4] 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 clustering, spatial independent component analysis (ICA), seed based, and others.[5] Synchronized brain regions may also be identified using long-range synchronization of the EEG, MEG, or other dynamic brain signals.[6]

The set of identified brain areas that are linked together in a large-scale network varies with cognitive function.[7] 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,[6] 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 number and composition of the coalitions will vary with the algorithm and parameters used to identify them.[8][9] 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.[8] The most common and stable networks are enumerated below. The regions participating in a functional network may be dynamically reconfigured.[5][10]

Disruptions in activity in various networks have been implicated in neuropsychiatric disorders such as depression, Alzheimer's, autism spectrum disorder, schizophrenia and bipolar disorder.[11]


FMRI scanning shows 10 large-scale brain networks.

The following seven networks have been identified by at least three studies and are coincident with the seven networks in the widely cited 2011 paper by Yeo et al.[8]

Default mode[edit]

  • 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.[6][10][12][1][13][14][15][8][16][17]

Dorsal attention[edit]

  • This network is involved in the voluntary deployment of attention and reorientation to unexpected events.[1][13][14][8][16][18][19] Within the dorsal attention network, the intraparietal sulcus and frontal eye fields influence the visual areas of the brain. These influencing factors allow for the orientation of attention.[20][18][17]

Ventral attention[edit]

  • 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.[18] 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.[18][21]
  • Other parcellation uses[14][18][8][16][19][17]


  • The salience network consists of several structures, including the anterior (bilateral) insula, dorsal anterior cingulate cortex, and three subcortical structures which are the ventral striatum, substantia nigra/ventral tegmental region.[22][23] It plays the key role of monitoring the salience of external inputs and internal brain events.[1][6][10][13][15][8][16] Specifically it aids in directing attention by identifying important biological and cognitive events.[23][17]


  • This network initiates and modulates cognitive control and comprises 18 sub-regions of the brain.[24] There is a strong correlation between fluid intelligence and the involvement of the fronto-parietal network with other networks.[25]
  • Other parcellation uses[8][16][10][26][17]



Several other brain networks have also been identified: auditory,[13][15] motor,[13] right executive,[13][15] posterior default mode,[13] left fronto-parietal,[14] cerebellar,[14][15] spatial attention,[1][6] attention,[10] language,[6][19] left executive,[15] sensorimotor network,[15] somatomotor,[8][16][10] lateral visual,[13][14][15] temporal,[8][16] visual perception,[19] and visual imagery.[19]

See also[edit]


  1. ^ a b c d e Riedl, Valentin; Utz, Lukas; Castrillón, Gabriel; Grimmer, Timo; Rauschecker, Josef P.; Ploner, Markus; Friston, Karl J.; Drzezga, Alexander; Sorg, Christian (January 12, 2016). "Metabolic connectivity mapping reveals effective connectivity in the resting human brain". PNAS. 113 (2): 428–433. Bibcode:2016PNAS..113..428R. doi:10.1073/pnas.1513752113. PMC 4720331. PMID 26712010.
  2. ^ Foster, Brett L.; Parvizi, Josef (2012-03-01). "Resting oscillations and cross-frequency coupling in the human posteromedial cortex". NeuroImage. 60 (1): 384–391. doi:10.1016/j.neuroimage.2011.12.019. ISSN 1053-8119. PMC 3596417. PMID 22227048.
  3. ^ Buckner, Randy L.; Andrews‐Hanna, Jessica R.; Schacter, Daniel L. (2008). "The Brain's Default Network". Annals of the New York Academy of Sciences. 1124 (1): 1–38. Bibcode:2008NYASA1124....1B. doi:10.1196/annals.1440.011. ISSN 1749-6632. PMID 18400922. S2CID 3167595.
  4. ^ Morris, Peter G.; Smith, Stephen M.; Barnes, Gareth R.; Stephenson, Mary C.; Hale, Joanne R.; Price, Darren; Luckhoo, Henry; Woolrich, Mark; Brookes, Matthew J. (2011-10-04). "Investigating the electrophysiological basis of resting state networks using magnetoencephalography". Proceedings of the National Academy of Sciences. 108 (40): 16783–16788. Bibcode:2011PNAS..10816783B. doi:10.1073/pnas.1112685108. ISSN 0027-8424. PMC 3189080. PMID 21930901.
  5. ^ a b Petersen, Steven; Sporns, Olaf (October 2015). "Brain Networks and Cognitive Architectures". Neuron. 88 (1): 207–219. doi:10.1016/j.neuron.2015.09.027. PMC 4598639. PMID 26447582.
  6. ^ a b c d e f Bressler, Steven L.; Menon, Vinod (June 2010). "Large scale brain networks in cognition: emerging methods and principles". Trends in Cognitive Sciences. 14 (6): 233–290. doi:10.1016/j.tics.2010.04.004. PMID 20493761. S2CID 5967761. Retrieved 24 January 2016. CS1 maint: discouraged parameter (link)
  7. ^ Bressler, Steven L. (2008). "Neurocognitive networks". Scholarpedia. 3 (2): 1567. Bibcode:2008SchpJ...3.1567B. doi:10.4249/scholarpedia.1567.
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