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 or other signal fluctuations.[1] 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.[2] 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.[3] 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,[2] 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. Disruptions in activity in various networks have been implicated in neuropsychiatric disorders such as depression, Alzheimer's, autism spectrum disorder, schizophrenia and bipolar disorder.[4]


fMRI scanning shows 10 large scale brain networks.

The following four 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.[5][1][2][6][7][8]
  • Dorsal attention: voluntary deployment of attention and reorientation to unexpected events.[1][6][7][9]
  • Salience: monitors the salience of external inputs and internal brain events.[1][2][6][8]
  • Lateral visual: important in complex emotional stimuli.[6][7][8]

Several other brain networks have also been identified: auditory,[6][8] motor,[6] right executive,[6][8] posterior default mode,[6] left fronto-parietal,[7] cerebellar,[7][8] ventral attention,[7][9] spatial attention,[1][2] language,[2] left executive,[8] and sensorimotor network.[8] 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”.[10]

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. doi:10.1073/pnas.1513752113. PMC 4720331Freely accessible. Retrieved 24 January 2016. 
  2. ^ 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. Retrieved 24 January 2016. 
  3. ^ Bressler, Steven L. "Neurocognitive networks". Scholarpedia. 3 (2): 1567. doi:10.4249/scholarpedia.1567. Retrieved 23 March 2016. 
  4. ^ Menon, Vinod (2011-09-09). Large-scale brain networks and psychopathology: A unifying triple network model. 15. 
  5. ^ "The serendipitous discovery of the brain's default network". NeuroImage. 62 (2): 1137–1145. 2012-08-15. doi:10.1016/j.neuroimage.2011.10.035. ISSN 1053-8119. 
  6. ^ a b c d e f g h Yuan, Rui; Di, Xin; Taylor, Paul A.; Gohel, Suril; Tsai, Yuan-Hsiung; Biswal, Bharat B. (30 April 2015). "Functional topography of the thalamocortical system in human". Brain Structure and Function. 221: 1971–1984. doi:10.1007/s00429-015-1018-7. Retrieved 24 January 2016. 
  7. ^ a b c d e f Bell, Peter T.; Shine, James M. (2015-11-09). "Estimating Large-Scale Network Convergence in the Human Functional Connectome". Brain Connectivity. 5 (9): 565–74. doi:10.1089/brain.2015.0348. PMID 26005099. 
  8. ^ a b c d e f g h Heine, Lizette; Soddu, Andrea; Gomez, Francisco; Vanhaudenhuyse, Audrey; Tshibanda, Luaba; Thonnard, Marie; Charland-Verville, Vanessa; Kirsch, Murielle; Laureys, Steven; Demertzi, Athena (2012). "Resting state networks and consciousness. Alterations of multiple resting state network connectivity in physiological, pharmacological and pathological consciousness states". Frontiers in Psychology. 3. doi:10.3389/fpsyg.2012.00295. 
  9. ^ a b Vossel, Simone; Geng, Joy J.; Fink, Gereon R. (2014). "Dorsal and Ventral Attention Systems: Distinct Neural Circuits but Collaborative Roles". The Neuroscientist. 20 (2): 150–159. doi:10.1177/1073858413494269. PMC 4107817Freely accessible. PMID 23835449. 
  10. ^ Ofengenden, Tzofit (2014) Memory formation and belief. Dialogues in Philosophy, Mental and Neuro Sciences, 7(2):34-44