Biology:Large-scale brain network

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Short description: 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[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 cluster analysis, 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.

The Organization for Human Brain Mapping has the Workgroup for HArmonized Taxonomy of NETworks (WHATNET) group to work towards a consensus regarding network nomenclature.[8] WHATNET conducted a survey in 2021 which showed a large degree of agreement about the name and topography of three networks: “somato network”, “default network” and “visual network.” Other networks had less agreement. Several issues make the work of creating a common atlas for networks difficult. Some of those issues are the variability of spatial and time scales, variability across individuals, and the dynamic nature of some networks.[9]

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.[10][11] 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.[10] The most common and stable networks are enumerated below. The regions participating in a functional network may be dynamically reconfigured.[5][12]

Disruptions in activity in various networks have been implicated in neuropsychiatric disorders such as depression, Alzheimer's, autism spectrum disorder, schizophrenia, ADHD[13] and bipolar disorder.[14]

Commonly Identified networks

An example that identified 10 large-scale brain networks from resting state fMRI activity through independent component analysis.[15]

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.[16] Uddin, Yeo, and Spreng proposed in 2019[17] that the following six networks should be defined as core networks based on converging evidences from multiple studies[18][10][19] to facilitate communication between researchers.

Default Mode (Medial frontoparietal)

Main page: Biology:Default mode network
  • 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][12][20][1][21][22][15][10][23][24]

Salience (Midcingulo-Insular)

Main page: Salience network
  • 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.[25][26] It plays the key role of monitoring the salience of external inputs and internal brain events.[1][6][12][21][15][10][23] Specifically, it aids in directing attention by identifying important biological and cognitive events.[26][24]
  • This network includes the ventral attention network, which primarily includes the temporoparietal junction and the ventral frontal cortex of the right hemisphere.[17][27] These areas respond when behaviorally relevant stimuli occur unexpectedly.[27] The ventral attention network is inhibited during focused attention in which top-down processing is being used, such as when 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.[27][28]

Attention (Dorsal frontoparietal)

Main page: Biology:Dorsal attention network
  • This network is involved in the voluntary, top-down deployment of attention.[1][21][22][10][23][27][29] 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.[30][27][24]

Control (Lateral frontoparietal)

Main page: Biology:Frontoparietal network
  • This network initiates and modulates cognitive control and comprises 18 sub-regions of the brain.[31] There is a strong correlation between fluid intelligence and the involvement of the fronto-parietal network with other networks.[32]
  • Versions of this network have also been called the central executive (or executive control) network and the cognitive control network.[17]

Sensorimotor or Somatomotor (Pericentral)

Main page: Biology:Sensorimotor network

Visual (Occipital)

Further information: Visual cortex
  • This network handles visual information processing.[33]

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.[17]

See also

References

  1. ↑ 1.0 1.1 1.2 1.3 1.4 Riedl, Valentin; Utz, Lukas; Castrillón, Gabriel; Grimmer, Timo; Rauschecker, Josef P.; Ploner, Markus; Friston, Karl J.; Drzezga, Alexander et al. (January 12, 2016). "Metabolic connectivity mapping reveals effective connectivity in the resting human brain". PNAS 113 (2): 428–433. doi:10.1073/pnas.1513752113. PMID 26712010. Bibcode: 2016PNAS..113..428R. 
  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. PMID 22227048. 
  3. ↑ Buckner, Randy L.; Andrews‐Hanna, Jessica R.; Schacter, Daniel L. (2008). "The Brain's Default Network" (in en). Annals of the New York Academy of Sciences 1124 (1): 1–38. doi:10.1196/annals.1440.011. ISSN 1749-6632. PMID 18400922. Bibcode: 2008NYASA1124....1B. 
  4. ↑ Morris, Peter G.; Smith, Stephen M.; Barnes, Gareth R.; Stephenson, Mary C.; Hale, Joanne R.; Price, Darren; Luckhoo, Henry; Woolrich, Mark et al. (2011-10-04). "Investigating the electrophysiological basis of resting state networks using magnetoencephalography" (in en). Proceedings of the National Academy of Sciences 108 (40): 16783–16788. doi:10.1073/pnas.1112685108. ISSN 0027-8424. PMID 21930901. Bibcode: 2011PNAS..10816783B. 
  5. ↑ 5.0 5.1 Petersen, Steven; Sporns, Olaf (October 2015). "Brain Networks and Cognitive Architectures". Neuron 88 (1): 207–219. doi:10.1016/j.neuron.2015.09.027. PMID 26447582. 
  6. ↑ 6.0 6.1 6.2 6.3 6.4 6.5 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. http://www.cell.com/trends/cognitive-sciences/issue?pii=S1364-6613(10)X0005-5. Retrieved 24 January 2016. 
  7. ↑ Bressler, Steven L. (2008). "Neurocognitive networks". Scholarpedia 3 (2): 1567. doi:10.4249/scholarpedia.1567. Bibcode: 2008SchpJ...3.1567B. 
  8. ↑ Uddin, Lucina (2022-10-10). "A Brain Network by Any Other Name". Journal of Cognitive Neuroscience 2022 (10): 363–364. doi:10.1162/jocn_a_01925. PMID 36223250. https://doi.org/10.1162/jocn_a_01925. 
  9. ↑ Uddin, LQ; Betzel, Richard F.; Cohen, Jessica R.; Damoiselastx, Jessica S.; De Brigard, Felipe; Eickhoff, Simon B.; Fornito, Alex; Gratton, Caterina et al. (October 2023). "Controversies and progress on standardization of large-scale brain network nomenclature". Network Neuroscience 7 (3): 864-903. doi:10.1162/netn_a_00323. 
  10. ↑ 10.0 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 Yeo, B. T. Thomas; Krienen, Fenna M.; Sepulcre, Jorge; Sabuncu, Mert R.; Lashkari, Danial; Hollinshead, Marisa; Roffman, Joshua L.; Smoller, Jordan W. et al. (2011-09-01). "The organization of the human cerebral cortex estimated by intrinsic functional connectivity". Journal of Neurophysiology 106 (3): 1125–1165. doi:10.1152/jn.00338.2011. PMID 21653723. Bibcode: 2011NatSD...2E0031H. 
  11. ↑ Abou Elseoud, Ahmed; Littow, Harri; Remes, Jukka; Starck, Tuomo; Nikkinen, Juha; Nissilä, Juuso; Timonen, Markku; Tervonen, Osmo et al. (2011-06-03). "Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity". Frontiers in Systems Neuroscience 5: 37. doi:10.3389/fnsys.2011.00037. PMID 21687724. 
  12. ↑ 12.0 12.1 12.2 12.3 12.4 Bassett, Daniella; Bertolero, Max (July 2019). "How Matter Becomes Mind". Scientific American 321 (1): 32. https://www.scientificamerican.com/. Retrieved 23 June 2019. 
  13. ↑ Griffiths, Kristi R.; Braund, Taylor A.; Kohn, Michael R.; Clarke, Simon; Williams, Leanne M.; Korgaonkar, Mayuresh S. (2 March 2021). "Structural brain network topology underpinning ADHD and response to methylphenidate treatment". Translational Psychiatry 11 (1): 150. doi:10.1038/s41398-021-01278-x. PMID 33654073. 
  14. ↑ Menon, Vinod (2011-09-09). "Large-scale brain networks and psychopathology: A unifying triple network model". Trends in Cognitive Sciences 15 (10): 483–506. doi:10.1016/j.tics.2011.08.003. PMID 21908230. https://www.researchgate.net/publication/51639686. 
  15. ↑ 15.0 15.1 15.2 15.3 15.4 15.5 15.6 15.7 Heine, Lizette; Soddu, Andrea; Gomez, Francisco; Vanhaudenhuyse, Audrey; Tshibanda, Luaba; Thonnard, Marie; Charland-Verville, Vanessa; Kirsch, Murielle et al. (2012). "Resting state networks and consciousness. Alterations of multiple resting state network connectivity in physiological, pharmacological and pathological consciousness states.". Frontiers in Psychology 3: 295. doi:10.3389/fpsyg.2012.00295. PMID 22969735. 
  16. ↑ Eickhoff, SB; Yeo, BTT; Genon, S (November 2018). "Imaging-based parcellations of the human brain.". Nature Reviews. Neuroscience 19 (11): 672–686. doi:10.1038/s41583-018-0071-7. PMID 30305712. http://juser.fz-juelich.de/record/856633/files/Eickhoff_Yeo_Genon_NRN_MainManuscriptInclFigures.pdf. 
  17. ↑ 17.0 17.1 17.2 17.3 17.4 Uddin, LQ; Yeo, BTT; Spreng, RN (November 2019). "Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks.". Brain Topography 32 (6): 926–942. doi:10.1007/s10548-019-00744-6. PMID 31707621. 
  18. ↑ Doucet, GE; Lee, WH; Frangou, S (2019-10-15). "Evaluation of the spatial variability in the major resting-state networks across human brain functional atlases.". Human Brain Mapping 40 (15): 4577–4587. doi:10.1002/hbm.24722. PMID 31322303. 
  19. ↑ Smith, SM; Fox, PT; Miller, KL; Glahn, DC; Fox, PM; Mackay, CE; Filippini, N; Watkins, KE et al. (2009-08-04). "Correspondence of the brain's functional architecture during activation and rest.". Proceedings of the National Academy of Sciences of the United States of America 106 (31): 13040–5. doi:10.1073/pnas.0905267106. PMID 19620724. Bibcode: 2009PNAS..10613040S. 
  20. ↑ Buckner, Randy L. (2012-08-15). "The serendipitous discovery of the brain's default network" (in en). NeuroImage 62 (2): 1137–1145. doi:10.1016/j.neuroimage.2011.10.035. ISSN 1053-8119. PMID 22037421. 
  21. ↑ 21.0 21.1 21.2 21.3 21.4 21.5 21.6 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 (4): 1971–1984. doi:10.1007/s00429-015-1018-7. PMID 25924563. 
  22. ↑ 22.0 22.1 22.2 22.3 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. 
  23. ↑ 23.0 23.1 23.2 23.3 23.4 Shafiei, Golia; Zeighami, Yashar; Clark, Crystal A.; Coull, Jennifer T.; Nagano-Saito, Atsuko; Leyton, Marco; Dagher, Alain; Mišić, Bratislav (2018-10-01). "Dopamine Signaling Modulates the Stability and Integration of Intrinsic Brain Networks". Cerebral Cortex 29 (1): 397–409. doi:10.1093/cercor/bhy264. PMID 30357316. 
  24. ↑ 24.0 24.1 24.2 24.3 Bailey, Stephen K.; Aboud, Katherine S.; Nguyen, Tin Q.; Cutting, Laurie E. (13 December 2018). "Applying a network framework to the neurobiology of reading and dyslexia". Journal of Neurodevelopmental Disorders 10 (1): 37. doi:10.1186/s11689-018-9251-z. PMID 30541433. 
  25. ↑ Steimke, Rosa; Nomi, Jason S.; Calhoun, Vince D.; Stelzel, Christine; Paschke, Lena M.; Gaschler, Robert; Goschke, Thomas; Walter, Henrik et al. (2017-12-01). "Salience network dynamics underlying successful resistance of temptation" (in en). Social Cognitive and Affective Neuroscience 12 (12): 1928–1939. doi:10.1093/scan/nsx123. ISSN 1749-5016. PMID 29048582. 
  26. ↑ 26.0 26.1 Menon, V. (2015-01-01), "Salience Network", in Toga, Arthur W., Brain Mapping, Academic Press, pp. 597–611, doi:10.1016/B978-0-12-397025-1.00052-X, ISBN 978-0-12-397316-0, http://www.sciencedirect.com/science/article/pii/B978012397025100052X, retrieved 2019-12-08 
  27. ↑ 27.0 27.1 27.2 27.3 27.4 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. PMID 23835449. 
  28. ↑ Shulman, Gordon L.; McAvoy, Mark P.; Cowan, Melanie C.; Astafiev, Serguei V.; Tansy, Aaron P.; d'Avossa, Giovanni; Corbetta, Maurizio (2003-11-01). "Quantitative Analysis of Attention and Detection Signals During Visual Search". Journal of Neurophysiology 90 (5): 3384–3397. doi:10.1152/jn.00343.2003. ISSN 0022-3077. PMID 12917383. 
  29. ↑ 29.0 29.1 29.2 Hutton, John S.; Dudley, Jonathan; Horowitz-Kraus, Tzipi; DeWitt, Tom; Holland, Scott K. (1 September 2019). "Functional Connectivity of Attention, Visual, and Language Networks During Audio, Illustrated, and Animated Stories in Preschool-Age Children". Brain Connectivity 9 (7): 580–592. doi:10.1089/brain.2019.0679. PMID 31144523. 
  30. ↑ Fox, Michael D.; Corbetta, Maurizio; Snyder, Abraham Z.; Vincent, Justin L.; Raichle, Marcus E. (2006-06-27). "Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems" (in en). Proceedings of the National Academy of Sciences 103 (26): 10046–10051. doi:10.1073/pnas.0604187103. ISSN 0027-8424. PMID 16788060. Bibcode: 2006PNAS..10310046F. 
  31. ↑ Scolari, Miranda; Seidl-Rathkopf, Katharina N; Kastner, Sabine (2015-02-01). "Functions of the human frontoparietal attention network: Evidence from neuroimaging". Current Opinion in Behavioral Sciences. Cognitive control 1: 32–39. doi:10.1016/j.cobeha.2014.08.003. ISSN 2352-1546. PMID 27398396. 
  32. ↑ Marek, Scott; Dosenbach, Nico U. F. (June 2018). "The frontoparietal network: function, electrophysiology, and importance of individual precision mapping". Dialogues in Clinical Neuroscience 20 (2): 133–140. doi:10.31887/DCNS.2018.20.2/smarek. ISSN 1294-8322. PMID 30250390. 
  33. ↑ Yang, Yan-li; Deng, Hong-xia; Xing, Gui-yang; Xia, Xiao-luan; Li, Hai-fang (2015). "Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state". Neural Regeneration Research 10 (2): 298–307. doi:10.4103/1673-5374.152386. PMID 25883631. 



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