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Large-scale brain network

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 edit

 
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) 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][12][20][1][21][22][15][10][23][24]

Salience (Midcingulo-Insular) edit

  • 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) edit

  • 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) edit

  • 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) edit

Visual (Occipital) edit

  • This network handles visual information processing.[33]

Other networks edit

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 edit

References edit

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  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. PMC 6771873. PMID 31322303.
  19. ^ Smith, SM; Fox, PT; Miller, KL; Glahn, DC; Fox, PM; Mackay, CE; Filippini, N; Watkins, KE; Toro, R; Laird, AR; Beckmann, CF (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. Bibcode:2009PNAS..10613040S. doi:10.1073/pnas.0905267106. PMC 2722273. PMID 19620724.
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large, scale, brain, network, also, known, intrinsic, brain, networks, collections, widespread, brain, regions, showing, functional, connectivity, statistical, analysis, fmri, bold, signal, other, recording, methods, such, emerging, paradigm, neuroscience, tha. 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 Contents 1 Commonly Identified networks 1 1 Default Mode Medial frontoparietal 1 2 Salience Midcingulo Insular 1 3 Attention Dorsal frontoparietal 1 4 Control Lateral frontoparietal 1 5 Sensorimotor or Somatomotor Pericentral 1 6 Visual Occipital 2 Other networks 3 See also 4 ReferencesCommonly Identified networks edit nbsp 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 edit Main article 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 edit Main article 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 edit Main article 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 edit Main article 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 edit Main article Sensorimotor network This network processes somatosensory information and coordinates motion 15 10 23 12 21 The auditory cortex may be included 17 10 Visual Occipital edit Further information Visual cortex This network handles visual information processing 33 Other networks editDifferent methods and data have identified several other brain networks many of which greatly overlap or are subsets of more well characterized core networks 17 Limbic 12 10 24 Auditory 21 15 Right left executive 21 15 Cerebellar 22 15 Spatial attention 1 6 Language 6 29 Lateral visual 21 22 15 Temporal 10 23 Visual perception imagery 29 See also editComplex network Neural networkReferences edit a b c d e Riedl Valentin Utz Lukas Castrillon 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 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 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 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 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 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 Bressler Steven L 2008 Neurocognitive networks Scholarpedia 3 2 1567 Bibcode 2008SchpJ 3 1567B doi 10 4249 scholarpedia 1567 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 S2CID 252844955 Uddin LQ Betzel Richard F Cohen Jessica R Damoiselastx Jessica S De Brigard Felipe Eickhoff Simon B Fornito Alex Gratton Caterina Gordon Evan M Laird Angela R Larson Prior Linda McIntosh A Randal Nickerson Lisa D Pessoa Luiz Pinho Ana Luisa Poldrack Russell A Razi Adeel Sadaghiani Sepideh Shine James M Yendiki Anastasia Yeo BTT Spreng RN 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 a b c d e f g h i j Yeo B T Thomas Krienen Fenna M Sepulcre Jorge Sabuncu Mert R Lashkari Danial Hollinshead Marisa Roffman Joshua L Smoller Jordan W Zollei Lilla Polimeni Jonathan R Fischl Bruce Liu Hesheng Buckner Randy L 2011 09 01 The organization of the human cerebral cortex estimated by intrinsic functional connectivity Journal of Neurophysiology 106 3 1125 1165 Bibcode 2011NatSD 2E0031H doi 10 1152 jn 00338 2011 PMC 3174820 PMID 21653723 Abou Elseoud Ahmed Littow Harri Remes Jukka Starck Tuomo Nikkinen Juha Nissila Juuso Timonen Markku Tervonen Osmo Kiviniemi Vesa 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 PMC 3109774 PMID 21687724 a b c d e Bassett Daniella Bertolero Max July 2019 How Matter Becomes Mind Scientific American 321 1 32 Retrieved 23 June 2019 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 PMC 7925571 PMID 33654073 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 S2CID 26653572 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 3 295 doi 10 3389 fpsyg 2012 00295 PMC 3427917 PMID 22969735 Eickhoff SB Yeo BTT Genon S November 2018 Imaging based parcellations of the human brain PDF Nature Reviews Neuroscience 19 11 672 686 doi 10 1038 s41583 018 0071 7 PMID 30305712 S2CID 52954265 a b c d e 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 PMC 7325607 PMID 31707621 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 PMC 6771873 PMID 31322303 Smith SM Fox PT Miller KL Glahn DC Fox PM Mackay CE Filippini N Watkins KE Toro R Laird AR Beckmann CF 2009 08 04 Correspondence of the brain s functional architecture during activation and rest Proceedings of the 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25883631 Retrieved from https en wikipedia org w index php title Large scale brain network amp oldid 1202489269, wikipedia, wiki, book, books, library,

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