Whole-Brain Network Models: From Physics to Bedside
Computational neuroscience has come a long way from its humble origins in the pioneering work of Hodgkin and Huxley. Contemporary computational models of the brain span multiple spatiotemporal scales, from single neuronal compartments to models of social cognition. Each spatial scale comes with its...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2022.866517/full |
| _version_ | 1811256479235178496 |
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| author | Anagh Pathak Dipanjan Roy Arpan Banerjee |
| author_facet | Anagh Pathak Dipanjan Roy Arpan Banerjee |
| author_sort | Anagh Pathak |
| collection | DOAJ |
| description | Computational neuroscience has come a long way from its humble origins in the pioneering work of Hodgkin and Huxley. Contemporary computational models of the brain span multiple spatiotemporal scales, from single neuronal compartments to models of social cognition. Each spatial scale comes with its own unique set of promises and challenges. Here, we review models of large-scale neural communication facilitated by white matter tracts, also known as whole-brain models (WBMs). Whole-brain approaches employ inputs from neuroimaging data and insights from graph theory and non-linear systems theory to model brain-wide dynamics. Over the years, WBM models have shown promise in providing predictive insights into various facets of neuropathologies such as Alzheimer's disease, Schizophrenia, Epilepsy, Traumatic brain injury, while also offering mechanistic insights into large-scale cortical communication. First, we briefly trace the history of WBMs, leading up to the state-of-the-art. We discuss various methodological considerations for implementing a whole-brain modeling pipeline, such as choice of node dynamics, model fitting and appropriate parcellations. We then demonstrate the applicability of WBMs toward understanding various neuropathologies. We conclude by discussing ways of augmenting the biological and clinical validity of whole-brain models. |
| first_indexed | 2024-04-12T17:41:51Z |
| format | Article |
| id | doaj.art-d2716f2b98d644679a980ea6e6515af1 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-12T17:41:51Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-d2716f2b98d644679a980ea6e6515af12022-12-22T03:22:47ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882022-05-011610.3389/fncom.2022.866517866517Whole-Brain Network Models: From Physics to BedsideAnagh Pathak0Dipanjan Roy1Arpan Banerjee2National Brain Research Centre, Gurgaon, IndiaCentre for Brain Science and Applications, School of Artificial Intelligence and Data Science, Indian Institute of Technology, Jodhpur, IndiaNational Brain Research Centre, Gurgaon, IndiaComputational neuroscience has come a long way from its humble origins in the pioneering work of Hodgkin and Huxley. Contemporary computational models of the brain span multiple spatiotemporal scales, from single neuronal compartments to models of social cognition. Each spatial scale comes with its own unique set of promises and challenges. Here, we review models of large-scale neural communication facilitated by white matter tracts, also known as whole-brain models (WBMs). Whole-brain approaches employ inputs from neuroimaging data and insights from graph theory and non-linear systems theory to model brain-wide dynamics. Over the years, WBM models have shown promise in providing predictive insights into various facets of neuropathologies such as Alzheimer's disease, Schizophrenia, Epilepsy, Traumatic brain injury, while also offering mechanistic insights into large-scale cortical communication. First, we briefly trace the history of WBMs, leading up to the state-of-the-art. We discuss various methodological considerations for implementing a whole-brain modeling pipeline, such as choice of node dynamics, model fitting and appropriate parcellations. We then demonstrate the applicability of WBMs toward understanding various neuropathologies. We conclude by discussing ways of augmenting the biological and clinical validity of whole-brain models.https://www.frontiersin.org/articles/10.3389/fncom.2022.866517/fullwhole brain modelneural massneural fieldnetworkneuroimagingDTI |
| spellingShingle | Anagh Pathak Dipanjan Roy Arpan Banerjee Whole-Brain Network Models: From Physics to Bedside Frontiers in Computational Neuroscience whole brain model neural mass neural field network neuroimaging DTI |
| title | Whole-Brain Network Models: From Physics to Bedside |
| title_full | Whole-Brain Network Models: From Physics to Bedside |
| title_fullStr | Whole-Brain Network Models: From Physics to Bedside |
| title_full_unstemmed | Whole-Brain Network Models: From Physics to Bedside |
| title_short | Whole-Brain Network Models: From Physics to Bedside |
| title_sort | whole brain network models from physics to bedside |
| topic | whole brain model neural mass neural field network neuroimaging DTI |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2022.866517/full |
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