Literature DB >> 35471545

Whole-Brain Modelling: Past, Present, and Future.

John D Griffiths1,2, Sorenza P Bastiaens3,4, Neda Kaboodvand5.   

Abstract

Whole-Brain Modelling is a scientific field with a short history and a long past. Its various disciplinary roots and conceptual ingredients extend back to as early as the 1940s. It was not until the late 2000s, however, that a nascent paradigm emerged in roughly its current form-concurrently, and in many ways joined at the hip, with its sister field of macro-connectomics. This period saw a handful of seminal papers authored by a certain motley crew of notable theoretical and cognitive neuroscientists, which have served to define much of the landscape of whole-brain modelling as it stands at the start of the 2020s. At the same time, the field has over the past decade expanded in a dozen or more fascinating new methodological, theoretical, and clinical directions. In this chapter we offer a potted Past, Present, and Future of whole-brain modelling, noting what we take to be some of its greatest successes, hardest challenges, and most exciting opportunities.
© 2022. Springer Nature Switzerland AG.

Entities:  

Keywords:  Connectome; Mean-field; Neural field; Neural mass; Neuroimaging

Mesh:

Year:  2022        PMID: 35471545     DOI: 10.1007/978-3-030-89439-9_13

Source DB:  PubMed          Journal:  Adv Exp Med Biol        ISSN: 0065-2598            Impact factor:   3.650


  123 in total

1.  Emergence of scaling in random networks

Authors: 
Journal:  Science       Date:  1999-10-15       Impact factor: 47.728

2.  Real-time automated EEG tracking of brain states using neural field theory.

Authors:  R G Abeysuriya; P A Robinson
Journal:  J Neurosci Methods       Date:  2015-10-31       Impact factor: 2.390

3.  Network diffusion accurately models the relationship between structural and functional brain connectivity networks.

Authors:  Farras Abdelnour; Henning U Voss; Ashish Raj
Journal:  Neuroimage       Date:  2013-12-30       Impact factor: 6.556

4.  Physiologically based arousal state estimation and dynamics.

Authors:  R G Abeysuriya; C J Rennie; P A Robinson
Journal:  J Neurosci Methods       Date:  2015-06-11       Impact factor: 2.390

5.  Functional brain connectivity is predictable from anatomic network's Laplacian eigen-structure.

Authors:  Farras Abdelnour; Michael Dayan; Orrin Devinsky; Thomas Thesen; Ashish Raj
Journal:  Neuroimage       Date:  2018-02-14       Impact factor: 6.556

6.  Computational modeling of resting-state activity demonstrates markers of normalcy in children with prenatal or perinatal stroke.

Authors:  Mohit H Adhikari; Anjali Raja Beharelle; Alessandra Griffa; Patric Hagmann; Ana Solodkin; Anthony R McIntosh; Steven L Small; Gustavo Deco
Journal:  J Neurosci       Date:  2015-06-10       Impact factor: 6.167

7.  Optimization of surgical intervention outside the epileptogenic zone in the Virtual Epileptic Patient (VEP).

Authors:  Sora An; Fabrice Bartolomei; Maxime Guye; Viktor Jirsa
Journal:  PLoS Comput Biol       Date:  2019-06-26       Impact factor: 4.475

8.  Modeling the impact of lesions in the human brain.

Authors:  Jeffrey Alstott; Michael Breakspear; Patric Hagmann; Leila Cammoun; Olaf Sporns
Journal:  PLoS Comput Biol       Date:  2009-06-12       Impact factor: 4.475

9.  A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs.

Authors:  Sophie Achard; Raymond Salvador; Brandon Whitcher; John Suckling; Ed Bullmore
Journal:  J Neurosci       Date:  2006-01-04       Impact factor: 6.167

10.  A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks.

Authors:  Romesh G Abeysuriya; Jonathan Hadida; Stamatios N Sotiropoulos; Saad Jbabdi; Robert Becker; Benjamin A E Hunt; Matthew J Brookes; Mark W Woolrich
Journal:  PLoS Comput Biol       Date:  2018-02-23       Impact factor: 4.475

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