Data-driven discovery of canonical large-scale brain dynamics.
brain dynamics
computational modeling
fMRI
resting state
sleep
Journal
Cerebral cortex communications
ISSN: 2632-7376
Titre abrégé: Cereb Cortex Commun
Pays: United States
ID NLM: 101767128
Informations de publication
Date de publication:
2022
2022
Historique:
received:
09
09
2022
revised:
28
10
2022
accepted:
30
10
2022
entrez:
8
12
2022
pubmed:
9
12
2022
medline:
9
12
2022
Statut:
epublish
Résumé
Human behavior and cognitive function correlate with complex patterns of spatio-temporal brain dynamics, which can be simulated using computational models with different degrees of biophysical realism. We used a data-driven optimization algorithm to determine and classify the types of local dynamics that enable the reproduction of different observables derived from functional magnetic resonance recordings. The phase space analysis of the resulting equations revealed a predominance of stable spiral attractors, which optimized the similarity to the empirical data in terms of the synchronization, metastability, and functional connectivity dynamics. For stable limit cycles, departures from harmonic oscillations improved the fit in terms of functional connectivity dynamics. Eigenvalue analyses showed that proximity to a bifurcation improved the accuracy of the simulation for wakefulness, whereas deep sleep was associated with increased stability. Our results provide testable predictions that constrain the landscape of suitable biophysical models, while supporting noise-driven dynamics close to a bifurcation as a canonical mechanism underlying the complex fluctuations that characterize endogenous brain activity.
Identifiants
pubmed: 36479448
doi: 10.1093/texcom/tgac045
pii: tgac045
pmc: PMC9721525
doi:
Types de publication
Journal Article
Langues
eng
Pagination
tgac045Informations de copyright
© The Author(s) 2022. Published by Oxford University Press.
Références
J Neurosci. 2015 Jul 29;35(30):10866-77
pubmed: 26224868
Biol Psychiatry Cogn Neurosci Neuroimaging. 2018 Sep;3(9):777-787
pubmed: 30093344
Brain Sci. 2020 Sep 10;10(9):
pubmed: 32927678
PLoS Comput Biol. 2008 Aug 29;4(8):e1000092
pubmed: 18769680
Neuroimage. 2020 Jul 15;215:116833
pubmed: 32289454
PLoS One. 2016 Aug 18;11(8):e0157292
pubmed: 27536987
Phys Rev Lett. 2020 Dec 4;125(23):238101
pubmed: 33337222
Science. 2005 Sep 30;309(5744):2228-32
pubmed: 16195466
J Neurosci. 2011 Apr 27;31(17):6353-61
pubmed: 21525275
PLoS Comput Biol. 2008 Oct;4(10):e1000196
pubmed: 18846206
Science. 1998 Dec 4;282(5395):1846-51
pubmed: 9836628
Nat Rev Neurosci. 2011 Jan;12(1):43-56
pubmed: 21170073
Chaos. 2021 Feb;31(2):023127
pubmed: 33653038
Neuron. 2014 Dec 3;84(5):892-905
pubmed: 25475184
Sci Rep. 2017 Jun 8;7(1):3095
pubmed: 28596608
Trends Cogn Sci. 2011 May;15(5):200-9
pubmed: 21497128
AJNR Am J Neuroradiol. 2001 Aug;22(7):1326-33
pubmed: 11498421
PLoS Comput Biol. 2012;8(8):e1002634
pubmed: 22912567
Neuroimage. 2002 Jan;15(1):273-89
pubmed: 11771995
Curr Opin Neurobiol. 2013 Apr;23(2):162-71
pubmed: 23294553
Nat Commun. 2022 Apr 19;13(1):2019
pubmed: 35440540
Nat Rev Neurosci. 2009 Mar;10(3):186-98
pubmed: 19190637
Curr Biol. 2018 Oct 8;28(19):3065-3074.e6
pubmed: 30270185
PLoS Comput Biol. 2021 Jul 27;17(7):e1009139
pubmed: 34314430
Magn Reson Med. 2000 Jul;44(1):162-7
pubmed: 10893535
Neuroimage. 2017 Jan 15;145(Pt B):377-388
pubmed: 27477535
Trends Neurosci. 2000 May;23(5):216-22
pubmed: 10782127
Neuroimage. 2022 Apr 15;250:118928
pubmed: 35101596
Proc Natl Acad Sci U S A. 2009 Jun 23;106(25):10302-7
pubmed: 19497858
Chaos. 2018 Oct;28(10):106321
pubmed: 30384618
Phys Rev Res. 2020 Apr-Jun;2(2):
pubmed: 33718881
Proc Natl Acad Sci U S A. 2020 Apr 28;117(17):9566-9576
pubmed: 32284420
IEEE Trans Image Process. 2004 Apr;13(4):600-12
pubmed: 15376593
Curr Opin Neurol. 2016 Aug;29(4):429-36
pubmed: 27224088
Sci Rep. 2017 Jul 5;7(1):4634
pubmed: 28680119