Simulating human sleep spindle MEG and EEG from ion channel and circuit level dynamics.
Cortex
EEG
Forward model
Human
MEG
Sleep
Spindle
Thalamus
Journal
Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558
Informations de publication
Date de publication:
15 03 2019
15 03 2019
Historique:
received:
23
05
2018
revised:
03
10
2018
accepted:
04
10
2018
pubmed:
10
10
2018
medline:
29
7
2020
entrez:
10
10
2018
Statut:
ppublish
Résumé
Although they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain. We present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects' MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors. The simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted. Previous models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales. This multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.
Sections du résumé
BACKGROUND
Although they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain.
METHOD
We present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects' MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors.
RESULTS
The simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted.
COMPARISON WITH EXISTING METHODS
Previous models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales.
CONCLUSIONS
This multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.
Identifiants
pubmed: 30300700
pii: S0165-0270(18)30310-8
doi: 10.1016/j.jneumeth.2018.10.002
pmc: PMC6380919
mid: NIHMS993389
pii:
doi:
Substances chimiques
Ion Channels
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
46-57Subventions
Organisme : NINDS NIH HHS
ID : T32 NS061847
Pays : United States
Organisme : NIMH NIH HHS
ID : T32 MH020002
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB009282
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH099645
Pays : United States
Organisme : NIMH NIH HHS
ID : RF1 MH117155
Pays : United States
Informations de copyright
Copyright © 2018 Elsevier B.V. All rights reserved.
Références
Nat Neurosci. 2018 Sep;21(9):1251-1259
pubmed: 30082915
PLoS Comput Biol. 2018 Jun 27;14(6):e1006171
pubmed: 29949575
J Neurophysiol. 2012 Aug;108(4):956-75
pubmed: 22539822
IEEE Trans Med Imaging. 2005 Jan;24(1):12-28
pubmed: 15638183
J Neurosci. 1992 Oct;12(10):3804-17
pubmed: 1403085
Proc Natl Acad Sci U S A. 1997 Jan 21;94(2):719-23
pubmed: 9012851
J Neurophysiol. 2010 Jul;104(1):179-88
pubmed: 20427615
Cereb Cortex. 2000 Dec;10(12):1185-99
pubmed: 11073868
Neuroimage. 2015 Jan 15;105:1-12
pubmed: 25450108
Neuroimage. 2015 May 1;111:49-58
pubmed: 25680520
Elife. 2016 Nov 15;5:
pubmed: 27855061
J Physiol Paris. 2003 Jul-Nov;97(4-6):641-58
pubmed: 15242672
Nat Rev Neurosci. 2010 Feb;11(2):114-26
pubmed: 20046194
J Neurophysiol. 1996 Sep;76(3):2049-70
pubmed: 8890314
Hum Brain Mapp. 2000 Dec;11(4):286-93
pubmed: 11144757
Philos Trans R Soc Lond B Biol Sci. 2002 Dec 29;357(1428):1659-73
pubmed: 12626002
J Neurosci. 2018 Mar 21;38(12):3013-3025
pubmed: 29449429
Neuron. 2011 Dec 8;72(5):859-72
pubmed: 22153380
J Neurosci. 2002 Oct 1;22(19):8691-704
pubmed: 12351744
J Comput Neurosci. 2018 Feb;44(1):1-24
pubmed: 29230640
J Neurosci. 2011 Jun 22;31(25):9124-34
pubmed: 21697364
J Neurosci. 2011 Dec 7;31(49):17821-34
pubmed: 22159098
Neuroimage. 2017 Feb 1;146:236-245
pubmed: 27840241
Comput Intell Neurosci. 2011;2011:156869
pubmed: 21253357
J Neurophysiol. 2013 Mar;109(6):1683
pubmed: 23503557
Hum Brain Mapp. 2010 Jan;31(1):140-9
pubmed: 19639553
Brain Topogr. 2010 Sep;23(3):227-32
pubmed: 20640882
Biol Psychiatry. 2016 Oct 15;80(8):599-608
pubmed: 26602589
Nature. 2014 Jul 24;511(7510):421-7
pubmed: 25056061
Neuroimage. 2001 Dec;14(6):1424-31
pubmed: 11707098
Nat Rev Neurosci. 2013 Nov;14(11):770-85
pubmed: 24135696
PLoS Comput Biol. 2014 Sep 25;10(9):e1003855
pubmed: 25255217
Neuroimage. 1999 Feb;9(2):179-94
pubmed: 9931268
Clin Neurophysiol. 2011 Feb;122(2):229-35
pubmed: 20637689
Sci Rep. 2018 Feb 1;8(1):2055
pubmed: 29391596
Nat Commun. 2017 May 25;8:15499
pubmed: 28541306
PLoS Comput Biol. 2016 Sep 01;12(9):e1005022
pubmed: 27584827
Biol Psychiatry. 2012 Jan 15;71(2):154-61
pubmed: 21967958
J Neurosci. 2016 Apr 13;36(15):4231-47
pubmed: 27076422
Proc Natl Acad Sci U S A. 2018 Jun 26;115(26):6858-6863
pubmed: 29884650
J Neurophysiol. 1992 Oct;68(4):1373-83
pubmed: 1279135
J Neuroeng Rehabil. 2007 Nov 30;4:46
pubmed: 18053144
Science. 1993 Oct 29;262(5134):679-85
pubmed: 8235588
IEEE Trans Pattern Anal Mach Intell. 2009 Jun;31(6):1006-16
pubmed: 19372606
Cell. 1993 Jan;72 Suppl:55-63
pubmed: 8094037
Clin Neurophysiol. 2002 Dec;113(12):1937-47
pubmed: 12464331
Front Neuroinform. 2014 Jan 16;7:41
pubmed: 24474916
Neuroimage. 2013 Oct 15;80:144-68
pubmed: 23702415
Hum Brain Mapp. 2011 Dec;32(12):2270-1
pubmed: 21826758
Elife. 2016 Nov 16;5:
pubmed: 27849520
Med Biol Eng Comput. 1994 Jan;32(1):35-42
pubmed: 8182960
J Neurosci. 2012 Apr 11;32(15):5250-63
pubmed: 22496571
J Neurosci. 2013 Sep 4;33(36):14489-500
pubmed: 24005300
Biomed Eng Online. 2010 Sep 06;9:45
pubmed: 20819204
J Physiol. 1990 Dec;431:291-318
pubmed: 1712843
Trends Neurosci. 2001 Oct;24(10):595-601
pubmed: 11576674
IEEE Trans Biomed Eng. 1998 Sep;45(9):1135-45
pubmed: 9735563
Nat Rev Neurosci. 2006 Feb;7(2):153-60
pubmed: 16429124
J Comp Neurol. 2014 Jan 1;522(1):225-59
pubmed: 23983048
J Neurosci. 1996 Jan;16(1):169-85
pubmed: 8613783
Hum Brain Mapp. 2011 Dec;32(12):2217-27
pubmed: 21337472
J Neurophysiol. 2013 Mar;109(6):1681-2
pubmed: 23503556
J Neurophysiol. 2008 Sep;100(3):1562-75
pubmed: 18632897
eNeuro. 2015 Sep 17;2(4):
pubmed: 26465003
J Neurosci Methods. 2001 Mar 30;106(1):69-79
pubmed: 11248342
J Neurosci Methods. 2006 Jun 30;154(1-2):116-33
pubmed: 16436298
J Biol Phys. 2008 Aug;34(3-4):279-99
pubmed: 19669478
Magn Reson Imaging. 2004 Dec;22(10):1533-8
pubmed: 15707802
Neuroimage. 2012 Feb 1;59(3):2464-74
pubmed: 21959078
Brain Topogr. 2013 Jul;26(3):378-96
pubmed: 23355112
Neuron. 2013 Dec 4;80(5):1112-28
pubmed: 24314724
J Physiol. 2012 Aug 15;590(16):3987-4010
pubmed: 22641778
Brain Res. 2000 Dec 15;886(1-2):208-223
pubmed: 11119697
J Comput Neurosci. 2004 Sep-Oct;17(2):203-23
pubmed: 15306740
J Neurophysiol. 2012 Aug;108(4):953-5
pubmed: 22572946
Nature. 2016 Aug 11;536(7615):171-178
pubmed: 27437579
J Neurophysiol. 2000 Aug;84(2):1076-87
pubmed: 10938329
Brain Connect. 2013;3(2):121-45
pubmed: 23442172
Neuroimage. 2012 Aug 15;62(2):774-81
pubmed: 22248573
Science. 1996 Nov 1;274(5288):771-4
pubmed: 8864114
J Neurophysiol. 1998 Feb;79(2):999-1016
pubmed: 9463458
Trends Neurosci. 2017 Apr;40(4):208-218
pubmed: 28314445
J Neurosci Methods. 2004 Mar 15;134(1):9-21
pubmed: 15102499