State Changes During Resting-State (Magneto)encephalographic Studies: The Effect of Drowsiness on Spectral, Connectivity, and Network Analyses.
EEG
drowsiness
graph connectivity analysis
magnetoencephalography (MEG)
spectral power analysis
Journal
Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481
Informations de publication
Date de publication:
2022
2022
Historique:
received:
24
09
2021
accepted:
28
03
2022
entrez:
5
7
2022
pubmed:
6
7
2022
medline:
6
7
2022
Statut:
epublish
Résumé
A common problem in resting-state neuroimaging studies is that subjects become drowsy or fall asleep. Although this could drastically affect neurophysiological measurements, such as magnetoencephalography (MEG), its specific impact remains understudied. We aimed to systematically investigate how often drowsiness is present during resting-state MEG recordings, and how the state changes alter quantitative estimates of oscillatory activity, functional connectivity, and network topology. About 8-min MEG recordings of 19 healthy subjects, split into ~13-s epochs, were scored for the presence of eyes-open (EO), alert eyes-closed (A-EC), or drowsy eyes-closed (D-EC) states. After projection to source-space, results of spectral, functional connectivity, and network analyses in 6 canonical frequency bands were compared between these states on a global and regional levels. Functional connectivity was analyzed using the phase lag index (PLI) and corrected amplitude envelope correlation (AECc), and network topology was analyzed using the minimum spanning tree (MST). Drowsiness was present in >55% of all epochs that did not fulfill the AASM criteria for sleep. There were clear differences in Drowsiness during eyes-closed resting-state MEG recordings is present in the majority of epochs, despite the instructions to stay awake. This has considerable influence on spectral properties, but much less so on functional connectivity and network topology. These findings are important for interpreting the results of EEG/MEG studies using spectral analyses in neurological disease, where recordings should be evaluated for the presence of drowsiness. For connectivity analyses or studies on network topology, this seems of far less importance.
Sections du résumé
Background
UNASSIGNED
A common problem in resting-state neuroimaging studies is that subjects become drowsy or fall asleep. Although this could drastically affect neurophysiological measurements, such as magnetoencephalography (MEG), its specific impact remains understudied. We aimed to systematically investigate how often drowsiness is present during resting-state MEG recordings, and how the state changes alter quantitative estimates of oscillatory activity, functional connectivity, and network topology.
Methods
UNASSIGNED
About 8-min MEG recordings of 19 healthy subjects, split into ~13-s epochs, were scored for the presence of eyes-open (EO), alert eyes-closed (A-EC), or drowsy eyes-closed (D-EC) states. After projection to source-space, results of spectral, functional connectivity, and network analyses in 6 canonical frequency bands were compared between these states on a global and regional levels. Functional connectivity was analyzed using the phase lag index (PLI) and corrected amplitude envelope correlation (AECc), and network topology was analyzed using the minimum spanning tree (MST).
Results
UNASSIGNED
Drowsiness was present in >55% of all epochs that did not fulfill the AASM criteria for sleep. There were clear differences in
Conclusions
UNASSIGNED
Drowsiness during eyes-closed resting-state MEG recordings is present in the majority of epochs, despite the instructions to stay awake. This has considerable influence on spectral properties, but much less so on functional connectivity and network topology. These findings are important for interpreting the results of EEG/MEG studies using spectral analyses in neurological disease, where recordings should be evaluated for the presence of drowsiness. For connectivity analyses or studies on network topology, this seems of far less importance.
Identifiants
pubmed: 35784839
doi: 10.3389/fnins.2022.782474
pmc: PMC9245543
doi:
Types de publication
Journal Article
Langues
eng
Pagination
782474Informations de copyright
Copyright © 2022 Strijbis, Timar, Schoonhoven, Nauta, Kulik, de Ruiter, Schoonheim, Hillebrand and Stam.
Déclaration de conflit d'intérêts
MS has received compensation for consulting services or speaker honoraria from ExceMed, MedDay, Atara, Sanofi-Genzyme, and Biogen. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Références
J Clin Sleep Med. 2017 May 15;13(5):665-666
pubmed: 28416048
Phys Med Biol. 2006 Apr 7;51(7):1759-68
pubmed: 16552102
J Clin Neurophysiol. 2019 Jul;36(4):250-256
pubmed: 31274687
Psychophysiology. 2002 May;39(3):313-21
pubmed: 12212650
Proc Natl Acad Sci U S A. 2016 Apr 5;113(14):3867-72
pubmed: 27001844
Brain. 2007 Jul;130(Pt 7):1847-60
pubmed: 17412733
Hum Brain Mapp. 2014 Jan;35(1):257-69
pubmed: 23008180
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:7994-7
pubmed: 26738147
Nat Neurosci. 2012 Jun;15(6):884-90
pubmed: 22561454
J Clin Neurophysiol. 1987 Oct;4(4):327-82
pubmed: 3316272
Brain Connect. 2016 Jul;6(6):448-60
pubmed: 27212454
Neurobiol Aging. 2012 May;33(5):1008.e25-31
pubmed: 22118944
Electroencephalogr Clin Neurophysiol. 1997 Mar;102(3):216-27
pubmed: 9129577
Mult Scler. 2019 Dec;25(14):1896-1906
pubmed: 30465461
Neuroimage. 2012 Feb 15;59(4):3909-21
pubmed: 22122866
Neuron. 2014 May 7;82(3):695-708
pubmed: 24811386
Psychol Forsch. 1970;33(2):85-99
pubmed: 5515904
Hum Brain Mapp. 2014 Dec;35(12):5946-61
pubmed: 25053254
J Neurosci Methods. 2009 Feb 15;177(1):203-11
pubmed: 18996412
IEEE Trans Biomed Eng. 2004 Oct;51(10):1726-34
pubmed: 15490820
PLoS One. 2014 Oct 06;9(10):e108648
pubmed: 25286380
Proc Natl Acad Sci U S A. 2010 Mar 30;107(13):6040-5
pubmed: 20304792
Alzheimers Res Ther. 2020 Jun 3;12(1):68
pubmed: 32493476
J Neurophysiol. 2011 Dec;106(6):2888-95
pubmed: 21880941
J Neural Eng. 2017 Oct;14(5):056012
pubmed: 28627505
Clin Neurophysiol. 2007 Nov;118(11):2519-24
pubmed: 17892969
Psychophysiology. 2012 Apr;49(4):574-82
pubmed: 22324302
Sci Rep. 2017 Aug 29;7(1):9685
pubmed: 28852152
Neuroimage. 2009 Oct 1;47(4):1460-8
pubmed: 19463959
BMC Neurosci. 2020 May 12;21(1):20
pubmed: 32398004
Brain Topogr. 2004 Summer;16(4):269-75
pubmed: 15379226
Neuroimage. 2014 Mar;88:308-18
pubmed: 24161625
Neuroimage. 2022 Feb 15;247:118823
pubmed: 34923132
Electroencephalogr Clin Neurophysiol. 1966 Nov;21(5):501-3
pubmed: 4162774
Alzheimers Dement. 2021 Sep;17(9):1528-1553
pubmed: 33860614
Int J Psychophysiol. 2014 Jun;92(3):129-38
pubmed: 24726900
Nat Neurosci. 2020 Dec;23(12):1473-1483
pubmed: 32958924
Neuroreport. 2000 May 15;11(7):1509-14
pubmed: 10841367
Hum Brain Mapp. 2018 Jun;39(6):2455-2471
pubmed: 29468769
J Neurosci Methods. 2021 Jan 15;348:108985
pubmed: 33164816
J Neural Eng. 2016 Jun;13(3):036015
pubmed: 27137952
Neuroimage. 2016 Sep;138:284-293
pubmed: 27262239
Electroencephalogr Clin Neurophysiol. 1991 Nov;79(5):382-92
pubmed: 1718711
Psychophysiology. 2005 Nov;42(6):691-7
pubmed: 16364064
Clin Neurophysiol. 2007 Aug;118(8):1691-704
pubmed: 17587643
Neuroimage. 2015 Jan 1;104:177-88
pubmed: 25451472
Cereb Cortex. 2009 Mar;19(3):524-36
pubmed: 18567609
Neuroimage. 2018 Jun;173:632-643
pubmed: 29477441
Brain Connect. 2013;3(1):50-60
pubmed: 23106635
Australas Phys Eng Sci Med. 2016 Sep;39(3):797-806
pubmed: 27550443
Hum Brain Mapp. 2008 Nov;29(11):1288-301
pubmed: 17894391
Brain Connect. 2019 Sep;9(7):539-553
pubmed: 31115272
Hum Brain Mapp. 2007 Nov;28(11):1178-93
pubmed: 17266107