The dynamic core hypothesis posits that consciousness is correlated with simultaneously integrated and differentiated assemblies of transiently synchronized brain regions. We represented time-dependent functional interactions using dynamic brain networks and assessed the integrity of the dynamic core by means of the size and flexibility of the largest multilayer module. As a first step, we constrained parameter selection using a newly developed benchmark for module detection in heterogeneous temporal networks. Next, we applied a multilayer modularity maximization algorithm to dynamic brain networks computed from functional magnetic resonance imaging (fMRI) data acquired during deep sleep and under propofol anesthesia. We found that unconsciousness reconfigured network flexibility and reduced the size of the largest spatiotemporal module, which we identified with the dynamic core. Our results represent a first characterization of modular brain network dynamics during states of unconsciousness measured with fMRI, adding support to the dynamic core hypothesis of human consciousness.

1.
W.
James
,
The Principles of Psychology
(
Cosimo Inc.
,
2007
), Vol. 1.
2.
G. M.
Edelman
and
G.
Tononi
, “Reentry and the dynamic core: Neural correlates of conscious experience,” in Neural Correlates of Consciousness: Empirical and Conceptual Questions (MIT Press, 2000), pp. 139–151.
3.
G.
Tononi
and
G. M.
Edelman
, “
Consciousness and complexity
,”
Science
282
,
1846
(
1998
).
4.
G. M.
Edelman
and
G.
Tononi
,
A Universe of Consciousness: How Matter Becomes Imagination
(
Basic Books
,
2008
).
5.
G.
Rees
,
G.
Kreiman
, and
C.
Koch
, “
Neural correlates of consciousness in humans
,”
Nat. Rev. Neurosci.
3
,
261
(
2002
).
6.
G.
Tononi
,
M.
Boly
,
M.
Massimini
, and
C.
Koch
, “
Integrated information theory: From consciousness to its physical substrate
,”
Nat. Rev. Neurosci.
17
,
450
(
2016
).
7.
M.
Tegmark
, “
Improved measures of integrated information
,”
PLoS Comput. Biol.
12
,
e1005123
(
2016
).
8.
R. L.
Carhart-Harris
,
R.
Leech
,
P. J.
Hellyer
,
M.
Shanahan
,
A.
Feilding
,
E.
Tagliazucchi
,
D. R.
Chialvo
, and
D.
Nutt
, “
The entropic brain: A theory of conscious states informed by neuroimaging research with psychedelic drugs
,”
Front. Hum. Neurosci.
8
,
20
(
2014
).
9.
M.
Schartner
,
A.
Seth
,
Q.
Noirhomme
,
M.
Boly
,
M.-A.
Bruno
,
S.
Laureys
, and
A.
Barrett
, “
Complexity of multi-dimensional spontaneous EEG decreases during propofol induced general anaesthesia
,”
PLoS One
10
,
e0133532
(
2015
).
10.
J.-R.
King
,
J. D.
Sitt
,
F.
Faugeras
,
B.
Rohaut
,
I.
El Karoui
,
L.
Cohen
,
L.
Naccache
, and
S.
Dehaene
, “
Information sharing in the brain indexes consciousness in noncommunicative patients
,”
Curr. Biol.
23
,
1914
(
2013
).
11.
A. G.
Casali
,
O.
Gosseries
,
M.
Rosanova
,
M.
Boly
,
S.
Sarasso
,
K. R.
Casali
,
S.
Casarotto
,
M.-A.
Bruno
,
S.
Laureys
,
G.
Tononi
, and
M.
Massimini
, “
A theoretically based index of consciousness independent of sensory processing and behavior
,”
Sci. Transl. Med.
5
,
198ra105
(
2013
).
12.
E.
Tagliazucchi
,
D. R.
Chialvo
,
M.
Siniatchkin
,
E.
Amico
,
J.-F.
Brichant
,
V.
Bonhomme
,
Q.
Noirhomme
,
H.
Laufs
, and
S.
Laureys
, “
Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics
,”
J. R. Soc. Interface
13
,
20151027
(
2016
).
13.
E.
Tagliazucchi
, “
The signatures of conscious access and its phenomenology are consistent with large-scale brain communication at criticality
,”
Conscious. Cogn.
55
,
136
(
2017
).
14.
H.
Bocaccio
,
C.
Pallavicini
,
M.
Castro
,
S.
Sánchez
,
G.
De Pino
,
H.
Laufs
,
M.
Villarreal
, and
E.
Tagliazucchi
, “
The avalanche-like behaviour of large-scale haemodynamic activity from wakefulness to deep sleep
,”
J. R. Soc. Interface
16
,
20190262
(
2019
).
15.
O.
Sporns
, “
Network attributes for segregation and integration in the human brain
,”
Curr. Opin. Neurobiol.
23
,
162
(
2013
).
16.
S. F.
Muldoon
and
D. S.
Bassett
, “
Network and multilayer network approaches to understanding human brain dynamics
,”
Philos. Sci.
83
,
710
(
2016
).
17.
U.
Braun
,
A.
Schäfer
,
H.
Walter
,
S.
Erk
,
N.
Romanczuk-Seiferth
,
L.
Haddad
,
J. I.
Schweiger
,
O.
Grimm
,
A.
Heinz
,
H.
Tost
et al., “
Dynamic reconfiguration of frontal brain networks during executive cognition in humans
,”
Proc. Natl. Acad. Sci. U.S.A.
112
,
011678
(
2015
).
18.
Q. K.
Telesford
,
M.-E.
Lynall
,
J.
Vettel
,
M. B.
Miller
,
S. T.
Grafton
, and
D. S.
Bassett
, “
Detection of functional brain network reconfiguration during task-driven cognitive states
,”
NeuroImage
142
,
198
(
2016
).
19.
D. S.
Bassett
,
N. F.
Wymbs
,
M. A.
Porter
,
P. J.
Mucha
,
J. M.
Carlson
, and
S. T.
Grafton
, “
Dynamic reconfiguration of human brain networks during learning
,”
Proc. Natl. Acad. Sci. U.S.A.
108
,
7641
(
2011
).
20.
M.
Pedersen
,
A.
Zalesky
,
A.
Omidvarnia
, and
G. D.
Jackson
, “
Multilayer network switching rate predicts brain performance
,”
Proc. Natl. Acad. Sci. U.S.A.
115
,
013376
(
2018
).
21.
G.
Gifford
,
N.
Crossley
,
M. J.
Kempton
,
S.
Morgan
,
P.
Dazzan
,
J.
Young
, and
P.
McGuire
, “
Resting state fMRI based multilayer network configuration in patients with schizophrenia
,”
NeuroImage
25
,
102169
(
2020
).
22.
P. J.
Mucha
,
T.
Richardson
,
K.
Macon
,
M. A.
Porter
, and
J.-P.
Onnela
, “
Community structure in time-dependent, multiscale, and multiplex networks
,”
Science
328
,
876
(
2010
).
23.
D. S.
Bassett
,
P.
Zurn
, and
J. I.
Gold
, “
On the nature and use of models in network neuroscience
,”
Nat. Rev. Neurosci.
19
,
566
(
2018
).
24.
A.
Lancichinetti
,
S.
Fortunato
, and
F.
Radicchi
, “
Benchmark graphs for testing community detection algorithms
,”
Phys. Rev. E
78
,
046110
(
2008
).
25.
R. B.
Berry
,
R.
Brooks
,
C. E.
Gamaldo
,
S. M.
Harding
,
C.
Marcus
,
B. V.
Vaughn
et al., “
The AASM manual for the scoring of sleep and associated events, rules, terminology and technical specifications, Darien, Illinois
,”
Am. Acad. Sleep Med.
176
,
2012
(
2012
).
26.
C.
Granell
,
R. K.
Darst
,
A.
Arenas
,
S.
Fortunato
, and
S.
Gómez
, “
Benchmark model to assess community structure in evolving networks
,”
Phys. Rev. E
92
,
012805
(
2015
).
27.
N.
Tzourio-Mazoyer
,
B.
Landeau
,
D.
Papathanassiou
,
F.
Crivello
,
O.
Etard
,
N.
Delcroix
,
B.
Mazoyer
, and
M.
Joliot
, “
Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain
,”
NeuroImage
15
,
273
(
2002
).
28.
N. A.
Crossley
,
A.
Mechelli
,
J.
Scott
,
F.
Carletti
,
P. T.
Fox
,
P.
McGuire
, and
E. T.
Bullmore
, “
The hubs of the human connectome are generally implicated in the anatomy of brain disorders
,”
Brain
137
,
2382
(
2014
).
29.
Y.
Benjamini
and
Y.
Hochberg
, “
Controlling the false discovery rate: A practical and powerful approach to multiple testing
,”
J. R. Stat. Soc.: Ser. B Stat. Methodol.
57
,
289
(
1995
).
30.
E.
Tagliazucchi
and
H.
Laufs
, “
Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep
,”
Neuron
82
,
695
(
2014
).
31.
F.
Cavanna
,
M. G.
Vilas
,
M.
Palmucci
, and
E.
Tagliazucchi
, “
Dynamic functional connectivity and brain metastability during altered states of consciousness
,”
NeuroImage
180
,
383
(
2018
).
32.
B. J.
He
, “
Scale-free brain activity: Past, present, and future
,”
Trends Cognit. Sci.
18
,
480
(
2014
).
33.
R. M.
Hutchison
,
T.
Womelsdorf
,
E. A.
Allen
,
P. A.
Bandettini
,
V. D.
Calhoun
,
M.
Corbetta
,
S.
Della Penna
,
J. H.
Duyn
,
G. H.
Glover
,
J.
Gonzalez-Castillo
et al., “
Dynamic functional connectivity: Promise, issues, and interpretations
,”
NeuroImage
80
,
360
(
2013
).
34.
E.
Tagliazucchi
,
F.
Von Wegner
,
A.
Morzelewski
,
V.
Brodbeck
, and
H.
Laufs
, “
Dynamic bold functional connectivity in humans and its electrophysiological correlates
,”
Front. Hum. Neurosci.
6
,
339
(
2012
).
35.
R.
Liégeois
,
E.
Ziegler
,
C.
Phillips
,
P.
Geurts
,
F.
Gómez
,
M. A.
Bahri
,
B. T.
Yeo
,
A.
Soddu
,
A.
Vanhaudenhuyse
,
S.
Laureys
et al., “
Cerebral functional connectivity periodically (de) synchronizes with anatomical constraints
,”
Brain Struct. Funct.
221
,
2985
(
2016
).
36.
I.
Diez
and
J.
Sepulcre
, “
Neurogenetic profiles delineate large-scale connectivity dynamics of the human brain
,”
Nat. Commun.
9
,
1
(
2018
).
37.
P.
Barttfeld
,
L.
Uhrig
,
J. D.
Sitt
,
M.
Sigman
,
B.
Jarraya
, and
S.
Dehaene
, “
Signature of consciousness in the dynamics of resting-state brain activity
,”
Proc. Natl. Acad. Sci. U.S.A.
112
,
887
(
2015
).
38.
E.
Tagliazucchi
and
E. J.
van Someren
, “
The large-scale functional connectivity correlates of consciousness and arousal during the healthy and pathological human sleep cycle
,”
NeuroImage
160
,
55
(
2017
).
39.
Z.
Huang
,
J.
Zhang
,
J.
Wu
,
G. A.
Mashour
, and
A. G.
Hudetz
, “
Temporal circuit of macroscale dynamic brain activity supports human consciousness
,”
Sci. Adv.
6
,
eaaz0087
(
2020
).
40.
A.
Demertzi
,
E.
Tagliazucchi
,
S.
Dehaene
,
G.
Deco
,
P.
Barttfeld
,
F.
Raimondo
,
C.
Martial
,
D.
Fernández-Espejo
,
B.
Rohaut
,
H.
Voss
et al., “
Human consciousness is supported by dynamic complex patterns of brain signal coordination
,”
Sci. Adv.
5
,
eaat7603
(
2019
).
41.
Y.
Ma
,
C.
Hamilton
, and
N.
Zhang
, “
Dynamic connectivity patterns in conscious and unconscious brain
,”
Brain Connect.
7
,
1
(
2017
).
42.
E.
Tagliazucchi
,
P.
Balenzuela
,
D.
Fraiman
, and
D. R.
Chialvo
, “
Criticality in large-scale brain fMRI dynamics unveiled by a novel point process analysis
,”
Front. Physiol.
3
,
15
(
2012
).
43.
E.
Tagliazucchi
,
M.
Siniatchkin
,
H.
Laufs
, and
D. R.
Chialvo
, “
The voxel-wise functional connectome can be efficiently derived from co-activations in a sparse spatio-temporal point-process
,”
Front. Neurosci.
10
,
381
(
2016
).
44.
X.
Liu
,
N.
Zhang
,
C.
Chang
, and
J. H.
Duyn
, “
Co-activation patterns in resting-state fMRI signals
,”
NeuroImage
180
,
485
(
2018
).
45.
B.
Hunyadi
,
M. W.
Woolrich
,
A. J.
Quinn
,
D.
Vidaurre
, and
M.
De Vos
, “
A dynamic system of brain networks revealed by fast transient EEG fluctuations and their fMRI correlates
,”
NeuroImage
185
,
72
(
2019
).
46.
R. F.
Betzel
and
D. S.
Bassett
, “
Multi-scale brain networks
,”
NeuroImage
160
,
73
(
2017
).
47.
L.
Cai
,
X.
Wei
,
J.
Wang
,
G.
Yi
,
M.
Lu
, and
Y.
Dong
, “
Characterization of network switching in disorder of consciousness at multiple time scales
,”
J. Neural Eng.
17
,
026024
(
2020
).
48.
H.
Laufs
,
A.
Kleinschmidt
,
A.
Beyerle
,
E.
Eger
,
A.
Salek-Haddadi
,
C.
Preibisch
, and
K.
Krakow
, “
EEG-correlated fMRI of human alpha activity
,”
NeuroImage
19
,
1463
(
2003
).
49.
J. C.
de Munck
,
S. I.
Gonçalves
,
L.
Huijboom
,
J. P.
Kuijer
,
P. J.
Pouwels
,
R. M.
Heethaar
, and
F. L.
da Silva
, “
The hemodynamic response of the alpha rhythm: An EEG/fMRI study
,”
NeuroImage
35
,
1142
(
2007
).
50.
R. I.
Goldman
,
J. M.
Stern
,
J.
Engel
, and
M. S.
Cohen
, “
Simultaneous EEG and fMRI of the alpha rhythm
,”
Neuroreport
13
,
2487
(
2002
).
51.
D.
Mantini
,
M. G.
Perrucci
,
C.
Del Gratta
,
G. L.
Romani
, and
M.
Corbetta
, “
Electrophysiological signatures of resting state networks in the human brain
,”
Proc. Natl. Acad. Sci. U.S.A.
104
,
013170
(
2007
).
52.
E.
Tagliazucchi
, “
Unconsciousness reconfigures modular brain network dynamics
,”
Zenodo
(
2021
).

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