Relationship of critical dynamics, functional connectivity, and states of consciousness in large-scale human brain networks
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Autor:innen
Lee, Heonsoo
Golkowski, Daniel
Berger, Sebastian
Ilg, Rüdiger
Lee, Joseph
Mashour, George A.
Lee, UnCheol
Avidan, Michael S.
Blain-Moraes, Stefanie
Autor:in (Körperschaft)
Publikationsdatum
2019
Typ der Arbeit
Studiengang
Sammlung
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
NeuroImage
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
188
Ausgabe / Nummer
Seiten / Dauer
228-238
Patentnummer
Verlag / Herausgebende Institution
Elsevier
Verlagsort / Veranstaltungsort
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Recent modeling and empirical studies support the hypothesis that large-scale brain networks function near a critical state. Similar functional connectivity patterns derived from resting state empirical data and brain network models at criticality provide further support. However, despite the strong implication of a relationship, there has been no principled explanation of how criticality shapes the characteristic functional connectivity in large-scale brain networks. Here, we hypothesized that the network science concept of partial phase locking is the underlying mechanism of optimal functional connectivity in the resting state. We further hypothesized that the characteristic connectivity of the critical state provides a theoretical boundary to quantify how far pharmacologically or pathologically perturbed brain connectivity deviates from its critical state, which could enable the differentiation of various states of consciousness with a theory-based metric. To test the hypothesis, we used a neuroanatomically informed brain network model with the resulting source signals projected to electroencephalogram (EEG)-like sensor signals with a forward model. Phase lag entropy (PLE), a measure of phase relation diversity, was estimated and the topography of PLE was analyzed. To measure the distance from criticality, the PLE topography at a critical state was compared with those of the EEG data from baseline consciousness, isoflurane anesthesia, ketamine anesthesia, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state. We demonstrate that the partial phase locking at criticality shapes the functional connectivity and asymmetric anterior-posterior PLE topography, with low (high) PLE for high (low) degree nodes. The topographical similarity and the strength of PLE differentiates various pharmacologic and pathologic states of consciousness. Moreover, this model-based EEG network analysis provides a novel metric to quantify how far a pharmacologically or pathologically perturbed brain network is away from critical state, rather than merely determining whether it is in a critical or non-critical state.
Schlagwörter
Criticality, Consciousness, Functional connectivity, Electroencephalogram, Disorders of consciousness, Anesthesia
Fachgebiet (DDC)
600 - Technik, Medizin, angewandte Wissenschaften
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
1053-8119
1095-9572
1095-9572
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Gold
Lizenz
Zitation
LEE, Heonsoo, Daniel GOLKOWSKI, Denis JORDAN, Sebastian BERGER, Rüdiger ILG, Joseph LEE, George A. MASHOUR, UnCheol LEE, Michael S. AVIDAN, Stefanie BLAIN-MORAES, Goodarz GOLMIRZAIE, Randall HARDIE, Rosemary HOGG, Ellen JANKE, Max B. KELZ, Kaitlyn MAIER, George A. MASHOUR, Hannah MAYBRIER, Andrew MCKINSTRY-WU, Maxwell MUENCH, Andrew OCHROCH, Ben J.A. PALANCA, Paul PICTON, E. Marlon SCHWARZ, Vijay TARNAL, Giancarlo VANINI und Phillip E. VLISIDES, 2019. Relationship of critical dynamics, functional connectivity, and states of consciousness in large-scale human brain networks. NeuroImage. 2019. Bd. 188, S. 228–238. DOI 10.1016/j.neuroimage.2018.12.011. Verfügbar unter: https://doi.org/10.26041/fhnw-9536