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  Ghost attractors in spontaneous brain activity: Recurrent excursions into functionally-relevant BOLD phase-locking states

Vohryzek, J., Deco, G., Cessac, B., Kringelbach, M. L., & Cabral, J. (2020). Ghost attractors in spontaneous brain activity: Recurrent excursions into functionally-relevant BOLD phase-locking states. Frontiers in Systems Neuroscience, 14: 20. doi:10.3389/fnsys.2020.00020.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-781A-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-781B-6
Genre: Journal Article

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 Creators:
Vohryzek, Jakub1, 2, Author
Deco, Gustavo3, 4, 5, 6, Author              
Cessac, Bruno7, Author
Kringelbach, Morten L.1, 2, Author
Cabral, Joana1, 2, 8, Author
Affiliations:
1Department of Psychiatry, University of Oxford, United Kingdom, ou_persistent22              
2Center for Music in the Brain, Aarhus University, Denmark, ou_persistent22              
3Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Spain, ou_persistent22              
4Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634551              
5Catalan Institution for Research and Advanced Studies (ICREA), University Pompeu Fabra, Barcelona, Spain, ou_persistent22              
6Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia, ou_persistent22              
7Biovision Team, University of Côte d'Azur, Nice, France, ou_persistent22              
8ICVS - Life and Health Sciences Research Institute, School of Health Sciences, University of Minho, Braga, Portugal, ou_persistent22              

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Free keywords: LEiDA; Ghost attractors; Dynamic functional connectivity; Dynamical system theory; Functional networks; Resting-state
 Abstract: Functionally relevant network patterns form transiently in brain activity during rest, where a given subset of brain areas exhibits temporally synchronized BOLD signals. To adequately assess the biophysical mechanisms governing intrinsic brain activity, a detailed characterization of the dynamical features of functional networks is needed from the experimental side to constrain theoretical models. In this work, we use an open-source fMRI dataset from 100 healthy participants from the Human Connectome Project and analyze whole-brain activity using Leading Eigenvector Dynamics Analysis (LEiDA), which serves to characterize brain activity at each time point by its whole-brain BOLD phase-locking pattern. Clustering these BOLD phase-locking patterns into a set of k states, we demonstrate that the cluster centroids closely overlap with reference functional subsystems. Borrowing tools from dynamical systems theory, we characterize spontaneous brain activity in the form of trajectories within the state space, calculating the Fractional Occupancy and the Dwell Times of each state, as well as the Transition Probabilities between states. Finally, we demonstrate that within-subject reliability is maximized when including the high frequency components of the BOLD signal (>0.1 Hz), indicating the existence of individual fingerprints in dynamical patterns evolving at least as fast as the temporal resolution of acquisition (here TR = 0.72 s). Our results reinforce the mechanistic scenario that resting-state networks are the expression of erratic excursions from a baseline synchronous steady state into weakly-stable partially-synchronized states – which we term ghost attractors. To better understand the rules governing the transitions between ghost attractors, we use methods from dynamical systems theory, giving insights into high-order mechanisms underlying brain function.

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Language(s): eng - English
 Dates: 2019-12-072020-03-252020-04-17
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.3389/fnsys.2020.00020
Other: eCollection 2020
PMID: 32362815
PMC: PMC7182014
 Degree: -

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Title: Frontiers in Systems Neuroscience
  Abbreviation : Front Syst Neurosci
Source Genre: Journal
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Publ. Info: Lausanne, Switzerland : Frontiers Research Foundation
Pages: - Volume / Issue: 14 Sequence Number: 20 Start / End Page: - Identifier: ISSN: 1662-5137
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5137