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Metastability, fractal scaling, and synergistic information processing: What phase relationships reveal about intrinsic brain activity

Research output: Contribution to journalArticlepeer-review

Fran Hancock, Joana Cabral, Andrea I Luppi, Fernando E Rosas, Pedro A M Mediano, Ottavia Dipasquale, Federico E Turkheimer

Original languageEnglish
Article number119433
Pages (from-to)119433
JournalNeuroImage
Volume259
Early online date1 Jul 2022
DOIs
E-pub ahead of print1 Jul 2022
Published1 Oct 2022

Bibliographical note

Funding Information: FH received no financial support for the research, authorship, and/or publication of this article. JC was funded by the Portuguese Foundation for Science and Technology (FCT) CEECIND/03325/2017, by the European Regional Development Fund (FEDER) through the Competitiveness Factors Operational Program (COMPETE), by FCT project UID/Multi/50026, by projects NORTE-01- 0145-FEDER-000013, and NORTE-01–0145-FEDER-000023 supported by the NORTE 2020 Programme under the Portugal 2020 Partnership Agreement through FEDER. AL is supported by a Gates Cambridge Scholarship. FR is supported by the Ad Astra Chandaria foundation. PM is funded by the Wellcome Trust (grant no.210920/Z/18/Z). Funding Information: All data used in this study was collected for the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essene and Kamil Ugurbil; 1U54MH091657) with funding from the sixteen NIH Institutes and Centers supporting the NIH Blueprint for Neuroscience Research; and by the McDonell Center for Systems Neuroscience at Washington University. Funding Information: The authors would like to acknowledge the use of the following freely available code:, MATLAB Toolbox dFCwalk https://github.com/FunDyn/dFCwalk. FluctuationAnalysis https://github.com/marlow17/FluctuationAnalysis. ICC Arash Salarian (2021). Intraclass Correlation Coefficient (ICC) (https://www.mathworks.com/matlabcentral/fileexchange/22099-intraclass-correlation-coefficient-icc), MATLAB Central File Exchange. Retrieved August 18, 2021. Corrplot Wei. T. Simko V. (2021). R package ‘corrplot’: Visualization of a Correlation Matrix. (Version 0.92), https://github.com/taiyun/corrplot. lmerTest (Kuznetsova et al. 2017) https://cran.r-project.org/web/packages/lmerTest/index.html. Performance (Lüdecke et al. 2021) https://github.com/easystats/performance. Permutation_htest_np https://users.aalto.fi/~eglerean/permutations.html. Shade Javier Montalt Tordera (2021). Filled area plot. (https://www.mathworks.com/matlabcentral/fileexchange/69652-filled-area-plot), MATLAB Central File Exchange. Retrieved November 19, 2021. Superbar Scott Lowe (2021). superbar (https://github.com/scottclowe/superbar), GitHub. Retrieved November 19, 2021. Ghost Attractors https://github.com/jvohryzek/GhostAttractors. The Matlab and R code developed for this analysis is available github.com/franhancock/Complexity-science-in-dFC together with the 5 phase-locking mode centroids for AAL parcellation in NIFTI and in Matlab format. FH received no financial support for the research, authorship, and/or publication of this article. JC was funded by the Portuguese Foundation for Science and Technology (FCT) CEECIND/03325/2017, by the European Regional Development Fund (FEDER) through the Competitiveness Factors Operational Program (COMPETE), by FCT project UID/Multi/50026, by projects NORTE-01- 0145-FEDER-000013, and NORTE-01–0145-FEDER-000023 supported by the NORTE 2020 Programme under the Portugal 2020 Partnership Agreement through FEDER. AL is supported by a Gates Cambridge Scholarship. FR is supported by the Ad Astra Chandaria foundation. PM is funded by the Wellcome Trust (grant no.210920/Z/18/Z). Fran Hancock: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Software; Visualization; Writing - original draft. Joana Cabral: Formal analysis, Methodology; Software; Writing - review & editing. Andrea I. Luppi: Writing - review & editing. Fernando E. Rosas: Formal analysis, Writing - review & editing. Pedro A.M. Mediano: Formal analysis, Methodology; Writing - review & editing. Ottavia Dipasquale: Writing - review & editing. Federico E. Turkheimer: Formal analysis; Supervision; Writing - review & editing. All authors participated in the discussion of the ideas, read and approved the submitted version. Publisher Copyright: © 2022 The Author(s)

King's Authors

Abstract

Dynamic functional connectivity (dFC) in resting-state fMRI holds promise to deliver candidate biomarkers for clinical applications. However, the reliability and interpretability of dFC metrics remain contested. Despite a myriad of methodologies and resulting measures, few studies have combined metrics derived from different conceptualizations of brain functioning within the same analysis - perhaps missing an opportunity for improved interpretability. Using a complexity-science approach, we assessed the reliability and interrelationships of a battery of phase-based dFC metrics including tools originating from dynamical systems, stochastic processes, and information dynamics approaches. Our analysis revealed novel relationships between these metrics, which allowed us to build a predictive model for integrated information using metrics from dynamical systems and information theory. Furthermore, global metastability - a metric reflecting simultaneous tendencies for coupling and decoupling - was found to be the most representative and stable metric in brain parcellations that included cerebellar regions. Additionally, spatiotemporal patterns of phase-locking were found to change in a slow, non-random, continuous manner over time. Taken together, our findings show that the majority of characteristics of resting-state fMRI dynamics reflect an interrelated dynamical and informational complexity profile, which is unique to each acquisition. This finding challenges the interpretation of results from cross-sectional designs for brain neuromarker discovery, suggesting that individual life-trajectories may be more informative than sample means.

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