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

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

doi:10.1016/j.neuroimage.2022.119433

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

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

Neuroimaging Department
Health Psychology Section, Psychology Department, Institute of Psychology, Psychiatry and Neuroscience, King's College London, United Kingdom.
King's College London
Department of Urology and Kidney Transplantation, Ospedali Riuniti BMM, Reggio Calabria, Italy; Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal; PT Government Associate Laboratory, Life and Health Sciences Research Institute, Braga, Portugal. Electronic address: [email protected].
University of Cambridge Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, and Cancer Research UK Cambridge Centre, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK; NIHR BioResource, Cambridge University Hospitals, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK.
Department of Materials, Imperial College London, London SW7 2AZ, United Kingdom; Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom; Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, United Kingdom.

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

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.

Original language	English
Article number	119433
Pages (from-to)	119433
Journal	NeuroImage
Volume	259
Early online date	1 Jul 2022
DOIs	https://doi.org/10.1016/j.neuroimage.2022.119433
Publication status	Published - 1 Oct 2022

Access to Document

10.1016/j.neuroimage.2022.119433

Cite this

@article{cb75c8931bdd4f95b9dc92b734cb47c4,

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

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.",

author = "Fran Hancock and Joana Cabral and Luppi, {Andrea I} and Rosas, {Fernando E} and Mediano, {Pedro A M} and Ottavia Dipasquale and Turkheimer, {Federico E}",

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 {\textquoteleft}corrplot{\textquoteright}: 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 (L{\"u}decke 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: {\textcopyright} 2022 The Author(s)",

year = "2022",

month = oct,

day = "1",

doi = "10.1016/j.neuroimage.2022.119433",

language = "English",

volume = "259",

pages = "119433",

journal = "NeuroImage",

issn = "1053-8119",

publisher = "ACADEMIC PRESS INC ELSEVIER SCIENCE",

}

TY - JOUR

T1 - Metastability, fractal scaling, and synergistic information processing

T2 - What phase relationships reveal about intrinsic brain activity

AU - Hancock, Fran

AU - Cabral, Joana

AU - Luppi, Andrea I

AU - Rosas, Fernando E

AU - Mediano, Pedro A M

AU - Dipasquale, Ottavia

AU - Turkheimer, Federico E

N1 - 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)

PY - 2022/10/1

Y1 - 2022/10/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85133952058&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2022.119433

DO - 10.1016/j.neuroimage.2022.119433

M3 - Article

C2 - 35781077

SN - 1053-8119

VL - 259

SP - 119433

JO - NeuroImage

JF - NeuroImage

M1 - 119433

ER -

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

Abstract

Access to Document

Other files and links

Fingerprint

Cite this