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      Questions and controversies in the study of time-varying functional connectivity in resting fMRI

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          Abstract

          The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain’s functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as “dynamic” or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.

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          Most cited references188

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          Multi-task connectivity reveals flexible hubs for adaptive task control

          Extensive evidence suggests the human ability to adaptively implement a wide variety of tasks is preferentially due to the operation of a fronto-parietal brain network. We hypothesized that this network’s adaptability is made possible by ‘flexible hubs’ – brain regions that rapidly update their pattern of global functional connectivity according to task demands. We utilized recent advances in characterizing brain network organization and dynamics to identify mechanisms consistent with the flexible hub theory. We found that the fronto-parietal network’s brain-wide functional connectivity pattern shifted more than other networks’ across a variety of task states, and that these connectivity patterns could be used to identify the current task. Further, these patterns were consistent across practiced and novel tasks, suggesting reuse of flexible hub connectivity patterns facilitates adaptive (novel) task performance. Together, these findings support a central role for fronto-parietal flexible hubs in cognitive control and adaptive implementation of task demands generally.
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            Large-scale cortical correlation structure of spontaneous oscillatory activity.

            Little is known about the brain-wide correlation of electrophysiological signals. We found that spontaneous oscillatory neuronal activity exhibited frequency-specific spatial correlation structure in the human brain. We developed an analysis approach that discounts spurious correlation of signal power caused by the limited spatial resolution of electrophysiological measures. We applied this approach to source estimates of spontaneous neuronal activity reconstructed from magnetoencephalography. Overall, correlation of power across cortical regions was strongest in the alpha to beta frequency range (8–32 Hz) and correlation patterns depended on the underlying oscillation frequency. Global hubs resided in the medial temporal lobe in the theta frequency range (4–6 Hz), in lateral parietal areas in the alpha to beta frequency range (8–23 Hz) and in sensorimotor areas for higher frequencies (32–45 Hz). Our data suggest that interactions in various large-scale cortical networks may be reflected in frequency-specific power envelope correlations.
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              Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity.

              Since initial reports regarding the impact of motion artifact on measures of functional connectivity, there has been a proliferation of participant-level confound regression methods to limit its impact. However, many of the most commonly used techniques have not been systematically evaluated using a broad range of outcome measures. Here, we provide a systematic evaluation of 14 participant-level confound regression methods in 393 youths. Specifically, we compare methods according to four benchmarks, including the residual relationship between motion and connectivity, distance-dependent effects of motion on connectivity, network identifiability, and additional degrees of freedom lost in confound regression. Our results delineate two clear trade-offs among methods. First, methods that include global signal regression minimize the relationship between connectivity and motion, but result in distance-dependent artifact. In contrast, censoring methods mitigate both motion artifact and distance-dependence, but use additional degrees of freedom. Importantly, less effective de-noising methods are also unable to identify modular network structure in the connectome. Taken together, these results emphasize the heterogeneous efficacy of existing methods, and suggest that different confound regression strategies may be appropriate in the context of specific scientific goals.
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                Author and article information

                Contributors
                Journal
                Netw Neurosci
                Netw Neurosci
                netn
                Network Neuroscience
                MIT Press (One Rogers Street, Cambridge, MA 02142-1209USAjournals-info@mit.edu )
                2472-1751
                2020
                2020
                : 4
                : 1
                : 30-69
                Affiliations
                [1]Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
                [2]Departments of Statistics and Psychiatry, University of Michigan, Ann Arbor, MI, USA
                [3]Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
                [4]Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
                [5]Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
                [6]Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
                [7]Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
                [8]University of Newcastle, Callaghan, NSW, 2308, Australia
                [9]QIMR Berghofer, Brisbane, Australia
                [10]Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
                [11]Department of Neurology and Neurological Sciences, Stanford University, Stanford CA, USA
                [12]Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Switzerland
                [13]Department of Radiology and Medical Informatics, University of Geneva, Switzerland
                [14]Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
                [15]Rotman Research Institute - Baycrest Centre, Toronto, Canada
                [16]Department of Psychology, University of Toronto, Toronto, Canada
                [17]Department of Psychology, Stanford University, Stanford, CA, USA
                [18]Brain and Mind Centre, University of Sydney, NSW, Australia
                [19]Department of Psychology, Stanford University, Stanford, CA, USA
                [20]Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
                [21]Stichting Solaris Onderzoek en Ontwikkeling, Nijmegen, The Netherlands
                [22]Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, The Netherlands
                [23]Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
                [24]The Mind Research Network, Albuquerque, NM, USA
                [25]Biomedical Statistics and Multimodal Signal Processing Unit, Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience, Johannes-Gutenberg-University Hospital, Mainz, Germany
                [26]Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
                [27]Monash Institute of Cognitive and Clinical Neurosciences and Monash Biomedical Imaging, Monash University, Clayton, Australia
                [28]Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
                [29]Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
                [30]Wellcome Trust Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, United Kingdom
                [31]Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
                [32]The Mind Research Network, Albuquerque, NM, USA
                [33]Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
                [34]Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, Georgia, USA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                [†]

                Indicates equal contribution.

                Handling Editor: Emily Finn

                Author information
                https://orcid.org/0000-0001-8012-6399
                https://orcid.org/0000-0003-2052-025X
                https://orcid.org/0000-0002-6183-4493
                https://orcid.org/0000-0001-9200-1681
                https://orcid.org/0000-0003-4943-3969
                https://orcid.org/0000-0001-5737-1660
                https://orcid.org/0000-0001-6707-0028
                https://orcid.org/0000-0003-3985-3898
                https://orcid.org/0000-0003-2289-0828
                https://orcid.org/0000-0002-1784-5662
                https://orcid.org/0000-0001-6755-0259
                https://orcid.org/0000-0003-1762-5499
                https://orcid.org/0000-0002-0533-6035
                https://orcid.org/0000-0003-1604-9143
                https://orcid.org/0000-0001-7058-4062
                https://orcid.org/0000-0002-4964-2237
                https://orcid.org/0000-0002-4679-7567
                https://orcid.org/0000-0001-6158-2663
                https://orcid.org/0000-0002-7899-7061
                https://orcid.org/0000-0002-0779-9439
                https://orcid.org/0000-0002-9650-2229
                https://orcid.org/0000-0003-3462-0147
                https://orcid.org/0000-0001-9058-0747
                Article
                netn_a_00116
                10.1162/netn_a_00116
                7006871
                32043043
                a1720d18-4526-4d80-93c6-4189d62a75d5
                © 2019 Massachusetts Institute of Technology

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.

                History
                : 17 May 2019
                : 22 November 2019
                Page count
                Figures: 2, Tables: 2, References: 272, Pages: 40
                Funding
                Funded by: National Science Foundation;
                Award ID: DGE 1106400
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                Categories
                Review Articles
                Custom metadata
                Lurie, D. J., Kessler, D., Bassett, D. S., Betzel, R. F., Breakspear, M., Keilholz, S., … Calhoun, V. D. (2020). Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Network Neuroscience, 4(1), 30–69. https://doi.org/10.1162/netn_a_00116

                functional connectivity,brain networks,brain dynamics,fmri,rest,review

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