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Iacopini I, Karsai M, Barrat A. The temporal dynamics of group interactions in higher-order social networks. Nat Commun 2024; 15:7391. [PMID: 39191743 DOI: 10.1038/s41467-024-50918-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 07/25/2024] [Indexed: 08/29/2024] Open
Abstract
Representing social systems as networks, starting from the interactions between individuals, sheds light on the mechanisms governing their dynamics. However, networks encode only pairwise interactions, while most social interactions occur among groups of individuals, requiring higher-order network representations. Despite the recent interest in higher-order networks, little is known about the mechanisms that govern the formation and evolution of groups, and how people move between groups. Here, we leverage empirical data on social interactions among children and university students to study their temporal dynamics at both individual and group levels, characterising how individuals navigate groups and how groups form and disaggregate. We find robust patterns across contexts and propose a dynamical model that closely reproduces empirical observations. These results represent a further step in understanding social systems, and open up research directions to study the impact of group dynamics on dynamical processes that evolve on top of them.
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Affiliation(s)
- Iacopo Iacopini
- Network Science Institute, Northeastern University London, London, E1W 1LP, United Kingdom.
- Department of Physics, Northeastern University, Boston, MA, 02115, USA.
- Department of Network and Data Science, Central European University, A-1100, Vienna, Austria.
| | - Márton Karsai
- Department of Network and Data Science, Central European University, A-1100, Vienna, Austria
- National Laboratory for Health Security, HUN-REN Alfréd Rényi Institute of Mathematics, 1053, Budapest, Hungary
| | - Alain Barrat
- Aix Marseille Univ, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
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2
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Douchamps V, di Volo M, Torcini A, Battaglia D, Goutagny R. Gamma oscillatory complexity conveys behavioral information in hippocampal networks. Nat Commun 2024; 15:1849. [PMID: 38418832 PMCID: PMC10902292 DOI: 10.1038/s41467-024-46012-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 02/09/2024] [Indexed: 03/02/2024] Open
Abstract
The hippocampus and entorhinal cortex exhibit rich oscillatory patterns critical for cognitive functions. In the hippocampal region CA1, specific gamma-frequency oscillations, timed at different phases of the ongoing theta rhythm, are hypothesized to facilitate the integration of information from varied sources and contribute to distinct cognitive processes. Here, we show that gamma elements -a multidimensional characterization of transient gamma oscillatory episodes- occur at any frequency or phase relative to the ongoing theta rhythm across all CA1 layers in male mice. Despite their low power and stochastic-like nature, individual gamma elements still carry behavior-related information and computational modeling suggests that they reflect neuronal firing. Our findings challenge the idea of rigid gamma sub-bands, showing that behavior shapes ensembles of irregular gamma elements that evolve with learning and depend on hippocampal layers. Widespread gamma diversity, beyond randomness, may thus reflect complexity, likely functional but invisible to classic average-based analyses.
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Affiliation(s)
- Vincent Douchamps
- Université de Strasbourg, Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA), CNRS, UMR 7364, Strasbourg, France
| | - Matteo di Volo
- Université Claude Bernard Lyon 1, Institut National de la Santé et de la Recherche Médicale, Stem Cell and Brain Research Institute, U1208, Bron, France
- CY Cergy Paris Université, Laboratoire de Physique Théorique et Modélisation (LPTM), CNRS, UMR 8089, 95302, Cergy-Pontoise, France
| | - Alessandro Torcini
- CY Cergy Paris Université, Laboratoire de Physique Théorique et Modélisation (LPTM), CNRS, UMR 8089, 95302, Cergy-Pontoise, France
- CNR - Consiglio Nazionale delle Ricerche, Istituto dei Sistemi Complessi, via Madonna del Piano 10, 50019, Sesto Fiorentino, Italy
| | - Demian Battaglia
- Université de Strasbourg, Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA), CNRS, UMR 7364, Strasbourg, France.
- Aix-Marseille Université, Institut de Neurosciences des Systèmes (INS), INSERM, UMR 1106, Marseille, France.
- University of Strasbourg Institute for Advanced Studies (USIAS), Strasbourg, France.
| | - Romain Goutagny
- Université de Strasbourg, Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA), CNRS, UMR 7364, Strasbourg, France.
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3
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Voges N, Lima V, Hausmann J, Brovelli A, Battaglia D. Decomposing Neural Circuit Function into Information Processing Primitives. J Neurosci 2024; 44:e0157232023. [PMID: 38050070 PMCID: PMC10866194 DOI: 10.1523/jneurosci.0157-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 09/01/2023] [Accepted: 09/19/2023] [Indexed: 12/06/2023] Open
Abstract
It is challenging to measure how specific aspects of coordinated neural dynamics translate into operations of information processing and, ultimately, cognitive functions. An obstacle is that simple circuit mechanisms-such as self-sustained or propagating activity and nonlinear summation of inputs-do not directly give rise to high-level functions. Nevertheless, they already implement simple the information carried by neural activity. Here, we propose that distinct functions, such as stimulus representation, working memory, or selective attention, stem from different combinations and types of low-level manipulations of information or information processing primitives. To test this hypothesis, we combine approaches from information theory with simulations of multi-scale neural circuits involving interacting brain regions that emulate well-defined cognitive functions. Specifically, we track the information dynamics emergent from patterns of neural dynamics, using quantitative metrics to detect where and when information is actively buffered, transferred or nonlinearly merged, as possible modes of low-level processing (storage, transfer and modification). We find that neuronal subsets maintaining representations in working memory or performing attentional gain modulation are signaled by their boosted involvement in operations of information storage or modification, respectively. Thus, information dynamic metrics, beyond detecting which network units participate in cognitive processing, also promise to specify how and when they do it, that is, through which type of primitive computation, a capability that may be exploited for the analysis of experimental recordings.
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Affiliation(s)
- Nicole Voges
- Institut de Neurosciences de La Timone, UMR 7289, CNRS, Aix-Marseille Université, Marseille 13005, France
- Institute for Language, Communication and the Brain (ILCB), Aix-Marseille Université, Marseille 13005, France
| | - Vinicius Lima
- Institut de Neurosciences des Systèmes (INS), UMR 1106, Aix-Marseille Université, Marseille 13005, France
| | - Johannes Hausmann
- R&D Department, Hyland Switzerland Sarl, Corcelles NE 2035, Switzerland
| | - Andrea Brovelli
- Institut de Neurosciences de La Timone, UMR 7289, CNRS, Aix-Marseille Université, Marseille 13005, France
- Institute for Language, Communication and the Brain (ILCB), Aix-Marseille Université, Marseille 13005, France
| | - Demian Battaglia
- Institute for Language, Communication and the Brain (ILCB), Aix-Marseille Université, Marseille 13005, France
- Institut de Neurosciences des Systèmes (INS), UMR 1106, Aix-Marseille Université, Marseille 13005, France
- University of Strasbourg Institute for Advanced Studies (USIAS), Strasbourg 67000, France
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4
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Sundiang M, Hatsopoulos NG, MacLean JN. Dynamic structure of motor cortical neuron coactivity carries behaviorally relevant information. Netw Neurosci 2023; 7:661-678. [PMID: 37397877 PMCID: PMC10312288 DOI: 10.1162/netn_a_00298] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 12/02/2022] [Indexed: 01/28/2024] Open
Abstract
Skillful, voluntary movements are underpinned by computations performed by networks of interconnected neurons in the primary motor cortex (M1). Computations are reflected by patterns of coactivity between neurons. Using pairwise spike time statistics, coactivity can be summarized as a functional network (FN). Here, we show that the structure of FNs constructed from an instructed-delay reach task in nonhuman primates is behaviorally specific: Low-dimensional embedding and graph alignment scores show that FNs constructed from closer target reach directions are also closer in network space. Using short intervals across a trial, we constructed temporal FNs and found that temporal FNs traverse a low-dimensional subspace in a reach-specific trajectory. Alignment scores show that FNs become separable and correspondingly decodable shortly after the Instruction cue. Finally, we observe that reciprocal connections in FNs transiently decrease following the Instruction cue, consistent with the hypothesis that information external to the recorded population temporarily alters the structure of the network at this moment.
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Affiliation(s)
- Marina Sundiang
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Nicholas G. Hatsopoulos
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
- University of Chicago Neuroscience Institute, Chicago, IL, USA
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
| | - Jason N. MacLean
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
- University of Chicago Neuroscience Institute, Chicago, IL, USA
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
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5
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Lucas M, Morris A, Townsend-Teague A, Tichit L, Habermann B, Barrat A. Inferring cell cycle phases from a partially temporal network of protein interactions. CELL REPORTS METHODS 2023; 3:100397. [PMID: 36936083 PMCID: PMC10014271 DOI: 10.1016/j.crmeth.2023.100397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/13/2022] [Accepted: 01/11/2023] [Indexed: 02/05/2023]
Abstract
The temporal organization of biological systems is key for understanding them, but current methods for identifying this organization are often ad hoc and require prior knowledge. We present Phasik, a method that automatically identifies this multiscale organization by combining time series data (protein or gene expression) and interaction data (protein-protein interaction network). Phasik builds a (partially) temporal network and uses clustering to infer temporal phases. We demonstrate the method's effectiveness by recovering well-known phases and sub-phases of the cell cycle of budding yeast and phase arrests of mutants. We also show its general applicability using temporal gene expression data from circadian rhythms in wild-type and mutant mouse models. We systematically test Phasik's robustness and investigate the effect of having only partial temporal information. As time-resolved, multiomics datasets become more common, this method will allow the study of temporal regulation in lesser-known biological contexts, such as development, metabolism, and disease.
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Affiliation(s)
- Maxime Lucas
- Aix Marseille University, CNRS, I2M UMR 7373, Turing Center for Living Systems, Marseille, France
- Aix Marseille University, CNRS, IBDM UMR 7288, Turing Center for Living Systems, Marseille, France
- Aix Marseille University, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
| | | | | | - Laurent Tichit
- Aix Marseille University, CNRS, I2M UMR 7373, Turing Center for Living Systems, Marseille, France
| | - Bianca Habermann
- Aix Marseille University, CNRS, IBDM UMR 7288, Turing Center for Living Systems, Marseille, France
| | - Alain Barrat
- Aix Marseille University, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
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6
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Pairwise and high-order dependencies in the cryptocurrency trading network. Sci Rep 2022; 12:18483. [PMID: 36323721 PMCID: PMC9628620 DOI: 10.1038/s41598-022-21192-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/23/2022] [Indexed: 01/06/2023] Open
Abstract
In this paper we analyse the effects of information flows in cryptocurrency markets. We first define a cryptocurrency trading network, i.e. the network made using cryptocurrencies as nodes and the Granger causality among their weekly log returns as links, later we analyse its evolution over time. In particular, with reference to years 2020 and 2021, we study the logarithmic US dollar price returns of the cryptocurrency trading network using both pairwise and high-order statistical dependencies, quantified by Granger causality and O-information, respectively. With reference to the former, we find that it shows peaks in correspondence of important events, like e.g., Covid-19 pandemic turbulence or occasional sudden prices rise. The corresponding network structure is rather stable, across weekly time windows in the period considered and the coins are the most influential nodes in the network. In the pairwise description of the network, stable coins seem to play a marginal role whereas, turning high-order dependencies, they appear in the highest number of synergistic information circuits, thus proving that they play a major role for high order effects. With reference to redundancy and synergy with the time evolution of the total transactions in US dollars, we find that their large volume in the first semester of 2021 seems to have triggered a transition in the cryptocurrency network toward a more complex dynamical landscape. Our results show that pairwise and high-order descriptions of complex financial systems provide complementary information for cryptocurrency analysis.
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7
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Voutsa V, Battaglia D, Bracken LJ, Brovelli A, Costescu J, Díaz Muñoz M, Fath BD, Funk A, Guirro M, Hein T, Kerschner C, Kimmich C, Lima V, Messé A, Parsons AJ, Perez J, Pöppl R, Prell C, Recinos S, Shi Y, Tiwari S, Turnbull L, Wainwright J, Waxenecker H, Hütt MT. Two classes of functional connectivity in dynamical processes in networks. J R Soc Interface 2021; 18:20210486. [PMID: 34665977 PMCID: PMC8526174 DOI: 10.1098/rsif.2021.0486] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 09/13/2021] [Indexed: 12/12/2022] Open
Abstract
The relationship between network structure and dynamics is one of the most extensively investigated problems in the theory of complex systems of recent years. Understanding this relationship is of relevance to a range of disciplines-from neuroscience to geomorphology. A major strategy of investigating this relationship is the quantitative comparison of a representation of network architecture (structural connectivity, SC) with a (network) representation of the dynamics (functional connectivity, FC). Here, we show that one can distinguish two classes of functional connectivity-one based on simultaneous activity (co-activity) of nodes, the other based on sequential activity of nodes. We delineate these two classes in different categories of dynamical processes-excitations, regular and chaotic oscillators-and provide examples for SC/FC correlations of both classes in each of these models. We expand the theoretical view of the SC/FC relationships, with conceptual instances of the SC and the two classes of FC for various application scenarios in geomorphology, ecology, systems biology, neuroscience and socio-ecological systems. Seeing the organisation of dynamical processes in a network either as governed by co-activity or by sequential activity allows us to bring some order in the myriad of observations relating structure and function of complex networks.
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Affiliation(s)
- Venetia Voutsa
- Department of Life Sciences and Chemistry, Jacobs University Bremen, 28759 Bremen, Germany
| | - Demian Battaglia
- Aix-Marseille Université, Inserm, Institut de Neurosciences des Systèmes (UMR 1106), Marseille, France
- University of Strasbourg Institute for Advanced Studies (USIAS), Strasbourg 67083, France
| | | | - Andrea Brovelli
- Aix-Marseille Université, CNRS, Institut de Neurosciences de la Timone (UMR 7289), Marseille, France
| | - Julia Costescu
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - Mario Díaz Muñoz
- Department of Sustainability, Governance and Methods, Modul University Vienna, 1190 Vienna, Austria
| | - Brian D. Fath
- Department of Biological Sciences, Towson University, Towson, Maryland 21252, USA
- Advancing Systems Analysis Program, International Institute for Applied Systems Analysis, Laxenburg 2361, Austria
- Department of Environmental Studies, Masaryk University, 60200 Brno, Czech Republic
| | - Andrea Funk
- Institute of Hydrobiology and Aquatic Ecosystem Management (IHG), University of Natural Resources and Life Sciences Vienna (BOKU), 1180 Vienna, Austria
- WasserCluster Lunz - Biologische Station GmbH, Dr. Carl Kupelwieser Promenade 5, 3293 Lunz am See, Austria
| | - Mel Guirro
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - Thomas Hein
- Institute of Hydrobiology and Aquatic Ecosystem Management (IHG), University of Natural Resources and Life Sciences Vienna (BOKU), 1180 Vienna, Austria
- WasserCluster Lunz - Biologische Station GmbH, Dr. Carl Kupelwieser Promenade 5, 3293 Lunz am See, Austria
| | - Christian Kerschner
- Department of Sustainability, Governance and Methods, Modul University Vienna, 1190 Vienna, Austria
- Department of Environmental Studies, Masaryk University, 60200 Brno, Czech Republic
| | - Christian Kimmich
- Department of Environmental Studies, Masaryk University, 60200 Brno, Czech Republic
- Regional Science and Environmental Research, Institute for Advanced Studies, 1080 Vienna, Austria
| | - Vinicius Lima
- Aix-Marseille Université, Inserm, Institut de Neurosciences des Systèmes (UMR 1106), Marseille, France
- Aix-Marseille Université, CNRS, Institut de Neurosciences de la Timone (UMR 7289), Marseille, France
| | - Arnaud Messé
- Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Germany
| | | | - John Perez
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - Ronald Pöppl
- Department of Geography and Regional Research, University of Vienna, Universitätsstr. 7, 1010 Vienna, Austria
| | - Christina Prell
- Department of Cultural Geography, University of Groningen, 9747 AD, Groningen, The Netherlands
| | - Sonia Recinos
- Institute of Hydrobiology and Aquatic Ecosystem Management (IHG), University of Natural Resources and Life Sciences Vienna (BOKU), 1180 Vienna, Austria
| | - Yanhua Shi
- Department of Environmental Studies, Masaryk University, 60200 Brno, Czech Republic
| | - Shubham Tiwari
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - Laura Turnbull
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - John Wainwright
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - Harald Waxenecker
- Department of Environmental Studies, Masaryk University, 60200 Brno, Czech Republic
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Jacobs University Bremen, 28759 Bremen, Germany
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Llano DA, Ma C, Di Fabrizio U, Taheri A, Stebbings KA, Yudintsev G, Xiao G, Kenyon RV, Berger-Wolf TY. A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse. Netw Neurosci 2021; 5:569-590. [PMID: 34189378 PMCID: PMC8233117 DOI: 10.1162/netn_a_00191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 03/11/2021] [Indexed: 12/12/2022] Open
Abstract
Network analysis of large-scale neuroimaging data is a particularly challenging computational problem. Here, we adapt a novel analytical tool, the community dynamic inference method (CommDy), for brain imaging data from young and aged mice. CommDy, which was inspired by social network theory, has been successfully used in other domains in biology; this report represents its first use in neuroscience. We used CommDy to investigate aging-related changes in network metrics in the auditory and motor cortices by using flavoprotein autofluorescence imaging in brain slices and in vivo. We observed that auditory cortical networks in slices taken from aged brains were highly fragmented compared to networks observed in young animals. CommDy network metrics were then used to build a random-forests classifier based on NMDA receptor blockade data, which successfully reproduced the aging findings, suggesting that the excitatory cortical connections may be altered during aging. A similar aging-related decline in network connectivity was also observed in spontaneous activity in the awake motor cortex, suggesting that the findings in the auditory cortex reflect general mechanisms during aging. These data suggest that CommDy provides a new dynamic network analytical tool to study the brain and that aging is associated with fragmentation of intracortical networks.
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Affiliation(s)
- Daniel A. Llano
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Beckman Institute for Advanced Science and Technology, Urbana, IL, USA
| | - Chihua Ma
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Umberto Di Fabrizio
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Aynaz Taheri
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Kevin A. Stebbings
- Neuroscience Program, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Beckman Institute for Advanced Science and Technology, Urbana, IL, USA
| | - Georgiy Yudintsev
- Neuroscience Program, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Beckman Institute for Advanced Science and Technology, Urbana, IL, USA
| | - Gang Xiao
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Beckman Institute for Advanced Science and Technology, Urbana, IL, USA
| | - Robert V. Kenyon
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Tanya Y. Berger-Wolf
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
- Current affiliation: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
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9
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Arbabyazd LM, Lombardo D, Blin O, Didic M, Battaglia D, Jirsa V. Dynamic Functional Connectivity as a complex random walk: Definitions and the dFCwalk toolbox. MethodsX 2020; 7:101168. [PMID: 33344179 PMCID: PMC7736993 DOI: 10.1016/j.mex.2020.101168] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 11/27/2020] [Indexed: 12/30/2022] Open
Abstract
•We have developed a framework to describe the dynamics of Functional Connectivity (dFC) estimated from brain activity time-series as a complex random walk in the space of possible functional networks. This conceptual and methodological framework considers dFC as a smooth reconfiguration process, combining "liquid" and "coordinated" aspects. Unlike other previous approaches, our method does not require the explicit extraction of discrete connectivity states.•In our previous work, we introduced several metrics for the quantitative characterization of the dFC random walk. First, dFC speed analyses extract the distribution of the time-resolved rate of reconfiguration of FC along time. These distributions have a clear peak (typical dFC speed, that can already serve as a biomarker) and fat tails (denoting deviations from Gaussianity that can be detected by suitable scaling analyses of FC network streams). Second, meta-connectivity (MC) analyses identify groups of functional links whose fluctuations co-vary in time and that define veritable dFC modules organized along specific dFC meta-hub controllers (differing from conventional FC modules and hubs). The decomposition of whole-brain dFC by MC allows performing dFC speed analyses separately for each of the detected dFC modules.•We present here blocks and pipelines for dFC random walk analyses that are made easily available through a dedicated MATLABⓇ toolbox (dFCwalk), openly downloadable. Although we applied such analyses mostly to fMRI resting state data, in principle our methods can be extended to any type of neural activity (from Local Field Potentials to EEG, MEG, fNIRS, etc.) or even non-neural time-series.
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Affiliation(s)
- Lucas M. Arbabyazd
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France
| | - Diego Lombardo
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France
| | - Olivier Blin
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France
- AP-HM, Timone, Service de Pharmacologie Clinique et Pharmacovigilance, F-13005 Marseille, France
| | - Mira Didic
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France
- AP-HM, Timone, Service de Neurologie et Neuropsychologie, F-13005 Marseille, France
| | - Demian Battaglia
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France
| | - Viktor Jirsa
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France
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