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Kumar G P, Panda R, Sharma K, Adarsh A, Annen J, Martial C, Faymonville ME, Laureys S, Sombrun C, Ganesan RA, Vanhaudenhuyse A, Gosseries O. Changes in high-order interaction measures of synergy and redundancy during non-ordinary states of consciousness induced by meditation, hypnosis, and auto-induced cognitive trance. Neuroimage 2024; 293:120623. [PMID: 38670442 DOI: 10.1016/j.neuroimage.2024.120623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/08/2024] [Accepted: 04/21/2024] [Indexed: 04/28/2024] Open
Abstract
High-order interactions are required across brain regions to accomplish specific cognitive functions. These functional interdependencies are reflected by synergistic information that can be obtained by combining the information from all the sources considered and redundant information (i.e., common information provided by all the sources). However, electroencephalogram (EEG) functional connectivity is limited to pairwise interactions thereby precluding the estimation of high-order interactions. In this multicentric study, we used measures of synergistic and redundant information to study in parallel the high-order interactions between five EEG electrodes during three non-ordinary states of consciousness (NSCs): Rajyoga meditation (RM), hypnosis, and auto-induced cognitive trance (AICT). We analyzed EEG data from 22 long-term Rajyoga meditators, nine volunteers undergoing hypnosis, and 21 practitioners of AICT. We here report the within-group changes in synergy and redundancy for each NSC in comparison with their respective baseline. During RM, synergy increased at the whole brain level in the delta and theta bands. Redundancy decreased in frontal, right central, and posterior electrodes in delta, and frontal, central, and posterior electrodes in beta1 and beta2 bands. During hypnosis, synergy decreased in mid-frontal, temporal, and mid-centro-parietal electrodes in the delta band. The decrease was also observed in the beta2 band in the left frontal and right parietal electrodes. During AICT, synergy decreased in delta and theta bands in left-frontal, right-frontocentral, and posterior electrodes. The decrease was also observed at the whole brain level in the alpha band. However, redundancy changes during hypnosis and AICT were not significant. The subjective reports of absorption and dissociation during hypnosis and AICT, as well as the mystical experience questionnaires during AICT, showed no correlation with the high-order measures. The proposed study is the first exploratory attempt to utilize the concepts of synergy and redundancy in NSCs. The differences in synergy and redundancy during different NSCs warrant further studies to relate the extracted measures with the phenomenology of the NSCs.
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Affiliation(s)
- Pradeep Kumar G
- MILE Lab, Department of Electrical Engineering, Indian Institute of Science, Bengaluru, India
| | - Rajanikant Panda
- Coma Science Group, GIGA-Consciousness, University of Liege, Liege, Belgium; Sensation & Perception Research Group, GIGA-Consciousness, University of Liege, Liege, Belgium; Centre du Cerveau, University Hospital of Liege, Liege, Belgium
| | - Kanishka Sharma
- MILE Lab, Department of Electrical Engineering, Indian Institute of Science, Bengaluru, India
| | - A Adarsh
- MILE Lab, Department of Electrical Engineering, Indian Institute of Science, Bengaluru, India
| | - Jitka Annen
- Coma Science Group, GIGA-Consciousness, University of Liege, Liege, Belgium; Centre du Cerveau, University Hospital of Liege, Liege, Belgium
| | - Charlotte Martial
- Coma Science Group, GIGA-Consciousness, University of Liege, Liege, Belgium; Centre du Cerveau, University Hospital of Liege, Liege, Belgium
| | - Marie-Elisabeth Faymonville
- Sensation & Perception Research Group, GIGA-Consciousness, University of Liege, Liege, Belgium; Arsene Bruny Integrated Oncological Center, University Hospital of Liege, Liege, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA-Consciousness, University of Liege, Liege, Belgium; Centre du Cerveau, University Hospital of Liege, Liege, Belgium
| | | | - Ramakrishnan Angarai Ganesan
- MILE Lab, Department of Electrical Engineering, Indian Institute of Science, Bengaluru, India; Centre for Neuroscience, Indian Institute of Science, Bengaluru, India
| | - Audrey Vanhaudenhuyse
- Sensation & Perception Research Group, GIGA-Consciousness, University of Liege, Liege, Belgium; Algology Interdisciplinary Center, University Hospital of Liege, Liege, Belgium
| | - Olivia Gosseries
- Coma Science Group, GIGA-Consciousness, University of Liege, Liege, Belgium; Sensation & Perception Research Group, GIGA-Consciousness, University of Liege, Liege, Belgium; Centre du Cerveau, University Hospital of Liege, Liege, Belgium.
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2
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Guichet C, Banjac S, Achard S, Mermillod M, Baciu M. Modeling the neurocognitive dynamics of language across the lifespan. Hum Brain Mapp 2024; 45:e26650. [PMID: 38553863 PMCID: PMC10980845 DOI: 10.1002/hbm.26650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 02/08/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
Healthy aging is associated with a heterogeneous decline across cognitive functions, typically observed between language comprehension and language production (LP). Examining resting-state fMRI and neuropsychological data from 628 healthy adults (age 18-88) from the CamCAN cohort, we performed state-of-the-art graph theoretical analysis to uncover the neural mechanisms underlying this variability. At the cognitive level, our findings suggest that LP is not an isolated function but is modulated throughout the lifespan by the extent of inter-cognitive synergy between semantic and domain-general processes. At the cerebral level, we show that default mode network (DMN) suppression coupled with fronto-parietal network (FPN) integration is the way for the brain to compensate for the effects of dedifferentiation at a minimal cost, efficiently mitigating the age-related decline in LP. Relatedly, reduced DMN suppression in midlife could compromise the ability to manage the cost of FPN integration. This may prompt older adults to adopt a more cost-efficient compensatory strategy that maintains global homeostasis at the expense of LP performances. Taken together, we propose that midlife represents a critical neurocognitive juncture that signifies the onset of LP decline, as older adults gradually lose control over semantic representations. We summarize our findings in a novel synergistic, economical, nonlinear, emergent, cognitive aging model, integrating connectomic and cognitive dimensions within a complex system perspective.
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Affiliation(s)
| | - Sonja Banjac
- Université Grenoble Alpes, CNRS LPNC UMR 5105GrenobleFrance
| | - Sophie Achard
- LJK, UMR CNRS 5224, Université Grenoble AlpesGrenobleFrance
| | | | - Monica Baciu
- Université Grenoble Alpes, CNRS LPNC UMR 5105GrenobleFrance
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Luppi AI, Rosas FE, Mediano PAM, Menon DK, Stamatakis EA. Information decomposition and the informational architecture of the brain. Trends Cogn Sci 2024; 28:352-368. [PMID: 38199949 DOI: 10.1016/j.tics.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/09/2023] [Accepted: 11/17/2023] [Indexed: 01/12/2024]
Abstract
To explain how the brain orchestrates information-processing for cognition, we must understand information itself. Importantly, information is not a monolithic entity. Information decomposition techniques provide a way to split information into its constituent elements: unique, redundant, and synergistic information. We review how disentangling synergistic and redundant interactions is redefining our understanding of integrative brain function and its neural organisation. To explain how the brain navigates the trade-offs between redundancy and synergy, we review converging evidence integrating the structural, molecular, and functional underpinnings of synergy and redundancy; their roles in cognition and computation; and how they might arise over evolution and development. Overall, disentangling synergistic and redundant information provides a guiding principle for understanding the informational architecture of the brain and cognition.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Fernando E Rosas
- Department of Informatics, University of Sussex, Brighton, UK; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - David K Menon
- Department of Medicine, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
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Kemp JT, Kline AG, Bettencourt LMA. Information synergy maximizes the growth rate of heterogeneous groups. PNAS Nexus 2024; 3:pgae072. [PMID: 38420213 PMCID: PMC10901557 DOI: 10.1093/pnasnexus/pgae072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 02/02/2024] [Indexed: 03/02/2024]
Abstract
Collective action and group formation are fundamental behaviors among both organisms cooperating to maximize their fitness and people forming socioeconomic organizations. Researchers have extensively explored social interaction structures via game theory and homophilic linkages, such as kin selection and scalar stress, to understand emergent cooperation in complex systems. However, we still lack a general theory capable of predicting how agents benefit from heterogeneous preferences, joint information, or skill complementarities in statistical environments. Here, we derive general statistical dynamics for the origin of cooperation based on the management of resources and pooled information. Specifically, we show how groups that optimally combine complementary agent knowledge about resources in statistical environments maximize their growth rate. We show that these advantages are quantified by the information synergy embedded in the conditional probability of environmental states given agents' signals, such that groups with a greater diversity of signals maximize their collective information. It follows that, when constraints are placed on group formation, agents must intelligently select with whom they cooperate to maximize the synergy available to their own signal. Our results show how the general properties of information underlie the optimal collective formation and dynamics of groups of heterogeneous agents across social and biological phenomena.
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Affiliation(s)
- Jordan T Kemp
- Department of Physics, University of Chicago, 5720 S Ellis Ave #201, Chicago, IL 60637, USA
| | - Adam G Kline
- Department of Physics, University of Chicago, 5720 S Ellis Ave #201, Chicago, IL 60637, USA
| | - Luís M A Bettencourt
- Department of Ecology & Evolution, University of Chicago, 1101 E 57th St, Chicago, IL 60637, USA
- Mansueto Institute for Urban Innovation, University of Chicago, 1155 E 60th Street, Chicago, IL 60637, USA
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Koçillari L, Celotto M, Francis NA, Mukherjee S, Babadi B, Kanold PO, Panzeri S. Behavioural relevance of redundant and synergistic stimulus information between functionally connected neurons in mouse auditory cortex. Brain Inform 2023; 10:34. [PMID: 38052917 PMCID: PMC10697912 DOI: 10.1186/s40708-023-00212-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/02/2023] [Indexed: 12/07/2023] Open
Abstract
Measures of functional connectivity have played a central role in advancing our understanding of how information is transmitted and processed within the brain. Traditionally, these studies have focused on identifying redundant functional connectivity, which involves determining when activity is similar across different sites or neurons. However, recent research has highlighted the importance of also identifying synergistic connectivity-that is, connectivity that gives rise to information not contained in either site or neuron alone. Here, we measured redundant and synergistic functional connectivity between neurons in the mouse primary auditory cortex during a sound discrimination task. Specifically, we measured directed functional connectivity between neurons simultaneously recorded with calcium imaging. We used Granger Causality as a functional connectivity measure. We then used Partial Information Decomposition to quantify the amount of redundant and synergistic information about the presented sound that is carried by functionally connected or functionally unconnected pairs of neurons. We found that functionally connected pairs present proportionally more redundant information and proportionally less synergistic information about sound than unconnected pairs, suggesting that their functional connectivity is primarily redundant. Further, synergy and redundancy coexisted both when mice made correct or incorrect perceptual discriminations. However, redundancy was much higher (both in absolute terms and in proportion to the total information available in neuron pairs) in correct behavioural choices compared to incorrect ones, whereas synergy was higher in absolute terms but lower in relative terms in correct than in incorrect behavioural choices. Moreover, the proportion of redundancy reliably predicted perceptual discriminations, with the proportion of synergy adding no extra predictive power. These results suggest a crucial contribution of redundancy to correct perceptual discriminations, possibly due to the advantage it offers for information propagation, and also suggest a role of synergy in enhancing information level during correct discriminations.
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Affiliation(s)
- Loren Koçillari
- Istituto Italiano Di Tecnologia, 38068, Rovereto, Italy.
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251, Hamburg, Germany.
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf (UKE), 20246, Hamburg, Germany.
| | - Marco Celotto
- Istituto Italiano Di Tecnologia, 38068, Rovereto, Italy
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251, Hamburg, Germany
- Department of Pharmacy and Biotechnology, University of Bologna, 40126, Bologna, Italy
| | - Nikolas A Francis
- Department of Biology and Brain and Behavior Institute, University of Maryland, College Park, MD, 20742, USA
| | - Shoutik Mukherjee
- Department of Electrical and Computer Engineering and Institute for Systems Research, University of Maryland, College Park, MD, 20742, USA
| | - Behtash Babadi
- Department of Electrical and Computer Engineering and Institute for Systems Research, University of Maryland, College Park, MD, 20742, USA
| | - Patrick O Kanold
- Department of Biomedical Engineering and Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251, Hamburg, Germany.
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Sparacino L, Faes L, Mijatović G, Parla G, Lo Re V, Miraglia R, de Ville de Goyet J, Sparacia G. Statistical Approaches to Identify Pairwise and High-Order Brain Functional Connectivity Signatures on a Single-Subject Basis. Life (Basel) 2023; 13:2075. [PMID: 37895456 PMCID: PMC10608185 DOI: 10.3390/life13102075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/21/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
Keeping up with the shift towards personalized neuroscience essentially requires the derivation of meaningful insights from individual brain signal recordings by analyzing the descriptive indexes of physio-pathological states through statistical methods that prioritize subject-specific differences under varying experimental conditions. Within this framework, the current study presents a methodology for assessing the value of the single-subject fingerprints of brain functional connectivity, assessed both by standard pairwise and novel high-order measures. Functional connectivity networks, which investigate the inter-relationships between pairs of brain regions, have long been a valuable tool for modeling the brain as a complex system. However, their usefulness is limited by their inability to detect high-order dependencies beyond pairwise correlations. In this study, by leveraging multivariate information theory, we confirm recent evidence suggesting that the brain contains a plethora of high-order, synergistic subsystems that would go unnoticed using a pairwise graph structure. The significance and variations across different conditions of functional pairwise and high-order interactions (HOIs) between groups of brain signals are statistically verified on an individual level through the utilization of surrogate and bootstrap data analyses. The approach is illustrated on the single-subject recordings of resting-state functional magnetic resonance imaging (rest-fMRI) signals acquired using a pediatric patient with hepatic encephalopathy associated with a portosystemic shunt and undergoing liver vascular shunt correction. Our results show that (i) the proposed single-subject analysis may have remarkable clinical relevance for subject-specific investigations and treatment planning, and (ii) the possibility of investigating brain connectivity and its post-treatment functional developments at a high-order level may be essential to fully capture the complexity and modalities of the recovery.
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Affiliation(s)
- Laura Sparacino
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (L.S.); (L.F.)
| | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (L.S.); (L.F.)
| | - Gorana Mijatović
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia;
| | - Giuseppe Parla
- Radiology Service, IRCCS-ISMETT, 90127 Palermo, Italy; (G.P.); (R.M.)
| | | | - Roberto Miraglia
- Radiology Service, IRCCS-ISMETT, 90127 Palermo, Italy; (G.P.); (R.M.)
| | - Jean de Ville de Goyet
- Department for the Treatment and Study of Pediatric Abdominal Diseases and Abdominal Transplantation, IRCCS-ISMETT, 90127 Palermo, Italy;
| | - Gianvincenzo Sparacia
- Radiology Service, IRCCS-ISMETT, 90127 Palermo, Italy; (G.P.); (R.M.)
- Radiology Service, BiND, University of Palermo, 90128 Palermo, Italy
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Varley TF, Pope M, Puxeddu MG, Faskowitz J, Sporns O. Partial entropy decomposition reveals higher-order information structures in human brain activity. Proc Natl Acad Sci U S A 2023; 120:e2300888120. [PMID: 37467265 PMCID: PMC10372615 DOI: 10.1073/pnas.2300888120] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 06/06/2023] [Indexed: 07/21/2023] Open
Abstract
The standard approach to modeling the human brain as a complex system is with a network, where the basic unit of interaction is a pairwise link between two brain regions. While powerful, this approach is limited by the inability to assess higher-order interactions involving three or more elements directly. In this work, we explore a method for capturing higher-order dependencies in multivariate data: the partial entropy decomposition (PED). Our approach decomposes the joint entropy of the whole system into a set of nonnegative atoms that describe the redundant, unique, and synergistic interactions that compose the system's structure. PED gives insight into the mathematics of functional connectivity and its limitation. When applied to resting-state fMRI data, we find robust evidence of higher-order synergies that are largely invisible to standard functional connectivity analyses. Our approach can also be localized in time, allowing a frame-by-frame analysis of how the distributions of redundancies and synergies change over the course of a recording. We find that different ensembles of regions can transiently change from being redundancy-dominated to synergy-dominated and that the temporal pattern is structured in time. These results provide strong evidence that there exists a large space of unexplored structures in human brain data that have been largely missed by a focus on bivariate network connectivity models. This synergistic structure is dynamic in time and likely will illuminate interesting links between brain and behavior. Beyond brain-specific application, the PED provides a very general approach for understanding higher-order structures in a variety of complex systems.
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Affiliation(s)
- Thomas F. Varley
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN47405
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN47405
| | - Maria Pope
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN47405
- Program in Neuroscience, Indiana University, Bloomington, IN47405
| | - Maria Grazia Puxeddu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN47405
| | - Joshua Faskowitz
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN47405
- Program in Neuroscience, Indiana University, Bloomington, IN47405
| | - Olaf Sporns
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN47405
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN47405
- Program in Neuroscience, Indiana University, Bloomington, IN47405
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Abramov DM, Tsallis C, Lima HS. Neural complexity through a nonextensive statistical-mechanical approach of human electroencephalograms. Sci Rep 2023; 13:10318. [PMID: 37365196 DOI: 10.1038/s41598-023-37219-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/18/2023] [Indexed: 06/28/2023] Open
Abstract
The brain is a complex system whose understanding enables potentially deeper approaches to mental phenomena. Dynamics of wide classes of complex systems have been satisfactorily described within q-statistics, a current generalization of Boltzmann-Gibbs (BG) statistics. Here, we study human electroencephalograms of typical human adults (EEG), very specifically their inter-occurrence times across an arbitrarily chosen threshold of the signal (observed, for instance, at the midparietal location in scalp). The distributions of these inter-occurrence times differ from those usually emerging within BG statistical mechanics. They are instead well approached within the q-statistical theory, based on non-additive entropies characterized by the index q. The present method points towards a suitable tool for quantitatively accessing brain complexity, thus potentially opening useful studies of the properties of both typical and altered brain physiology.
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Affiliation(s)
- Dimitri Marques Abramov
- Laboratório de Neurobiologia e Neurofisiologia Clínica, Instituto Nacional da Saude da Criança, da Mulher e do Adolescente Fernandes Figueira, Fundacao Oswaldo Cruz, Avenida Rui Barbosa 716, Flamengo, Rio de Janeiro, 22250-020, Brazil.
| | - Constantino Tsallis
- Centro Brasileiro de Pesquisas Fisicas and National Institute of Science and Technology for Complex Systems, Rua Xavier Sigaud 150, Rio de Janeiro, 22290-180, Brazil
- Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM, 87501, USA
- Complexity Science Hub Vienna, Josefstädter Strasse 39, 1080, Vienna, Austria
| | - Henrique Santos Lima
- Centro Brasileiro de Pesquisas Fisicas and National Institute of Science and Technology for Complex Systems, Rua Xavier Sigaud 150, Rio de Janeiro, 22290-180, Brazil
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