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Menesse G, Torres JJ. Information dynamics of in silico EEG Brain Waves: Insights into oscillations and functions. PLoS Comput Biol 2024; 20:e1012369. [PMID: 39236071 PMCID: PMC11407780 DOI: 10.1371/journal.pcbi.1012369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 09/17/2024] [Accepted: 07/26/2024] [Indexed: 09/07/2024] Open
Abstract
The relation between electroencephalography (EEG) rhythms, brain functions, and behavioral correlates is well-established. Some physiological mechanisms underlying rhythm generation are understood, enabling the replication of brain rhythms in silico. This offers a pathway to explore connections between neural oscillations and specific neuronal circuits, potentially yielding fundamental insights into the functional properties of brain waves. Information theory frameworks, such as Integrated Information Decomposition (Φ-ID), relate dynamical regimes with informational properties, providing deeper insights into neuronal dynamic functions. Here, we investigate wave emergence in an excitatory/inhibitory (E/I) balanced network of integrate and fire neurons with short-term synaptic plasticity. This model produces a diverse range of EEG-like rhythms, from low δ waves to high-frequency oscillations. Through Φ-ID, we analyze the network's information dynamics and its relation with different emergent rhythms, elucidating the system's suitability for functions such as robust information transfer, storage, and parallel operation. Furthermore, our study helps to identify regimes that may resemble pathological states due to poor informational properties and high randomness. We found, e.g., that in silico β and δ waves are associated with maximum information transfer in inhibitory and excitatory neuron populations, respectively, and that the coexistence of excitatory θ, α, and β waves is associated to information storage. Additionally, we observed that high-frequency oscillations can exhibit either high or poor informational properties, potentially shedding light on ongoing discussions regarding physiological versus pathological high-frequency oscillations. In summary, our study demonstrates that dynamical regimes with similar oscillations may exhibit vastly different information dynamics. Characterizing information dynamics within these regimes serves as a potent tool for gaining insights into the functions of complex neuronal networks. Finally, our findings suggest that the use of information dynamics in both model and experimental data analysis, could help discriminate between oscillations associated with cognitive functions and those linked to neuronal disorders.
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Affiliation(s)
- Gustavo Menesse
- Department of Electromagnetism and Physics of the Matter & Institute Carlos I for Theoretical and Computational Physics, University of Granada, Granada, Spain
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Asunción, San Lorenzo, Paraguay
| | - Joaquín J Torres
- Department of Electromagnetism and Physics of the Matter & Institute Carlos I for Theoretical and Computational Physics, University of Granada, Granada, Spain
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2
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Herzog R, Barbey FM, Islam MN, Rueda-Delgado L, Nolan H, Prado P, Krylova M, Izyurov I, Javaheripour N, Danyeli LV, Sen ZD, Walter M, O'Donnell P, Buhl DL, Murphy B, Ibanez A. High-order brain interactions in ketamine during rest and task: a double-blinded cross-over design using portable EEG on male participants. Transl Psychiatry 2024; 14:310. [PMID: 39068157 PMCID: PMC11283531 DOI: 10.1038/s41398-024-03029-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 07/12/2024] [Accepted: 07/17/2024] [Indexed: 07/30/2024] Open
Abstract
Ketamine is a dissociative anesthetic that induces a shift in global consciousness states and related brain dynamics. Portable low-density EEG systems could be used to monitor these effects. However, previous evidence is almost null and lacks adequate methods to address global dynamics with a small number of electrodes. This study delves into brain high-order interactions (HOI) to explore the effects of ketamine using portable EEG. In a double-blinded cross-over design, 30 male adults (mean age = 25.57, SD = 3.74) were administered racemic ketamine and compared against saline infusion as a control. Both task-driven (auditory oddball paradigm) and resting-state EEG were recorded. HOI were computed using advanced multivariate information theory tools, allowing us to quantify nonlinear statistical dependencies between all possible electrode combinations. Ketamine induced an increase in redundancy in brain dynamics (copies of the same information that can be retrieved from 3 or more electrodes), most significantly in the alpha frequency band. Redundancy was more evident during resting state, associated with a shift in conscious states towards more dissociative tendencies. Furthermore, in the task-driven context (auditory oddball), the impact of ketamine on redundancy was more significant for predictable (standard stimuli) compared to deviant ones. Finally, associations were observed between ketamine's HOI and experiences of derealization. Ketamine appears to increase redundancy and HOI across psychometric measures, suggesting these effects are correlated with alterations in consciousness towards dissociation. In comparisons with event-related potential (ERP) or standard functional connectivity metrics, HOI represent an innovative method to combine all signal spatial interactions obtained from low-density dry EEG in drug interventions, as it is the only approach that exploits all possible combinations between electrodes. This research emphasizes the potential of complexity measures coupled with portable EEG devices in monitoring shifts in consciousness, especially when paired with low-density configurations, paving the way for better understanding and monitoring of pharmacological-induced changes.
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Affiliation(s)
- Rubén Herzog
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile.
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Paris, France.
| | | | | | | | - Hugh Nolan
- Cumulus Neuroscience Ltd, Dublin, Ireland
| | - Pavel Prado
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Marina Krylova
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Igor Izyurov
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Nooshin Javaheripour
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Lena Vera Danyeli
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Zümrüt Duygu Sen
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- German Center for Mental Health (DZPG), partner site Halle-Jena-Magdeburg, Jena, Germany
| | - Patricio O'Donnell
- Neuroscience Drug Discovery Unit, Takeda Pharmaceuticals, Cambridge, MA, 02390, USA
| | - Derek L Buhl
- Neuroscience Drug Discovery Unit, Takeda Pharmaceuticals, Cambridge, MA, 02390, USA
| | | | - Agustin Ibanez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile.
- Global Brain Health Institute, UCSF and Trinity College Dublin, Dublin, Ireland.
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3
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard J, Carhart-Harris RL, Williams GB, Craig MM, Finoia P, Owen AM, Naci L, Menon DK, Bor D, Stamatakis EA. A synergistic workspace for human consciousness revealed by Integrated Information Decomposition. eLife 2024; 12:RP88173. [PMID: 39022924 PMCID: PMC11257694 DOI: 10.7554/elife.88173] [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] [Indexed: 07/20/2024] Open
Abstract
How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a 'synergistic global workspace', comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brain's default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.
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Affiliation(s)
- Andrea I Luppi
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Pedro AM Mediano
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Fernando E Rosas
- Center for Psychedelic Research, Department of Brain Science, Imperial College LondonLondonUnited Kingdom
- Center for Complexity Science, Imperial College LondonLondonUnited Kingdom
- Data Science Institute, Imperial College LondonLondonUnited Kingdom
| | - Judith Allanson
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's HospitalCambridgeUnited Kingdom
| | - John Pickard
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's HospitalCambridgeUnited Kingdom
| | - Robin L Carhart-Harris
- Center for Psychedelic Research, Department of Brain Science, Imperial College LondonLondonUnited Kingdom
- Psychedelics Division - Neuroscape, Department of Neurology, University of CaliforniaSan FranciscoUnited States
| | - Guy B Williams
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
| | - Michael M Craig
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Paola Finoia
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
| | - Adrian M Owen
- Department of Psychology and Department of Physiology and Pharmacology, The Brain and Mind Institute, University of Western OntarioLondonCanada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Lloyd Building, Trinity CollegeDublinIreland
| | - David K Menon
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
| | - Daniel Bor
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Emmanuel A Stamatakis
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
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4
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Luppi AI, Rosas FE, Mediano PAM, Demertzi A, Menon DK, Stamatakis EA. Unravelling consciousness and brain function through the lens of time, space, and information. Trends Neurosci 2024; 47:551-568. [PMID: 38824075 DOI: 10.1016/j.tins.2024.05.007] [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: 02/15/2024] [Revised: 04/29/2024] [Accepted: 05/09/2024] [Indexed: 06/03/2024]
Abstract
Disentangling how cognitive functions emerge from the interplay of brain dynamics and network architecture is among the major challenges that neuroscientists face. Pharmacological and pathological perturbations of consciousness provide a lens to investigate these complex challenges. Here, we review how recent advances about consciousness and the brain's functional organisation have been driven by a common denominator: decomposing brain function into fundamental constituents of time, space, and information. Whereas unconsciousness increases structure-function coupling across scales, psychedelics may decouple brain function from structure. Convergent effects also emerge: anaesthetics, psychedelics, and disorders of consciousness can exhibit similar reconfigurations of the brain's unimodal-transmodal functional axis. Decomposition approaches reveal the potential to translate discoveries across species, with computational modelling providing a path towards mechanistic integration.
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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; St John's College, University of Cambridge, Cambridge, UK; Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK.
| | - Fernando E Rosas
- Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Department of Informatics, University of Sussex, Brighton, UK; Center for Psychedelic Research, Imperial College London, London, UK
| | | | - Athena Demertzi
- Physiology of Cognition Lab, GIGA-Cyclotron Research Center In Vivo Imaging, University of Liège, Liège 4000, Belgium; Psychology and Neuroscience of Cognition Research Unit, University of Liège, Liège 4000, Belgium; National Fund for Scientific Research (FNRS), Brussels 1000, Belgium
| | - David K Menon
- Division of Anaesthesia, 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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5
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Millán AP, Sun H, Torres JJ, Bianconi G. Triadic percolation induces dynamical topological patterns in higher-order networks. PNAS NEXUS 2024; 3:pgae270. [PMID: 39035037 PMCID: PMC11259606 DOI: 10.1093/pnasnexus/pgae270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 06/27/2024] [Indexed: 07/23/2024]
Abstract
Triadic interactions are higher-order interactions which occur when a set of nodes affects the interaction between two other nodes. Examples of triadic interactions are present in the brain when glia modulate the synaptic signals among neuron pairs or when interneuron axo-axonic synapses enable presynaptic inhibition and facilitation, and in ecosystems when one or more species can affect the interaction among two other species. On random graphs, triadic percolation has been recently shown to turn percolation into a fully fledged dynamical process in which the size of the giant component undergoes a route to chaos. However, in many real cases, triadic interactions are local and occur on spatially embedded networks. Here, we show that triadic interactions in spatial networks induce a very complex spatio-temporal modulation of the giant component which gives rise to triadic percolation patterns with significantly different topology. We classify the observed patterns (stripes, octopus, and small clusters) with topological data analysis and we assess their information content (entropy and complexity). Moreover, we illustrate the multistability of the dynamics of the triadic percolation patterns, and we provide a comprehensive phase diagram of the model. These results open new perspectives in percolation as they demonstrate that in presence of spatial triadic interactions, the giant component can acquire a time-varying topology. Hence, this work provides a theoretical framework that can be applied to model realistic scenarios in which the giant component is time dependent as in neuroscience.
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Affiliation(s)
- Ana P Millán
- Electromagnetism and Matter Physics Department, Institute “Carlos I” for Theoretical and Computational Physics, University of Granada, Granada E-18071, Spain
| | - Hanlin Sun
- Nordita, KTH Royal Institute of Technology and Stockholm University, Stockholm SE-106 91, Sweden
| | - Joaquín J Torres
- Electromagnetism and Matter Physics Department, Institute “Carlos I” for Theoretical and Computational Physics, University of Granada, Granada E-18071, Spain
| | - Ginestra Bianconi
- Centre for Complex Systems, School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, UK
- The Alan Turing Institute, London NW1 2DB, UK
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6
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Wang H, Liu Y, Ding Y. Identifying Diagnostic Biomarkers for Autism Spectrum Disorder From Higher-order Interactions Using the PED Algorithm. Neuroinformatics 2024; 22:285-296. [PMID: 38771433 DOI: 10.1007/s12021-024-09662-w] [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] [Accepted: 03/23/2024] [Indexed: 05/22/2024]
Abstract
In the field of neuroimaging, more studies of abnormalities in brain regions of the autism spectrum disorder (ASD) usually focused on two brain regions connected, and less on abnormalities of higher-order interactions of brain regions. To explore the complex relationships of brain regions, we used the partial entropy decomposition (PED) algorithm to capture higher-order interactions by computing the higher-order dependencies of all three brain regions (triads). We proposed a method for examining the effect of individual brain regions on triads based on the PED and surrogate tests. The key triads were discovered by analyzing the effects. Further, the hypergraph modularity maximization algorithm revealed the higher-order brain structures, of which the link between right thalamus and left thalamus in ASD was more loose compared with the typical control (TC). Redundant key triad (left cerebellum crus 1 and left precuneus and right inferior occipital gyrus) exhibited a discernible attenuation in interaction in ASD, while the synergistic key triad (right cerebellum crus 1 and left postcentral gyrus and left lingual gyrus) indicated a notable decline. The results of classification model further confirmed the potential of the key triads as diagnostic biomarkers.
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Affiliation(s)
- Hao Wang
- School of Science, Jiangnan University, Wuxi, Jiangsu, China
| | - Yanting Liu
- School of Science, Jiangnan University, Wuxi, Jiangsu, China
| | - Yanrui Ding
- School of Science, Jiangnan University, Wuxi, Jiangsu, China.
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7
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Varley TF, Bongard J. Evolving higher-order synergies reveals a trade-off between stability and information-integration capacity in complex systems. CHAOS (WOODBURY, N.Y.) 2024; 34:063127. [PMID: 38865092 DOI: 10.1063/5.0200425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 05/21/2024] [Indexed: 06/13/2024]
Abstract
There has recently been an explosion of interest in how "higher-order" structures emerge in complex systems comprised of many interacting elements (often called "synergistic" information). This "emergent" organization has been found in a variety of natural and artificial systems, although at present, the field lacks a unified understanding of what the consequences of higher-order synergies and redundancies are for systems under study. Typical research treats the presence (or absence) of synergistic information as a dependent variable and report changes in the level of synergy in response to some change in the system. Here, we attempt to flip the script: rather than treating higher-order information as a dependent variable, we use evolutionary optimization to evolve boolean networks with significant higher-order redundancies, synergies, or statistical complexity. We then analyze these evolved populations of networks using established tools for characterizing discrete dynamics: the number of attractors, the average transient length, and the Derrida coefficient. We also assess the capacity of the systems to integrate information. We find that high-synergy systems are unstable and chaotic, but with a high capacity to integrate information. In contrast, evolved redundant systems are extremely stable, but have negligible capacity to integrate information. Finally, the complex systems that balance integration and segregation (known as Tononi-Sporns-Edelman complexity) show features of both chaosticity and stability, with a greater capacity to integrate information than the redundant systems while being more stable than the random and synergistic systems. We conclude that there may be a fundamental trade-off between the robustness of a system's dynamics and its capacity to integrate information (which inherently requires flexibility and sensitivity) and that certain kinds of complexity naturally balance this trade-off.
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Affiliation(s)
- Thomas F Varley
- Department of Computer Science, University of Vermont, Burlington, Vermont 05405, USA
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont 05405, USA
| | - Josh Bongard
- Department of Computer Science, University of Vermont, Burlington, Vermont 05405, USA
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont 05405, USA
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8
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Wang X, Krieger-Redwood K, Lyu B, Lowndes R, Wu G, Souter NE, Wang X, Kong R, Shafiei G, Bernhardt BC, Cui Z, Smallwood J, Du Y, Jefferies E. The Brain's Topographical Organization Shapes Dynamic Interaction Patterns That Support Flexible Behavior Based on Rules and Long-Term Knowledge. J Neurosci 2024; 44:e2223232024. [PMID: 38527807 PMCID: PMC11140685 DOI: 10.1523/jneurosci.2223-23.2024] [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: 11/15/2023] [Revised: 03/14/2024] [Accepted: 03/17/2024] [Indexed: 03/27/2024] Open
Abstract
Adaptive behavior relies both on specific rules that vary across situations and stable long-term knowledge gained from experience. The frontoparietal control network (FPCN) is implicated in the brain's ability to balance these different influences on action. Here, we investigate how the topographical organization of the cortex supports behavioral flexibility within the FPCN. Functional properties of this network might reflect its juxtaposition between the dorsal attention network (DAN) and the default mode network (DMN), two large-scale systems implicated in top-down attention and memory-guided cognition, respectively. Our study tests whether subnetworks of FPCN are topographically proximal to the DAN and the DMN, respectively, and how these topographical differences relate to functional differences: the proximity of each subnetwork is anticipated to play a pivotal role in generating distinct cognitive modes relevant to working memory and long-term memory. We show that FPCN subsystems share multiple anatomical and functional similarities with their neighboring systems (DAN and DMN) and that this topographical architecture supports distinct interaction patterns that give rise to different patterns of functional behavior. The FPCN acts as a unified system when long-term knowledge supports behavior but becomes segregated into discrete subsystems with different patterns of interaction when long-term memory is less relevant. In this way, our study suggests that the topographical organization of the FPCN and the connections it forms with distant regions of cortex are important influences on how this system supports flexible behavior.
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Affiliation(s)
- Xiuyi Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
- Department of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom
| | - Katya Krieger-Redwood
- Department of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom
| | - Baihan Lyu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Rebecca Lowndes
- Department of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom
| | - Guowei Wu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Nicholas E Souter
- Department of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom
| | - Xiaokang Wang
- Department of Biomedical Engineering, University of California, Davis, California 95616
| | - Ru Kong
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore
| | - Golia Shafiei
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Jonathan Smallwood
- Department of Psychology, Queens University, Kingston, Ontario K7L 3N6, Canada
| | - Yi Du
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
- Chinese Institute for Brain Research, Beijing 102206, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China
| | - Elizabeth Jefferies
- Department of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom
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Pinto H, Lazic I, Antonacci Y, Pernice R, Gu D, Barà C, Faes L, Rocha AP. Testing dynamic correlations and nonlinearity in bivariate time series through information measures and surrogate data analysis. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1385421. [PMID: 38835949 PMCID: PMC11148466 DOI: 10.3389/fnetp.2024.1385421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 04/22/2024] [Indexed: 06/06/2024]
Abstract
The increasing availability of time series data depicting the evolution of physical system properties has prompted the development of methods focused on extracting insights into the system behavior over time, discerning whether it stems from deterministic or stochastic dynamical systems. Surrogate data testing plays a crucial role in this process by facilitating robust statistical assessments. This ensures that the observed results are not mere occurrences by chance, but genuinely reflect the inherent characteristics of the underlying system. The initial process involves formulating a null hypothesis, which is tested using surrogate data in cases where assumptions about the underlying distributions are absent. A discriminating statistic is then computed for both the original data and each surrogate data set. Significantly deviating values between the original data and the surrogate data ensemble lead to the rejection of the null hypothesis. In this work, we present various surrogate methods designed to assess specific statistical properties in random processes. Specifically, we introduce methods for evaluating the presence of autodependencies and nonlinear dynamics within individual processes, using Information Storage as a discriminating statistic. Additionally, methods are introduced for detecting coupling and nonlinearities in bivariate processes, employing the Mutual Information Rate for this purpose. The surrogate methods introduced are first tested through simulations involving univariate and bivariate processes exhibiting both linear and nonlinear dynamics. Then, they are applied to physiological time series of Heart Period (RR intervals) and respiratory flow (RESP) variability measured during spontaneous and paced breathing. Simulations demonstrated that the proposed methods effectively identify essential dynamical features of stochastic systems. The real data application showed that paced breathing, at low breathing rate, increases the predictability of the individual dynamics of RR and RESP and dampens nonlinearity in their coupled dynamics.
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Affiliation(s)
- Helder Pinto
- Departamento de Matemática, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
- Centro de Matemática da Universidade do Porto (CMUP), Porto, Portugal
| | - Ivan Lazic
- Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
| | - Yuri Antonacci
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Danlei Gu
- Beijing Jiaotong University, Beijing, China
| | - Chiara Barà
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Luca Faes
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Ana Paula Rocha
- Departamento de Matemática, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
- Centro de Matemática da Universidade do Porto (CMUP), Porto, Portugal
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10
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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] [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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Benozzo D, Baron G, Coletta L, Chiuso A, Gozzi A, Bertoldo A. Macroscale coupling between structural and effective connectivity in the mouse brain. Sci Rep 2024; 14:3142. [PMID: 38326324 PMCID: PMC10850485 DOI: 10.1038/s41598-024-51613-7] [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: 07/27/2023] [Accepted: 01/07/2024] [Indexed: 02/09/2024] Open
Abstract
Exploring how the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the major goals of modern neuroscience. At the macroscale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be considered: the directionality of the structural connectome and limitations in explaining networks functions through an undirected measure such as FC. Here, we employed an accurate directed SC of the mouse brain acquired through viral tracers and compared it with single-subject effective connectivity (EC) matrices derived from a dynamic causal model (DCM) applied to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their respective couplings by conditioning on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks; only within sensory motor networks did we observe connections that align in terms of both effective and structural strength.
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Affiliation(s)
- Danilo Benozzo
- Department of Information Engineering, University of Padova, Padua, Italy.
| | - Giorgia Baron
- Department of Information Engineering, University of Padova, Padua, Italy
| | - Ludovico Coletta
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandro Chiuso
- Department of Information Engineering, University of Padova, Padua, Italy
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padova, Padua, Italy.
- Padova Neuroscience Center (PNC), Padua, Italy.
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Varley TF. Generalized decomposition of multivariate information. PLoS One 2024; 19:e0297128. [PMID: 38315691 PMCID: PMC10843128 DOI: 10.1371/journal.pone.0297128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/28/2023] [Indexed: 02/07/2024] Open
Abstract
Since its introduction, the partial information decomposition (PID) has emerged as a powerful, information-theoretic technique useful for studying the structure of (potentially higher-order) interactions in complex systems. Despite its utility, the applicability of the PID is restricted by the need to assign elements as either "sources" or "targets", as well as the specific structure of the mutual information itself. Here, I introduce a generalized information decomposition that relaxes the source/target distinction while still satisfying the basic intuitions about information. This approach is based on the decomposition of the Kullback-Leibler divergence, and consequently allows for the analysis of any information gained when updating from an arbitrary prior to an arbitrary posterior. As a result, any information-theoretic measure that can be written as a linear combination of Kullback-Leibler divergences admits a decomposition in the style of Williams and Beer, including the total correlation, the negentropy, and the mutual information as special cases. This paper explores how the generalized information decomposition can reveal novel insights into existing measures, as well as the nature of higher-order synergies. We show that synergistic information is intimately related to the well-known Tononi-Sporns-Edelman (TSE) complexity, and that synergistic information requires a similar integration/segregation balance as a high TSE complexity. Finally, I end with a discussion of how this approach fits into other attempts to generalize the PID and the possibilities for empirical applications.
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Affiliation(s)
- Thomas F. Varley
- Department of Computer Science, University of Vermont, Burlington, VT, United States of America
- Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States of America
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Scagliarini T, Sparacino L, Faes L, Marinazzo D, Stramaglia S. Gradients of O-information highlight synergy and redundancy in physiological applications. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1335808. [PMID: 38264338 PMCID: PMC10803408 DOI: 10.3389/fnetp.2023.1335808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024]
Abstract
The study of high order dependencies in complex systems has recently led to the introduction of statistical synergy, a novel quantity corresponding to a form of emergence in which patterns at large scales are not traceable from lower scales. As a consequence, several works in the last years dealt with the synergy and its counterpart, the redundancy. In particular, the O-information is a signed metric that measures the balance between redundant and synergistic statistical dependencies. In spite of its growing use, this metric does not provide insight about the role played by low-order scales in the formation of high order effects. To fill this gap, the framework for the computation of the O-information has been recently expanded introducing the so-called gradients of this metric, which measure the irreducible contribution of a variable (or a group of variables) to the high order informational circuits of a system. Here, we review the theory behind the O-information and its gradients and present the potential of these concepts in the field of network physiology, showing two new applications relevant to brain functional connectivity probed via functional resonance imaging and physiological interactions among the variability of heart rate, arterial pressure, respiration and cerebral blood flow.
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Affiliation(s)
- Tomas Scagliarini
- Dipartimento di Fisica e Astronomia G. Galilei, Università degli Studi di Padova, Padova, Italy
| | - Laura Sparacino
- Dipartimento di Ingegneria, Università di Palermo, Palermo, Italy
| | - Luca Faes
- Dipartimento di Ingegneria, Università di Palermo, Palermo, Italy
| | | | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Center of Innovative Technologies for Signal Detection and Processing (TIRES), Università degli Studi di Bari Aldo Moro, Bari, Italy
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