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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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Coronel‐Oliveros C, Gómez RG, Ranasinghe K, Sainz‐Ballesteros A, Legaz A, Fittipaldi S, Cruzat J, Herzog R, Yener G, Parra M, Aguillon D, Lopera F, Santamaria‐Garcia H, Moguilner S, Medel V, Orio P, Whelan R, Tagliazucchi E, Prado P, Ibañez A. Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole-brain modeling. Alzheimers Dement 2024; 20:3228-3250. [PMID: 38501336 PMCID: PMC11095480 DOI: 10.1002/alz.13788] [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: 06/16/2023] [Revised: 02/08/2024] [Accepted: 02/15/2024] [Indexed: 03/20/2024]
Abstract
INTRODUCTION Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) lack mechanistic biophysical modeling in diverse, underrepresented populations. Electroencephalography (EEG) is a high temporal resolution, cost-effective technique for studying dementia globally, but lacks mechanistic models and produces non-replicable results. METHODS We developed a generative whole-brain model that combines EEG source-level metaconnectivity, anatomical priors, and a perturbational approach. This model was applied to Global South participants (AD, bvFTD, and healthy controls). RESULTS Metaconnectivity outperformed pairwise connectivity and revealed more viscous dynamics in patients, with altered metaconnectivity patterns associated with multimodal disease presentation. The biophysical model showed that connectome disintegration and hypoexcitability triggered altered metaconnectivity dynamics and identified critical regions for brain stimulation. We replicated the main results in a second subset of participants for validation with unharmonized, heterogeneous recording settings. DISCUSSION The results provide a novel agenda for developing mechanistic model-inspired characterization and therapies in clinical, translational, and computational neuroscience settings.
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Affiliation(s)
- Carlos Coronel‐Oliveros
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Global Brain Health Institute (GBHI)University of California San Francisco (UCSFA)San FranciscoCaliforniaUSA
- Trinity College DublinDublinIreland
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV)Universidad de ValparaísoValparaísoChile
| | - Raúl Gónzalez Gómez
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Center for Social and Cognitive NeuroscienceSchool of Psychology, Universidad Adolfo IbáñezSantiagoChile
| | - Kamalini Ranasinghe
- Memory and Aging CenterDepartment of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | | | - Agustina Legaz
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Provincia de Buenos AiresVictoriaArgentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Global Brain Health Institute (GBHI)University of California San Francisco (UCSFA)San FranciscoCaliforniaUSA
- Trinity College DublinDublinIreland
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Provincia de Buenos AiresVictoriaArgentina
| | - Josephine Cruzat
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
| | - Rubén Herzog
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
| | - Gorsev Yener
- Izmir University of Economics, Faculty of Medicine, Fevzi Çakmak, Balçova/İzmirSakaryaTurkey
- Dokuz Eylül University, Brain Dynamics Multidisciplinary Research Center, KonakAlsancakTurkey
| | - Mario Parra
- School of Psychological Sciences and HealthUniversity of StrathclydeGlasgowScotland
| | - David Aguillon
- Neuroscience Research Group, University of AntioquiaBogotáColombia
| | - Francisco Lopera
- Neuroscience Research Group, University of AntioquiaBogotáColombia
| | - Hernando Santamaria‐Garcia
- Pontificia Universidad Javeriana, PhD Program of NeuroscienceBogotáColombia
- Hospital Universitario San Ignacio, Center for Memory and Cognition IntellectusBogotáColombia
| | - Sebastián Moguilner
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Provincia de Buenos AiresVictoriaArgentina
| | - Vicente Medel
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Brain and Mind Centre, The University of SydneySydneyNew South WalesAustralia
- Department of NeuroscienceUniversidad de Chile, IndependenciaSantiagoChile
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV)Universidad de ValparaísoValparaísoChile
- Instituto de NeurocienciaFacultad de Ciencias, Universidad de Valparaíso, Playa AnchaValparaísoChile
| | - Robert Whelan
- Global Brain Health Institute (GBHI)University of California San Francisco (UCSFA)San FranciscoCaliforniaUSA
- Trinity College DublinDublinIreland
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Buenos Aires Physics Institute and Physics DepartmentUniversity of Buenos Aires, Intendente Güiraldes 2160 – Ciudad UniversitariaBuenos AiresArgentina
| | - Pavel Prado
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la RehabilitaciónUniversidad San Sebastián, Región MetropolitanaSantiagoChile
| | - Agustín Ibañez
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Global Brain Health Institute (GBHI)University of California San Francisco (UCSFA)San FranciscoCaliforniaUSA
- Trinity College DublinDublinIreland
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Provincia de Buenos AiresVictoriaArgentina
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
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3
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Coronel-Oliveros C, Medel V, Whitaker GA, Astudillo A, Gallagher D, Z-Rivera L, Prado P, El-Deredy W, Orio P, Weinstein A. Elevating understanding: Linking high-altitude hypoxia to brain aging through EEG functional connectivity and spectral analyses. Netw Neurosci 2024; 8:275-292. [PMID: 38562297 PMCID: PMC10927308 DOI: 10.1162/netn_a_00352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 11/17/2023] [Indexed: 04/04/2024] Open
Abstract
High-altitude hypoxia triggers brain function changes reminiscent of those in healthy aging and Alzheimer's disease, compromising cognition and executive functions. Our study sought to validate high-altitude hypoxia as a model for assessing brain activity disruptions akin to aging. We collected EEG data from 16 healthy volunteers during acute high-altitude hypoxia (at 4,000 masl) and at sea level, focusing on relative changes in power and aperiodic slope of the EEG spectrum due to hypoxia. Additionally, we examined functional connectivity using wPLI, and functional segregation and integration using graph theory tools. High altitude led to slower brain oscillations, that is, increased δ and reduced α power, and flattened the 1/f aperiodic slope, indicating higher electrophysiological noise, akin to healthy aging. Notably, functional integration strengthened in the θ band, exhibiting unique topographical patterns at the subnetwork level, including increased frontocentral and reduced occipitoparietal integration. Moreover, we discovered significant correlations between subjects' age, 1/f slope, θ band integration, and observed robust effects of hypoxia after adjusting for age. Our findings shed light on how reduced oxygen levels at high altitudes influence brain activity patterns resembling those in neurodegenerative disorders and aging, making high-altitude hypoxia a promising model for comprehending the brain in health and disease.
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Affiliation(s)
- Carlos Coronel-Oliveros
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Global Brain Health Institute (GBHI), University of California, San Francisco (UCSF), San Francisco, CA, USA and Trinity College Dublin, Dublin, Ireland
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile
| | - Vicente Medel
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
- Department of Neuroscience, Universidad de Chile, Santiago, Chile
| | - Grace Alma Whitaker
- Advanced Center for Electrical and Electronics Engineering (AC3E), Federico Santa María Technical University, Valparaíso, Chile
- Chair of Acoustics and Haptics, Technische Universität Dresden, Dresden, Germany
| | - Aland Astudillo
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile
- NICM Health Research Institute, Western Sydney University, Penrith, New South Wales, Australia
| | - David Gallagher
- School of Psychology, Liverpool John Moores University, Liverpool, England
| | - Lucía Z-Rivera
- Advanced Center for Electrical and Electronics Engineering (AC3E), Federico Santa María Technical University, Valparaíso, Chile
| | - Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Wael El-Deredy
- Advanced Center for Electrical and Electronics Engineering (AC3E), Federico Santa María Technical University, Valparaíso, Chile
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile
- Instituto de Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Alejandro Weinstein
- Advanced Center for Electrical and Electronics Engineering (AC3E), Federico Santa María Technical University, Valparaíso, Chile
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile
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Hindriks R, Broeders TAA, Schoonheim MM, Douw L, Santos F, van Wieringen W, Tewarie PKB. Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants. Hum Brain Mapp 2024; 45:e26663. [PMID: 38520377 PMCID: PMC10960559 DOI: 10.1002/hbm.26663] [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: 05/25/2023] [Revised: 02/12/2024] [Accepted: 03/08/2024] [Indexed: 03/25/2024] Open
Abstract
Blood-level oxygenation-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most common modality to study functional connectivity in the human brain. Most research to date has focused on connectivity between pairs of brain regions. However, attention has recently turned towards connectivity involving more than two regions, that is, higher-order connectivity. It is not yet clear how higher-order connectivity can best be quantified. The measures that are currently in use cannot distinguish between pairwise (i.e., second-order) and higher-order connectivity. We show that genuine higher-order connectivity can be quantified by using multivariate cumulants. We explore the use of multivariate cumulants for quantifying higher-order connectivity and the performance of block bootstrapping for statistical inference. In particular, we formulate a generative model for fMRI signals exhibiting higher-order connectivity and use it to assess bias, standard errors, and detection probabilities. Application to resting-state fMRI data from the Human Connectome Project demonstrates that spontaneous fMRI signals are organized into higher-order networks that are distinct from second-order resting-state networks. Application to a clinical cohort of patients with multiple sclerosis further demonstrates that cumulants can be used to classify disease groups and explain behavioral variability. Hence, we present a novel framework to reliably estimate genuine higher-order connectivity in fMRI data which can be used for constructing hyperedges, and finally, which can readily be applied to fMRI data from populations with neuropsychiatric disease or cognitive neuroscientific experiments.
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Affiliation(s)
- Rikkert Hindriks
- Department of Mathematics, Faculty of ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Tommy A. A. Broeders
- Department of Anatomy and Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Menno M. Schoonheim
- Department of Anatomy and Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Fernando Santos
- Dutch Institute for Emergent Phenomena (DIEP)Institute for Advanced Studies, University of AmsterdamAmsterdamThe Netherlands
- Korteweg de Vries Institute for MathematicsUniversity of AmsterdamAmsterdamthe Netherlands
| | - Wessel van Wieringen
- Department of Epidemiology and BiostatisticsAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Prejaas K. B. Tewarie
- Sir Peter Mansfield Imaging CenterSchool of Physics, University of NottinghamNottinghamUnited Kingdom
- Clinical Neurophysiology GroupUniversity of TwenteEnschedeThe Netherlands
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5
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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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6
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Jimenez-Marin A, Diez I, Erramuzpe A, Stramaglia S, Bonifazi P, Cortes JM. Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain. Sci Data 2024; 11:256. [PMID: 38424112 PMCID: PMC10904384 DOI: 10.1038/s41597-024-03060-2] [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: 08/10/2023] [Accepted: 02/12/2024] [Indexed: 03/02/2024] Open
Abstract
The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions, in conjunction with modular transcriptomic profiles, which are essential for comprehensive biological interpretation. To mitigate this limitation, our contribution encompasses the provision of an open-access dataset consisting of derivative matrices of functional and structural connectivity across multiple scales, accompanied by code that facilitates the investigation of their interrelations. We also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.
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Affiliation(s)
- Antonio Jimenez-Marin
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Ibai Diez
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, United States of America
| | - Asier Erramuzpe
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain
| | - Sebastiano Stramaglia
- Dipartamento Interateneo di Fisica, Universita Degli Studi di Bari Aldo Moro, INFN, Bari, Italy
| | - Paolo Bonifazi
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain
| | - Jesus M Cortes
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain.
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain.
- Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain.
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7
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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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Whole-brain modeling explains the context-dependent effects of cholinergic neuromodulation. Neuroimage 2023; 265:119782. [PMID: 36464098 DOI: 10.1016/j.neuroimage.2022.119782] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/08/2022] [Accepted: 11/30/2022] [Indexed: 12/04/2022] Open
Abstract
Integration and segregation are two fundamental principles of brain organization. The brain manages the transitions and balance between different functional segregated or integrated states through neuromodulatory systems. Recently, computational and experimental studies suggest a pro-segregation effect of cholinergic neuromodulation. Here, we studied the effects of the cholinergic system on brain functional connectivity using both empirical fMRI data and computational modeling. First, we analyzed the effects of nicotine on functional connectivity and network topology in healthy subjects during resting-state conditions and during an attentional task. Then, we employed a whole-brain neural mass model interconnected using a human connectome to simulate the effects of nicotine and investigate causal mechanisms for these changes. The drug effect was modeled decreasing both the global coupling and local feedback inhibition parameters, consistent with the known cellular effects of acetylcholine. We found that nicotine incremented functional segregation in both empirical and simulated data, and the effects are context-dependent: observed during the task, but not in the resting state. In-task performance correlates with functional segregation, establishing a link between functional network topology and behavior. Furthermore, we found in the empirical data that the regional density of the nicotinic acetylcholine α4β2 correlates with the decrease in functional nodal strength by nicotine during the task. Our results confirm that cholinergic neuromodulation promotes functional segregation in a context-dependent fashion, and suggest that this segregation is suited for simple visual-attentional tasks.
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