1
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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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2
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Irankhah R, Mehrabbeik M, Parastesh F, Rajagopal K, Jafari S, Kurths J. Synchronization enhancement subjected to adaptive blinking coupling. CHAOS (WOODBURY, N.Y.) 2024; 34:023120. [PMID: 38377293 DOI: 10.1063/5.0188366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/25/2024] [Indexed: 02/22/2024]
Abstract
Synchronization holds a significant role, notably within chaotic systems, in various contexts where the coordinated behavior of systems plays a pivotal and indispensable role. Hence, many studies have been dedicated to investigating the underlying mechanism of synchronization of chaotic systems. Networks with time-varying coupling, particularly those with blinking coupling, have been proven essential. The reason is that such coupling schemes introduce dynamic variations that enhance adaptability and robustness, making them applicable in various real-world scenarios. This paper introduces a novel adaptive blinking coupling, wherein the coupling adapts dynamically based on the most influential variable exhibiting the most significant average disparity. To ensure an equitable selection of the most effective coupling at each time instance, the average difference of each variable is normalized to the synchronous solution's range. Due to this adaptive coupling selection, synchronization enhancement is expected to be observed. This hypothesis is assessed within networks of identical systems, encompassing Lorenz, Rössler, Chen, Hindmarsh-Rose, forced Duffing, and forced van der Pol systems. The results demonstrated a substantial improvement in synchronization when employing adaptive blinking coupling, particularly when applying the normalization process.
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Affiliation(s)
- Reza Irankhah
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 159163-4311, Iran
| | - Mahtab Mehrabbeik
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 159163-4311, Iran
| | - Fatemeh Parastesh
- Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai 600069, India
| | - Karthikeyan Rajagopal
- Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai 600069, India
| | - Sajad Jafari
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 159163-4311, Iran
- Health Technology Research Institute, Amirkabir University of Technology (Tehran Polytechnic), Tehran 159163-4311, Iran
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research, Potsdam 14473, Germany
- Institute of Physics, Humboldt University of Berlin, Berlin 12489, Germany
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3
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Bollt E, Fish J, Kumar A, Roque Dos Santos E, Laurienti PJ. Fractal basins as a mechanism for the nimble brain. Sci Rep 2023; 13:20860. [PMID: 38012212 PMCID: PMC10682042 DOI: 10.1038/s41598-023-45664-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/22/2023] [Indexed: 11/29/2023] Open
Abstract
An interesting feature of the brain is its ability to respond to disparate sensory signals from the environment in unique ways depending on the environmental context or current brain state. In dynamical systems, this is an example of multi-stability, the ability to switch between multiple stable states corresponding to specific patterns of brain activity/connectivity. In this article, we describe chimera states, which are patterns consisting of mixed synchrony and incoherence, in a brain-inspired dynamical systems model composed of a network with weak individual interactions and chaotic/periodic local dynamics. We illustrate the mechanism using synthetic time series interacting on a realistic anatomical brain network derived from human diffusion tensor imaging. We introduce the so-called vector pattern state (VPS) as an efficient way of identifying chimera states and mapping basin structures. Clustering similar VPSs for different initial conditions, we show that coexisting attractors of such states reveal intricately "mingled" fractal basin boundaries that are immediately reachable. This could explain the nimble brain's ability to rapidly switch patterns between coexisting attractors.
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Affiliation(s)
- Erik Bollt
- Department of Electrical and Computer Engineering, Clarkson University, 8 Clarkson Ave., Potsdam, NY, 13699, USA.
- Clarkson Center for Complex Systems Science, Clarkson University, 8 Clarkson Ave., Potsdam, NY, 13699, USA.
| | - Jeremie Fish
- Department of Electrical and Computer Engineering, Clarkson University, 8 Clarkson Ave., Potsdam, NY, 13699, USA
- Clarkson Center for Complex Systems Science, Clarkson University, 8 Clarkson Ave., Potsdam, NY, 13699, USA
| | - Anil Kumar
- Department of Electrical and Computer Engineering, Clarkson University, 8 Clarkson Ave., Potsdam, NY, 13699, USA
- Clarkson Center for Complex Systems Science, Clarkson University, 8 Clarkson Ave., Potsdam, NY, 13699, USA
| | - Edmilson Roque Dos Santos
- Department of Electrical and Computer Engineering, Clarkson University, 8 Clarkson Ave., Potsdam, NY, 13699, USA
- Clarkson Center for Complex Systems Science, Clarkson University, 8 Clarkson Ave., Potsdam, NY, 13699, USA
- Instituto de Ciências Matemáticas e Computação, Universidade de São Paulo, Av. Trab. São Carlense, 400, São Carlos, SP, 13566-590, Brazil
| | - Paul J Laurienti
- Department of Radiology, Wake Forest University School of Medicine, 475 Vine Street, Winston-Salem, NC, 27101, USA
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4
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Bitra VR, Challa SR, Adiukwu PC, Rapaka D. Tau trajectory in Alzheimer's disease: Evidence from the connectome-based computational models. Brain Res Bull 2023; 203:110777. [PMID: 37813312 DOI: 10.1016/j.brainresbull.2023.110777] [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: 05/23/2023] [Revised: 07/08/2023] [Accepted: 10/06/2023] [Indexed: 10/11/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an impairment of cognition and memory. Current research on connectomics have now related changes in the network organization in AD to the patterns of accumulation and spread of amyloid and tau, providing insights into the neurobiological mechanisms of the disease. In addition, network analysis and modeling focus on particular use of graphs to provide intuition into key organizational principles of brain structure, that stipulate how neural activity propagates along structural connections. The utility of connectome-based computational models aids in early predicting, tracking the progression of biomarker-directed AD neuropathology. In this article, we present a short review of tau trajectory, the connectome changes in tau pathology, and the dependent recent connectome-based computational modelling approaches for tau spreading, reproducing pragmatic findings, and developing significant novel tau targeted therapies.
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Affiliation(s)
- Veera Raghavulu Bitra
- School of Pharmacy, Faculty of Health Sciences, University of Botswana, P/Bag-0022, Gaborone, Botswana.
| | - Siva Reddy Challa
- Department of Cancer Biology and Pharmacology, University of Illinois College of Medicine, Peoria, IL 61614, USA; KVSR Siddartha College of Pharmaceutical Sciences, Vijayawada, Andhra Pradesh, India
| | - Paul C Adiukwu
- School of Pharmacy, Faculty of Health Sciences, University of Botswana, P/Bag-0022, Gaborone, Botswana
| | - Deepthi Rapaka
- Pharmacology Division, D.D.T. College of Medicine, Gaborone, Botswana.
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5
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Castaldo F, Páscoa Dos Santos F, Timms RC, Cabral J, Vohryzek J, Deco G, Woolrich M, Friston K, Verschure P, Litvak V. Multi-modal and multi-model interrogation of large-scale functional brain networks. Neuroimage 2023; 277:120236. [PMID: 37355200 PMCID: PMC10958139 DOI: 10.1016/j.neuroimage.2023.120236] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 06/26/2023] Open
Abstract
Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.
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Affiliation(s)
- Francesca Castaldo
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom.
| | - Francisco Páscoa Dos Santos
- Eodyne Systems SL, Barcelona, Spain; Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ryan C Timms
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - Portuguese Government Associate Laboratory, Braga/Guimarães, Portugal; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, United United Kingdom
| | - Jakub Vohryzek
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, United United Kingdom; Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gustavo Deco
- Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Mark Woolrich
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Paul Verschure
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
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6
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Fuscà M, Siebenhühner F, Wang SH, Myrov V, Arnulfo G, Nobili L, Palva JM, Palva S. Brain criticality predicts individual levels of inter-areal synchronization in human electrophysiological data. Nat Commun 2023; 14:4736. [PMID: 37550300 PMCID: PMC10406818 DOI: 10.1038/s41467-023-40056-9] [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: 12/07/2022] [Accepted: 07/10/2023] [Indexed: 08/09/2023] Open
Abstract
Neuronal oscillations and their synchronization between brain areas are fundamental for healthy brain function. Yet, synchronization levels exhibit large inter-individual variability that is associated with behavioral variability. We test whether individual synchronization levels are predicted by individual brain states along an extended regime of critical-like dynamics - the Griffiths phase (GP). We use computational modelling to assess how synchronization is dependent on brain criticality indexed by long-range temporal correlations (LRTCs). We analyze LRTCs and synchronization of oscillations from resting-state magnetoencephalography and stereo-electroencephalography data. Synchronization and LRTCs are both positively linearly and quadratically correlated among healthy subjects, while in epileptogenic areas they are negatively linearly correlated. These results show that variability in synchronization levels is explained by the individual position along the GP with healthy brain areas operating in its subcritical and epileptogenic areas in its supercritical side. We suggest that the GP is fundamental for brain function allowing individual variability while retaining functional advantages of criticality.
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Affiliation(s)
- Marco Fuscà
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Felix Siebenhühner
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University, and Helsinki University Hospital, Helsinki, Finland
| | - Sheng H Wang
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- CEA, NeuroSpin, Gif-sur-Yvette, France
- MIND team, Inria, Université Paris-Saclay, Bures-sur-Yvette, France
| | - Vladislav Myrov
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Dept. of Informatics, Bioengineering, Robotics and System engineering, University of Genoa, Genoa, Italy
| | - Lino Nobili
- Child Neuropsychiatry Unit, IRCCS, Istituto G. Gaslini, Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy
- "Claudio Munari" Epilepsy Surgery Centre, Niguarda Hospital, Milan, Italy
| | - J Matias Palva
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Satu Palva
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK.
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
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7
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Northoff G, Klar P, Bein M, Safron A. As without, so within: how the brain's temporo-spatial alignment to the environment shapes consciousness. Interface Focus 2023; 13:20220076. [PMID: 37065263 PMCID: PMC10102730 DOI: 10.1098/rsfs.2022.0076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 03/02/2023] [Indexed: 04/18/2023] Open
Abstract
Consciousness is constituted by a structure that includes contents as foreground and the environment as background. This structural relation between the experiential foreground and background presupposes a relationship between the brain and the environment, often neglected in theories of consciousness. The temporo-spatial theory of consciousness addresses the brain-environment relation by a concept labelled 'temporo-spatial alignment'. Briefly, temporo-spatial alignment refers to the brain's neuronal activity's interaction with and adaption to interoceptive bodily and exteroceptive environmental stimuli, including their symmetry as key for consciousness. Combining theory and empirical data, this article attempts to demonstrate the yet unclear neuro-phenomenal mechanisms of temporo-spatial alignment. First, we suggest three neuronal layers of the brain's temporo-spatial alignment to the environment. These neuronal layers span across a continuum from longer to shorter timescales. (i) The background layer comprises longer and more powerful timescales mediating topographic-dynamic similarities between different subjects' brains. (ii) The intermediate layer includes a mixture of medium-scaled timescales allowing for stochastic matching between environmental inputs and neuronal activity through the brain's intrinsic neuronal timescales and temporal receptive windows. (iii) The foreground layer comprises shorter and less powerful timescales for neuronal entrainment of stimuli temporal onset through neuronal phase shifting and resetting. Second, we elaborate on how the three neuronal layers of temporo-spatial alignment correspond to their respective phenomenal layers of consciousness. (i) The inter-subjectively shared contextual background of consciousness. (ii) An intermediate layer that mediates the relationship between different contents of consciousness. (iii) A foreground layer that includes specific fast-changing contents of consciousness. Overall, temporo-spatial alignment may provide a mechanism whose different neuronal layers modulate corresponding phenomenal layers of consciousness. Temporo-spatial alignment can provide a bridging principle for linking physical-energetic (free energy), dynamic (symmetry), neuronal (three layers of distinct time-space scales) and phenomenal (form featured by background-intermediate-foreground) mechanisms of consciousness.
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Affiliation(s)
- Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, TheRoyal's Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada K1Z 7K4
- Mental Health Centre, Zhejiang University School of Medicine, Hangzhou 310053, People's Republic of China
- Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou 310053, People's Republic of China
| | - Philipp Klar
- Medical Faculty, C. & O. Vogt-Institute for Brain Research, Heinrich Heine University of Düsseldorf, 40225 Düsseldorf, Germany
| | - Magnus Bein
- Department of Biology and Department of Psychiatry, McGill University, Quebec, Canada H3A 0G4
| | - Adam Safron
- Center for Psychedelic and Consciousness Research, Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA
- Institute for Advanced Consciousness Studies, Santa Monica, CA 90403, USA
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8
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Schirner M, Deco G, Ritter P. Learning how network structure shapes decision-making for bio-inspired computing. Nat Commun 2023; 14:2963. [PMID: 37221168 DOI: 10.1038/s41467-023-38626-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 05/10/2023] [Indexed: 05/25/2023] Open
Abstract
To better understand how network structure shapes intelligent behavior, we developed a learning algorithm that we used to build personalized brain network models for 650 Human Connectome Project participants. We found that participants with higher intelligence scores took more time to solve difficult problems, and that slower solvers had higher average functional connectivity. With simulations we identified a mechanistic link between functional connectivity, intelligence, processing speed and brain synchrony for trading accuracy with speed in dependence of excitation-inhibition balance. Reduced synchrony led decision-making circuits to quickly jump to conclusions, while higher synchrony allowed for better integration of evidence and more robust working memory. Strict tests were applied to ensure reproducibility and generality of the obtained results. Here, we identify links between brain structure and function that enable to learn connectome topology from noninvasive recordings and map it to inter-individual differences in behavior, suggesting broad utility for research and clinical applications.
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Affiliation(s)
- Michael Schirner
- Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany.
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Einstein Center Digital Future, Wilhelmstraße 67, 10117, Berlin, Germany.
| | - Gustavo Deco
- Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience Group, University of Pompeu Fabra, Barcelona, Spain
- Catalan Institution for Research and Advanced Studies, Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Clayton, Melbourne, VIC, Australia
| | - Petra Ritter
- Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany.
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Einstein Center Digital Future, Wilhelmstraße 67, 10117, Berlin, Germany.
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9
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Klar P, Çatal Y, Langner R, Huang Z, Northoff G. Scale-free dynamics in the core-periphery topography and task alignment decline from conscious to unconscious states. Commun Biol 2023; 6:499. [PMID: 37161021 PMCID: PMC10170069 DOI: 10.1038/s42003-023-04879-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/26/2023] [Indexed: 05/11/2023] Open
Abstract
Scale-free physiological processes are ubiquitous in the human organism. Resting-state functional MRI studies observed the loss of scale-free dynamics under anesthesia. In contrast, the modulation of scale-free dynamics during task-related activity remains an open question. We investigate scale-free dynamics in the cerebral cortex's unimodal periphery and transmodal core topography in rest and task states during three conscious levels (awake, sedation, and anesthesia) complemented by computational modelling (Stuart-Landau model). The empirical findings demonstrate that the loss of the brain's intrinsic scale-free dynamics in the core-periphery topography during anesthesia, where pink noise transforms into white noise, disrupts the brain's neuronal alignment with the task's temporal structure. The computational model shows that the stimuli's scale-free dynamics, namely pink noise distinguishes from brown and white noise, also modulate task-related activity. Together, we provide evidence for two mechanisms of consciousness, temporo-spatial nestedness and alignment, suggested by the Temporo-Spatial Theory of Consciousness (TTC).
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Affiliation(s)
- Philipp Klar
- Medical Faculty, C. & O. Vogt-Institute for Brain Research, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany.
| | - Yasir Çatal
- The Royal's Institute of Mental Health Research & University of Ottawa. Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, Ottawa, 145 Carling Avenue, Rm. 6435, Ottawa, ON, K1Z 7K4, Canada
| | - Robert Langner
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Zirui Huang
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Georg Northoff
- The Royal's Institute of Mental Health Research & University of Ottawa. Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, Ottawa, 145 Carling Avenue, Rm. 6435, Ottawa, ON, K1Z 7K4, Canada
- Centre for Cognition and Brain Disorders, Hangzhou Normal University, Tianmu Road 305, Hangzhou, Zhejiang Province, 310013, China
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10
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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. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.22.529400. [PMID: 36865122 PMCID: PMC9980133 DOI: 10.1101/2023.02.22.529400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
How the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the biggest questions of modern neuroscience. At the macro-scale 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 taken into account: the directionality of the structural connectome and the limitations of describing network functions in terms of FC. Here, we employed an accurate directed SC of the mouse brain obtained by means of viral tracers, and related it with single-subject effective connectivity (EC) matrices computed by applying a recently developed DCM to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their couplings by conditioning both 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 the connections within sensory motor networks align both in terms of effective and structural strength.
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Affiliation(s)
- Danilo Benozzo
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Giorgia Baron
- Department of Information Engineering, University of Padova, Padova, 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, Padova, 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, Padova, Italy
- Padova Neuroscience Center, Padova, Italy
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11
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Piccinini J, Deco G, Kringelbach M, Laufs H, Sanz Perl Y, Tagliazucchi E. Data-driven discovery of canonical large-scale brain dynamics. Cereb Cortex Commun 2022; 3:tgac045. [PMID: 36479448 PMCID: PMC9721525 DOI: 10.1093/texcom/tgac045] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/28/2022] [Accepted: 10/30/2022] [Indexed: 09/23/2023] Open
Abstract
Human behavior and cognitive function correlate with complex patterns of spatio-temporal brain dynamics, which can be simulated using computational models with different degrees of biophysical realism. We used a data-driven optimization algorithm to determine and classify the types of local dynamics that enable the reproduction of different observables derived from functional magnetic resonance recordings. The phase space analysis of the resulting equations revealed a predominance of stable spiral attractors, which optimized the similarity to the empirical data in terms of the synchronization, metastability, and functional connectivity dynamics. For stable limit cycles, departures from harmonic oscillations improved the fit in terms of functional connectivity dynamics. Eigenvalue analyses showed that proximity to a bifurcation improved the accuracy of the simulation for wakefulness, whereas deep sleep was associated with increased stability. Our results provide testable predictions that constrain the landscape of suitable biophysical models, while supporting noise-driven dynamics close to a bifurcation as a canonical mechanism underlying the complex fluctuations that characterize endogenous brain activity.
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Affiliation(s)
- Juan Piccinini
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 Ciudad Universitaria, CABA, Argentina
- National Scientific and Technical Research Council (CONICET), Godoy Cruz 2290, CABA, Argentina
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
- Department of Information and Communication Technologies, Universitat Pompeu Fabra Plaça de la Mercè, 10-12, 08002 Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avancats (ICREA), Passeig Lluís Companys 23 08010 Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
- School of Psychological Sciences, Monash University, Wellington Rd, Clayton VIC 3800, Australia
| | - Morten Kringelbach
- Department of Psychiatry, University of Oxford, Warneford Hospital, Warneford Ln, Headington, Oxford OX3 7JX, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Building 1710, Universitetsbyen 3, 8000 Aarhus C, Denmark
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar 4710-057 Braga, Portugal
- Centre for Eudaimonia and Human Flourishing, University of Oxford, 7 Stoke Pl, Headington, Oxford OX3 9BX, United Kingdom
| | - Helmut Laufs
- Neurology Department, Schleswig-Holstein University Hospital, University of Kiel, Haus D, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Yonatan Sanz Perl
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 Ciudad Universitaria, CABA, Argentina
- National Scientific and Technical Research Council (CONICET), Godoy Cruz 2290, CABA, Argentina
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Vito Dumas 284 Victoria, Buenos Aires, Argentina
- Paris Brain Institute (ICM), Hôpital Pitié, 47 Bd de l'Hôpital, 75013 Paris, France
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 Ciudad Universitaria, CABA, Argentina
- National Scientific and Technical Research Council (CONICET), Godoy Cruz 2290, CABA, Argentina
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Diagonal las Torres 2640, Peñalolén Av. Presidente Errázuriz 3485, Las Condes, Santiago, Chile
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