1
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Kim JZ, Larsen B, Parkes L. Shaping dynamical neural computations using spatiotemporal constraints. Biochem Biophys Res Commun 2024; 728:150302. [PMID: 38968771 DOI: 10.1016/j.bbrc.2024.150302] [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: 11/28/2023] [Revised: 03/21/2024] [Accepted: 04/11/2024] [Indexed: 07/07/2024]
Abstract
Dynamics play a critical role in computation. The principled evolution of states over time enables both biological and artificial networks to represent and integrate information to make decisions. In the past few decades, significant multidisciplinary progress has been made in bridging the gap between how we understand biological versus artificial computation, including how insights gained from one can translate to the other. Research has revealed that neurobiology is a key determinant of brain network architecture, which gives rise to spatiotemporally constrained patterns of activity that underlie computation. Here, we discuss how neural systems use dynamics for computation, and claim that the biological constraints that shape brain networks may be leveraged to improve the implementation of artificial neural networks. To formalize this discussion, we consider a natural artificial analog of the brain that has been used extensively to model neural computation: the recurrent neural network (RNN). In both the brain and the RNN, we emphasize the common computational substrate atop which dynamics occur-the connectivity between neurons-and we explore the unique computational advantages offered by biophysical constraints such as resource efficiency, spatial embedding, and neurodevelopment.
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Affiliation(s)
- Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY, 14853, USA.
| | - Bart Larsen
- Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, USA
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ, 08854, USA.
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2
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Wang H, Chen J, Yuan Z, Huang Y, Lin F. NHSMM-MAR-sdNC: A novel data-driven computational framework for state-dependent effective connectivity analysis. Med Image Anal 2024; 97:103290. [PMID: 39094462 DOI: 10.1016/j.media.2024.103290] [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: 05/07/2023] [Revised: 02/08/2024] [Accepted: 07/25/2024] [Indexed: 08/04/2024]
Abstract
The brain exhibits intrinsic dynamics characterized by spontaneous spatiotemporal reorganization of neural activity or metastability, which is associated closely with functional integration and segregation. Compared to dynamic functional connectivity, state-dependent effective connectivity (i.e., dynamic effective connectivity) is more suitable for exploring the metastability as its ability to infer causalities between brain regions. However, methods for state-dependent effective connectivity are scarce and urgently needed. In this study, a novel data-driven computational framework, named NHSMM-MAR-sdNC integrating nonparametric hidden semi-Markov model combined with multivariate autoregressive model and state-dependent new causality, is proposed to investigate the state-dependent effective connectivity. The framework is not constrained by any biological assumptions. Furthermore, state number can be inferred from the observed data directly and the state duration distributions will be estimated explicitly rather than restricted by geometric form, which overcomes limitations of hidden Markov model. Experimental results of synthetic data show that the framework can identify the state number adaptively and the state-dependent causality networks accurately. The dynamics of state-related causality networks are also revealed by the new method on real-world resting-state fMRI data. Our method provides a new data-driven computational framework for identifying state-dependent effective connectivity, which will facilitate the identification and assessment of metastability and itinerant dynamics of the brain.
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Affiliation(s)
- Houxiang Wang
- School of Science, Wuhan University of Technology, Wuhan Hubei, 430071, China
| | - Jiaqing Chen
- School of Science, Wuhan University of Technology, Wuhan Hubei, 430071, China.
| | - Zihao Yuan
- School of Science, Wuhan University of Technology, Wuhan Hubei, 430071, China
| | - Yangxin Huang
- School of Public Health, University of South Florida, Tampa, FL 33612, USA
| | - Fuchun Lin
- National Center for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, Hubei 430071, China.
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3
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Parkes L, Kim JZ, Stiso J, Brynildsen JK, Cieslak M, Covitz S, Gur RE, Gur RC, Pasqualetti F, Shinohara RT, Zhou D, Satterthwaite TD, Bassett DS. A network control theory pipeline for studying the dynamics of the structural connectome. Nat Protoc 2024:10.1038/s41596-024-01023-w. [PMID: 39075309 DOI: 10.1038/s41596-024-01023-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 05/16/2024] [Indexed: 07/31/2024]
Abstract
Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.
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Affiliation(s)
- Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia K Brynildsen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dale Zhou
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
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4
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Vogel JW, Alexander-Bloch AF, Wagstyl K, Bertolero MA, Markello RD, Pines A, Sydnor VJ, Diaz-Papkovich A, Hansen JY, Evans AC, Bernhardt B, Misic B, Satterthwaite TD, Seidlitz J. Deciphering the functional specialization of whole-brain spatiomolecular gradients in the adult brain. Proc Natl Acad Sci U S A 2024; 121:e2219137121. [PMID: 38861593 PMCID: PMC11194492 DOI: 10.1073/pnas.2219137121] [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: 11/08/2022] [Accepted: 04/27/2024] [Indexed: 06/13/2024] Open
Abstract
Cortical arealization arises during neurodevelopment from the confluence of molecular gradients representing patterned expression of morphogens and transcription factors. However, whether similar gradients are maintained in the adult brain remains unknown. Here, we uncover three axes of topographic variation in gene expression in the adult human brain that specifically capture previously identified rostral-caudal, dorsal-ventral, and medial-lateral axes of early developmental patterning. The interaction of these spatiomolecular gradients i) accurately reconstructs the position of brain tissue samples, ii) delineates known functional territories, and iii) can model the topographical variation of diverse cortical features. The spatiomolecular gradients are distinct from canonical cortical axes differentiating the primary sensory cortex from the association cortex, but radiate in parallel with the axes traversed by local field potentials along the cortex. We replicate all three molecular gradients in three independent human datasets as well as two nonhuman primate datasets and find that each gradient shows a distinct developmental trajectory across the lifespan. The gradients are composed of several well-known transcription factors (e.g., PAX6 and SIX3), and a small set of genes shared across gradients are strongly enriched for multiple diseases. Together, these results provide insight into the developmental sculpting of functionally distinct brain regions, governed by three robust transcriptomic axes embedded within brain parenchyma.
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Affiliation(s)
- Jacob W. Vogel
- Department of Clinical Sciences Malmö, SciLifeLab, Lund University, Lund, Sweden202 13
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
| | - Aaron F. Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA19104
- Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA19104
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, LondonWC1N 3AR, United Kingdom
| | - Maxwell A. Bertolero
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
| | - Ross D. Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QCH3A 2B4, Canada
| | - Adam Pines
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA94305
| | - Valerie J. Sydnor
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
| | - Alex Diaz-Papkovich
- Quantitative Life Sciences, McGill University, Montreal, QCH3A 1E3, Canada
- McGill Genome Centre, McGill University, Montreal, QCH3A 0G1, Canada
| | - Justine Y. Hansen
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QCH3A 2B4, Canada
| | - Alan C. Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QCH3A 2B4, Canada
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QCH3A 2B4, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QCH3A 2B4, Canada
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
- Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA19104
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA19104
- Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA19104
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5
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Bagheri A, Pasande M, Bello K, Araabi BN, Akhondi-Asl A. Discovering the effective connectome of the brain with dynamic Bayesian DAG learning. Neuroimage 2024; 297:120684. [PMID: 38880310 DOI: 10.1016/j.neuroimage.2024.120684] [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: 12/04/2023] [Revised: 05/30/2024] [Accepted: 06/10/2024] [Indexed: 06/18/2024] Open
Abstract
Understanding the complex mechanisms of the brain can be unraveled by extracting the Dynamic Effective Connectome (DEC). Recently, score-based Directed Acyclic Graph (DAG) discovery methods have shown significant improvements in extracting the causal structure and inferring effective connectivity. However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data. In this paper, we introduce Bayesian Dynamic DAG learning with M-matrices Acyclicity characterization (BDyMA) method to address the challenges in discovering DEC. The presented dynamic DAG enables us to discover direct feedback loop edges as well. Leveraging an unconstrained framework in the BDyMA method leads to more accurate results in detecting high-dimensional networks, achieving sparser outcomes, making it particularly suitable for extracting DEC. Additionally, the score function of the BDyMA method allows the incorporation of prior knowledge into the process of dynamic causal discovery which further enhances the accuracy of results. Comprehensive simulations on synthetic data and experiments on Human Connectome Project (HCP) data demonstrate that our method can handle both of the two main challenges, yielding more accurate and reliable DEC compared to state-of-the-art and traditional methods. Additionally, we investigate the trustworthiness of DTI data as prior knowledge for DEC discovery and show the improvements in DEC discovery when the DTI data is incorporated into the process.
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Affiliation(s)
- Abdolmahdi Bagheri
- School of Electrical and Computer Engineering, University of Tehran, College of Engineering, Tehran, Iran.
| | - Mohammad Pasande
- School of Electrical and Computer Engineering, University of Tehran, College of Engineering, Tehran, Iran
| | - Kevin Bello
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA
| | - Babak Nadjar Araabi
- School of Electrical and Computer Engineering, University of Tehran, College of Engineering, Tehran, Iran
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6
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Mohan UR, Zhang H, Ermentrout B, Jacobs J. The direction of theta and alpha travelling waves modulates human memory processing. Nat Hum Behav 2024; 8:1124-1135. [PMID: 38459263 DOI: 10.1038/s41562-024-01838-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 01/24/2024] [Indexed: 03/10/2024]
Abstract
To support a range of behaviours, the brain must flexibly coordinate neural activity across widespread brain regions. One potential mechanism for this coordination is a travelling wave, in which a neural oscillation propagates across the brain while organizing the order and timing of activity across regions. Although travelling waves are present across the brain in various species, their potential functional relevance has remained unknown. Here, using rare direct human brain recordings, we demonstrate a distinct functional role for travelling waves of theta- and alpha-band (2-13 Hz) oscillations in the cortex. Travelling waves propagate in different directions during separate cognitive processes. In episodic memory, travelling waves tended to propagate in a posterior-to-anterior direction during successful memory encoding and in an anterior-to-posterior direction during recall. Because travelling waves of oscillations correspond to local neuronal spiking, these patterns indicate that rhythmic pulses of activity move across the brain in different directions for separate behaviours. More broadly, our results suggest a fundamental role for travelling waves and oscillations in dynamically coordinating neural connectivity, by flexibly organizing the timing and directionality of network interactions across the cortex to support cognition and behaviour.
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Affiliation(s)
- Uma R Mohan
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD, USA
| | | | - Bard Ermentrout
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joshua Jacobs
- Department of Biomedical Engineering, Columbia University, New York City, NY, USA.
- Department of Neurological Surgery, Columbia University, New York City, NY, USA.
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7
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Koller DP, Schirner M, Ritter P. Human connectome topology directs cortical traveling waves and shapes frequency gradients. Nat Commun 2024; 15:3570. [PMID: 38670965 PMCID: PMC11053146 DOI: 10.1038/s41467-024-47860-x] [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: 04/29/2023] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Traveling waves and neural oscillation frequency gradients are pervasive in the human cortex. While the direction of traveling waves has been linked to brain function and dysfunction, the factors that determine this direction remain elusive. We hypothesized that structural connectivity instrength gradients - defined as the gradually varying sum of incoming connection strengths across the cortex - could shape both traveling wave direction and frequency gradients. We confirm the presence of instrength gradients in the human connectome across diverse cohorts and parcellations. Using a cortical network model, we demonstrate how these instrength gradients direct traveling waves and shape frequency gradients. Our model fits resting-state MEG functional connectivity best in a regime where instrength-directed traveling waves and frequency gradients emerge. We further show how structural subnetworks of the human connectome generate opposing wave directions and frequency gradients observed in the alpha and beta bands. Our findings suggest that structural connectivity instrength gradients affect both traveling wave direction and frequency gradients.
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Grants
- P.R. acknowledges funding from the following sources: Digital Europe Grant TEF-Health # 101100700, H2020 Research and Innovation Action Grant Human Brain Project SGA2 785907, H2020 Research and Innovation Action Grant Human Brain Project SGA3 945539, H2020 Research and Innovation Action Grant EOSC VirtualBrainCloud 826421, H2020 Research and Innovation Action Grant AISN 101057655, H2020 Research Infrastructures Grant EBRAINS-PREP 101079717, H2020 European Innovation Council PHRASE 101058240, H2020 Research Infrastructures Grant EBRAIN-Health 101058516, H2020 European Research Council Grant ERC BrainModes 683049, JPND ERA PerMed PatternCog 2522FSB904, Berlin Institute of Health & Foundation Charité, Johanna Quandt Excellence Initiative, German Research Foundation SFB 1436 (project ID 425899996), German Research Foundation SFB 1315 (project ID 327654276), German Research Foundation SFB 936 (project ID 178316478), German Research Foundation SFB-TRR 295 (project ID 424778381) German Research Foundation SPP Computational Connectomics RI 2073/6-1, RI 2073/10-2, RI 2073/9-1.
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Affiliation(s)
- Dominik P Koller
- 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.
| | - 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, 10115, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Einstein Center Digital Future, Wilhelmstraße 67, 10117, Berlin, Germany
| | - 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, 10115, 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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8
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Papo D, Buldú JM. Does the brain behave like a (complex) network? I. Dynamics. Phys Life Rev 2024; 48:47-98. [PMID: 38145591 DOI: 10.1016/j.plrev.2023.12.006] [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: 12/08/2023] [Accepted: 12/10/2023] [Indexed: 12/27/2023]
Abstract
Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.
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Affiliation(s)
- D Papo
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy; Center for Translational Neurophysiology, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy.
| | - J M Buldú
- Complex Systems Group & G.I.S.C., Universidad Rey Juan Carlos, Madrid, Spain
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9
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Irastorza-Valera L, Benítez JM, Montáns FJ, Saucedo-Mora L. An Agent-Based Model to Reproduce the Boolean Logic Behaviour of Neuronal Self-Organised Communities through Pulse Delay Modulation and Generation of Logic Gates. Biomimetics (Basel) 2024; 9:101. [PMID: 38392147 PMCID: PMC10886514 DOI: 10.3390/biomimetics9020101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/16/2024] [Accepted: 02/04/2024] [Indexed: 02/24/2024] Open
Abstract
The human brain is arguably the most complex "machine" to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain's structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence frameworks inspired by its logical functioning. In this article, an approach to model some aspects of the brain learning and signal processing is presented, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model to demonstrate how dynamic neuroplasticity, neural inhibition and neuron migration can reshape the brain's logical connectivity to synchronise signal processing and obtain certain target latencies. This work showcases the importance of dynamic logical and biophysical remodelling in brain plasticity. Combining mathematical (agents, graph theory, topology and backpropagation) and biomedical ingredients (metastability, neuroplasticity and migration), these preliminary results prove complex brain phenomena can be reproduced-under pertinent simplifications-via affordable computations, which can be construed as a starting point for more ambitiously accurate simulations.
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Affiliation(s)
- Luis Irastorza-Valera
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain
- PIMM Laboratory, Arts et Métiers Institute of Technology, 151 Bd de l'Hôpital, 75013 Paris, France
| | - José María Benítez
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain
| | - Francisco J Montáns
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain
- Department of Mechanical and Aerospace Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Luis Saucedo-Mora
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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10
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Gutzen R, De Bonis G, De Luca C, Pastorelli E, Capone C, Allegra Mascaro AL, Resta F, Manasanch A, Pavone FS, Sanchez-Vives MV, Mattia M, Grün S, Paolucci PS, Denker M. A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets. CELL REPORTS METHODS 2024; 4:100681. [PMID: 38183979 PMCID: PMC10831958 DOI: 10.1016/j.crmeth.2023.100681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/11/2023] [Accepted: 12/11/2023] [Indexed: 01/08/2024]
Abstract
Neuroscience is moving toward a more integrative discipline where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integrating such heterogeneous data into analysis workflows such that consistent and comparable conclusions can be distilled as an experimental basis for models and theories. Here, we propose a solution in the context of slow-wave activity (<1 Hz), which occurs during unconscious brain states like sleep and general anesthesia and is observed across diverse experimental approaches. We address the issue of integrating and comparing heterogeneous data by conceptualizing a general pipeline design that is adaptable to a variety of inputs and applications. Furthermore, we present the Collaborative Brain Wave Analysis Pipeline (Cobrawap) as a concrete, reusable software implementation to perform broad, detailed, and rigorous comparisons of slow-wave characteristics across multiple, openly available electrocorticography (ECoG) and calcium imaging datasets.
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Affiliation(s)
- Robin Gutzen
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany; Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany.
| | - Giulia De Bonis
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy
| | - Chiara De Luca
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy; Institute of Neuroinformatics, University of Zürich and ETH Zürich, Zürich, Switzerland
| | - Elena Pastorelli
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy
| | - Cristiano Capone
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy
| | - Anna Letizia Allegra Mascaro
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Florence, Italy; Neuroscience Institute, National Research Council, Pisa, Italy
| | - Francesco Resta
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Florence, Italy; Department of Physics and Astronomy, University of Florence, Florence, Italy
| | - Arnau Manasanch
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Francesco Saverio Pavone
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Florence, Italy; Department of Physics and Astronomy, University of Florence, Florence, Italy; National Institute of Optics, National Research Council, Sesto Fiorentino, Italy
| | - Maria V Sanchez-Vives
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Maurizio Mattia
- National Center for Radiation Protection and Computational Physics, Istituto Superiore di Sanità (ISS), Rome, Italy
| | - Sonja Grün
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany; Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany
| | | | - Michael Denker
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
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11
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Guan S, Jiang R, Chen DY, Michael A, Meng C, Biswal B. Multifractal long-range dependence pattern of functional magnetic resonance imaging in the human brain at rest. Cereb Cortex 2023; 33:11594-11608. [PMID: 37851793 DOI: 10.1093/cercor/bhad393] [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: 09/12/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 10/20/2023] Open
Abstract
Long-range dependence is a prevalent phenomenon in various biological systems that characterizes the long-memory effect of temporal fluctuations. While recent research suggests that functional magnetic resonance imaging signal has fractal property, it remains unknown about the multifractal long-range dependence pattern of resting-state functional magnetic resonance imaging signals. The current study adopted the multifractal detrended fluctuation analysis on highly sampled resting-state functional magnetic resonance imaging scans to investigate long-range dependence profile associated with the whole-brain voxels as specific functional networks. Our findings revealed the long-range dependence's multifractal properties. Moreover, long-term persistent fluctuations are found for all stations with stronger persistency in whole-brain regions. Subsets with large fluctuations contribute more to the multifractal spectrum in the whole brain. Additionally, we found that the preprocessing with band-pass filtering provided significantly higher reliability for estimating long-range dependence. Our validation analysis confirmed that the optimal pipeline of long-range dependence analysis should include band-pass filtering and removal of daily temporal dependence. Furthermore, multifractal long-range dependence characteristics in healthy control and schizophrenia are different significantly. This work has provided an analytical pipeline for the multifractal long-range dependence in the resting-state functional magnetic resonance imaging signal. The findings suggest differential long-memory effects in the intrinsic functional networks, which may offer a neural marker finding for understanding brain function and pathology.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu 610041, China
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Medical Equipment Department, Xiangyang No.1 People's Hospital, Xiangyang 441000, China
| | - Donna Y Chen
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
| | - Andrew Michael
- Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, United States
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
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12
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Kim JZ, Larsen B, Parkes L. Shaping dynamical neural computations using spatiotemporal constraints. ARXIV 2023:arXiv:2311.15572v1. [PMID: 38076517 PMCID: PMC10705584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Dynamics play a critical role in computation. The principled evolution of states over time enables both biological and artificial networks to represent and integrate information to make decisions. In the past few decades, significant multidisciplinary progress has been made in bridging the gap between how we understand biological versus artificial computation, including how insights gained from one can translate to the other. Research has revealed that neurobiology is a key determinant of brain network architecture, which gives rise to spatiotemporally constrained patterns of activity that underlie computation. Here, we discuss how neural systems use dynamics for computation, and claim that the biological constraints that shape brain networks may be leveraged to improve the implementation of artificial neural networks. To formalize this discussion, we consider a natural artificial analog of the brain that has been used extensively to model neural computation: the recurrent neural network (RNN). In both the brain and the RNN, we emphasize the common computational substrate atop which dynamics occur-the connectivity between neurons-and we explore the unique computational advantages offered by biophysical constraints such as resource efficiency, spatial embedding, and neurodevelopment.
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Affiliation(s)
- Jason Z. Kim
- Department of Physics, Cornell University, Ithaca, NY 14853, USA
| | - Bart Larsen
- Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
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13
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Rué-Queralt J, Mancini V, Rochas V, Latrèche C, Uhlhaas PJ, Michel CM, Plomp G, Eliez S, Hagmann P. The coupling between the spatial and temporal scales of neural processes revealed by a joint time-vertex connectome spectral analysis. Neuroimage 2023; 280:120337. [PMID: 37604296 DOI: 10.1016/j.neuroimage.2023.120337] [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: 03/16/2023] [Revised: 08/10/2023] [Accepted: 08/16/2023] [Indexed: 08/23/2023] Open
Abstract
Brain oscillations are produced by the coordinated activity of large groups of neurons and different rhythms are thought to reflect different modes of information processing. These modes, in turn, are known to occur at different spatial scales. Nevertheless, how these rhythms support different spatial modes of information processing at the brain scale is not yet fully understood. Here we use "Joint Time-Vertex Spectral Analysis" to characterize the joint spectral content of brain activity both in time (temporal frequencies) and in space over the connectivity graph (spatial connectome harmonics). This method allows us to characterize the relationship between spatially localized or distributed neural processes on one side and their respective temporal frequency bands in source-reconstructed M/EEG signals. We explore this approach on two different datasets, an auditory steady-state response (ASSR) and a visual grating task. Our results suggest that different information processing mechanisms are carried out at different frequency bands: while spatially distributed activity (which may also be interpreted as integration) specifically occurs at low temporal frequencies (alpha and theta) and low graph spatial frequencies, localized electrical activity (i.e., segregation) is observed at high temporal frequencies (high and low gamma) over restricted high spatial graph frequencies. Crucially, the estimated contribution of the distributed and localized neural activity predicts performance in a behavioral task, demonstrating the neurophysiological relevance of the joint time-vertex spectral representation.
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Affiliation(s)
- Joan Rué-Queralt
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland; Perceptual Networks Lab, Department of Psychology, University of Fribourg, Fribourg, Switzerland
| | - Valentina Mancini
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland.
| | - Vincent Rochas
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland; Human Neuroscience Platform, Fondation Campus Biotech Geneva, Switzerland
| | - Caren Latrèche
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Peter J Uhlhaas
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, Scotland, United Kingdom; Department of Child and Adolescent Psychiatry, Psychosomatic Medicine and Psychotherapy, Charité Universitätsmedizin, Berlin, Germany
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
| | - Gijs Plomp
- Perceptual Networks Lab, Department of Psychology, University of Fribourg, Fribourg, Switzerland
| | - Stephan Eliez
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Department of Genetic Medicine and Development, University of Geneva School of Medicine, Geneva, Switzerland
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
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14
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Parkes L, Kim JZ, Stiso J, Brynildsen JK, Cieslak M, Covitz S, Gur RE, Gur RC, Pasqualetti F, Shinohara RT, Zhou D, Satterthwaite TD, Bassett DS. Using network control theory to study the dynamics of the structural connectome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.23.554519. [PMID: 37662395 PMCID: PMC10473719 DOI: 10.1101/2023.08.23.554519] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains dynamics. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter dynamics in a desired way. We have extensively developed and validated the application of NCT to the human structural connectome. Through these efforts, we have studied (i) how different aspects of connectome topology affect neural dynamics, (ii) whether NCT outputs cohere with empirical data on brain function and stimulation, and (iii) how NCT outputs vary across development and correlate with behavior and mental health symptoms. In this protocol, we introduce a framework for applying NCT to structural connectomes following two main pathways. Our primary pathway focuses on computing the control energy associated with transitioning between specific neural activity states. Our second pathway focuses on computing average controllability, which indexes nodes' general capacity to control dynamics. We also provide recommendations for comparing NCT outputs against null network models. Finally, we support this protocol with a Python-based software package called network control theory for python (nctpy).
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Affiliation(s)
- Linden Parkes
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
| | - Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY 14853, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
| | | | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA 92521, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dale Zhou
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, PA 19104, USA
- Department of Physics and Astronomy, University of Pennsylvania, PA 19104, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
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15
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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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16
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Fan L, Li Y, Huang ZG, Zhang W, Wu X, Liu T, Wang J. Low-frequency repetitive transcranial magnetic stimulation alters the individual functional dynamical landscape. Cereb Cortex 2023; 33:9583-9598. [PMID: 37376783 DOI: 10.1093/cercor/bhad228] [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: 04/26/2023] [Revised: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive approach to modulate brain activity and behavior in humans. Still, how individual resting-state brain dynamics after rTMS evolves across different functional configurations is rarely studied. Here, using resting state fMRI data from healthy subjects, we aimed to examine the effects of rTMS to individual large-scale brain dynamics. Using Topological Data Analysis based Mapper approach, we construct the precise dynamic mapping (PDM) for each participant. To reveal the relationship between PDM and canonical functional representation of the resting brain, we annotated the graph using relative activation proportion of a set of large-scale resting-state networks (RSNs) and assigned the single brain volume to corresponding RSN-dominant or a hub state (not any RSN was dominant). Our results show that (i) low-frequency rTMS could induce changed temporal evolution of brain states; (ii) rTMS didn't alter the hub-periphery configurations underlined resting-state brain dynamics; and (iii) the rTMS effects on brain dynamics differ across the left frontal and occipital lobe. In conclusion, low-frequency rTMS significantly alters the individual temporo-spatial dynamics, and our finding further suggested a potential target-dependent alteration of brain dynamics. This work provides a new perspective to comprehend the heterogeneous effect of rTMS.
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Affiliation(s)
- Liming Fan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Zi-Gang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Wenlong Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Xiaofeng Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
- The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, China
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17
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Batista BC, Morris AZ, Steinbock O. Pattern selection by material aging: Modeling chemical gardens in two and three dimensions. Proc Natl Acad Sci U S A 2023; 120:e2305172120. [PMID: 37399415 PMCID: PMC10334770 DOI: 10.1073/pnas.2305172120] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/18/2023] [Indexed: 07/05/2023] Open
Abstract
Chemical gardens are complex, often macroscopic, structures formed by precipitation reactions. Their thin walls compartmentalize the system and adjust in size and shape if the volume of the interior reactant solution is increased by osmosis or active injection. Spatial confinement to a thin layer is known to result in various patterns including self-extending filaments and flower-like patterns organized around a continuous, expanding front. Here, we describe a cellular automaton model for this type of self-organization, in which each lattice site is occupied by one of the two reactants or the precipitate. Reactant injection causes the random replacement of precipitate and generates an expanding near-circular precipitate front. If this process includes an age bias favoring the replacement of fresh precipitate, thin-walled filaments arise and grow-like in the experiments-at the leading tip. In addition, the inclusion of a buoyancy effect allows the model to capture various branched and unbranched chemical garden shapes in two and three dimensions. Our results provide a model of chemical garden structures and highlight the importance of temporal changes in the self-healing membrane material.
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Affiliation(s)
- Bruno C. Batista
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL32306-4390
| | - Amari Z. Morris
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL32306-4390
| | - Oliver Steinbock
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL32306-4390
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18
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Xu Y, Long X, Feng J, Gong P. Interacting spiral wave patterns underlie complex brain dynamics and are related to cognitive processing. Nat Hum Behav 2023:10.1038/s41562-023-01626-5. [PMID: 37322235 DOI: 10.1038/s41562-023-01626-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 05/12/2023] [Indexed: 06/17/2023]
Abstract
The large-scale activity of the human brain exhibits rich and complex patterns, but the spatiotemporal dynamics of these patterns and their functional roles in cognition remain unclear. Here by characterizing moment-by-moment fluctuations of human cortical functional magnetic resonance imaging signals, we show that spiral-like, rotational wave patterns (brain spirals) are widespread during both resting and cognitive task states. These brain spirals propagate across the cortex while rotating around their phase singularity centres, giving rise to spatiotemporal activity dynamics with non-stationary features. The properties of these brain spirals, such as their rotational directions and locations, are task relevant and can be used to classify different cognitive tasks. We also demonstrate that multiple, interacting brain spirals are involved in coordinating the correlated activations and de-activations of distributed functional regions; this mechanism enables flexible reconfiguration of task-driven activity flow between bottom-up and top-down directions during cognitive processing. Our findings suggest that brain spirals organize complex spatiotemporal dynamics of the human brain and have functional correlates to cognitive processing.
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Affiliation(s)
- Yiben Xu
- School of Physics, University of Sydney, Sydney, New South Wales, Australia
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, New South Wales, Australia
| | - Xian Long
- School of Physics, University of Sydney, Sydney, New South Wales, Australia
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, New South Wales, Australia
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, New South Wales, Australia.
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, New South Wales, Australia.
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19
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Pang JC, Aquino KM, Oldehinkel M, Robinson PA, Fulcher BD, Breakspear M, Fornito A. Geometric constraints on human brain function. Nature 2023; 618:566-574. [PMID: 37258669 PMCID: PMC10266981 DOI: 10.1038/s41586-023-06098-1] [Citation(s) in RCA: 68] [Impact Index Per Article: 68.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 04/18/2023] [Indexed: 06/02/2023]
Abstract
The anatomy of the brain necessarily constrains its function, but precisely how remains unclear. The classical and dominant paradigm in neuroscience is that neuronal dynamics are driven by interactions between discrete, functionally specialized cell populations connected by a complex array of axonal fibres1-3. However, predictions from neural field theory, an established mathematical framework for modelling large-scale brain activity4-6, suggest that the geometry of the brain may represent a more fundamental constraint on dynamics than complex interregional connectivity7,8. Here, we confirm these theoretical predictions by analysing human magnetic resonance imaging data acquired under spontaneous and diverse task-evoked conditions. Specifically, we show that cortical and subcortical activity can be parsimoniously understood as resulting from excitations of fundamental, resonant modes of the brain's geometry (that is, its shape) rather than from modes of complex interregional connectivity, as classically assumed. We then use these geometric modes to show that task-evoked activations across over 10,000 brain maps are not confined to focal areas, as widely believed, but instead excite brain-wide modes with wavelengths spanning over 60 mm. Finally, we confirm predictions that the close link between geometry and function is explained by a dominant role for wave-like activity, showing that wave dynamics can reproduce numerous canonical spatiotemporal properties of spontaneous and evoked recordings. Our findings challenge prevailing views and identify a previously underappreciated role of geometry in shaping function, as predicted by a unifying and physically principled model of brain-wide dynamics.
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Affiliation(s)
- James C Pang
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.
| | - Kevin M Aquino
- School of Physics, University of Sydney, Camperdown, New South Wales, Australia
- BrainKey Inc., San Francisco, CA, USA
| | - Marianne Oldehinkel
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Peter A Robinson
- School of Physics, University of Sydney, Camperdown, New South Wales, Australia
| | - Ben D Fulcher
- School of Physics, University of Sydney, Camperdown, New South Wales, Australia
| | - Michael Breakspear
- School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, Callaghan, New South Wales, Australia
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, New South Wales, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
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20
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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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21
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Zheng Y, Tang S, Zheng H, Wang X, Liu L, Yang Y, Zhen Y, Zheng Z. Noise improves the association between effects of local stimulation and structural degree of brain networks. PLoS Comput Biol 2023; 19:e1010866. [PMID: 37167331 PMCID: PMC10205011 DOI: 10.1371/journal.pcbi.1010866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/23/2023] [Accepted: 04/20/2023] [Indexed: 05/13/2023] Open
Abstract
Stimulation to local areas remarkably affects brain activity patterns, which can be exploited to investigate neural bases of cognitive function and modify pathological brain statuses. There has been growing interest in exploring the fundamental action mechanisms of local stimulation. Nevertheless, how noise amplitude, an essential element in neural dynamics, influences stimulation-induced brain states remains unknown. Here, we systematically examine the effects of local stimulation by using a large-scale biophysical model under different combinations of noise amplitudes and stimulation sites. We demonstrate that noise amplitude nonlinearly and heterogeneously tunes the stimulation effects from both regional and network perspectives. Furthermore, by incorporating the role of the anatomical network, we show that the peak frequencies of unstimulated areas at different stimulation sites averaged across noise amplitudes are highly positively related to structural connectivity. Crucially, the association between the overall changes in functional connectivity as well as the alterations in the constraints imposed by structural connectivity with the structural degree of stimulation sites is nonmonotonically influenced by the noise amplitude, with the association increasing in specific noise amplitude ranges. Moreover, the impacts of local stimulation of cognitive systems depend on the complex interplay between the noise amplitude and average structural degree. Overall, this work provides theoretical insights into how noise amplitude and network structure jointly modulate brain dynamics during stimulation and introduces possibilities for better predicting and controlling stimulation outcomes.
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Affiliation(s)
- Yi Zheng
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Shaoting Tang
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, P.R. China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, China
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
| | - Hongwei Zheng
- Beijing Academy of Blockchain and Edge Computing (BABEC), Beijing, China
| | - Xin Wang
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, P.R. China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
| | - Longzhao Liu
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, P.R. China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
| | - Yaqian Yang
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Yi Zhen
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Zhiming Zheng
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, P.R. China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, China
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
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22
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Wang S, Chang C. Complex topology meets simple statistics. Nat Neurosci 2023; 26:732-734. [PMID: 37095400 DOI: 10.1038/s41593-023-01295-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Affiliation(s)
- Shiyu Wang
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Catie Chang
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
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23
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Functional re-organization of hippocampal-cortical gradients during naturalistic memory processes. Neuroimage 2023; 271:119996. [PMID: 36863548 DOI: 10.1016/j.neuroimage.2023.119996] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 02/12/2023] [Accepted: 02/27/2023] [Indexed: 03/04/2023] Open
Abstract
The functional organization of the hippocampus mirrors that of the cortex, changing smoothly along connectivity gradients and abruptly at inter-areal boundaries. Hippocampal-dependent cognitive processes require flexible integration of these hippocampal gradients into functionally related cortical networks. To understand the cognitive relevance of this functional embedding, we acquired fMRI data while participants viewed brief news clips, either containing or lacking recently familiarized cues. Participants were 188 healthy mid-life adults and 31 adults with mild cognitive impairment (MCI) or Alzheimer's disease (AD). We employed a recently developed technique - connectivity gradientography - to study gradually changing patterns of voxel to whole brain functional connectivity and their sudden transitions. We observed that functional connectivity gradients of the anterior hippocampus map onto connectivity gradients across the default mode network during these naturalistic stimuli. The presence of familiar cues in the news clips accentuates a stepwise transition across the boundary from the anterior to the posterior hippocampus. This functional transition is shifted in the posterior direction in the left hippocampus of individuals with MCI or AD. These findings shed new light on the functional integration of hippocampal connectivity gradients into large-scale cortical networks, how these adapt with memory context and how these change in the presence of neurodegenerative disease.
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24
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Yu Y, Oh Y, Kounios J, Beeman M. Uncovering the Interplay of Oscillatory Processes During Creative Problem Solving: A Dynamic Modeling Approach. CREATIVITY RESEARCH JOURNAL 2023; 35:438-454. [PMID: 38145249 PMCID: PMC10745236 DOI: 10.1080/10400419.2023.2172871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
To solve a new problem, people spontaneously engage multiple cognitive processes. Previous work has identified a diverse set of oscillatory components critical at different stages of creative problem solving. In this project, we use hidden state modeling to untangle the roles of oscillation processes over time as people solve puzzles. Building on earlier work, we further developed analytical methods, such as incorporating source separating techniques and identifying the optimal number of states using cross-validation. We extracted brain states characterized by spatio-spectral topographies from time-resolved EEG spectral powers. The data driven approach allowed us to infer the dynamic, trial-by-trial, state sequences, and provided a comprehensive depiction of how various oscillation components interact recurrently throughout the trial. The properties of the states suggest their dissociable cognitive functions. For example, we identified three states with dominant activation in alpha bands but having distinct spatial distributions. People were differentially engaged in these states depending on the stages (e.g., onset or response) and outcomes of the trials (solved with insight or analysis). The current approach, applicable to many tasks requiring extended trial duration, can potentially reconcile findings from previous EEG studies and drive new hypotheses to further our understanding of the complex creative process.
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25
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Chesebro AG, Mujica-Parodi LR, Weistuch C. Ion gradient-driven bifurcations of a multi-scale neuronal model. CHAOS, SOLITONS, AND FRACTALS 2023; 167:113120. [PMID: 37662556 PMCID: PMC10470863 DOI: 10.1016/j.chaos.2023.113120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Metabolic limitations within the brain frequently arise in the context of aging and disease. As the largest consumers of energy within the brain, ion pumps that maintain the neuronal membrane potential are the most affected when energy supply becomes limited. To characterize the effects of such limitations, we analyze the ion gradients present in a conductance-based (Morris-Lecar) neural mass model. We show the existence and locations of Neimark-Sacker and period-doubling bifurcations in the sodium, calcium, and potassium reversal potentials and demonstrate that these bifurcations form physiologically relevant bounds of ion gradient variability. Within these bounds, we show how depolarization of the gradients causes decreased neural activity. We also show that the depolarization of ion gradients decreases inter-regional coherence, causing a shift in the critical point at which the coupling occurs and thereby inducing loss of synchrony between regions. In this way, we show that the Larter-Breakspear model captures ion gradient variability present at the microscale level and propagates these changes to the macroscale effects such as those observed in human neuroimaging studies.
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Affiliation(s)
- Anthony G. Chesebro
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, 11794, NY, USA
| | - Lilianne R. Mujica-Parodi
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, 11794, NY, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, 02129, MA, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, 11794, NY, USA
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, 11794, NY, USA
| | - Corey Weistuch
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, 10065, NY, USA
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26
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Wang C, Yang L, Lin Y, Wang C, Tian P. Alteration of resting-state network dynamics in autism spectrum disorder based on leading eigenvector dynamics analysis. Front Integr Neurosci 2023; 16:922577. [PMID: 36743477 PMCID: PMC9892631 DOI: 10.3389/fnint.2022.922577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 12/23/2022] [Indexed: 01/20/2023] Open
Abstract
Background Neurobiological models to explain the vulnerability of autism spectrum disorders (ASDs) are scarce, and previous resting-state functional magnetic resonance imaging (rs-fMRI) studies mostly examined static functional connectivity (FC). Given that FC constantly evolves, it is critical to probe FC dynamic differences in ASD patients. Methods We characterized recurring phase-locking (PL) states during rest in 45 ASD patients and 47 age- and sex-matched healthy controls (HCs) using Leading Eigenvector Dynamics Analysis (LEiDA) and probed the organization of PL states across different fine grain sizes. Results Our results identified five different groups of discrete resting-state functional networks, which can be defined as recurrent PL state overtimes. Specifically, ASD patients showed an increased probability of three PL states, consisting of the visual network (VIS), frontoparietal control network (FPN), default mode network (DMN), and ventral attention network (VAN). Correspondingly, ASD patients also showed a decreased probability of two PL states, consisting of the subcortical network (SUB), somatomotor network (SMN), FPN, and VAN. Conclusion Our findings suggested that the temporal reorganization of brain discrete networks was closely linked to sensory to cognitive systems of the brain. Our study provides new insights into the dynamics of brain networks and contributes to a deeper understanding of the neurological mechanisms of ASD.
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Affiliation(s)
- Chaoyan Wang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lu Yang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanan Lin
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Caihong Wang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Peichao Tian
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China,*Correspondence: Peichao Tian,
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27
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Sorrentino P, Rabuffo G, Baselice F, Troisi Lopez E, Liparoti M, Quarantelli M, Sorrentino G, Bernard C, Jirsa V. Dynamical interactions reconfigure the gradient of cortical timescales. Netw Neurosci 2023; 7:73-85. [PMID: 37334007 PMCID: PMC10270712 DOI: 10.1162/netn_a_00270] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 07/14/2022] [Indexed: 09/18/2023] Open
Abstract
The functional organization of the brain is usually presented with a back-to-front gradient of timescales, reflecting regional specialization with sensory areas (back) processing information faster than associative areas (front), which perform information integration. However, cognitive processes require not only local information processing but also coordinated activity across regions. Using magnetoencephalography recordings, we find that the functional connectivity at the edge level (between two regions) is also characterized by a back-to-front gradient of timescales following that of the regional gradient. Unexpectedly, we demonstrate a reverse front-to-back gradient when nonlocal interactions are prominent. Thus, the timescales are dynamic and can switch between back-to-front and front-to-back patterns.
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Affiliation(s)
- P. Sorrentino
- Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France
- Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy
| | - G. Rabuffo
- Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France
| | - F. Baselice
- Department of Engineering, Parthenope University of Naples, Naples, Italy
| | - E. Troisi Lopez
- Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy
- Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
| | - M. Liparoti
- Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy
- Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
| | - M. Quarantelli
- Biostructure and Bioimaging Institute, National Research Council, Naples, Italy
| | - G. Sorrentino
- Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy
- Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
| | - C. Bernard
- Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France
| | - V. Jirsa
- Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France
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28
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Portoles O, Qin Y, Hadida J, Woolrich M, Cao M, van Vugt M. Modulations of local synchrony over time lead to resting-state functional connectivity in a parsimonious large-scale brain model. PLoS One 2022; 17:e0275819. [PMID: 36288273 PMCID: PMC9604991 DOI: 10.1371/journal.pone.0275819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 09/24/2022] [Indexed: 11/30/2022] Open
Abstract
Biophysical models of large-scale brain activity are a fundamental tool for understanding the mechanisms underlying the patterns observed with neuroimaging. These models combine a macroscopic description of the within- and between-ensemble dynamics of neurons within a single architecture. A challenge for these models is accounting for modulations of within-ensemble synchrony over time. Such modulations in local synchrony are fundamental for modeling behavioral tasks and resting-state activity. Another challenge comes from the difficulty in parametrizing large scale brain models which hinders researching principles related with between-ensembles differences. Here we derive a parsimonious large scale brain model that can describe fluctuations of local synchrony. Crucially, we do not reduce within-ensemble dynamics to macroscopic variables first, instead we consider within and between-ensemble interactions similarly while preserving their physiological differences. The dynamics of within-ensemble synchrony can be tuned with a parameter which manipulates local connectivity strength. We simulated resting-state static and time-resolved functional connectivity of alpha band envelopes in models with identical and dissimilar local connectivities. We show that functional connectivity emerges when there are high fluctuations of local and global synchrony simultaneously (i.e. metastable dynamics). We also show that for most ensembles, leaning towards local asynchrony or synchrony correlates with the functional connectivity with other ensembles, with the exception of some regions belonging to the default-mode network.
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Affiliation(s)
- Oscar Portoles
- Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
- * E-mail:
| | - Yuzhen Qin
- Department of Mechanical Engineering, University of California, Riverside, California, United States of America
| | - Jonathan Hadida
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Mark Woolrich
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Ming Cao
- Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
| | - Marieke van Vugt
- Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
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29
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Rabuffo G, Sorrentino P, Bernard C, Jirsa V. Spontaneous neuronal avalanches as a correlate of access consciousness. Front Psychol 2022; 13:1008407. [PMID: 36337573 PMCID: PMC9634647 DOI: 10.3389/fpsyg.2022.1008407] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 10/04/2022] [Indexed: 09/03/2023] Open
Abstract
Decades of research have advanced our understanding of the biophysical mechanisms underlying consciousness. However, an overarching framework bridging between models of consciousness and the large-scale organization of spontaneous brain activity is still missing. Based on the observation that spontaneous brain activity dynamically switches between epochs of segregation and large-scale integration of information, we hypothesize a brain-state dependence of conscious access, whereby the presence of either segregated or integrated states marks distinct modes of information processing. We first review influential works on the neuronal correlates of consciousness, spontaneous resting-state brain activity and dynamical system theory. Then, we propose a test experiment to validate our hypothesis that conscious access occurs in aperiodic cycles, alternating windows where new incoming information is collected but not experienced, to punctuated short-lived integration events, where conscious access to previously collected content occurs. In particular, we suggest that the integration events correspond to neuronal avalanches, which are collective bursts of neuronal activity ubiquitously observed in electrophysiological recordings. If confirmed, the proposed framework would link the physics of spontaneous cortical dynamics, to the concept of ignition within the global neuronal workspace theory, whereby conscious access manifest itself as a burst of neuronal activity.
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Affiliation(s)
- Giovanni Rabuffo
- Institut de Neurosciences des Systemes, Aix-Marseille University, Marseille, France
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30
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Déli É, Peters JF, Kisvárday Z. How the Brain Becomes the Mind: Can Thermodynamics Explain the Emergence and Nature of Emotions? ENTROPY (BASEL, SWITZERLAND) 2022; 24:1498. [PMID: 37420518 DOI: 10.3390/e24101498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 07/09/2023]
Abstract
The neural systems' electric activities are fundamental for the phenomenology of consciousness. Sensory perception triggers an information/energy exchange with the environment, but the brain's recurrent activations maintain a resting state with constant parameters. Therefore, perception forms a closed thermodynamic cycle. In physics, the Carnot engine is an ideal thermodynamic cycle that converts heat from a hot reservoir into work, or inversely, requires work to transfer heat from a low- to a high-temperature reservoir (the reversed Carnot cycle). We analyze the high entropy brain by the endothermic reversed Carnot cycle. Its irreversible activations provide temporal directionality for future orientation. A flexible transfer between neural states inspires openness and creativity. In contrast, the low entropy resting state parallels reversible activations, which impose past focus via repetitive thinking, remorse, and regret. The exothermic Carnot cycle degrades mental energy. Therefore, the brain's energy/information balance formulates motivation, sensed as position or negative emotions. Our work provides an analytical perspective of positive and negative emotions and spontaneous behavior from the free energy principle. Furthermore, electrical activities, thoughts, and beliefs lend themselves to a temporal organization, an orthogonal condition to physical systems. Here, we suggest that an experimental validation of the thermodynamic origin of emotions might inspire better treatment options for mental diseases.
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Affiliation(s)
- Éva Déli
- Department of Anatomy, Histology, and Embryology, University of Debrecen, 4032 Debrecen, Hungary
| | - James F Peters
- Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
- Department of Mathematics, Adiyaman University, Adiyaman 02040, Turkey
| | - Zoltán Kisvárday
- Department of Anatomy, Histology, and Embryology, University of Debrecen, 4032 Debrecen, Hungary
- ELKH Neuroscience Research Group, University of Debrecen, 4032 Debrecen, Hungary
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31
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Srivastava P, Fotiadis P, Parkes L, Bassett DS. The expanding horizons of network neuroscience: From description to prediction and control. Neuroimage 2022; 258:119250. [PMID: 35659996 PMCID: PMC11164099 DOI: 10.1016/j.neuroimage.2022.119250] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 04/15/2022] [Accepted: 04/25/2022] [Indexed: 01/11/2023] Open
Abstract
The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.
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Affiliation(s)
- Pragya Srivastava
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neuroscience, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA; Santa Fe Institute, Santa Fe NM 87501, USA.
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32
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Saggar M, Shine JM, Liégeois R, Dosenbach NUF, Fair D. Precision dynamical mapping using topological data analysis reveals a hub-like transition state at rest. Nat Commun 2022; 13:4791. [PMID: 35970984 PMCID: PMC9378660 DOI: 10.1038/s41467-022-32381-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 07/27/2022] [Indexed: 01/01/2023] Open
Abstract
In the absence of external stimuli, neural activity continuously evolves from one configuration to another. Whether these transitions or explorations follow some underlying arrangement or lack a predictable ordered plan remains to be determined. Here, using fMRI data from highly sampled individuals (~5 hours of resting-state data per individual), we aimed to reveal the rules that govern transitions in brain activity at rest. Our Topological Data Analysis based Mapper approach characterized a highly visited transition state of the brain that acts as a switch between different neural configurations to organize the spontaneous brain activity. Further, while the transition state was characterized by a uniform representation of canonical resting-state networks (RSNs), the periphery of the landscape was dominated by a subject-specific combination of RSNs. Altogether, we revealed rules or principles that organize spontaneous brain activity using a precision dynamics approach.
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Affiliation(s)
- Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
| | - James M Shine
- Brain and Mind Center, The University of Sydney, Sydney, NSW, Australia
| | - Raphaël Liégeois
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nico U F Dosenbach
- Departments of Neurology, Radiology, Pediatrics and Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | - Damien Fair
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA
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33
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Abstract
A complex system is often associated with emergence of new phenomena from the interactions between the system's components. General anesthesia reduces brain complexity and so inhibits the emergence of consciousness. An understanding of complexity is necessary for the interpretation of brain monitoring algorithms. Complexity indices capture the "difficulty" of understanding brain activity over time and/or space. Complexity-entropy plots reveal the types of complexity indices and their balance of randomness and structure. Lempel-Ziv complexity is a common index of temporal complexity for single-channel electroencephalogram containing both power spectral and nonlinear effects, revealed by phase-randomized surrogate data. Computing spatial complexities involves forming a connectivity matrix and calculating the complexity of connectivity patterns. Spatiotemporal complexity can be estimated in multiple ways including temporal or spatial concatenation, estimation of state switching, or integrated information. This article illustrates the concept and application of various complexities by providing working examples; a website with interactive demonstrations has also been created.
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Cabral J, Castaldo F, Vohryzek J, Litvak V, Bick C, Lambiotte R, Friston K, Kringelbach ML, Deco G. Metastable oscillatory modes emerge from synchronization in the brain spacetime connectome. COMMUNICATIONS PHYSICS 2022; 5:184. [PMID: 38288392 PMCID: PMC7615562 DOI: 10.1038/s42005-022-00950-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 06/20/2022] [Indexed: 01/31/2024]
Abstract
A rich repertoire of oscillatory signals is detected from human brains with electro- and magnetoencephalography (EEG/MEG). However, the principles underwriting coherent oscillations and their link with neural activity remain under debate. Here, we revisit the mechanistic hypothesis that transient brain rhythms are a signature of metastable synchronization, occurring at reduced collective frequencies due to delays between brain areas. We consider a system of damped oscillators in the presence of background noise - approximating the short-lived gamma-frequency oscillations generated within neuronal circuits - coupled according to the diffusion weighted tractography between brain areas. Varying the global coupling strength and conduction speed, we identify a critical regime where spatially and spectrally resolved metastable oscillatory modes (MOMs) emerge at sub-gamma frequencies, approximating the MEG power spectra from 89 healthy individuals at rest. Further, we demonstrate that the frequency, duration, and scale of MOMs - as well as the frequency-specific envelope functional connectivity - can be controlled by global parameters, while the connectome structure remains unchanged. Grounded in the physics of delay-coupled oscillators, these numerical analyses demonstrate how interactions between locally generated fast oscillations in the connectome spacetime structure can lead to the emergence of collective brain rhythms organized in space and time.
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Affiliation(s)
- Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- ICVS/3B’s - Portuguese Government Associate Laboratory, Braga/Guimarães, Portugal
| | - Francesca Castaldo
- Wellcome Centre for Human Neuroimaging, University College London, Queen Square Institute of Neurology, London, UK
| | - Jakub Vohryzek
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, University College London, Queen Square Institute of Neurology, London, UK
| | - Christian Bick
- Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience – Systems & Network Neuroscience, Amsterdam, The Netherlands
- Mathematical Institute, University of Oxford, Oxford, UK
- Department of Mathematics, University of Exeter, Exeter, UK
| | | | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, Queen Square Institute of Neurology, London, UK
| | - Morten L. Kringelbach
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Gustavo Deco
- Center 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
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35
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Ramezanian-Panahi M, Abrevaya G, Gagnon-Audet JC, Voleti V, Rish I, Dumas G. Generative Models of Brain Dynamics. Front Artif Intell 2022; 5:807406. [PMID: 35910192 PMCID: PMC9335006 DOI: 10.3389/frai.2022.807406] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 06/10/2022] [Indexed: 01/28/2023] Open
Abstract
This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.
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Affiliation(s)
| | - Germán Abrevaya
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Instituto de Física de Buenos Aires (IFIBA), CONICET, Universidad de Buenos Aires, Buenos Aires, Argentina
| | | | - Vikram Voleti
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
| | - Irina Rish
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
| | - Guillaume Dumas
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
- Department of Psychiatry, CHU Sainte-Justine Research Center, Mila-Quebec AI Institute, Université de Montréal, Montréal, QC, Canada
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36
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Uncovering hidden resting state dynamics: A new perspective on auditory verbal hallucinations. Neuroimage 2022; 255:119188. [PMID: 35398281 DOI: 10.1016/j.neuroimage.2022.119188] [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: 12/01/2021] [Revised: 02/25/2022] [Accepted: 03/13/2022] [Indexed: 11/24/2022] Open
Abstract
In the absence of sensory stimulation, the brain transits between distinct functional networks. Network dynamics such as transition patterns and the time the brain stays in each network link to cognition and behavior and are subject to much investigation. Auditory verbal hallucinations (AVH), the temporally fluctuating unprovoked experience of hearing voices, are associated with aberrant resting state network activity. However, we lack a clear understanding of how different networks contribute to aberrant activity over time. An accurate characterization of latent network dynamics and their relation to neurocognitive changes necessitates methods that capture the sub-second temporal fluctuations of the networks' functional connectivity signatures. Here, we critically evaluate the assumptions and sensitivity of several approaches commonly used to assess temporal dynamics of brain connectivity states in M/EEG and fMRI research, highlighting methodological constraints and their clinical relevance to AVH. Identifying altered brain connectivity states linked to AVH can facilitate the detection of predictive disease markers and ultimately be valuable for generating individual risk profiles, differential diagnosis, targeted intervention, and treatment strategies.
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37
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Aquino KM, Fulcher B, Oldham S, Parkes L, Gollo L, Deco G, Fornito A. On the intersection between data quality and dynamical modelling of large-scale fMRI signals. Neuroimage 2022; 256:119051. [PMID: 35276367 DOI: 10.1016/j.neuroimage.2022.119051] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 01/23/2022] [Accepted: 03/01/2022] [Indexed: 12/25/2022] Open
Abstract
Large-scale dynamics of the brain are routinely modelled using systems of nonlinear dynamical equations that describe the evolution of population-level activity, with distinct neural populations often coupled according to an empirically measured structural connectivity matrix. This modelling approach has been used to generate insights into the neural underpinnings of spontaneous brain dynamics, as recorded with techniques such as resting state functional MRI (fMRI). In fMRI, researchers have many degrees of freedom in the way that they can process the data and recent evidence indicates that the choice of pre-processing steps can have a major effect on empirical estimates of functional connectivity. However, the potential influence of such variations on modelling results are seldom considered. Here we show, using three popular whole-brain dynamical models, that different choices during fMRI preprocessing can dramatically affect model fits and interpretations of findings. Critically, we show that the ability of these models to accurately capture patterns in fMRI dynamics is mostly driven by the degree to which they fit global signals rather than interesting sources of coordinated neural dynamics. We show that widespread deflections can arise from simple global synchronisation. We introduce a simple two-parameter model that captures these fluctuations and performs just as well as more complex, multi-parameter biophysical models. From our combined analyses of data and simulations, we describe benchmarks to evaluate model fit and validity. Although most models are not resilient to denoising, we show that relaxing the approximation of homogeneous neural populations by more explicitly modelling inter-regional effective connectivity can improve model accuracy at the expense of increased model complexity. Our results suggest that many complex biophysical models may be fitting relatively trivial properties of the data, and underscore a need for tighter integration between data quality assurance and model development.
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Affiliation(s)
- Kevin M Aquino
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria 3168, Australia; School of Physics, University of Sydney, New South Wales, 2006 Australia.
| | - Ben Fulcher
- School of Physics, University of Sydney, New South Wales, 2006 Australia
| | - Stuart Oldham
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria 3168, Australia
| | - Linden Parkes
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104 USA
| | - Leonardo Gollo
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria 3168, Australia
| | - Gustavo Deco
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria 3168, Australia; Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona 08010, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Barcelona 08010, Spain
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria 3168, Australia
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38
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Cakan C, Dimulescu C, Khakimova L, Obst D, Flöel A, Obermayer K. Spatiotemporal Patterns of Adaptation-Induced Slow Oscillations in a Whole-Brain Model of Slow-Wave Sleep. Front Comput Neurosci 2022; 15:800101. [PMID: 35095451 PMCID: PMC8790481 DOI: 10.3389/fncom.2021.800101] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 12/16/2021] [Indexed: 11/13/2022] Open
Abstract
During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations (SOs) between up- and down-states travel across the cortex. While an isolated piece of cortex can produce SOs, the brain-wide propagation of these oscillations are thought to be mediated by the long-range axonal connections. We address the mechanism of how SOs emerge and recruit large parts of the brain using a whole-brain model constructed from empirical connectivity data in which SOs are induced independently in each brain area by a local adaptation mechanism. Using an evolutionary optimization approach, good fits to human resting-state fMRI data and sleep EEG data are found at values of the adaptation strength close to a bifurcation where the model produces a balance between local and global SOs with realistic spatiotemporal statistics. Local oscillations are more frequent, last shorter, and have a lower amplitude. Global oscillations spread as waves of silence across the undirected brain graph, traveling from anterior to posterior regions. These traveling waves are caused by heterogeneities in the brain network in which the connection strengths between brain areas determine which areas transition to a down-state first, and thus initiate traveling waves across the cortex. Our results demonstrate the utility of whole-brain models for explaining the origin of large-scale cortical oscillations and how they are shaped by the connectome.
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Affiliation(s)
- Caglar Cakan
- Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Cristiana Dimulescu
- Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Liliia Khakimova
- Department of Neurology, University Medicine, Greifswald, Germany
| | - Daniela Obst
- Department of Neurology, University Medicine, Greifswald, Germany
| | - Agnes Flöel
- Department of Neurology, University Medicine, Greifswald, Germany
- German Center for Neurodegenerative Diseases, Greifswald, Germany
| | - Klaus Obermayer
- Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
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39
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Cortical traveling waves reflect state-dependent hierarchical sequencing of local regions in the human connectome network. Sci Rep 2022; 12:334. [PMID: 35013416 PMCID: PMC8748796 DOI: 10.1038/s41598-021-04169-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/14/2021] [Indexed: 11/08/2022] Open
Abstract
Recent human studies using electrocorticography have demonstrated that alpha and theta band oscillations form traveling waves on the cortical surface. According to neural synchronization theories, the cortical traveling waves may group local cortical regions and sequence them by phase synchronization; however these contributions have not yet been assessed. This study aimed to evaluate the functional contributions of traveling waves using connectome-based network modeling. In the simulation, we observed stable traveling waves on the entire cortical surface wherein the topographical pattern of these phases was substantially correlated with the empirically obtained resting-state networks, and local radial waves also appeared within the size of the empirical networks (< 50 mm). Importantly, individual regions in the entire network were instantaneously sequenced by their internal frequencies, and regions with higher intrinsic frequency were seen in the earlier phases of the traveling waves. Based on the communication-through-coherence theory, this phase configuration produced a hierarchical organization of each region by unidirectional communication between the arbitrarily paired regions. In conclusion, cortical traveling waves reflect the intrinsic frequency-dependent hierarchical sequencing of local regions, global traveling waves sequence the set of large-scale cortical networks, and local traveling waves sequence local regions within individual cortical networks.
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40
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Jung H, Wager TD, Carter RM. Novel Cognitive Functions Arise at the Convergence of Macroscale Gradients. J Cogn Neurosci 2021; 34:381-396. [PMID: 34942643 DOI: 10.1162/jocn_a_01803] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Functions in higher-order brain regions are the source of extensive debate. Although past trends have been to describe the brain-especially posterior cortical areas-in terms of a set of functional modules, a new emerging paradigm focuses on the integration of proximal functions. In this review, we synthesize emerging evidence that a variety of novel functions in the higher-order brain regions are due to convergence: convergence of macroscale gradients brings feature-rich representations into close proximity, presenting an opportunity for novel functions to arise. Using the TPJ as an example, we demonstrate that convergence is enabled via three properties of the brain: (1) hierarchical organization, (2) abstraction, and (3) equidistance. As gradients travel from primary sensory cortices to higher-order brain regions, information becomes abstracted and hierarchical, and eventually, gradients meet at a point maximally and equally distant from their sensory origins. This convergence, which produces multifaceted combinations, such as mentalizing another person's thought or projecting into a future space, parallels evolutionary and developmental characteristics in such regions, resulting in new cognitive and affective faculties.
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Affiliation(s)
- Heejung Jung
- University of Colorado Boulder.,Dartmouth College
| | - Tor D Wager
- University of Colorado Boulder.,Dartmouth College
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41
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Takeda Y, Hiroe N, Yamashita O. Whole-brain propagating patterns in human resting-state brain activities. Neuroimage 2021; 245:118711. [PMID: 34793956 DOI: 10.1016/j.neuroimage.2021.118711] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/15/2021] [Accepted: 11/04/2021] [Indexed: 11/17/2022] Open
Abstract
Repetitive propagating activities in resting-state brain activities have been widely observed in various species and regions. Because they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. "Whole-brain" propagating activities may also reflect a process that integrates information distributed over the entire brain, such as visual and motor information. Here we reveal whole-brain propagating activities from human resting-state magnetoencephalography (MEG) and electroencephalography (EEG) data. We simultaneously recorded the MEGs and EEGs and estimated the source currents from both measurements. Then using our recently proposed algorithm, we extracted repetitive spatiotemporal patterns from the source currents. The estimated patterns consisted of multiple frequency components, each of which transiently exhibited the frequency-specific resting-state networks (RSNs) of functional MRIs (fMRIs), such as the default mode and sensorimotor networks. A simulation test suggested that the spatiotemporal patterns reflected the phase alignment of the multiple frequency oscillators induced by the propagating activities along the anatomical connectivity. These results argue that whole-brain propagating activities transiently exhibited multiple RSNs in their multiple frequency components, suggesting that they reflected a process to integrate the information distributed over the frequencies and networks.
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Affiliation(s)
- Yusuke Takeda
- Computational Brain Dynamics Team, RIKEN Center for Advanced Intelligence Project, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan; Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
| | - Nobuo Hiroe
- Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
| | - Okito Yamashita
- Computational Brain Dynamics Team, RIKEN Center for Advanced Intelligence Project, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan; Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
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42
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Kim M, Kim H, Huang Z, Mashour GA, Jordan D, Ilg R, Lee U. Criticality Creates a Functional Platform for Network Transitions Between Internal and External Processing Modes in the Human Brain. Front Syst Neurosci 2021; 15:657809. [PMID: 34899199 PMCID: PMC8657781 DOI: 10.3389/fnsys.2021.657809] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 10/29/2021] [Indexed: 11/13/2022] Open
Abstract
Continuous switching between internal and external modes in the brain appears important for generating models of the self and the world. However, how the brain transitions between these two modes remains unknown. We propose that a large synchronization fluctuation of brain networks, emerging only near criticality (i.e., a balanced state between order and disorder), spontaneously creates temporal windows with distinct preferences for integrating the network's internal information or for processing external stimuli. Using a computational model, electroencephalography (EEG) analysis, and functional magnetic resonance imaging (fMRI) analysis during alterations of consciousness in humans, we report that synchronized and incoherent networks, respectively, bias toward internal and external information with specific network configurations. In the brain network model and EEG-based network, the network preferences are the most prominent at criticality and in conscious states associated with the bandwidth 4-12 Hz, with alternating functional network configurations. However, these network configurations are selectively disrupted in different states of consciousness such as general anesthesia, psychedelic states, minimally conscious states, and unresponsive wakefulness syndrome. The network preference for internal information integration is only significant in conscious states and psychedelic states, but not in other unconscious states, suggesting the importance of internal information integration in maintaining consciousness. The fMRI co-activation pattern analysis shows that functional networks that are sensitive to external stimuli-such as default mode, dorsal attentional, and frontoparietal networks-are activated in incoherent states, while insensitive networks, such as global activation and deactivation networks, are dominated in highly synchronized states. We suggest that criticality produces a functional platform for the brain's capability for continuous switching between two modes, which is crucial for the emergence of consciousness.
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Affiliation(s)
- Minkyung Kim
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Hyoungkyu Kim
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Zirui Huang
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
| | - George A Mashour
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States.,Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, United States
| | - Denis Jordan
- Applied Mathematics and Statistics, University of Applied Sciences Northwestern Switzerland, Muttenz, Switzerland.,Department of Anesthesiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,Department of Neurology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Rüdiger Ilg
- Applied Mathematics and Statistics, University of Applied Sciences Northwestern Switzerland, Muttenz, Switzerland.,Department of Anesthesiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,Department of Neurology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - UnCheol Lee
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
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43
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Cakan C, Jajcay N, Obermayer K. neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09931-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Abstractneurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e., the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is made possible using a parameter exploration module, which allows one to characterize a model’s behavior as a function of changing parameters. An optimization module is provided for fitting models to multimodal empirical data using evolutionary algorithms. neurolib is designed to be extendable and allows for easy implementation of custom neural mass models, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure–function relationship of brain networks, and for performing in-silico optimization of whole-brain models.
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Sadoun A, Chauhan T, Zhang YF, Gallois Y, Marx M, Deguine O, Barone P, Strelnikov K. Intensity patterns at the peaks of brain activity in fMRI and PET are highly correlated with neural models of spatial integration. Eur J Neurosci 2021; 54:7141-7151. [PMID: 34550613 PMCID: PMC9291889 DOI: 10.1111/ejn.15469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 09/12/2021] [Accepted: 09/15/2021] [Indexed: 11/30/2022]
Abstract
Spatial integration during the brain's cognitive activity prompts changes in energy used by different neuroglial populations. Nevertheless, the organisation of such integration in 3D ‐brain activity remains undescribed from a quantitative standpoint. In response, we applied a cross‐correlation between brain activity and integrative models, which yielded a deeper understanding of information integration in functional brain mapping. We analysed four datasets obtained via fundamentally different neuroimaging techniques (functional magnetic resonance imaging [fMRI] and positron emission tomography [PET]) and found that models of spatial integration with an increasing input to each step of integration were significantly more correlated with brain activity than models with a constant input to each step of integration. In addition, marking the voxels with the maximal correlation, we found exceptionally high intersubject consistency with the initial brain activity at the peaks. Our method demonstrated for the first time that the network of peaks of brain activity is organised strictly according to the models of spatial integration independent of neuroimaging techniques. The highest correlation with models integrating an increasing at each step input suggests that brain activity reflects a network of integrative processes where the results of integration in some neuroglial populations serve as an input to other neuroglial populations.
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Affiliation(s)
- Amirouche Sadoun
- UMR 5549, Centre National de la Recherche Scientifique, Université de Toulouse 3, Toulouse, France.,Centre de Recherche Cerveau et Cognition, Université de Toulouse 3, Université Paul Sabatier, Toulouse, France
| | - Tushar Chauhan
- UMR 5549, Centre National de la Recherche Scientifique, Université de Toulouse 3, Toulouse, France.,Centre de Recherche Cerveau et Cognition, Université de Toulouse 3, Université Paul Sabatier, Toulouse, France
| | - Yi Fan Zhang
- UMR 5549, Centre National de la Recherche Scientifique, Université de Toulouse 3, Toulouse, France.,Centre de Recherche Cerveau et Cognition, Université de Toulouse 3, Université Paul Sabatier, Toulouse, France
| | - Yohan Gallois
- Service d'Oto-Rhino-Laryngologie et Oto-Neurologie, CHU de Toulouse, Université de Toulouse 3, Toulouse, France
| | - Mathieu Marx
- Service d'Oto-Rhino-Laryngologie et Oto-Neurologie, CHU de Toulouse, Université de Toulouse 3, Toulouse, France
| | - Olivier Deguine
- UMR 5549, Centre National de la Recherche Scientifique, Université de Toulouse 3, Toulouse, France.,Centre de Recherche Cerveau et Cognition, Université de Toulouse 3, Université Paul Sabatier, Toulouse, France.,Service d'Oto-Rhino-Laryngologie et Oto-Neurologie, CHU de Toulouse, Université de Toulouse 3, Toulouse, France
| | - Pascal Barone
- UMR 5549, Centre National de la Recherche Scientifique, Université de Toulouse 3, Toulouse, France.,Centre de Recherche Cerveau et Cognition, Université de Toulouse 3, Université Paul Sabatier, Toulouse, France
| | - Kuzma Strelnikov
- UMR 5549, Centre National de la Recherche Scientifique, Université de Toulouse 3, Toulouse, France.,Centre de Recherche Cerveau et Cognition, Université de Toulouse 3, Université Paul Sabatier, Toulouse, France.,Service d'Oto-Rhino-Laryngologie et Oto-Neurologie, CHU de Toulouse, Université de Toulouse 3, Toulouse, France
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Mancini M, Tian Q, Fan Q, Cercignani M, Huang SY. Dissecting whole-brain conduction delays through MRI microstructural measures. Brain Struct Funct 2021; 226:2651-2663. [PMID: 34390416 PMCID: PMC8448685 DOI: 10.1007/s00429-021-02358-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/28/2021] [Indexed: 01/24/2023]
Abstract
Network models based on structural connectivity have been increasingly used as the blueprint for large-scale simulations of the human brain. As the nodes of this network are distributed through the cortex and interconnected by white matter pathways with different characteristics, modeling the associated conduction delays becomes important. The goal of this study is to estimate and characterize these delays directly from the brain structure. To achieve this, we leveraged microstructural measures from a combination of advanced magnetic resonance imaging acquisitions and computed the main determinants of conduction velocity, namely axonal diameter and myelin content. Using the model proposed by Rushton, we used these measures to calculate the conduction velocity and estimated the associated delays using tractography. We observed that both the axonal diameter and conduction velocity distributions presented a rather constant trend across different connection lengths, with resulting delays that scale linearly with the connection length. Relying on insights from graph theory and Kuramoto simulations, our results support the approximation of constant conduction velocity but also show path- and region-specific differences.
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Affiliation(s)
- Matteo Mancini
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK. .,Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK. .,NeuroPoly Lab, Polytechnique Montréal, Montréal, Canada.
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Mara Cercignani
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
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46
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Robinson PA, Henderson JA, Gabay NC, Aquino KM, Babaie-Janvier T, Gao X. Determination of Dynamic Brain Connectivity via Spectral Analysis. Front Hum Neurosci 2021; 15:655576. [PMID: 34335207 PMCID: PMC8323754 DOI: 10.3389/fnhum.2021.655576] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 06/03/2021] [Indexed: 11/30/2022] Open
Abstract
Spectral analysis based on neural field theory is used to analyze dynamic connectivity via methods based on the physical eigenmodes that are the building blocks of brain dynamics. These approaches integrate over space instead of averaging over time and thereby greatly reduce or remove the temporal averaging effects, windowing artifacts, and noise at fine spatial scales that have bedeviled the analysis of dynamical functional connectivity (FC). The dependences of FC on dynamics at various timescales, and on windowing, are clarified and the results are demonstrated on simple test cases, demonstrating how modes provide directly interpretable insights that can be related to brain structure and function. It is shown that FC is dynamic even when the brain structure and effective connectivity are fixed, and that the observed patterns of FC are dominated by relatively few eigenmodes. Common artifacts introduced by statistical analyses that do not incorporate the physical nature of the brain are discussed and it is shown that these are avoided by spectral analysis using eigenmodes. Unlike most published artificially discretized “resting state networks” and other statistically-derived patterns, eigenmodes overlap, with every mode extending across the whole brain and every region participating in every mode—just like the vibrations that give rise to notes of a musical instrument. Despite this, modes are independent and do not interact in the linear limit. It is argued that for many purposes the intrinsic limitations of covariance-based FC instead favor the alternative of tracking eigenmode coefficients vs. time, which provide a compact representation that is directly related to biophysical brain dynamics.
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Affiliation(s)
- Peter A Robinson
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - James A Henderson
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Natasha C Gabay
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Kevin M Aquino
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Tara Babaie-Janvier
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Xiao Gao
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia.,Department of Biomedical Engineering, University of Melbourne, Parkville, VIC, Australia
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Safron A. The Radically Embodied Conscious Cybernetic Bayesian Brain: From Free Energy to Free Will and Back Again. ENTROPY (BASEL, SWITZERLAND) 2021; 23:783. [PMID: 34202965 PMCID: PMC8234656 DOI: 10.3390/e23060783] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 05/12/2021] [Accepted: 05/27/2021] [Indexed: 11/24/2022]
Abstract
Drawing from both enactivist and cognitivist perspectives on mind, I propose that explaining teleological phenomena may require reappraising both "Cartesian theaters" and mental homunculi in terms of embodied self-models (ESMs), understood as body maps with agentic properties, functioning as predictive-memory systems and cybernetic controllers. Quasi-homuncular ESMs are suggested to constitute a major organizing principle for neural architectures due to their initial and ongoing significance for solutions to inference problems in cognitive (and affective) development. Embodied experiences provide foundational lessons in learning curriculums in which agents explore increasingly challenging problem spaces, so answering an unresolved question in Bayesian cognitive science: what are biologically plausible mechanisms for equipping learners with sufficiently powerful inductive biases to adequately constrain inference spaces? Drawing on models from neurophysiology, psychology, and developmental robotics, I describe how embodiment provides fundamental sources of empirical priors (as reliably learnable posterior expectations). If ESMs play this kind of foundational role in cognitive development, then bidirectional linkages will be found between all sensory modalities and frontal-parietal control hierarchies, so infusing all senses with somatic-motoric properties, thereby structuring all perception by relevant affordances, so solving frame problems for embodied agents. Drawing upon the Free Energy Principle and Active Inference framework, I describe a particular mechanism for intentional action selection via consciously imagined (and explicitly represented) goal realization, where contrasts between desired and present states influence ongoing policy selection via predictive coding mechanisms and backward-chained imaginings (as self-realizing predictions). This embodied developmental legacy suggests a mechanism by which imaginings can be intentionally shaped by (internalized) partially-expressed motor acts, so providing means of agentic control for attention, working memory, imagination, and behavior. I further describe the nature(s) of mental causation and self-control, and also provide an account of readiness potentials in Libet paradigms wherein conscious intentions shape causal streams leading to enaction. Finally, I provide neurophenomenological handlings of prototypical qualia including pleasure, pain, and desire in terms of self-annihilating free energy gradients via quasi-synesthetic interoceptive active inference. In brief, this manuscript is intended to illustrate how radically embodied minds may create foundations for intelligence (as capacity for learning and inference), consciousness (as somatically-grounded self-world modeling), and will (as deployment of predictive models for enacting valued goals).
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Affiliation(s)
- Adam Safron
- Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, Baltimore, MD 21218, USA;
- Kinsey Institute, Indiana University, Bloomington, IN 47405, USA
- Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA
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48
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FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency. Sci Rep 2021; 11:12160. [PMID: 34108523 PMCID: PMC8190312 DOI: 10.1038/s41598-021-91513-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 05/24/2021] [Indexed: 02/05/2023] Open
Abstract
Neural modelling tools are increasingly employed to describe, explain, and predict the human brain's behavior. Among them, spiking neural networks (SNNs) make possible the simulation of neural activity at the level of single neurons, but their use is often threatened by the resources needed in terms of processing capabilities and memory. Emerging applications where a low energy burden is required (e.g. implanted neuroprostheses) motivate the exploration of new strategies able to capture the relevant principles of neuronal dynamics in reduced and efficient models. The recent Leaky Integrate-and-Fire with Latency (LIFL) spiking neuron model shows some realistic neuronal features and efficiency at the same time, a combination of characteristics that may result appealing for SNN-based brain modelling. In this paper we introduce FNS, the first LIFL-based SNN framework, which combines spiking/synaptic modelling with the event-driven approach, allowing us to define heterogeneous neuron groups and multi-scale connectivity, with delayed connections and plastic synapses. FNS allows multi-thread, precise simulations, integrating a novel parallelization strategy and a mechanism of periodic dumping. We evaluate the performance of FNS in terms of simulation time and used memory, and compare it with those obtained with neuronal models having a similar neurocomputational profile, implemented in NEST, showing that FNS performs better in both scenarios. FNS can be advantageously used to explore the interaction within and between populations of spiking neurons, even for long time-scales and with a limited hardware configuration.
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Song Y, Zhou D, Li S. Maximum Entropy Principle Underlies Wiring Length Distribution in Brain Networks. Cereb Cortex 2021; 31:4628-4641. [PMID: 33999124 DOI: 10.1093/cercor/bhab110] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/16/2021] [Accepted: 03/17/2021] [Indexed: 11/14/2022] Open
Abstract
A brain network comprises a substantial amount of short-range connections with an admixture of long-range connections. The portion of long-range connections in brain networks is observed to be quantitatively dissimilar across species. It is hypothesized that the length of connections is constrained by the spatial embedding of brain networks, yet fundamental principles that underlie the wiring length distribution remain unclear. By quantifying the structural diversity of a brain network using Shannon's entropy, here we show that the wiring length distribution across multiple species-including Drosophila, mouse, macaque, human, and C. elegans-follows the maximum entropy principle (MAP) under the constraints of limited wiring material and the spatial locations of brain areas or neurons. In addition, by considering stochastic axonal growth, we propose a network formation process capable of reproducing wiring length distributions of the 5 species, thereby implementing MAP in a biologically plausible manner. We further develop a generative model incorporating MAP, and show that, for the 5 species, the generated network exhibits high similarity to the real network. Our work indicates that the brain connectivity evolves to be structurally diversified by maximizing entropy to support efficient interareal communication, providing a potential organizational principle of brain networks.
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Affiliation(s)
- Yuru Song
- Neuroscience Graduate Program, University of California, San Diego, CA, USA
| | - Douglas Zhou
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.,Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.,Ministry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Songting Li
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.,Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.,Ministry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai 200240, China
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50
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Byrne Á, Ross J, Nicks R, Coombes S. Mean-Field Models for EEG/MEG: From Oscillations to Waves. Brain Topogr 2021; 35:36-53. [PMID: 33993357 PMCID: PMC8813727 DOI: 10.1007/s10548-021-00842-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 04/21/2021] [Indexed: 11/24/2022]
Abstract
Neural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of within-population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.
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Affiliation(s)
- Áine Byrne
- School of Mathematics and Statistics, Science Centre, University College Dublin, South Belfield, Dublin 4, Ireland.
| | - James Ross
- School of Mathematical Sciences, Centre for Mathematical Medicine and Biology, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Rachel Nicks
- School of Mathematical Sciences, Centre for Mathematical Medicine and Biology, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Stephen Coombes
- School of Mathematical Sciences, Centre for Mathematical Medicine and Biology, University of Nottingham, Nottingham, NG7 2RD, UK
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