1
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Izzi JVR, Ferreira RF, Girardi VA, Pena RFO. Identifying Effective Connectivity between Stochastic Neurons with Variable-Length Memory Using a Transfer Entropy Rate Estimator. Brain Sci 2024; 14:442. [PMID: 38790421 PMCID: PMC11119028 DOI: 10.3390/brainsci14050442] [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: 03/23/2024] [Revised: 04/22/2024] [Accepted: 04/26/2024] [Indexed: 05/26/2024] Open
Abstract
Information theory explains how systems encode and transmit information. This article examines the neuronal system, which processes information via neurons that react to stimuli and transmit electrical signals. Specifically, we focus on transfer entropy to measure the flow of information between sequences and explore its use in determining effective neuronal connectivity. We analyze the causal relationships between two discrete time series, X:=Xt:t∈Z and Y:=Yt:t∈Z, which take values in binary alphabets. When the bivariate process (X,Y) is a jointly stationary ergodic variable-length Markov chain with memory no larger than k, we demonstrate that the null hypothesis of the test-no causal influence-requires a zero transfer entropy rate. The plug-in estimator for this function is identified with the test statistic of the log-likelihood ratios. Since under the null hypothesis, this estimator follows an asymptotic chi-squared distribution, it facilitates the calculation of p-values when applied to empirical data. The efficacy of the hypothesis test is illustrated with data simulated from a neuronal network model, characterized by stochastic neurons with variable-length memory. The test results identify biologically relevant information, validating the underlying theory and highlighting the applicability of the method in understanding effective connectivity between neurons.
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Affiliation(s)
- João V. R. Izzi
- Department of Statistics, Federal University of São Carlos, São Carlos 13565-905, SP, Brazil
| | - Ricardo F. Ferreira
- Department of Statistics, Federal University of São Carlos, São Carlos 13565-905, SP, Brazil
| | - Victor A. Girardi
- Department of Statistics, Federal University of São Carlos, São Carlos 13565-905, SP, Brazil
| | - Rodrigo F. O. Pena
- Department of Biological Sciences, Florida Atlantic University, Jupiter, FL 33458, USA
- Stiles-Nicholson Brain Institute, Florida Atlantic University, Jupiter, FL 33458, USA
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2
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Chang YJ, Chen YI, Yeh HC, Santacruz SR. Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics. Sci Rep 2024; 14:5145. [PMID: 38429297 PMCID: PMC10907713 DOI: 10.1038/s41598-024-54593-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 02/14/2024] [Indexed: 03/03/2024] Open
Abstract
Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-scale to reveal the connections between the dynamical patterns and the behaviors, investigations of neural population dynamics are mainly limited to single-scale analysis. Our goal is to develop a cross-scale dynamical model for the collective activity of neuronal populations. Here we introduce a bio-inspired deep learning approach, termed NeuroBondGraph Network (NBGNet), to capture cross-scale dynamics that can infer and map the neural data from multiple scales. Our model not only exhibits more than an 11-fold improvement in reconstruction accuracy, but also predicts synchronous neural activity and preserves correlated low-dimensional latent dynamics. We also show that the NBGNet robustly predicts held-out data across a long time scale (2 weeks) without retraining. We further validate the effective connectivity defined from our model by demonstrating that neural connectivity during motor behaviour agrees with the established neuroanatomical hierarchy of motor control in the literature. The NBGNet approach opens the door to revealing a comprehensive understanding of brain computation, where network mechanisms of multi-scale activity are critical.
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Affiliation(s)
- Yin-Jui Chang
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Yuan-I Chen
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Hsin-Chih Yeh
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
- Texas Materials Institute, The University of Texas at Austin, Austin, TX, USA
| | - Samantha R Santacruz
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
- Institute for Neuroscience, The University of Texas at Austin, Austin, TX, USA.
- Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.
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3
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Voges N, Lima V, Hausmann J, Brovelli A, Battaglia D. Decomposing Neural Circuit Function into Information Processing Primitives. J Neurosci 2024; 44:e0157232023. [PMID: 38050070 PMCID: PMC10866194 DOI: 10.1523/jneurosci.0157-23.2023] [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: 01/27/2023] [Revised: 09/01/2023] [Accepted: 09/19/2023] [Indexed: 12/06/2023] Open
Abstract
It is challenging to measure how specific aspects of coordinated neural dynamics translate into operations of information processing and, ultimately, cognitive functions. An obstacle is that simple circuit mechanisms-such as self-sustained or propagating activity and nonlinear summation of inputs-do not directly give rise to high-level functions. Nevertheless, they already implement simple the information carried by neural activity. Here, we propose that distinct functions, such as stimulus representation, working memory, or selective attention, stem from different combinations and types of low-level manipulations of information or information processing primitives. To test this hypothesis, we combine approaches from information theory with simulations of multi-scale neural circuits involving interacting brain regions that emulate well-defined cognitive functions. Specifically, we track the information dynamics emergent from patterns of neural dynamics, using quantitative metrics to detect where and when information is actively buffered, transferred or nonlinearly merged, as possible modes of low-level processing (storage, transfer and modification). We find that neuronal subsets maintaining representations in working memory or performing attentional gain modulation are signaled by their boosted involvement in operations of information storage or modification, respectively. Thus, information dynamic metrics, beyond detecting which network units participate in cognitive processing, also promise to specify how and when they do it, that is, through which type of primitive computation, a capability that may be exploited for the analysis of experimental recordings.
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Affiliation(s)
- Nicole Voges
- Institut de Neurosciences de La Timone, UMR 7289, CNRS, Aix-Marseille Université, Marseille 13005, France
- Institute for Language, Communication and the Brain (ILCB), Aix-Marseille Université, Marseille 13005, France
| | - Vinicius Lima
- Institut de Neurosciences des Systèmes (INS), UMR 1106, Aix-Marseille Université, Marseille 13005, France
| | - Johannes Hausmann
- R&D Department, Hyland Switzerland Sarl, Corcelles NE 2035, Switzerland
| | - Andrea Brovelli
- Institut de Neurosciences de La Timone, UMR 7289, CNRS, Aix-Marseille Université, Marseille 13005, France
- Institute for Language, Communication and the Brain (ILCB), Aix-Marseille Université, Marseille 13005, France
| | - Demian Battaglia
- Institute for Language, Communication and the Brain (ILCB), Aix-Marseille Université, Marseille 13005, France
- Institut de Neurosciences des Systèmes (INS), UMR 1106, Aix-Marseille Université, Marseille 13005, France
- University of Strasbourg Institute for Advanced Studies (USIAS), Strasbourg 67000, France
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4
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Liu ZQ, Shafiei G, Baillet S, Misic B. Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks. Neuroimage 2023; 278:120276. [PMID: 37451374 DOI: 10.1016/j.neuroimage.2023.120276] [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: 03/04/2023] [Revised: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
The relationship between structural and functional connectivity in the brain is a key question in connectomics. Here we quantify patterns of structure-function coupling across the neocortex, by comparing structural connectivity estimated using diffusion MRI with functional connectivity estimated using both neurophysiological (MEG-based) and haemodynamic (fMRI-based) recordings. We find that structure-function coupling is heterogeneous across brain regions and frequency bands. The link between structural and functional connectivity is generally stronger in multiple MEG frequency bands compared to resting state fMRI. Structure-function coupling is greater in slower and intermediate frequency bands compared to faster frequency bands. We also find that structure-function coupling systematically follows the archetypal sensorimotor-association hierarchy, as well as patterns of laminar differentiation, peaking in granular layer IV. Finally, structure-function coupling is better explained using structure-informed inter-regional communication metrics than using structural connectivity alone. Collectively, these results place neurophysiological and haemodynamic structure-function relationships in a common frame of reference and provide a starting point for a multi-modal understanding of structure-function coupling in the brain.
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Affiliation(s)
- Zhen-Qi Liu
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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5
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Hernández-Recio S, Muñoz-Arnaiz R, López-Madrona V, Makarova J, Herreras O. Uncorrelated bilateral cortical input becomes timed across hippocampal subfields for long waves whereas gamma waves are largely ipsilateral. Front Cell Neurosci 2023; 17:1217081. [PMID: 37576568 PMCID: PMC10412937 DOI: 10.3389/fncel.2023.1217081] [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: 05/04/2023] [Accepted: 07/11/2023] [Indexed: 08/15/2023] Open
Abstract
The role of interhemispheric connections along successive segments of cortico-hippocampal circuits is poorly understood. We aimed to obtain a global picture of spontaneous transfer of activity during non-theta states across several nodes of the bilateral circuit in anesthetized rats. Spatial discrimination techniques applied to bilateral laminar field potentials (FP) across the CA1/Dentate Gyrus provided simultaneous left and right readouts in five FP generators that reflect activity in specific hippocampal afferents and associative pathways. We used a battery of correlation and coherence analyses to extract complementary aspects at different time scales and frequency bands. FP generators exhibited varying bilateral correlation that was high in CA1 and low in the Dentate Gyrus. The submillisecond delays indicate coordination but not support for synaptic dependence of one side on another. The time and frequency characteristics of bilateral coupling were specific to each generator. The Schaffer generator was strongly bilaterally coherent for both sharp waves and gamma waves, although the latter maintained poor amplitude co-variation. The lacunosum-moleculare generator was composed of up to three spatially overlapping activities, and globally maintained high bilateral coherence for long but not short (gamma) waves. These two CA1 generators showed no ipsilateral relationship in any frequency band. In the Dentate Gyrus, strong bilateral coherence was observed only for input from the medial entorhinal areas, while those from the lateral entorhinal areas were largely asymmetric, for both alpha and gamma waves. Granger causality testing showed strong bidirectional relationships between all homonymous bilateral generators except the lateral entorhinal input and a local generator in the Dentate Gyrus. It also revealed few significant relationships between ipsilateral generators, most notably the anticipation of lateral entorhinal cortex toward all others. Thus, with the notable exception of the lateral entorhinal areas, there is a marked interhemispheric coherence primarily for slow envelopes of activity, but not for pulse-like gamma waves, except in the Schafer segment. The results are consistent with essentially different streams of activity entering from and returning to the cortex on each side, with slow waves reflecting times of increased activity exchange between hemispheres and fast waves generally reflecting ipsilateral processing.
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Affiliation(s)
- Sara Hernández-Recio
- Laboratory of Experimental and Computational Neurophysiology, Department of Translational Neuroscience, Cajal Institute, CSIC, Madrid, Spain
- Program in Neuroscience, Autónoma de Madrid University-Cajal Institute, Madrid, Spain
| | - Ricardo Muñoz-Arnaiz
- Laboratory of Experimental and Computational Neurophysiology, Department of Translational Neuroscience, Cajal Institute, CSIC, Madrid, Spain
| | | | - Julia Makarova
- Laboratory of Experimental and Computational Neurophysiology, Department of Translational Neuroscience, Cajal Institute, CSIC, Madrid, Spain
| | - Oscar Herreras
- Laboratory of Experimental and Computational Neurophysiology, Department of Translational Neuroscience, Cajal Institute, CSIC, Madrid, Spain
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6
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Clark KB. Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks. BIOLOGY 2023; 12:biology12030352. [PMID: 36979044 PMCID: PMC10045557 DOI: 10.3390/biology12030352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/07/2023] [Accepted: 02/13/2023] [Indexed: 02/25/2023]
Abstract
In The cognitive-emotional brain, Pessoa overlooks continuum effects on nonlinear brain network connectivity by eschewing neural field theories and physiologically derived constructs representative of neuronal plasticity. The absence of this content, which is so very important for understanding the dynamic structure-function embedding and partitioning of brains, diminishes the rich competitive and cooperative nature of neural networks and trivializes Pessoa’s arguments, and similar arguments by other authors, on the phylogenetic and operational significance of an optimally integrated brain filled with variable-strength neural connections. Riemannian neuromanifolds, containing limit-imposing metaplastic Hebbian- and antiHebbian-type control variables, simulate scalable network behavior that is difficult to capture from the simpler graph-theoretic analysis preferred by Pessoa and other neuroscientists. Field theories suggest the partitioning and performance benefits of embedded cognitive-emotional networks that optimally evolve between exotic classical and quantum computational phases, where matrix singularities and condensations produce degenerate structure-function homogeneities unrealistic of healthy brains. Some network partitioning, as opposed to unconstrained embeddedness, is thus required for effective execution of cognitive-emotional network functions and, in our new era of neuroscience, should be considered a critical aspect of proper brain organization and operation.
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Affiliation(s)
- Kevin B. Clark
- Cures Within Reach, Chicago, IL 60602, USA;
- Felidae Conservation Fund, Mill Valley, CA 94941, USA
- Campus and Domain Champions Program, Multi-Tier Assistance, Training, and Computational Help (MATCH) Track, National Science Foundation’s Advanced Cyberinfrastructure Coordination Ecosystem: Services and Support (ACCESS), https://access-ci.org/
- Expert Network, Penn Center for Innovation, University of Pennsylvania, Philadelphia, PA 19104, USA
- Network for Life Detection (NfoLD), NASA Astrobiology Program, NASA Ames Research Center, Mountain View, CA 94035, USA
- Multi-Omics and Systems Biology & Artificial Intelligence and Machine Learning Analysis Working Groups, NASA GeneLab, NASA Ames Research Center, Mountain View, CA 94035, USA
- Frontier Development Lab, NASA Ames Research Center, Mountain View, CA 94035, USA & SETI Institute, Mountain View, CA 94043, USA
- Peace Innovation Institute, The Hague 2511, Netherlands & Stanford University, Palo Alto, CA 94305, USA
- Shared Interest Group for Natural and Artificial Intelligence (sigNAI), Max Planck Alumni Association, 14057 Berlin, Germany
- Biometrics and Nanotechnology Councils, Institute for Electrical and Electronics Engineers (IEEE), New York, NY 10016, USA
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7
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Madadi Asl M, Valizadeh A, Tass PA. Decoupling of interacting neuronal populations by time-shifted stimulation through spike-timing-dependent plasticity. PLoS Comput Biol 2023; 19:e1010853. [PMID: 36724144 PMCID: PMC9891531 DOI: 10.1371/journal.pcbi.1010853] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 01/05/2023] [Indexed: 02/02/2023] Open
Abstract
The synaptic organization of the brain is constantly modified by activity-dependent synaptic plasticity. In several neurological disorders, abnormal neuronal activity and pathological synaptic connectivity may significantly impair normal brain function. Reorganization of neuronal circuits by therapeutic stimulation has the potential to restore normal brain dynamics. Increasing evidence suggests that the temporal stimulation pattern crucially determines the long-lasting therapeutic effects of stimulation. Here, we tested whether a specific pattern of brain stimulation can enable the suppression of pathologically strong inter-population synaptic connectivity through spike-timing-dependent plasticity (STDP). More specifically, we tested how introducing a time shift between stimuli delivered to two interacting populations of neurons can effectively decouple them. To that end, we first used a tractable model, i.e., two bidirectionally coupled leaky integrate-and-fire (LIF) neurons, to theoretically analyze the optimal range of stimulation frequency and time shift for decoupling. We then extended our results to two reciprocally connected neuronal populations (modules) where inter-population delayed connections were modified by STDP. As predicted by the theoretical results, appropriately time-shifted stimulation causes a decoupling of the two-module system through STDP, i.e., by unlearning pathologically strong synaptic interactions between the two populations. Based on the overall topology of the connections, the decoupling of the two modules, in turn, causes a desynchronization of the populations that outlasts the cessation of stimulation. Decoupling effects of the time-shifted stimulation can be realized by time-shifted burst stimulation as well as time-shifted continuous simulation. Our results provide insight into the further optimization of a variety of multichannel stimulation protocols aiming at a therapeutic reshaping of diseased brain networks.
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Affiliation(s)
- Mojtaba Madadi Asl
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran
| | - Alireza Valizadeh
- Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Peter A. Tass
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States of America
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8
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Sych Y, Fomins A, Novelli L, Helmchen F. Dynamic reorganization of the cortico-basal ganglia-thalamo-cortical network during task learning. Cell Rep 2022; 40:111394. [PMID: 36130513 PMCID: PMC9513804 DOI: 10.1016/j.celrep.2022.111394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 05/31/2022] [Accepted: 08/29/2022] [Indexed: 11/19/2022] Open
Abstract
Adaptive behavior is coordinated by neuronal networks that are distributed across multiple brain regions such as in the cortico-basal ganglia-thalamo-cortical (CBGTC) network. Here, we ask how cross-regional interactions within such mesoscale circuits reorganize when an animal learns a new task. We apply multi-fiber photometry to chronically record simultaneous activity in 12 or 48 brain regions of mice trained in a tactile discrimination task. With improving task performance, most regions shift their peak activity from the time of reward-related action to the reward-predicting stimulus. By estimating cross-regional interactions using transfer entropy, we reveal that functional networks encompassing basal ganglia, thalamus, neocortex, and hippocampus grow and stabilize upon learning, especially at stimulus presentation time. The internal globus pallidus, ventromedial thalamus, and several regions in the frontal cortex emerge as salient hub regions. Our results highlight the learning-related dynamic reorganization that brain networks undergo when task-appropriate mesoscale network dynamics are established for goal-oriented behavior. Multi-fiber photometry reveals brain network adaptations during learning Activity in most regions temporally shifts from reward to predictive stimulus Cross-regional interactions in the CBGTC network increase and stabilize with learning Internal pallidum, VM thalamus, and prefrontal cortex regions emerge as hubs
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Affiliation(s)
- Yaroslav Sych
- Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, 8057 Zurich, Switzerland.
| | - Aleksejs Fomins
- Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, 8057 Zurich, Switzerland; Neuroscience Center Zurich, 8057 Zurich, Switzerland
| | - Leonardo Novelli
- Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, 8057 Zurich, Switzerland
| | - Fritjof Helmchen
- Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, 8057 Zurich, Switzerland; Neuroscience Center Zurich, 8057 Zurich, Switzerland.
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9
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Fang F, Godlewska B, Cho RY, Savitz SI, Selvaraj S, Zhang Y. Personalizing repetitive transcranial magnetic stimulation for precision depression treatment based on functional brain network controllability and optimal control analysis. Neuroimage 2022; 260:119465. [PMID: 35835338 DOI: 10.1016/j.neuroimage.2022.119465] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 06/05/2022] [Accepted: 07/11/2022] [Indexed: 11/16/2022] Open
Abstract
Brain neuromodulation effectively treats neurological diseases and psychiatric disorders such as Depression. However, due to patient heterogeneity, neuromodulation treatment outcomes are often highly variable, requiring patient-specific stimulation protocols throughout the recovery stages to optimize treatment outcomes. Therefore, it is critical to personalize neuromodulation protocol to optimize the patient-specific stimulation targets and parameters by accommodating inherent interpatient variability and intersession alteration during treatments. The study aims to develop a personalized repetitive transcranial magnetic stimulation (rTMS) protocol and evaluate its feasibility in optimizing the treatment efficiency using an existing dataset from an antidepressant experimental imaging study in depression. The personalization of the rTMS treatment protocol was achieved by personalizing both stimulation targets and parameters via a novel approach integrating the functional brain network controllability analysis and optimal control analysis. First, the functional brain network controllability analysis was performed to identify the optimal rTMS stimulation target from the effective connectivity network constructed from patient-specific resting-state functional magnetic resonance imaging data. The optimal control algorithm was then applied to optimize the rTMS stimulation parameters based on the optimized target. The performance of the proposed personalized rTMS technique was evaluated using datasets collected from a longitudinal antidepressant experimental imaging study in depression (n = 20). Simulation models demonstrated that the proposed personalized rTMS protocol outperformed the standard rTMS treatment by efficiently steering a depressive resting brain state to a healthy resting brain state, indicated by the significantly less control energy needed and higher model fitting accuracy achieved. The node with the maximum average controllability of each patient was designated as the optimal target region for the personalized rTMS protocol. Our results also demonstrated the theoretical feasibility of achieving comparable neuromodulation efficacy by stimulating a single node compared to stimulating multiple driver nodes. The findings support the feasibility of developing personalized neuromodulation protocols to more efficiently treat depression and other neurological diseases.
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Affiliation(s)
- Feng Fang
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
| | - Beata Godlewska
- Department of Psychiatry, Medical Sciences Division, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Raymond Y Cho
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, and Menninger Clinic, Houston, TX, United States
| | - Sean I Savitz
- Department of Neurology, The McGovern Medical School of UT Health Houston, Houston, TX, United States
| | - Sudhakar Selvaraj
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, The McGovern Medical School of UT Health Houston, Houston, TX, United States
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA.
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10
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Javadzadeh M, Hofer SB. Dynamic causal communication channels between neocortical areas. Neuron 2022; 110:2470-2483.e7. [PMID: 35690063 PMCID: PMC9616801 DOI: 10.1016/j.neuron.2022.05.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 03/26/2022] [Accepted: 05/12/2022] [Indexed: 11/08/2022]
Abstract
Processing of sensory information depends on the interactions between hierarchically connected neocortical regions, but it remains unclear how the activity in one area causally influences the activity dynamics in another and how rapidly such interactions change with time. Here, we show that the communication between the primary visual cortex (V1) and high-order visual area LM is context-dependent and surprisingly dynamic over time. By momentarily silencing one area while recording activity in the other, we find that both areas reliably affected changing subpopulations of target neurons within one hundred milliseconds while mice observed a visual stimulus. The influence of LM feedback on V1 responses became even more dynamic when the visual stimuli predicted a reward, causing fast changes in the geometry of V1 population activity and affecting stimulus coding in a context-dependent manner. Therefore, the functional interactions between cortical areas are not static but unfold through rapidly shifting communication subspaces whose dynamics depend on context when processing sensory information. Optogenetic perturbations reveal the causal structure of long-range cortical influences How visual areas influence each other changes dynamically over tens of milliseconds Feedback to V1 improves visual stimulus encoding required for behavior The dynamics of feedback influences depend on the behavioral context
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Affiliation(s)
- Mitra Javadzadeh
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK.
| | - Sonja B Hofer
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK.
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11
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Reyner-Parra D, Huguet G. Phase-locking patterns underlying effective communication in exact firing rate models of neural networks. PLoS Comput Biol 2022; 18:e1009342. [PMID: 35584147 PMCID: PMC9154197 DOI: 10.1371/journal.pcbi.1009342] [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: 07/22/2021] [Revised: 05/31/2022] [Accepted: 04/25/2022] [Indexed: 11/19/2022] Open
Abstract
Macroscopic oscillations in the brain have been observed to be involved in many cognitive tasks but their role is not completely understood. One of the suggested functions of the oscillations is to dynamically modulate communication between neural circuits. The Communication Through Coherence (CTC) theory proposes that oscillations reflect rhythmic changes in excitability of the neuronal populations. Thus, populations need to be properly phase-locked so that input volleys arrive at the peaks of excitability of the receiving population to communicate effectively. Here, we present a modeling study to explore synchronization between neuronal circuits connected with unidirectional projections. We consider an Excitatory-Inhibitory (E-I) network of quadratic integrate-and-fire neurons modeling a Pyramidal-Interneuronal Network Gamma (PING) rhythm. The network receives an external periodic input from either one or two sources, simulating the inputs from other oscillating neural groups. We use recently developed mean-field models which provide an exact description of the macroscopic activity of the spiking network. This low-dimensional mean field model allows us to use tools from bifurcation theory to identify the phase-locked states between the input and the target population as a function of the amplitude, frequency and coherence of the inputs. We identify the conditions for optimal phase-locking and effective communication. We find that inputs with high coherence can entrain the network for a wider range of frequencies. Besides, faster oscillatory inputs than the intrinsic network gamma cycle show more effective communication than inputs with similar frequency. Our analysis further shows that the entrainment of the network by inputs with higher frequency is more robust to distractors, thus giving them an advantage to entrain the network and communicate effectively. Finally, we show that pulsatile inputs can switch between attended inputs in selective attention. Oscillations are ubiquitous in the brain and are involved in several cognitive tasks but their role is not completely understood. The Communication Through Coherence theory proposes that background oscillations in the brain regulate the information flow between neural populations. The oscillators that are properly phase-locked so that inputs arrive at the peaks of excitability of the receiving population communicate effectively. In this paper, we study the emerging phase-locking patterns of a network generating PING oscillations under external periodic forcing, simulating the oscillatory input from other neural groups. We identify the conditions for optimal phase-locking and effective communication. Namely, we find that inputs with higher frequency and coherence have an adavantage to entrain the network and we quantify how robust are to distractors. Furthermore, we show how selective attention can be implemented by means of phase locking and we show that pulsatile inputs can switch between attended inputs.
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Affiliation(s)
- David Reyner-Parra
- Departament de Matemàtiques, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Gemma Huguet
- Departament de Matemàtiques, Universitat Politècnica de Catalunya, Barcelona, Spain
- Institut de Matemàtiques de la UPC - Barcelona Tech (IMTech), Barcelona, Spain
- Centre de Recerca Matemàtica, Barcelona, Spain
- * E-mail:
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12
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Winkler M, Dumont G, Schöll E, Gutkin B. Phase response approaches to neural activity models with distributed delay. BIOLOGICAL CYBERNETICS 2022; 116:191-203. [PMID: 34853889 DOI: 10.1007/s00422-021-00910-9] [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: 07/08/2021] [Accepted: 10/31/2021] [Indexed: 06/13/2023]
Abstract
In weakly coupled neural oscillator networks describing brain dynamics, the coupling delay is often distributed. We present a theoretical framework to calculate the phase response curve of distributed-delay induced limit cycles with infinite-dimensional phase space. Extending previous works, in which non-delayed or discrete-delay systems were investigated, we develop analytical results for phase response curves of oscillatory systems with distributed delay using Gaussian and log-normal delay distributions. We determine the scalar product and normalization condition for the linearized adjoint of the system required for the calculation of the phase response curve. As a paradigmatic example, we apply our technique to the Wilson-Cowan oscillator model of excitatory and inhibitory neuronal populations under the two delay distributions. We calculate and compare the phase response curves for the Gaussian and log-normal delay distributions. The phase response curves obtained from our adjoint calculations match those compiled by the direct perturbation method, thereby proving that the theory of weakly coupled oscillators can be applied successfully for distributed-delay-induced limit cycles. We further use the obtained phase response curves to derive phase interaction functions and determine the possible phase locked states of multiple inter-coupled populations to illuminate different synchronization scenarios. In numerical simulations, we show that the coupling delay distribution can impact the stability of the synchronization between inter-coupled gamma-oscillatory networks.
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Affiliation(s)
- Marius Winkler
- Group for Neural Theory, LNC INSERM U960, DEC, Ecole Normale Supérieure PSL* University, 24 rue Lhomond, 75005, Paris, France
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstrasse 36, 10623, Berlin, Germany
| | - Grégory Dumont
- Group for Neural Theory, LNC INSERM U960, DEC, Ecole Normale Supérieure PSL* University, 24 rue Lhomond, 75005, Paris, France
| | - Eckehard Schöll
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstrasse 36, 10623, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Humboldt-Universität, Philippstraße 13, 10115, Berlin, Germany
- Potsdam Institute for Climate Impact Research, Telegrafenberg A 31, 14473, Potsdam, Germany
| | - Boris Gutkin
- Group for Neural Theory, LNC INSERM U960, DEC, Ecole Normale Supérieure PSL* University, 24 rue Lhomond, 75005, Paris, France.
- Center for Cognition and Decision Making, Institue for Cognitive Neuroscience, NRU Higher School of Economics, Krivokolenniy sidewalk 3, 101000, Moscow, Russia.
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Powanwe AS, Longtin A. Mutual information resonances in delay-coupled limit cycle and quasi-cycle brain rhythms. BIOLOGICAL CYBERNETICS 2022; 116:129-146. [PMID: 35486195 DOI: 10.1007/s00422-022-00932-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
We elucidate how coupling delays and noise impact phase and mutual information relationships between two stochastic brain rhythms. This impact depends on the dynamical regime of each PING-based rhythm, as well as on network heterogeneity and coupling asymmetry. The number of peaks at positive and negative time lags in the delayed mutual information between the two bi-directionally communicating rhythms defines our measure of flexibility of information sharing and reflects the number of ways in which the two networks can alternately lead one another. We identify two distinct mechanisms for the appearance of qualitatively similar flexible information sharing. The flexibility in the quasi-cycle regime arises from the coupling delay-induced bimodality of the phase difference distribution, and the related bimodal mutual information. It persists in the presence of asymmetric coupling and heterogeneity but is limited to two routes of information sharing. The second mechanism in noisy limit cycle regime is not induced by the delay. However, delay-coupling and heterogeneity enable communication routes at multiple time lags. Noise disrupts the shared compromise frequency, allowing the expression of individual network frequencies which leads to a slow beating pattern. Simulations of an envelope-phase description for delay-coupled quasi-cycles yield qualitatively similar properties as for the full system. Near the bifurcation from in-phase to out-of-phase behaviour, a single preferred phase difference can coexist with two information sharing routes; further, the phase laggard can be the mutual information leader, or vice versa. Overall, the coupling delay endows a two-rhythm system with an array of lead-lag relationships and mutual information resonances that exist in spite of the noise and across the Hopf bifurcation. These beg to be mapped out experimentally with the help of our predictions.
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Affiliation(s)
- Arthur S Powanwe
- Department of Physics, University of Ottawa, 150 Louis Pasteur, Ottawa, ON, K1N6N5, Canada.
- Centre for Neural Dynamics, University of Ottawa, Ottawa, Canada.
| | - André Longtin
- Department of Physics, University of Ottawa, 150 Louis Pasteur, Ottawa, ON, K1N6N5, Canada
- Department of Cellular and Molecular Medicine, 451 Smyth Road, Ottawa, ON, K1H8M5, Canada
- Centre for Neural Dynamics, University of Ottawa, Ottawa, Canada
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Voutsa V, Battaglia D, Bracken LJ, Brovelli A, Costescu J, Díaz Muñoz M, Fath BD, Funk A, Guirro M, Hein T, Kerschner C, Kimmich C, Lima V, Messé A, Parsons AJ, Perez J, Pöppl R, Prell C, Recinos S, Shi Y, Tiwari S, Turnbull L, Wainwright J, Waxenecker H, Hütt MT. Two classes of functional connectivity in dynamical processes in networks. J R Soc Interface 2021; 18:20210486. [PMID: 34665977 PMCID: PMC8526174 DOI: 10.1098/rsif.2021.0486] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 09/13/2021] [Indexed: 12/12/2022] Open
Abstract
The relationship between network structure and dynamics is one of the most extensively investigated problems in the theory of complex systems of recent years. Understanding this relationship is of relevance to a range of disciplines-from neuroscience to geomorphology. A major strategy of investigating this relationship is the quantitative comparison of a representation of network architecture (structural connectivity, SC) with a (network) representation of the dynamics (functional connectivity, FC). Here, we show that one can distinguish two classes of functional connectivity-one based on simultaneous activity (co-activity) of nodes, the other based on sequential activity of nodes. We delineate these two classes in different categories of dynamical processes-excitations, regular and chaotic oscillators-and provide examples for SC/FC correlations of both classes in each of these models. We expand the theoretical view of the SC/FC relationships, with conceptual instances of the SC and the two classes of FC for various application scenarios in geomorphology, ecology, systems biology, neuroscience and socio-ecological systems. Seeing the organisation of dynamical processes in a network either as governed by co-activity or by sequential activity allows us to bring some order in the myriad of observations relating structure and function of complex networks.
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Affiliation(s)
- Venetia Voutsa
- Department of Life Sciences and Chemistry, Jacobs University Bremen, 28759 Bremen, Germany
| | - Demian Battaglia
- Aix-Marseille Université, Inserm, Institut de Neurosciences des Systèmes (UMR 1106), Marseille, France
- University of Strasbourg Institute for Advanced Studies (USIAS), Strasbourg 67083, France
| | | | - Andrea Brovelli
- Aix-Marseille Université, CNRS, Institut de Neurosciences de la Timone (UMR 7289), Marseille, France
| | - Julia Costescu
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - Mario Díaz Muñoz
- Department of Sustainability, Governance and Methods, Modul University Vienna, 1190 Vienna, Austria
| | - Brian D. Fath
- Department of Biological Sciences, Towson University, Towson, Maryland 21252, USA
- Advancing Systems Analysis Program, International Institute for Applied Systems Analysis, Laxenburg 2361, Austria
- Department of Environmental Studies, Masaryk University, 60200 Brno, Czech Republic
| | - Andrea Funk
- Institute of Hydrobiology and Aquatic Ecosystem Management (IHG), University of Natural Resources and Life Sciences Vienna (BOKU), 1180 Vienna, Austria
- WasserCluster Lunz - Biologische Station GmbH, Dr. Carl Kupelwieser Promenade 5, 3293 Lunz am See, Austria
| | - Mel Guirro
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - Thomas Hein
- Institute of Hydrobiology and Aquatic Ecosystem Management (IHG), University of Natural Resources and Life Sciences Vienna (BOKU), 1180 Vienna, Austria
- WasserCluster Lunz - Biologische Station GmbH, Dr. Carl Kupelwieser Promenade 5, 3293 Lunz am See, Austria
| | - Christian Kerschner
- Department of Sustainability, Governance and Methods, Modul University Vienna, 1190 Vienna, Austria
- Department of Environmental Studies, Masaryk University, 60200 Brno, Czech Republic
| | - Christian Kimmich
- Department of Environmental Studies, Masaryk University, 60200 Brno, Czech Republic
- Regional Science and Environmental Research, Institute for Advanced Studies, 1080 Vienna, Austria
| | - Vinicius Lima
- Aix-Marseille Université, Inserm, Institut de Neurosciences des Systèmes (UMR 1106), Marseille, France
- Aix-Marseille Université, CNRS, Institut de Neurosciences de la Timone (UMR 7289), Marseille, France
| | - Arnaud Messé
- Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Germany
| | | | - John Perez
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - Ronald Pöppl
- Department of Geography and Regional Research, University of Vienna, Universitätsstr. 7, 1010 Vienna, Austria
| | - Christina Prell
- Department of Cultural Geography, University of Groningen, 9747 AD, Groningen, The Netherlands
| | - Sonia Recinos
- Institute of Hydrobiology and Aquatic Ecosystem Management (IHG), University of Natural Resources and Life Sciences Vienna (BOKU), 1180 Vienna, Austria
| | - Yanhua Shi
- Department of Environmental Studies, Masaryk University, 60200 Brno, Czech Republic
| | - Shubham Tiwari
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - Laura Turnbull
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - John Wainwright
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - Harald Waxenecker
- Department of Environmental Studies, Masaryk University, 60200 Brno, Czech Republic
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Jacobs University Bremen, 28759 Bremen, Germany
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Mechanisms of Flexible Information Sharing through Noisy Oscillations. BIOLOGY 2021; 10:biology10080764. [PMID: 34439996 PMCID: PMC8389573 DOI: 10.3390/biology10080764] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/11/2021] [Accepted: 07/31/2021] [Indexed: 01/08/2023]
Abstract
Simple Summary To properly interact with our environment, the brain must be able to identify external stimuli, process them, and make the right decisions all in a short time. This may involve several brain regions interacting together by sharing information birectionally via rhythmic activity. Such flexibility requires the functional connectivity between the areas to be dynamic, and a key question is the relevant parameter and operating regimes that make this possible in spite of fixed structural connectivity. Working towards this goal, we consider two coupled brain regions, each of which exhibits a noisy rhythm, a commonly observed type of neural activity. Such rhythms can be induced by the stochasticity in the neural circuitry, or be autonomously generated through nonlinearities and not necessitating noise. For these two types of rhythms, we computed the amount of information shared between the brain areas and the preferred direction(s) of sharing. We found that without the coupling delay, the flexibility needed by the brain to perform cognitive tasks requires the rhythms to be autogenerated rather than noise-induced. This is the case even with asymmetry or heterogeneity. This suggests that the importance of the dynamical regime has to be taken into account when modeling interacting neural rhythms from an information theoretical point of view. Abstract Brain areas must be able to interact and share information in a time-varying, dynamic manner on a fast timescale. Such flexibility in information sharing has been linked to the synchronization of rhythm phases between areas. One definition of flexibility is the number of local maxima in the delayed mutual information curve between two connected areas. However, the precise relationship between phase synchronization and information sharing is not clear, nor is the flexibility in the face of the fixed structural connectivity and noise. Here, we consider two coupled oscillatory excitatory-inhibitory networks connected through zero-delay excitatory connections, each of which mimics a rhythmic brain area. We numerically compute phase-locking and delayed mutual information between the phases of excitatory local field potential (LFPs) of the two networks, which measures the shared information and its direction. The flexibility in information sharing is shown to depend on the dynamical origin of oscillations, and its properties in different regimes are found to persist in the presence of asymmetry in the connectivity as well as system heterogeneity. For coupled noise-induced rhythms (quasi-cycles), phase synchronization is robust even in the presence of asymmetry and heterogeneity. However, they do not show flexibility, in contrast to noise-perturbed rhythms (noisy limit cycles), which are shown here to exhibit two local information maxima, i.e., flexibility. For quasi-cycles, phase difference and information measures for the envelope-phase dynamics obtained from previous analytical work using the Stochastic Averaging Method (SAM) are found to be in good qualitative agreement with those obtained from the original dynamics. The relation between phase synchronization and communication patterns is not trivial, particularly in the noisy limit cycle regime. There, complex patterns of information sharing can be observed for a single value of the phase difference. The mechanisms reported here can be extended to I-I networks since their phase synchronizations are similar. Our results set the stage for investigating information sharing between several connected noisy rhythms in neural and other complex biological networks.
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Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling. eNeuro 2021; 8:ENEURO.0475-20.2021. [PMID: 34045210 PMCID: PMC8260273 DOI: 10.1523/eneuro.0475-20.2021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/08/2021] [Accepted: 04/12/2021] [Indexed: 12/18/2022] Open
Abstract
Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring “virtual FC” from empirical SC or “virtual SC” from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.
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Pariz A, Fischer I, Valizadeh A, Mirasso C. Transmission delays and frequency detuning can regulate information flow between brain regions. PLoS Comput Biol 2021; 17:e1008129. [PMID: 33857135 PMCID: PMC8049288 DOI: 10.1371/journal.pcbi.1008129] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 02/16/2021] [Indexed: 12/28/2022] Open
Abstract
Brain networks exhibit very variable and dynamical functional connectivity and flexible configurations of information exchange despite their overall fixed structure. Brain oscillations are hypothesized to underlie time-dependent functional connectivity by periodically changing the excitability of neural populations. In this paper, we investigate the role of the connection delay and the detuning between the natural frequencies of neural populations in the transmission of signals. Based on numerical simulations and analytical arguments, we show that the amount of information transfer between two oscillating neural populations could be determined by their connection delay and the mismatch in their oscillation frequencies. Our results highlight the role of the collective phase response curve of the oscillating neural populations for the efficacy of signal transmission and the quality of the information transfer in brain networks.
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Affiliation(s)
- Aref Pariz
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, Palma de Mallorca, Spain
| | - Ingo Fischer
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, Palma de Mallorca, Spain
| | - Alireza Valizadeh
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
- School of biological sciences, Institute for research in fundamental sciences (IPM), Tehran, Iran
- * E-mail: (AV); (CM)
| | - Claudio Mirasso
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, Palma de Mallorca, Spain
- * E-mail: (AV); (CM)
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18
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Saraf S, Young LS. Malleability of gamma rhythms enhances population-level correlations. J Comput Neurosci 2021; 49:189-205. [PMID: 33818659 DOI: 10.1007/s10827-021-00779-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 12/23/2020] [Accepted: 01/27/2021] [Indexed: 12/12/2022]
Abstract
An important problem in systems neuroscience is to understand how information is communicated among brain regions, and it has been proposed that communication is mediated by neuronal oscillations, such as rhythms in the gamma band. We sought to investigate this idea by using a network model with two components, a source (sending) and a target (receiving) component, both built to resemble local populations in the cerebral cortex. To measure the effectiveness of communication, we used population-level correlations in spike times between the source and target. We found that after correcting for a response time that is independent of initial conditions, spike-time correlations between the source and target are significant, due in large measure to the alignment of firing events in their gamma rhythms. But, we also found that regular oscillations cannot produce the results observed in our model simulations of cortical neurons. Surprisingly, it is the irregularity of gamma rhythms, the absence of internal clocks, together with the malleability of these rhythms and their tendency to align with external pulses - features that are known to be present in gamma rhythms in the real cortex - that produced the results observed. These findings and the mechanistic explanations we offered are our primary results. Our secondary result is a mathematical relationship between correlations and the sizes of the samples used for their calculation. As improving technology enables recording simultaneously from increasing numbers of neurons, this relationship could be useful for interpreting results from experimental recordings.
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Affiliation(s)
- Sonica Saraf
- Center for Neural Science, New York University, 10003, New York, USA
| | - Lai-Sang Young
- Courant Institute of Mathematical Sciences, New York University, New York, 10012, USA.
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Scheid BH, Ashourvan A, Stiso J, Davis KA, Mikhail F, Pasqualetti F, Litt B, Bassett DS. Time-evolving controllability of effective connectivity networks during seizure progression. Proc Natl Acad Sci U S A 2021; 118:e2006436118. [PMID: 33495341 PMCID: PMC7865160 DOI: 10.1073/pnas.2006436118] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Over one third of the estimated 3 million people with epilepsy in the United States are medication resistant. Responsive neurostimulation from chronically implanted electrodes provides a promising treatment alternative to resective surgery. However, determining optimal personalized stimulation parameters, including when and where to intervene to guarantee a positive patient outcome, is a major open challenge. Network neuroscience and control theory offer useful tools that may guide improvements in parameter selection for control of anomalous neural activity. Here we use a method to characterize dynamic controllability across consecutive effective connectivity (EC) networks based on regularized partial correlations between implanted electrodes during the onset, propagation, and termination regimes of 34 seizures. We estimate regularized partial correlation adjacency matrices from 1-s time windows of intracranial electrocorticography recordings using the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO). Average and modal controllability metrics calculated from each resulting EC network track the time-varying controllability of the brain on an evolving landscape of conditionally dependent network interactions. We show that average controllability increases throughout a seizure and is negatively correlated with modal controllability throughout. Our results support the hypothesis that the energy required to drive the brain to a seizure-free state from an ictal state is smallest during seizure onset, yet we find that applying control energy at electrodes in the seizure onset zone may not always be energetically favorable. Our work suggests that a low-complexity model of time-evolving controllability may offer insights for developing and improving control strategies targeting seizure suppression.
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Affiliation(s)
- Brittany H Scheid
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
| | - Arian Ashourvan
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104
| | - Kathryn A Davis
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Fadi Mikhail
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA 92521
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104;
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Santa Fe Institute, Santa Fe, NM 87501
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Mill RD, Gordon BA, Balota DA, Cole MW. Predicting dysfunctional age-related task activations from resting-state network alterations. Neuroimage 2020; 221:117167. [PMID: 32682094 PMCID: PMC7810059 DOI: 10.1016/j.neuroimage.2020.117167] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 06/25/2020] [Accepted: 07/11/2020] [Indexed: 11/12/2022] Open
Abstract
Alzheimer's disease (AD) is linked to changes in fMRI task activations and fMRI resting-state functional connectivity (restFC), which can emerge early in the illness timecourse. These fMRI correlates of unhealthy aging have been studied in largely separate subfields. Taking inspiration from neural network simulations, we propose a unifying mechanism wherein restFC alterations associated with AD disrupt the flow of activations between brain regions, leading to aberrant task activations. We apply this activity flow model in a large sample of clinically normal older adults, which was segregated into healthy (low-risk) and at-risk subgroups based on established imaging (positron emission tomography amyloid) and genetic (apolipoprotein) AD risk factors. Modeling the flow of healthy activations over at-risk AD connectivity effectively transformed the healthy aged activations into unhealthy (at-risk) aged activations. This enabled reliable prediction of at-risk AD task activations, and these predicted activations were related to individual differences in task behavior. These results support activity flow over altered intrinsic functional connections as a mechanism underlying Alzheimer's-related dysfunction, even in very early stages of the illness. Beyond these mechanistic insights, this approach raises clinical potential by enabling prediction of task activations and associated cognitive dysfunction in individuals without requiring them to perform in-scanner cognitive tasks.
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Affiliation(s)
- Ravi D Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Avenue, Newark, NJ 07102, USA.
| | - Brian A Gordon
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - David A Balota
- Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO 63130, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Avenue, Newark, NJ 07102, USA
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Papadopoulos L, Lynn CW, Battaglia D, Bassett DS. Relations between large-scale brain connectivity and effects of regional stimulation depend on collective dynamical state. PLoS Comput Biol 2020; 16:e1008144. [PMID: 32886673 PMCID: PMC7537889 DOI: 10.1371/journal.pcbi.1008144] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 10/06/2020] [Accepted: 07/12/2020] [Indexed: 01/09/2023] Open
Abstract
At the macroscale, the brain operates as a network of interconnected neuronal populations, which display coordinated rhythmic dynamics that support interareal communication. Understanding how stimulation of different brain areas impacts such activity is important for gaining basic insights into brain function and for further developing therapeutic neurmodulation. However, the complexity of brain structure and dynamics hinders predictions regarding the downstream effects of focal stimulation. More specifically, little is known about how the collective oscillatory regime of brain network activity—in concert with network structure—affects the outcomes of perturbations. Here, we combine human connectome data and biophysical modeling to begin filling these gaps. By tuning parameters that control collective system dynamics, we identify distinct states of simulated brain activity and investigate how the distributed effects of stimulation manifest at different dynamical working points. When baseline oscillations are weak, the stimulated area exhibits enhanced power and frequency, and due to network interactions, activity in this excited frequency band propagates to nearby regions. Notably, beyond these linear effects, we further find that focal stimulation causes more distributed modifications to interareal coherence in a band containing regions’ baseline oscillation frequencies. Importantly, depending on the dynamical state of the system, these broadband effects can be better predicted by functional rather than structural connectivity, emphasizing a complex interplay between anatomical organization, dynamics, and response to perturbation. In contrast, when the network operates in a regime of strong regional oscillations, stimulation causes only slight shifts in power and frequency, and structural connectivity becomes most predictive of stimulation-induced changes in network activity patterns. In sum, this work builds upon and extends previous computational studies investigating the impacts of stimulation, and underscores the fact that both the stimulation site, and, crucially, the regime of brain network dynamics, can influence the network-wide responses to local perturbations. Stimulation can be used to alter brain activity and is a therapeutic option for certain neurological conditions. However, predicting the distributed effects of local perturbations is difficult. Previous studies show that responses to stimulation depend on anatomical (or structural) coupling. In addition to structure, here we consider how stimulation effects also depend on the brain’s collective dynamical (or functional) state, arising from the coordination of rhythmic activity across large-scale networks. In a whole-brain computational model, we show that global responses to regional stimulation can indeed be contingent upon and differ across various dynamical working points. Notably, depending on the network’s oscillatory regime, stimulation can accelerate the activity of the stimulated site, and lead to widespread effects at both the new, excited frequency, as well as in a much broader frequency range including areas’ baseline frequencies. While structural connectivity is a good predictor of “excited band” changes, in some states “baseline band” effects can be better predicted by functional connectivity, which depends upon the system’s oscillatory regime. By integrating and extending past efforts, our results thus indicate that dynamical—in additional to structural—brain organization plays a role in governing how focal stimulation modulates interactions between distributed network elements.
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Affiliation(s)
- Lia Papadopoulos
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Christopher W. Lynn
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Demian Battaglia
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France
| | - Danielle S. Bassett
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
- * E-mail:
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Machado JN, Matias FS. Phase bistability between anticipated and delayed synchronization in neuronal populations. Phys Rev E 2020; 102:032412. [PMID: 33075861 DOI: 10.1103/physreve.102.032412] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 08/26/2020] [Indexed: 06/11/2023]
Abstract
Two dynamical systems unidirectionally coupled in a sender-receiver configuration can synchronize with a nonzero phase lag. In particular, the system can exhibit anticipated synchronization (AS), which is characterized by a negative phase lag, if the receiver also receives a delayed negative self-feedback. Recently, AS was shown to occur between cortical-like neuronal populations in which the self-feedback is mediated by inhibitory synapses. In this biologically plausible scenario, a transition from the usual delayed synchronization (with positive phase lag) to AS can be mediated by the inhibitory conductances in the receiver population. Here we show that depending on the relation between excitatory and inhibitory synaptic conductances the system can also exhibit phase bistability between anticipated and delayed synchronization. Furthermore, we show that the amount of noise at the receiver and the synaptic conductances can mediate the transition from stable phase locking to a bistable regime and eventually to a phase drift. We suggest that our spiking neuronal populations model could be potentially useful to study phase bistability in cortical regions related to bistable perception.
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Affiliation(s)
- Júlio Nunes Machado
- Instituto de Física, Universidade Federal de Alagoas, Maceió, Alagoas 57072-970, Brazil
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23
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Kreiter AK. Synchrony, flexible network configuration, and linking neural events to behavior. CURRENT OPINION IN PHYSIOLOGY 2020. [DOI: 10.1016/j.cophys.2020.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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24
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Battaglia D, Boudou T, Hansen ECA, Lombardo D, Chettouf S, Daffertshofer A, McIntosh AR, Zimmermann J, Ritter P, Jirsa V. Dynamic Functional Connectivity between order and randomness and its evolution across the human adult lifespan. Neuroimage 2020; 222:117156. [PMID: 32698027 DOI: 10.1016/j.neuroimage.2020.117156] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 05/25/2020] [Accepted: 07/07/2020] [Indexed: 12/14/2022] Open
Abstract
Functional Connectivity (FC) during resting-state or task conditions is not static but inherently dynamic. Yet, there is no consensus on whether fluctuations in FC may resemble isolated transitions between discrete FC states rather than continuous changes. This quarrel hampers advancing the study of dynamic FC. This is unfortunate as the structure of fluctuations in FC can certainly provide more information about developmental changes, aging, and progression of pathologies. We merge the two perspectives and consider dynamic FC as an ongoing network reconfiguration, including a stochastic exploration of the space of possible steady FC states. The statistical properties of this random walk deviate both from a purely "order-driven" dynamics, in which the mean FC is preserved, and from a purely "randomness-driven" scenario, in which fluctuations of FC remain uncorrelated over time. Instead, dynamic FC has a complex structure endowed with long-range sequential correlations that give rise to transient slowing and acceleration epochs in the continuous flow of reconfiguration. Our analysis for fMRI data in healthy elderly revealed that dynamic FC tends to slow down and becomes less complex as well as more random with increasing age. These effects appear to be strongly associated with age-related changes in behavioural and cognitive performance.
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Affiliation(s)
- Demian Battaglia
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France.
| | - Thomas Boudou
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France; ENSTA ParisTech, F-91762, Palaiseau, France.
| | - Enrique C A Hansen
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France; Institut de biologie de l'Ecole normale supérieure (IBENS), École Normale Supérieure, CNRS, INSERM, PSL Université Paris, F-75005, Paris, France.
| | - Diego Lombardo
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France.
| | - Sabrina Chettouf
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, D-10117, Berlin, Germany; Bernstein Center for Computational Neuroscience, D-10117, Berlin, Germany; Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, 1081 BT, Amsterdam, the Netherlands.
| | - Andreas Daffertshofer
- Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, 1081 BT, Amsterdam, the Netherlands.
| | - Anthony R McIntosh
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, M6A 2E1, Canada.
| | - Joelle Zimmermann
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, D-10117, Berlin, Germany; Rotman Research Institute, Baycrest Centre, Toronto, Ontario, M6A 2E1, Canada.
| | - Petra Ritter
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, D-10117, Berlin, Germany; Bernstein Center for Computational Neuroscience, D-10117, Berlin, Germany.
| | - Viktor Jirsa
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France.
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Castro S, El-Deredy W, Battaglia D, Orio P. Cortical ignition dynamics is tightly linked to the core organisation of the human connectome. PLoS Comput Biol 2020; 16:e1007686. [PMID: 32735580 PMCID: PMC7423150 DOI: 10.1371/journal.pcbi.1007686] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 08/12/2020] [Accepted: 06/15/2020] [Indexed: 12/04/2022] Open
Abstract
The capability of cortical regions to flexibly sustain an "ignited" state of activity has been discussed in relation to conscious perception or hierarchical information processing. Here, we investigate how the intrinsic propensity of different regions to get ignited is determined by the specific topological organisation of the structural connectome. More specifically, we simulated the resting-state dynamics of mean-field whole-brain models and assessed how dynamic multistability and ignition differ between a reference model embedding a realistic human connectome, and alternative models based on a variety of randomised connectome ensembles. We found that the strength of global excitation needed to first trigger ignition in a subset of regions is substantially smaller for the model embedding the empirical human connectome. Furthermore, when increasing the strength of excitation, the propagation of ignition outside of this initial core-which is able to self-sustain its high activity-is way more gradual than for any of the randomised connectomes, allowing for graded control of the number of ignited regions. We explain both these assets in terms of the exceptional weighted core-shell organisation of the empirical connectome, speculating that this topology of human structural connectivity may be attuned to support enhanced ignition dynamics.
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Affiliation(s)
- Samy Castro
- Centro Interdisciplinario de Neurociencias de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias, mención Neurociencia, Universidad de Valparaíso, Valparaíso, Chile
| | - Wael El-Deredy
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile
| | - Demian Battaglia
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, INSERM UMR 1106, Marseille, France
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencias de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Instituto de Neurociencias, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
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26
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Primal-size neural circuits in meta-periodic interaction. Cogn Neurodyn 2020; 15:359-367. [PMID: 33854649 DOI: 10.1007/s11571-020-09613-6] [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: 10/07/2019] [Revised: 06/08/2020] [Accepted: 06/23/2020] [Indexed: 10/23/2022] Open
Abstract
Experimental observations of simultaneous activity in large cortical areas have seemed to justify a large network approach in early studies of neural information codes and memory capacity. This approach has overlooked, however, the segregated nature of cortical structure and functionality. Employing graph-theoretic results, we show that, given the estimated number of neurons in the human brain, there are only a few primal sizes that can be attributed to neural circuits under probabilistically sparse connectivity. The significance of this finding is that neural circuits of relatively small primal sizes in cyclic interaction, implied by inhibitory interneuron potentiation and excitatory inter-circuit potentiation, generate relatively long non-repetitious sequences of asynchronous primal-length periods. The meta-periodic nature of such circuit interaction translates into meta-periodic firing-rate dynamics, representing cortical information. It is finally shown that interacting neural circuits of primal sizes 7 or less exhaust most of the capacity of the human brain, with relatively little room to spare for circuits of larger primal sizes. This also appears to ratify experimental findings on the human working memory capacity.
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Hong C, Wei X, Wang J, Deng B, Yu H, Che Y. Training Spiking Neural Networks for Cognitive Tasks: A Versatile Framework Compatible With Various Temporal Codes. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:1285-1296. [PMID: 31247574 DOI: 10.1109/tnnls.2019.2919662] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Recent studies have demonstrated the effectiveness of supervised learning in spiking neural networks (SNNs). A trainable SNN provides a valuable tool not only for engineering applications but also for theoretical neuroscience studies. Here, we propose a modified SpikeProp learning algorithm, which ensures better learning stability for SNNs and provides more diverse network structures and coding schemes. Specifically, we designed a spike gradient threshold rule to solve the well-known gradient exploding problem in SNN training. In addition, regulation rules on firing rates and connection weights are proposed to control the network activity during training. Based on these rules, biologically realistic features such as lateral connections, complex synaptic dynamics, and sparse activities are included in the network to facilitate neural computation. We demonstrate the versatility of this framework by implementing three well-known temporal codes for different types of cognitive tasks, namely, handwritten digit recognition, spatial coordinate transformation, and motor sequence generation. Several important features observed in experimental studies, such as selective activity, excitatory-inhibitory balance, and weak pairwise correlation, emerged in the trained model. This agreement between experimental and computational results further confirmed the importance of these features in neural function. This work provides a new framework, in which various neural behaviors can be modeled and the underlying computational mechanisms can be studied.
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Attention Selectively Gates Afferent Signal Transmission to Area V4. J Neurosci 2019; 38:3441-3452. [PMID: 29618546 DOI: 10.1523/jneurosci.2221-17.2018] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Revised: 02/14/2018] [Accepted: 02/15/2018] [Indexed: 11/21/2022] Open
Abstract
Selective attention allows focusing on only part of the incoming sensory information. Neurons in the extrastriate visual cortex reflect such selective processing when different stimuli are simultaneously present in their large receptive fields. Their spiking response then resembles the response to the attended stimulus when presented in isolation. Unclear is where in the neuronal pathway attention intervenes to achieve such selective signal routing and processing. To investigate this question, we tagged two equivalent visual stimuli by independent broadband luminance noise and used the spectral coherence of these behaviorally irrelevant signals with the field potential of a local neuronal population in male macaque monkeys' area V4 as a measure for their respective causal influences. This new experimental paradigm revealed that signal transmission was considerably weaker for the not-attended stimulus. Furthermore, our results show that attention does not need to modulate responses in the input populations sending signals to V4 to selectively represent a stimulus, nor do they suggest a change of the V4 neurons' output gain depending on their feature similarity with the stimuli. Our results rather imply that selective attention uses a gating mechanism comprising the synaptic "inputs" that transmit signals from upstream areas into the V4 neurons. A minimal model implementing attention-dependent routing by gamma-band synchrony replicated the attentional gating effect and the signals' spectral transfer characteristics. It supports the proposal that selective interareal gamma-band synchrony subserves signal routing and explains our experimental finding that attention selectively gates signals already at the level of afferent synaptic input.SIGNIFICANCE STATEMENT Depending on the behavioral context, the brain needs to channel the flow of information through its networks of massively interconnected neurons. We designed an experiment that allows to causally assess routing of information originating from an attended object. We found that attention "gates" signals at the interplay between afferent fibers and the local neurons. A minimal model demonstrated that coherent gamma-rhythmic activity (∼60 Hz) between local neurons and their afferent-providing input neurons can realize the gating. Importantly, the attended signals did not need to be amplified already in an earlier processing stage, nor did they get amplified by a simple output response modulation. The method provides a useful tool to study mechanisms of dynamic network configuration underlying cognitive processes.
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29
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Khambhati AN, Kahn AE, Costantini J, Ezzyat Y, Solomon EA, Gross RE, Jobst BC, Sheth SA, Zaghloul KA, Worrell G, Seger S, Lega BC, Weiss S, Sperling MR, Gorniak R, Das SR, Stein JM, Rizzuto DS, Kahana MJ, Lucas TH, Davis KA, Tracy JI, Bassett DS. Functional control of electrophysiological network architecture using direct neurostimulation in humans. Netw Neurosci 2019; 3:848-877. [PMID: 31410383 PMCID: PMC6663306 DOI: 10.1162/netn_a_00089] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 04/14/2019] [Indexed: 01/30/2023] Open
Abstract
Chronically implantable neurostimulation devices are becoming a clinically viable option for treating patients with neurological disease and psychiatric disorders. Neurostimulation offers the ability to probe and manipulate distributed networks of interacting brain areas in dysfunctional circuits. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. By integrating multimodal intracranial recordings and diffusion-weighted imaging from patients with drug-resistant epilepsy, we test hypothesized structural and functional rules that predict altered patterns of synchronized local field potentials. We demonstrate the ability to predictably reconfigure functional interactions depending on stimulation strength and location. Stimulation of areas with structurally weak connections largely modulates the functional hubness of downstream areas and concurrently propels the brain towards more difficult-to-reach dynamical states. By using focal perturbations to bridge large-scale structure, function, and markers of behavior, our findings suggest that stimulation may be tuned to influence different scales of network interactions driving cognition. Brain stimulation devices capable of perturbing the physiological state of neural systems are rapidly gaining popularity for their potential to treat neurological and psychiatric disease. A root problem is that underlying dysfunction spans a large-scale network of brain regions, requiring the ability to control the complex interactions between multiple brain areas. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. We demonstrate the ability to predictably reconfigure patterns of interactions between functional brain areas by modulating the strength and location of stimulation. Our findings have high significance for designing stimulation protocols capable of modulating distributed neural circuits in the human brain.
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Affiliation(s)
- Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Ari E Kahn
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia Costantini
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Youssef Ezzyat
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ethan A Solomon
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert E Gross
- Department of Neurosurgery, Emory University Hospital, Atlanta, GA, USA
| | - Barbara C Jobst
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, National Institutes of Health, Bethesda, MD, USA
| | | | - Sarah Seger
- Department of Neurosurgery, University of Texas, Southwestern Medical Center, Dallas, TX, USA
| | - Bradley C Lega
- Department of Neurosurgery, University of Texas, Southwestern Medical Center, Dallas, TX, USA
| | - Shennan Weiss
- Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Michael R Sperling
- Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Richard Gorniak
- Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Sandhitsu R Das
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Joel M Stein
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel S Rizzuto
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Timothy H Lucas
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Joseph I Tracy
- Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
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30
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Pinto MA, Rosso OA, Matias FS. Inhibitory autapse mediates anticipated synchronization between coupled neurons. Phys Rev E 2019; 99:062411. [PMID: 31330650 DOI: 10.1103/physreve.99.062411] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Indexed: 06/10/2023]
Abstract
Two identical autonomous dynamical systems unidirectionally coupled in a sender-receiver configuration can exhibit anticipated synchronization (AS) if the receiver neuron also receives a delayed negative self-feedback. Recently, AS was shown to occur in a three-neuron motif with standard chemical synapses where the delayed inhibition was provided by an interneuron. Here, we show that a two-neuron model in the presence of an inhibitory autapse, which is a massive self-innervation present in the cortical architecture, may present AS. The GABAergic autapse regulates the internal dynamics of the receiver neuron and acts as the negative delayed self-feedback required by dynamical systems in order to exhibit AS. In this biologically plausible scenario, a smooth transition from the usual delayed synchronization (DS) to AS typically occurs when the inhibitory conductance is increased. The phenomenon is shown to be robust when model parameters are varied within a physiological range. For extremely large values of the inhibitory autapse the system undergoes to a phase-drift regime in which the receiver is faster than the sender. Furthermore, we show that the inhibitory autapse promotes a faster internal dynamics of the free-running Receiver when the two neurons are uncoupled, which could be the mechanism underlying anticipated synchronization and the DS-AS transition.
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Affiliation(s)
- Marcel A Pinto
- Instituto de Física, Universidade Federal de Alagoas, Maceió, Alagoas 57072-970, Brazil
| | - Osvaldo A Rosso
- Instituto de Física, Universidade Federal de Alagoas, Maceió, Alagoas 57072-970, Brazil
- Departamento de Informática en Salud, Hospital Italiano de Buenos Aires and CONICET, C1199ABB, Ciudad Autónoma de Buenos Aires, Argentina
| | - Fernanda S Matias
- Instituto de Física, Universidade Federal de Alagoas, Maceió, Alagoas 57072-970, Brazil
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31
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Dumont G, Gutkin B. Macroscopic phase resetting-curves determine oscillatory coherence and signal transfer in inter-coupled neural circuits. PLoS Comput Biol 2019; 15:e1007019. [PMID: 31071085 PMCID: PMC6529019 DOI: 10.1371/journal.pcbi.1007019] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 05/21/2019] [Accepted: 04/10/2019] [Indexed: 01/05/2023] Open
Abstract
Macroscopic oscillations of different brain regions show multiple phase relationships that are persistent across time and have been implicated in routing information. While multiple cellular mechanisms influence the network oscillatory dynamics and structure the macroscopic firing motifs, one of the key questions is to identify the biophysical neuronal and synaptic properties that permit such motifs to arise. A second important issue is how the different neural activity coherence states determine the communication between the neural circuits. Here we analyse the emergence of phase-locking within bidirectionally delayed-coupled spiking circuits in which global gamma band oscillations arise from synaptic coupling among largely excitable neurons. We consider both the interneuronal (ING) and the pyramidal-interneuronal (PING) population gamma rhythms and the inter coupling targeting the pyramidal or the inhibitory neurons. Using a mean-field approach together with an exact reduction method, we reduce each spiking network to a low dimensional nonlinear system and derive the macroscopic phase resetting-curves (mPRCs) that determine how the phase of the global oscillation responds to incoming perturbations. This is made possible by the use of the quadratic integrate-and-fire model together with a Lorentzian distribution of the bias current. Depending on the type of gamma (PING vs. ING), we show that incoming excitatory inputs can either speed up the macroscopic oscillation (phase advance; type I PRC) or induce both a phase advance and a delay (type II PRC). From there we determine the structure of macroscopic coherence states (phase-locking) of two weakly synaptically-coupled networks. To do so we derive a phase equation for the coupled system which links the synaptic mechanisms to the coherence states of the system. We show that a synaptic transmission delay is a necessary condition for symmetry breaking, i.e. a non-symmetric phase lag between the macroscopic oscillations. This potentially provides an explanation to the experimentally observed variety of gamma phase-locking modes. Our analysis further shows that symmetry-broken coherence states can lead to a preferred direction of signal transfer between the oscillatory networks where this directionality also depends on the timing of the signal. Hence we suggest a causal theory for oscillatory modulation of functional connectivity between cortical circuits. Large scale brain oscillations emerge from synaptic interactions within neuronal circuits. Over the past years, such macroscopic rhythms have been suggested to play a crucial role in routing the flow of information across cortical regions, resulting in a functional connectome. The underlying mechanism is cortical oscillations that bind together following a well-known motif called phase-locking. While there is significant experimental support for multiple phase-locking modes in the brain, it is still unclear what is the underlying mechanism that permits macroscopic rhythms to phase lock. In the present paper we take up with this issue, and to show that, one can study the emergent macroscopic phase-locking within the mathematical framework of weakly coupled oscillators. We find that under synaptic delays, fully symmetrically coupled networks can display symmetry-broken states of activity, where one network starts to lead in phase the second (also sometimes known as stuttering states). When we analyse how incoming transient signals affect the coupled system, we find that in the symmetry-broken state, the effect depends strongly on which network is targeted (the leader or the follower) as well as the timing of the input. Hence we show how the dynamics of the emergent phase-locked activity imposes a functional directionality on how signals are processed. We thus offer clarification on the synaptic and circuit properties responsible for the emergence of multiple phase-locking patterns and provide support for its functional implication in information transfer.
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Affiliation(s)
- Grégory Dumont
- Group for Neural Theory, LNC INSERM U960, DEC, Ecole Normale Supérieure PSL* University, Paris, France
- * E-mail: (GD); (BG)
| | - Boris Gutkin
- Group for Neural Theory, LNC INSERM U960, DEC, Ecole Normale Supérieure PSL* University, Paris, France
- Center for Cognition and Decision Making, Institute for Cognitive Neuroscience, NRU Higher School of Economics, Moscow, Russia
- * E-mail: (GD); (BG)
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Nayak L, Dasgupta A, Das R, Ghosh K, De RK. Computational neuroscience and neuroinformatics: Recent progress and resources. J Biosci 2018; 43:1037-1054. [PMID: 30541962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The human brain and its temporal behavior correlated with development, structure, and function is a complex natural system even for its own kind. Coding and automation are necessary for modeling, analyzing and understanding the 86.1 +/- 8.1 +/- billion neurons, an almost equal number of non-neuronal glial cells, and the neuronal networks of the human brain comprising about 100 trillion connections. 'Computational neuroscience' which is heavily dependent on biology, physics, mathematics and computation addresses such problems while the archival, retrieval and merging of the huge amount of generated data in the form of clinical records, scientific literature, and specialized databases are carried out by 'neuroinformatics' approaches. Neuroinformatics is thus an interface between computer science and experimental neuroscience. This article provides an introduction to computational neuroscience and neuroinformatics fields along with their state-ofthe- art tools, software, and resources. Furthermore, it describes a few innovative applications of these fields in predicting and detecting brain network organization, complex brain disorder diagnosis, large-scale 3D simulation of the brain, brain- computer, and brain-to-brain interfaces. It provides an integrated overview of the fields in a non-technical way, appropriate for broad general readership. Moreover, the article is an updated unified resource of the existing knowledge and sources for researchers stepping into these fields.
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Affiliation(s)
- Losiana Nayak
- Machine Intelligence Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Kolkata 700 108, India
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Drebitz E, Haag M, Grothe I, Mandon S, Kreiter AK. Attention Configures Synchronization Within Local Neuronal Networks for Processing of the Behaviorally Relevant Stimulus. Front Neural Circuits 2018; 12:71. [PMID: 30210309 PMCID: PMC6123385 DOI: 10.3389/fncir.2018.00071] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 08/09/2018] [Indexed: 11/13/2022] Open
Abstract
The need for fast and dynamic processing of relevant information imposes high demands onto the flexibility and efficiency of the nervous system. A good example for such flexibility is the attention-dependent selection of relevant sensory information. Studies investigating attentional modulations of neuronal responses to simultaneously arriving input showed that neurons respond, as if only the attended stimulus would be present within their receptive fields (RF). However, attention also improves neuronal representation and behavioral performance, when only one stimulus is present. Thus, attention serves for selecting relevant input and changes the neuronal processing of signals representing selected stimuli, ultimately leading to a more efficient behavioral performance. Here, we tested the hypothesis that attention configures the strength of functional coupling between a local neuronal network's neurons specifically for effective processing of signals representing attended stimuli. This coupling is measured as the strength of γ-synchronization between these neurons. The hypothesis predicts that the pattern of synchronization in local networks should depend on which stimulus is attended. Furthermore, we expect this pattern to be similar for the attended stimulus presented alone or together with irrelevant stimuli in the RF. To test these predictions, we recorded spiking-activity and local field potentials (LFP) with closely spaced electrodes in area V4 of monkeys performing a demanding attention task. Our results show that the γ-band phase coherence (γ-PhC) between spiking-activity and the LFP, as well as the spiking-activity of two groups of neurons, strongly depended on which of the two stimuli in the RF was attended. The γ-PhC was almost identical for the attended stimulus presented either alone or together with a distractor. The functional relevance of dynamic γ-band synchronization is further supported by the observation of strongly degraded γ-PhC before behavioral errors, while firing rates were barely affected. These qualitatively different results point toward a failure of attention-dependent top-down mechanisms to correctly synchronize the local neuronal network in V4, even though this network receives the correctly selected input. These findings support the idea of a flexible, demand-dependent dynamic configuration of local neuronal networks, for performing different functions, even on the same sensory input.
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Affiliation(s)
- Eric Drebitz
- Center for Cognitive Science, Brain Research Institute, University of Bremen, Bremen, Germany
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35
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Gilson M. Analysis of fMRI data using noise-diffusion network models: a new covariance-coding perspective. BIOLOGICAL CYBERNETICS 2018; 112:153-161. [PMID: 29204807 DOI: 10.1007/s00422-017-0741-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 11/21/2017] [Indexed: 06/07/2023]
Abstract
Since the middle of the 1990s, studies of resting-state fMRI/BOLD data have explored the correlation patterns of activity across the whole brain, which is referred to as functional connectivity (FC). Among the many methods that have been developed to interpret FC, a recently proposed model-based approach describes the propagation of fluctuating BOLD activity within the recurrently connected brain network by inferring the effective connectivity (EC). In this model, EC quantifies the strengths of directional interactions between brain regions, viewed from the proxy of BOLD activity. In addition, the tuning procedure for the model provides estimates for the local variability (input variances) to explain how the observed FC is generated. Generalizing, the network dynamics can be studied in the context of an input-output mapping-determined by EC-for the second-order statistics of fluctuating nodal activities. The present paper focuses on the following detection paradigm: observing output covariances, how discriminative is the (estimated) network model with respect to various input covariance patterns? An application with the model fitted to experimental fMRI data-movie viewing versus resting state-illustrates that changes in local variability and changes in brain coordination go hand in hand.
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36
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The temporal dynamics involved in object representation updating to predict change. PROGRESS IN BRAIN RESEARCH 2017; 236:269-285. [PMID: 29157416 DOI: 10.1016/bs.pbr.2017.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2023]
Abstract
The synchronization of cortically disparate neural assemblies at frequencies in the gamma-band range (30-70Hz) is considered to be involved in the perceptual organization of the environment. In support of this Elliott (2014) demonstrated improved detection of a target stimulus when this target was primed in a matrix that flickered at specific frequencies in the gamma-band range, each found to be separated by regular intervals which correspond with a 6.69Hz period. This can be explained in terms of the interaction of the stimulus (and stimulus-induced) rhythm with a slow endogenous theta rhythm. When the interaction is in phase between these rhythms and target presentation time is slightly ahead of the priming stimulus presentation, improved detection times are recorded indicating an anticipatory response. However, when these rhythms are out of phase and the target is presented during or slightly after priming stimulus presentation, improved responding also occurs, suggesting a retroactive response is facilitated. Research in the auditory domain supports these findings (Aksentijevic et al., 2011). The conclusions of this research suggest that synchronization of neural assemblies contributes to the temporal code necessary to facilitate representation updating in order to respond to a dynamic environment and anticipate the logical next event.
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37
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Weber I, Florin E, von Papen M, Timmermann L. The influence of filtering and downsampling on the estimation of transfer entropy. PLoS One 2017; 12:e0188210. [PMID: 29149201 PMCID: PMC5693301 DOI: 10.1371/journal.pone.0188210] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2017] [Accepted: 11/02/2017] [Indexed: 01/24/2023] Open
Abstract
Transfer entropy (TE) provides a generalized and model-free framework to study Wiener-Granger causality between brain regions. Because of its nonparametric character, TE can infer directed information flow also from nonlinear systems. Despite its increasing number of applications in neuroscience, not much is known regarding the influence of common electrophysiological preprocessing on its estimation. We test the influence of filtering and downsampling on a recently proposed nearest neighborhood based TE estimator. Different filter settings and downsampling factors were tested in a simulation framework using a model with a linear coupling function and two nonlinear models with sigmoid and logistic coupling functions. For nonlinear coupling and progressively lower low-pass filter cut-off frequencies up to 72% false negative direct connections and up to 26% false positive connections were identified. In contrast, for the linear model, a monotonic increase was only observed for missed indirect connections (up to 86%). High-pass filtering (1 Hz, 2 Hz) had no impact on TE estimation. After low-pass filtering interaction delays were significantly underestimated. Downsampling the data by a factor greater than the assumed interaction delay erased most of the transmitted information and thus led to a very high percentage (67–100%) of false negative direct connections. Low-pass filtering increases the number of missed connections depending on the filters cut-off frequency. Downsampling should only be done if the sampling factor is smaller than the smallest assumed interaction delay of the analyzed network.
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Affiliation(s)
- Immo Weber
- Department of Neurology, University Hospital Giessen & Marburg, Marburg, Germany
- * E-mail: (IW); (LT)
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany
| | - Michael von Papen
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany
- Institute of Geophysics & Meteorology, University of Cologne, Cologne, Germany
| | - Lars Timmermann
- Department of Neurology, University Hospital Giessen & Marburg, Marburg, Germany
- * E-mail: (IW); (LT)
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38
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Fasoli D, Cattani A, Panzeri S. Transitions between asynchronous and synchronous states: a theory of correlations in small neural circuits. J Comput Neurosci 2017; 44:25-43. [PMID: 29124505 PMCID: PMC5770155 DOI: 10.1007/s10827-017-0667-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 09/04/2017] [Accepted: 10/10/2017] [Indexed: 12/11/2022]
Abstract
The study of correlations in neural circuits of different size, from the small size of cortical microcolumns to the large-scale organization of distributed networks studied with functional imaging, is a topic of central importance to systems neuroscience. However, a theory that explains how the parameters of mesoscopic networks composed of a few tens of neurons affect the underlying correlation structure is still missing. Here we consider a theory that can be applied to networks of arbitrary size with multiple populations of homogeneous fully-connected neurons, and we focus its analysis to a case of two populations of small size. We combine the analysis of local bifurcations of the dynamics of these networks with the analytical calculation of their cross-correlations. We study the correlation structure in different regimes, showing that a variation of the external stimuli causes the network to switch from asynchronous states, characterized by weak correlation and low variability, to synchronous states characterized by strong correlations and wide temporal fluctuations. We show that asynchronous states are generated by strong stimuli, while synchronous states occur through critical slowing down when the stimulus moves the network close to a local bifurcation. In particular, strongly positive correlations occur at the saddle-node and Andronov-Hopf bifurcations of the network, while strongly negative correlations occur when the network undergoes a spontaneous symmetry-breaking at the branching-point bifurcations. These results show how the correlation structure of firing-rate network models is strongly modulated by the external stimuli, even keeping the anatomical connections fixed. These results also suggest an effective mechanism through which biological networks may dynamically modulate the encoding and integration of sensory information.
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Affiliation(s)
- Diego Fasoli
- Laboratory of Neural Computation, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, 38068, Rovereto, Italy.
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, 08002, Barcelona, Spain.
| | - Anna Cattani
- Laboratory of Neural Computation, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, 38068, Rovereto, Italy
- Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy
| | - Stefano Panzeri
- Laboratory of Neural Computation, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, 38068, Rovereto, Italy
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39
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Gilson M, Deco G, Friston KJ, Hagmann P, Mantini D, Betti V, Romani GL, Corbetta M. Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions. Neuroimage 2017; 180:534-546. [PMID: 29024792 DOI: 10.1016/j.neuroimage.2017.09.061] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 08/25/2017] [Accepted: 09/28/2017] [Indexed: 01/20/2023] Open
Abstract
Our behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. However, the neural mechanisms governing the entrainment of functionally specialized brain areas remain poorly understood. In particular, the question arises whether observed changes in the regional activity for different cognitive conditions are explained by modifications of the inputs to the brain or its connectivity? We observe that transitions of fMRI activity between areas convey information about the tasks performed by 19 subjects, watching a movie versus a black screen (rest). We use a model-based framework that explains this spatiotemporal functional connectivity pattern by the local variability for 66 cortical regions and the network effective connectivity between them. We find that, among the estimated model parameters, movie viewing affects to a larger extent the local activity, which we interpret as extrinsic changes related to the increased stimulus load. However, detailed changes in the effective connectivity preserve a balance in the propagating activity and select specific pathways such that high-level brain regions integrate visual and auditory information, in particular boosting the communication between the two brain hemispheres. These findings speak to a dynamic coordination underlying the functional integration in the brain.
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Affiliation(s)
- Matthieu Gilson
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain.
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain; Institució Catalana de la Recerca i Estudis Avanats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona, 08010, Spain
| | - Karl J Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3BG, United Kingdom
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Rue du Bugnon 46, 1011, Lausanne, Switzerland; Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne (EPFL), Station 11, 1015, Lausanne, Switzerland
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 101 Tervuursevest, 3001, Leuven, Belgium; Department of Health Sciences and Technology, ETH Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland; Department of Experimental Psychology, Oxford University, 15 Parks Road, Oxford, OX1 3PH, United Kingdom
| | - Viviana Betti
- Department of Psychology, University of Rome La Sapienza, 00185, Rome, Italy; Fondazione Santa Lucia, Istituto Di Ricovero e Cura a Carattere Scientifico, 00142, Rome, Italy
| | - Gian Luca Romani
- Institute of Advanced Biomedical Technologies - G. dAnnunzio University Foundation, Department of Neuroscience Imaging and Clinical Science, G. dAnnunzio University, Via dei Vestini 31, Chieti, 66013, Italy
| | - Maurizio Corbetta
- Departments of Neurology, Radiology, Anatomy of Neurobiology, School of Medicine, Washington University, St. Louis, St Louis, USA
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40
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Lowet E, Roberts MJ, Peter A, Gips B, De Weerd P. A quantitative theory of gamma synchronization in macaque V1. eLife 2017; 6:26642. [PMID: 28857743 PMCID: PMC5779232 DOI: 10.7554/elife.26642] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 08/21/2017] [Indexed: 12/13/2022] Open
Abstract
Gamma-band synchronization coordinates brief periods of excitability in oscillating neuronal populations to optimize information transmission during sensation and cognition. Commonly, a stable, shared frequency over time is considered a condition for functional neural synchronization. Here, we demonstrate the opposite: instantaneous frequency modulations are critical to regulate phase relations and synchronization. In monkey visual area V1, nearby local populations driven by different visual stimulation showed different gamma frequencies. When similar enough, these frequencies continually attracted and repulsed each other, which enabled preferred phase relations to be maintained in periods of minimized frequency difference. Crucially, the precise dynamics of frequencies and phases across a wide range of stimulus conditions was predicted from a physics theory that describes how weakly coupled oscillators influence each other's phase relations. Hence, the fundamental mathematical principle of synchronization through instantaneous frequency modulations applies to gamma in V1 and is likely generalizable to other brain regions and rhythms.
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Affiliation(s)
- Eric Lowet
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Mark J Roberts
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Alina Peter
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
| | - Bart Gips
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Peter De Weerd
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands.,Maastricht Centre for Systems Biology, Maastricht University, Maastricht, Netherlands
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41
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Nakajima T, Arisawa H, Hosaka R, Mushiake H. Intended arm use influences interhemispheric correlation of β-oscillations in primate medial motor areas. J Neurophysiol 2017; 118:2865-2883. [PMID: 28855290 DOI: 10.1152/jn.00379.2016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Revised: 07/19/2017] [Accepted: 08/25/2017] [Indexed: 11/22/2022] Open
Abstract
To investigate the role of interhemispheric β-synchronization in the selection of motor effectors, we trained two monkeys to memorize and perform multiple two-movement sequences that included unimanual repetition and bimanual switching. We recorded local field potentials simultaneously in the bilateral supplementary motor area (SMA) and pre-SMA to examine how the β-power in both hemispheres and the interhemispheric relationship of β-oscillations depend on the prepared sequence of arm use. We found a significant ipsilateral enhancement of β-power for bimanual switching trials in the left hemisphere and an enhancement of β-power in the right SMA while preparing for unimanual repetition. Furthermore, interhemispheric synchrony in the SMA was significantly more enhanced while preparing unimanual repetition than while preparing bimanual switching. This enhancement of synchrony was detected in terms of β-phase but not in terms of modulation of β-power. Furthermore, the assessment of the interhemispheric phase difference revealed that the β-oscillation in the hemisphere contralateral to the instructed arm use significantly advanced its phase relative to that in the ipsilateral hemisphere. There was no arm use-dependent shift in phase difference in the pairwise recordings within each hemisphere. Both neurons with and without arm use-selective activity were phase-locked to the β-oscillation. These results imply that the degree of interhemispheric phase synchronization as well as phase differences and oscillatory power in the β-band may contribute to the selection of arm use depending on the behavioral conditions of sequential arm use.NEW & NOTEWORTHY We addressed interhemispheric relationships of β-oscillations during bimanual coordination. While monkeys prepared to initiate movement of the instructed arm, β-oscillations in the contralateral hemisphere showed a phase advance relative to the other hemisphere. Furthermore, the sequence of arm use influenced β-power and the degree of interhemispheric phase synchronization. Thus the dynamics of interhemispheric phases and power in β-oscillations may contribute to the specification of motor effectors in a given behavioral context.
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Affiliation(s)
- Toshi Nakajima
- Department of Physiology, Tohoku University School of Medicine, Sendai, Japan
| | - Haruka Arisawa
- Department of Physiology, Tohoku University School of Medicine, Sendai, Japan
| | - Ryosuke Hosaka
- Department of Applied Mathematics, Fukuoka University, Fukuoka, Japan; and.,Laboratory for Dynamics of Emergent Intelligence, RIKEN Brain Science Institute, Wako, Japan
| | - Hajime Mushiake
- Department of Physiology, Tohoku University School of Medicine, Sendai, Japan; .,Department of Applied Mathematics, Fukuoka University, Fukuoka, Japan; and
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42
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Demuru M, Gouw AA, Hillebrand A, Stam CJ, van Dijk BW, Scheltens P, Tijms BM, Konijnenberg E, Ten Kate M, den Braber A, Smit DJA, Boomsma DI, Visser PJ. Functional and effective whole brain connectivity using magnetoencephalography to identify monozygotic twin pairs. Sci Rep 2017; 7:9685. [PMID: 28852152 PMCID: PMC5575140 DOI: 10.1038/s41598-017-10235-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 08/01/2017] [Indexed: 01/08/2023] Open
Abstract
Resting-state functional connectivity patterns are highly stable over time within subjects. This suggests that such 'functional fingerprints' may have strong genetic component. We investigated whether the functional (FC) or effective (EC) connectivity patterns of one monozygotic twin could be used to identify the co-twin among a larger sample and determined the overlap in functional fingerprints within monozygotic (MZ) twin pairs using resting state magnetoencephalography (MEG). We included 32 cognitively normal MZ twin pairs from the Netherlands Twin Register who participate in the EMIF-AD preclinAD study (average age 68 years). Combining EC information across multiple frequency bands we obtained an identification rate over 75%. Since MZ twin pairs are genetically identical these results suggest a high genetic contribution to MEG-based EC patterns, leading to large similarities in brain connectivity patterns between two individuals even after 60 years of life or more.
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Affiliation(s)
- M Demuru
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
| | - A A Gouw
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and Magnetoencephalography Center, VU University Medical Center, Amsterdam, The Netherlands
| | - A Hillebrand
- Department of Clinical Neurophysiology and Magnetoencephalography Center, VU University Medical Center, Amsterdam, The Netherlands
| | - C J Stam
- Department of Clinical Neurophysiology and Magnetoencephalography Center, VU University Medical Center, Amsterdam, The Netherlands
| | - B W van Dijk
- Department of Clinical Neurophysiology and Magnetoencephalography Center, VU University Medical Center, Amsterdam, The Netherlands
| | - P Scheltens
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - B M Tijms
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - E Konijnenberg
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - M Ten Kate
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - A den Braber
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - D J A Smit
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
- Department of Psychiatry, Academic Medical Center, Amsterdam, The Netherlands
| | - D I Boomsma
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - P J Visser
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
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43
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Märtens M, Meier J, Hillebrand A, Tewarie P, Van Mieghem P. Brain network clustering with information flow motifs. APPLIED NETWORK SCIENCE 2017; 2:25. [PMID: 30443580 PMCID: PMC6214277 DOI: 10.1007/s41109-017-0046-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 07/26/2017] [Indexed: 06/09/2023]
Abstract
Recent work has revealed frequency-dependent global patterns of information flow by a network analysis of magnetoencephalography data of the human brain. However, it is unknown which properties on a small subgraph-scale of those functional brain networks are dominant at different frequencies bands. Motifs are the building blocks of networks on this level and have previously been identified as important features for healthy and abnormal brain function. In this study, we present a network construction that enables us to search and analyze motifs in different frequency bands. We give evidence that the bi-directional two-hop path is the most important motif for the information flow in functional brain networks. A clustering based on this motif exposes a spatially coherent yet frequency-dependent sub-division between the posterior, occipital and frontal brain regions.
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Affiliation(s)
- Marcus Märtens
- Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, P.O Box 5031, Delft, The Netherlands
| | - Jil Meier
- Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, P.O Box 5031, Delft, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and Magnetoencephalography Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Piet Van Mieghem
- Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, P.O Box 5031, Delft, The Netherlands
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44
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Dynamic Reconfiguration of Visuomotor-Related Functional Connectivity Networks. J Neurosci 2017; 37:839-853. [PMID: 28123020 DOI: 10.1523/jneurosci.1672-16.2016] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Revised: 11/03/2016] [Accepted: 11/12/2016] [Indexed: 11/21/2022] Open
Abstract
Cognitive functions arise from the coordination of large-scale brain networks. However, the principles governing interareal functional connectivity dynamics (FCD) remain elusive. Here, we tested the hypothesis that human executive functions arise from the dynamic interplay of multiple networks. To do so, we investigated FCD mediating a key executing function, known as arbitrary visuomotor mapping, using brain connectivity analyses of high-gamma activity recorded using MEG and intracranial EEG. Visuomotor mapping was found to arise from the dynamic interplay of three partly overlapping cortico-cortical and cortico-subcortical functional connectivity (FC) networks. First, visual and parietal regions coordinated with sensorimotor and premotor areas. Second, the dorsal frontoparietal circuit together with the sensorimotor and associative frontostriatal networks took the lead. Finally, cortico-cortical interhemispheric coordination among bilateral sensorimotor regions coupled with the left frontoparietal network and visual areas. We suggest that these networks reflect the processing of visual information, the emergence of visuomotor plans, and the processing of somatosensory reafference or action's outcomes, respectively. We thus demonstrated that visuomotor integration resides in the dynamic reconfiguration of multiple cortico-cortical and cortico-subcortical FC networks. More generally, we showed that visuomotor-related FC is nonstationary and displays switching dynamics and areal flexibility over timescales relevant for task performance. In addition, visuomotor-related FC is characterized by sparse connectivity with density <10%. To conclude, our results elucidate the relation between dynamic network reconfiguration and executive functions over short timescales and provide a candidate entry point toward a better understanding of cognitive architectures. SIGNIFICANCE STATEMENT Executive functions are supported by the dynamic coordination of neural activity over large-scale networks. The properties of large-scale brain coordination processes, however, remain unclear. Using tools combining MEG and intracranial EEG with brain connectivity analyses, we provide evidence that visuomotor behaviors, a hallmark of executive functions, are mediated by the interplay of multiple and spatially overlapping subnetworks. These subnetworks span visuomotor-related areas, the cortico-cortical and cortico-subcortical interactions of which evolve rapidly and reconfigure over timescales relevant for behavior. Visuomotor-related functional connectivity dynamics are characterized by sparse connections, nonstationarity, switching dynamics, and areal flexibility. We suggest that these properties represent key aspects of large-scale functional networks and cognitive architectures.
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45
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Cestnik R, Rosenblum M. Reconstructing networks of pulse-coupled oscillators from spike trains. Phys Rev E 2017; 96:012209. [PMID: 29347231 DOI: 10.1103/physreve.96.012209] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Indexed: 11/07/2022]
Abstract
We present an approach for reconstructing networks of pulse-coupled neuronlike oscillators from passive observation of pulse trains of all nodes. It is assumed that units are described by their phase response curves and that their phases are instantaneously reset by incoming pulses. Using an iterative procedure, we recover the properties of all nodes, namely their phase response curves and natural frequencies, as well as strengths of all directed connections.
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Affiliation(s)
- Rok Cestnik
- Department of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Strasse 24/25, D-14476 Potsdam-Golm, Germany.,Department of Human Movement Sciences, MOVE Research Institute Amsterdam, Vrije Universiteit Amsterdam, van der Boechorststraat 9, Amsterdam, Netherlands
| | - Michael Rosenblum
- Department of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Strasse 24/25, D-14476 Potsdam-Golm, Germany.,The Research Institute of Supercomputing, Lobachevsky National Research State University of Nizhny Novgorod, Russia
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46
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Kirst C, Modes CD, Magnasco MO. Shifting attention to dynamics: Self-reconfiguration of neural networks. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.04.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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47
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Palmigiano A, Geisel T, Wolf F, Battaglia D. Flexible information routing by transient synchrony. Nat Neurosci 2017; 20:1014-1022. [PMID: 28530664 DOI: 10.1038/nn.4569] [Citation(s) in RCA: 158] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Accepted: 04/25/2017] [Indexed: 12/20/2022]
Abstract
Perception, cognition and behavior rely on flexible communication between microcircuits in distinct cortical regions. The mechanisms underlying rapid information rerouting between such microcircuits are still unknown. It has been proposed that changing patterns of coherence between local gamma rhythms support flexible information rerouting. The stochastic and transient nature of gamma oscillations in vivo, however, is hard to reconcile with such a function. Here we show that models of cortical circuits near the onset of oscillatory synchrony selectively route input signals despite the short duration of gamma bursts and the irregularity of neuronal firing. In canonical multiarea circuits, we find that gamma bursts spontaneously arise with matched timing and frequency and that they organize information flow by large-scale routing states. Specific self-organized routing states can be induced by minor modulations of background activity.
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Affiliation(s)
- Agostina Palmigiano
- Max Planck Institute for Dynamics and Self-organization, Göttingen, Germany.,Bernstein Center for Computational Neuroscience, Göttingen, Germany.,Institute for Nonlinear Dynamics, Georg-August University School of Science, Göttingen, Germany.,SFB-889 Cellular Mechanisms of Sensory Processing, Göttingen, Germany
| | - Theo Geisel
- Max Planck Institute for Dynamics and Self-organization, Göttingen, Germany.,Bernstein Center for Computational Neuroscience, Göttingen, Germany.,Institute for Nonlinear Dynamics, Georg-August University School of Science, Göttingen, Germany
| | - Fred Wolf
- Max Planck Institute for Dynamics and Self-organization, Göttingen, Germany.,Bernstein Center for Computational Neuroscience, Göttingen, Germany.,Institute for Nonlinear Dynamics, Georg-August University School of Science, Göttingen, Germany.,SFB-889 Cellular Mechanisms of Sensory Processing, Göttingen, Germany
| | - Demian Battaglia
- Bernstein Center for Computational Neuroscience, Göttingen, Germany.,Aix-Marseille Université, Inserm, Institut de Neurosciences des Systèmes, Marseille, France
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48
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Kapucu FE, Mikkonen JE, Tanskanen JMA, Hyttinen JAK. Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:1595-1598. [PMID: 28268633 DOI: 10.1109/embc.2016.7591017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this paper, we study neuronal network analysis based on microelectrode measurements. We search for potential relations between time correlated changes in spectral distributions and synchrony for neuronal network activity. Spectral distribution is quantified by spectral entropy as a measure of uniformity/complexity and this measure is calculated as a function of time for the recorded neuronal signals, i.e., time variant spectral entropy. Time variant correlations in the spectral distributions between different parts of a neuronal network, i.e., of concurrent measurements via different microelectrodes, are calculated to express the relation with a single scalar. We demonstrate these relations with in vivo rat hippocampal recordings, and observe the time courses of the correlations between different regions of hippocampus in three sequential recordings. Additionally, we evaluate the results with a commonly employed causality analysis method to assess the possible correlated findings. Results show that time correlated spectral entropy reveals different levels of interrelations in neuronal networks, which can be interpreted as different levels of neuronal network synchrony.
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Ter Wal M, Tiesinga PH. Phase Difference between Model Cortical Areas Determines Level of Information Transfer. Front Comput Neurosci 2017; 11:6. [PMID: 28232796 PMCID: PMC5298997 DOI: 10.3389/fncom.2017.00006] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Accepted: 01/24/2017] [Indexed: 11/23/2022] Open
Abstract
Communication between cortical sites is mediated by long-range synaptic connections. However, these connections are relatively static, while everyday cognitive tasks demand a fast and flexible routing of information in the brain. Synchronization of activity between distant cortical sites has been proposed as the mechanism underlying such a dynamic communication structure. Here, we study how oscillatory activity affects the excitability and input-output relation of local cortical circuits and how it alters the transmission of information between cortical circuits. To this end, we develop model circuits showing fast oscillations by the PING mechanism, of which the oscillatory characteristics can be altered. We identify conditions for synchronization between two brain circuits and show that the level of intercircuit coherence and the phase difference is set by the frequency difference between the intrinsic oscillations. We show that the susceptibility of the circuits to inputs, i.e., the degree of change in circuit output following input pulses, is not uniform throughout the oscillation period and that both firing rate, frequency and power are differentially modulated by inputs arriving at different phases. As a result, an appropriate phase difference between the circuits is critical for the susceptibility windows of the circuits in the network to align and for information to be efficiently transferred. We demonstrate that changes in synchrony and phase difference can be used to set up or abolish information transfer in a network of cortical circuits.
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Affiliation(s)
- Marije Ter Wal
- Department of Neuroinformatics, Donders Institute, Radboud University Nijmegen, Netherlands
| | - Paul H Tiesinga
- Department of Neuroinformatics, Donders Institute, Radboud University Nijmegen, Netherlands
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Cannon J. Analytical Calculation of Mutual Information between Weakly Coupled Poisson-Spiking Neurons in Models of Dynamically Gated Communication. Neural Comput 2016; 29:118-145. [PMID: 27870617 DOI: 10.1162/neco_a_00915] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Mutual information is a commonly used measure of communication between neurons, but little theory exists describing the relationship between mutual information and the parameters of the underlying neuronal interaction. Such a theory could help us understand how specific physiological changes affect the capacity of neurons to synaptically communicate, and, in particular, they could help us characterize the mechanisms by which neuronal dynamics gate the flow of information in the brain. Here we study a pair of linear-nonlinear-Poisson neurons coupled by a weak synapse. We derive an analytical expression describing the mutual information between their spike trains in terms of synapse strength, neuronal activation function, the time course of postsynaptic currents, and the time course of the background input received by the two neurons. This expression allows mutual information calculations that would otherwise be computationally intractable. We use this expression to analytically explore the interaction of excitation, information transmission, and the convexity of the activation function. Then, using this expression to quantify mutual information in simulations, we illustrate the information-gating effects of neural oscillations and oscillatory coherence, which may either increase or decrease the mutual information across the synapse depending on parameters. Finally, we show analytically that our results can quantitatively describe the selection of one information pathway over another when multiple sending neurons project weakly to a single receiving neuron.
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Affiliation(s)
- Jonathan Cannon
- Department of Biology, Brandeis University, Waltham, MA 02453, U.S.A.
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