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John YJ, Sawyer KS, Srinivasan K, Müller EJ, Munn BR, Shine JM. It's about time: Linking dynamical systems with human neuroimaging to understand the brain. Netw Neurosci 2022; 6:960-979. [PMID: 36875012 PMCID: PMC9976648 DOI: 10.1162/netn_a_00230] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 01/04/2022] [Indexed: 11/04/2022] Open
Abstract
Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local, and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain's time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective, by focusing on dynamics instead of static snapshots of neural activity, and by embracing modeling approaches that map neural dynamics using "forward" models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology.
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Affiliation(s)
- Yohan J. John
- Neural Systems Laboratory, Department of Health Sciences, Boston University, Boston, MA, USA
| | - Kayle S. Sawyer
- Departments of Anatomy and Neurobiology, Boston University, Boston University, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Boston VA Healthcare System, Boston, MA, USA
- Sawyer Scientific, LLC, Boston, MA, USA
| | - Karthik Srinivasan
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Eli J. Müller
- Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
| | - Brandon R. Munn
- Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
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2
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Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics. Proc Natl Acad Sci U S A 2022; 119:e2201128119. [PMID: 35881787 PMCID: PMC9351497 DOI: 10.1073/pnas.2201128119] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Electrophysiological source imaging (ESI) is an indispensable tool for noninvasively studying brain function and dysfunction. The moderate to low spatial resolution of electroencephalography/magnetoencephalography recordings has been a major hindrance to their wide utility in the field. Although this challenge is mitigated significantly by ESI techniques, it is difficult for individuals without relevant training to select and optimize hyperparameters for these ESI solvers. We propose a deep learning–based source imaging methodology that incorporates current advances in biophysical computational models and neural networks into the ESI framework, and it requires minimal user intervention after the model is trained. Our work promises to enable precise and robust high-resolution spatiotemporal functional brain imaging for a variety of neuroscience research studies and clinical applications. Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as well as aiding the management of brain disorders. Here, we propose a non-conventional deep learning–based source imaging framework (DeepSIF) that provides robust and precise spatiotemporal estimates of underlying brain dynamics from noninvasive high-density electroencephalography (EEG) recordings. DeepSIF employs synthetic training data generated by biophysical models capable of modeling mesoscale brain dynamics. The rich characteristics of underlying brain sources are embedded in the realistic training data and implicitly learned by DeepSIF networks, avoiding complications associated with explicitly formulating and tuning priors in an optimization problem, as often is the case in conventional source imaging approaches. The performance of DeepSIF is evaluated by 1) a series of numerical experiments, 2) imaging sensory and cognitive brain responses in a total of 20 healthy subjects from three public datasets, and 3) rigorously validating DeepSIF’s capability in identifying epileptogenic regions in a cohort of 20 drug-resistant epilepsy patients by comparing DeepSIF results with invasive measurements and surgical resection outcomes. DeepSIF demonstrates robust and excellent performance, producing results that are concordant with common neuroscience knowledge about sensory and cognitive information processing as well as clinical findings about the location and extent of the epileptogenic tissue and outperforming conventional source imaging methods. The DeepSIF method, as a data-driven imaging framework, enables efficient and effective high-resolution functional imaging of spatiotemporal brain dynamics, suggesting its wide applicability and value to neuroscience research and clinical applications.
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Singh MF, Cole MW, Braver TS, Ching S. Developing control-theoretic objectives for large-scale brain dynamics and cognitive enhancement. ANNUAL REVIEWS IN CONTROL 2022; 54:363-376. [PMID: 38250171 PMCID: PMC10798814 DOI: 10.1016/j.arcontrol.2022.05.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
The development of technologies for brain stimulation provides a means for scientists and clinicians to directly actuate the brain and nervous system. Brain stimulation has shown intriguing potential in terms of modifying particular symptom clusters in patients and behavioral characteristics of subjects. The stage is thus set for optimization of these techniques and the pursuit of more nuanced stimulation objectives, including the modification of complex cognitive functions such as memory and attention. Control theory and engineering will play a key role in the development of these methods, guiding computational and algorithmic strategies for stimulation. In particular, realizing this goal will require new development of frameworks that allow for controlling not only brain activity, but also latent dynamics that underlie neural computation and information processing. In the current opinion, we review recent progress in brain stimulation and outline challenges and potential research pathways associated with exogenous control of cognitive function.
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Affiliation(s)
- Matthew F Singh
- Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, 63130, MO, USA
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, 07102, NJ, USA
- Psychological and Brain Science, Washington University in St. Louis, St. Louis, 63130, MO, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, 07102, NJ, USA
| | - Todd S Braver
- Psychological and Brain Science, Washington University in St. Louis, St. Louis, 63130, MO, USA
| | - ShiNung Ching
- Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, 63130, MO, USA
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4
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Triebkorn P, Stefanovski L, Dhindsa K, Diaz‐Cortes M, Bey P, Bülau K, Pai R, Spiegler A, Solodkin A, Jirsa V, McIntosh AR, Ritter P. Brain simulation augments machine-learning-based classification of dementia. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2022; 8:e12303. [PMID: 35601598 PMCID: PMC9107774 DOI: 10.1002/trc2.12303] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 02/20/2022] [Accepted: 04/15/2022] [Indexed: 01/24/2023]
Abstract
Introduction Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning (ML) and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer's disease (AD). Methods We enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local amyloid beta (Aβ) positron emission tomography (PET) with altered excitability. We use PET and magnetic resonance imaging (MRI) data from 33 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for ML classification. Results The combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the AD-typical spatial distribution. Discussion The cause-and-effect implementation of local hyperexcitation caused by Aβ can improve the ML-driven classification of AD and demonstrates TVB's ability to decode information in empirical data using connectivity-based brain simulation.
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Affiliation(s)
- Paul Triebkorn
- Berlin Institute of Health at Charité – Universitätsmedizin BerlinBerlinGermany
- Department of Neurology with Experimental NeurologyBrain Simulation Section, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
- Institut de Neurosciences des SystèmesAix Marseille UniversitéMarseilleFrance
| | - Leon Stefanovski
- Berlin Institute of Health at Charité – Universitätsmedizin BerlinBerlinGermany
- Department of Neurology with Experimental NeurologyBrain Simulation Section, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
| | - Kiret Dhindsa
- Berlin Institute of Health at Charité – Universitätsmedizin BerlinBerlinGermany
- Department of Neurology with Experimental NeurologyBrain Simulation Section, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
| | - Margarita‐Arimatea Diaz‐Cortes
- Berlin Institute of Health at Charité – Universitätsmedizin BerlinBerlinGermany
- Department of Neurology with Experimental NeurologyBrain Simulation Section, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
| | - Patrik Bey
- Berlin Institute of Health at Charité – Universitätsmedizin BerlinBerlinGermany
- Department of Neurology with Experimental NeurologyBrain Simulation Section, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
| | - Konstantin Bülau
- Berlin Institute of Health at Charité – Universitätsmedizin BerlinBerlinGermany
- Department of Neurology with Experimental NeurologyBrain Simulation Section, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
| | - Roopa Pai
- Berlin Institute of Health at Charité – Universitätsmedizin BerlinBerlinGermany
- Department of Neurology with Experimental NeurologyBrain Simulation Section, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
| | - Andreas Spiegler
- Berlin Institute of Health at Charité – Universitätsmedizin BerlinBerlinGermany
- Department of Neurology with Experimental NeurologyBrain Simulation Section, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
- Department of Neurophysiology and PathophysiologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Ana Solodkin
- Neuroscience, Behavioral and Brain Sciences, UT Dallas RichardsonDallasTexasUSA
| | - Viktor Jirsa
- Institut de Neurosciences des SystèmesAix Marseille UniversitéMarseilleFrance
| | | | - Petra Ritter
- Berlin Institute of Health at Charité – Universitätsmedizin BerlinBerlinGermany
- Department of Neurology with Experimental NeurologyBrain Simulation Section, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
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5
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Valle JAM, Madureira AL. Parameter Identification Problem in the Hodgkin-Huxley Model. Neural Comput 2022; 34:939-970. [PMID: 35231934 DOI: 10.1162/neco_a_01487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 11/16/2021] [Indexed: 11/04/2022]
Abstract
The Hodgkin-Huxley (H-H) landmark model is described by a system of four nonlinear differential equations that describes how action potentials in neurons are initiated and propagated. However, obtaining some of the parameters of the model requires a tedious combination of experiments and data tuning. In this letter, we propose the use of a minimal error iteration method to estimate some of the parameters in the H-H model, given the measurements of membrane potential. We provide numerical results showing that the approach approximates well some of the model's parameters, using the measured voltage as data, even in the presence of noise.
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6
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Stefanovski L, Meier JM, Pai RK, Triebkorn P, Lett T, Martin L, Bülau K, Hofmann-Apitius M, Solodkin A, McIntosh AR, Ritter P. Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain. Front Neuroinform 2021; 15:630172. [PMID: 33867964 PMCID: PMC8047422 DOI: 10.3389/fninf.2021.630172] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 03/08/2021] [Indexed: 12/18/2022] Open
Abstract
Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.
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Affiliation(s)
- Leon Stefanovski
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Jil Mona Meier
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Roopa Kalsank Pai
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Paul Triebkorn
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Tristram Lett
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Leon Martin
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Konstantin Bülau
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany
| | - Ana Solodkin
- Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, United States
| | | | - Petra Ritter
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
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7
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Aqil M, Atasoy S, Kringelbach ML, Hindriks R. Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome. PLoS Comput Biol 2021; 17:e1008310. [PMID: 33507899 PMCID: PMC7872285 DOI: 10.1371/journal.pcbi.1008310] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 02/09/2021] [Accepted: 12/11/2020] [Indexed: 12/22/2022] Open
Abstract
Tools from the field of graph signal processing, in particular the graph Laplacian operator, have recently been successfully applied to the investigation of structure-function relationships in the human brain. The eigenvectors of the human connectome graph Laplacian, dubbed "connectome harmonics", have been shown to relate to the functionally relevant resting-state networks. Whole-brain modelling of brain activity combines structural connectivity with local dynamical models to provide insight into the large-scale functional organization of the human brain. In this study, we employ the graph Laplacian and its properties to define and implement a large class of neural activity models directly on the human connectome. These models, consisting of systems of stochastic integrodifferential equations on graphs, are dubbed graph neural fields, in analogy with the well-established continuous neural fields. We obtain analytic predictions for harmonic and temporal power spectra, as well as functional connectivity and coherence matrices, of graph neural fields, with a technique dubbed CHAOSS (shorthand for Connectome-Harmonic Analysis Of Spatiotemporal Spectra). Combining graph neural fields with appropriate observation models allows for estimating model parameters from experimental data as obtained from electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). As an example application, we study a stochastic Wilson-Cowan graph neural field model on a high-resolution connectome graph constructed from diffusion tensor imaging (DTI) and structural MRI data. We show that the model equilibrium fluctuations can reproduce the empirically observed harmonic power spectrum of resting-state fMRI data, and predict its functional connectivity, with a high level of detail. Graph neural fields natively allow the inclusion of important features of cortical anatomy and fast computations of observable quantities for comparison with multimodal empirical data. They thus appear particularly suitable for modelling whole-brain activity at mesoscopic scales, and opening new potential avenues for connectome-graph-based investigations of structure-function relationships.
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Affiliation(s)
- Marco Aqil
- Department of Mathematics, Vrije Universiteit, Amsterdam, The Netherlands
| | - Selen Atasoy
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, University of Aarhus, Aarhus, Denmark
| | - Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, University of Aarhus, Aarhus, Denmark
| | - Rikkert Hindriks
- Department of Mathematics, Vrije Universiteit, Amsterdam, The Netherlands
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8
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Næss S, Halnes G, Hagen E, Hagler DJ, Dale AM, Einevoll GT, Ness TV. Biophysically detailed forward modeling of the neural origin of EEG and MEG signals. Neuroimage 2020; 225:117467. [PMID: 33075556 DOI: 10.1016/j.neuroimage.2020.117467] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 09/28/2020] [Accepted: 10/12/2020] [Indexed: 12/22/2022] Open
Abstract
Electroencephalography (EEG) and magnetoencephalography (MEG) are among the most important techniques for non-invasively studying cognition and disease in the human brain. These signals are known to originate from cortical neural activity, typically described in terms of current dipoles. While the link between cortical current dipoles and EEG/MEG signals is relatively well understood, surprisingly little is known about the link between different kinds of neural activity and the current dipoles themselves. Detailed biophysical modeling has played an important role in exploring the neural origin of intracranial electric signals, like extracellular spikes and local field potentials. However, this approach has not yet been taken full advantage of in the context of exploring the neural origin of the cortical current dipoles that are causing EEG/MEG signals. Here, we present a method for reducing arbitrary simulated neural activity to single current dipoles. We find that the method is applicable for calculating extracranial signals, but less suited for calculating intracranial electrocorticography (ECoG) signals. We demonstrate that this approach can serve as a powerful tool for investigating the neural origin of EEG/MEG signals. This is done through example studies of the single-neuron EEG contribution, the putative EEG contribution from calcium spikes, and from calculating EEG signals from large-scale neural network simulations. We also demonstrate how the simulated current dipoles can be used directly in combination with detailed head models, allowing for simulated EEG signals with an unprecedented level of biophysical details. In conclusion, this paper presents a framework for biophysically detailed modeling of EEG and MEG signals, which can be used to better our understanding of non-inasively measured neural activity in humans.
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Affiliation(s)
- Solveig Næss
- Department of Informatics, University of Oslo, Oslo 0316, Norway
| | - Geir Halnes
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway
| | - Espen Hagen
- Department of Physics, University of Oslo, Oslo 0316, Norway
| | - Donald J Hagler
- Department of Radiology, University of California, La Jolla, CA 92093, USA
| | - Anders M Dale
- Department of Radiology, University of California, La Jolla, CA 92093, USA; Department of Neurosciences, University of California, La Jolla, CA 92093, USA
| | - Gaute T Einevoll
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway; Department of Physics, University of Oslo, Oslo 0316, Norway.
| | - Torbjørn V Ness
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway.
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9
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Kang S, Hayashi Y, Bruyns-Haylett M, Delivopoulos E, Zheng Y. Model-Predicted Balance Between Neural Excitation and Inhibition Was Maintained Despite of Age-Related Decline in Sensory Evoked Local Field Potential in Rat Barrel Cortex. Front Syst Neurosci 2020; 14:24. [PMID: 32528256 PMCID: PMC7247833 DOI: 10.3389/fnsys.2020.00024] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 04/08/2020] [Indexed: 11/25/2022] Open
Abstract
The balance between neural excitation and inhibition has been shown to be crucial for normal brain function. However, it is unclear whether this balance is maintained through healthy aging. This study investigated the effect of aging on the temporal dynamics of the somatosensory evoked local field potential (LFP) in rats and tested the hypothesis that excitatory and inhibitory post-synaptic activities remain balanced during the aging process. The LFP signal was obtained from the barrel cortex of three different age groups of anesthetized rats (pre-adolescence: 4–6 weeks, young adult: 2–3 months, middle-aged adult: 10–20 months) under whisker pad stimulation. To confirm our previous finding that the initial segment of the evoked LFP was solely associated with excitatory post-synaptic activity, we micro-injected gabazine into the barrel cortex to block inhibition while LFP was collected continuously under the same stimulus condition. As expected, the initial slope of the evoked LFP in the granular layer was unaffected by gabazine injection. We subsequently estimated the excitatory and inhibitory post-synaptic activities through a balanced model of the LFP with delayed inhibition as an explicit constraint, and calculated the amplitude ratio of inhibition to excitation. We found an age-dependent slowing of the temporal dynamics in the somatosensory-evoked post-synaptic activity, as well as a significant age-related decrease in the amplitude of the excitatory component and a decreasing trend in the amplitude of the inhibitory component. Furthermore, the delay of inhibition with respect to excitation was significantly increased with age, but the amplitude ratio was maintained. Our findings suggest that aging reduces the amplitude of neural responses, but the balance between sensory evoked excitatory and inhibitory post-synaptic activities is maintained to support normal brain function during healthy aging. Further whole cell patch clamp experiments will be needed to confirm or refute these findings by measuring sensory evoked synaptic excitatory and inhibitory activities in vivo during the normal aging process.
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Affiliation(s)
- Sungmin Kang
- Biomedical Engineering, School of Biological Sciences, University of Reading, Reading, United Kingdom.,Centre for Integrative Neuroscience and Neurodynamics (CINN), University of Reading, Reading, United Kingdom
| | - Yurie Hayashi
- Biomedical Engineering, School of Biological Sciences, University of Reading, Reading, United Kingdom
| | - Michael Bruyns-Haylett
- Department of Bioengineering, Imperial College, South Kensington Campus, London, United Kingdom
| | - Evangelos Delivopoulos
- Biomedical Engineering, School of Biological Sciences, University of Reading, Reading, United Kingdom.,Centre for Integrative Neuroscience and Neurodynamics (CINN), University of Reading, Reading, United Kingdom
| | - Ying Zheng
- Biomedical Engineering, School of Biological Sciences, University of Reading, Reading, United Kingdom.,Centre for Integrative Neuroscience and Neurodynamics (CINN), University of Reading, Reading, United Kingdom
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10
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Computational modelling of the long-term effects of brain stimulation on the local and global structural connectivity of epileptic patients. PLoS One 2020; 15:e0221380. [PMID: 32027654 PMCID: PMC7004372 DOI: 10.1371/journal.pone.0221380] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 01/18/2020] [Indexed: 11/25/2022] Open
Abstract
Computational studies of the influence of different network parameters on the dynamic and topological network effects of brain stimulation can enhance our understanding of different outcomes between individuals. In this study, a brain stimulation session along with the subsequent post-stimulation brain activity is simulated for a period of one day using a network of modified Wilson-Cowan oscillators coupled according to diffusion imaging based structural connectivity. We use this computational model to examine how differences in the inter-region connectivity and the excitability of stimulated regions at the time of stimulation can affect post-stimulation behaviours. Our findings indicate that the initial inter-region connectivity can heavily affect the changes that stimulation induces in the connectivity of the network. Moreover, differences in the excitability of the stimulated regions seem to lead to different post-stimulation connectivity changes across the model network, including on the internal connectivity of non-stimulated regions.
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11
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Quindlen-Hotek JC, Kent AR, De Anda P, Kartha S, Benison AM, Winkelstein BA. Changes in Neuronal Activity in the Anterior Cingulate Cortex and Primary Somatosensory Cortex With Nonlinear Burst and Tonic Spinal Cord Stimulation. Neuromodulation 2020; 23:594-604. [PMID: 32027444 DOI: 10.1111/ner.13116] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 11/20/2019] [Accepted: 01/02/2020] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Although nonlinear burst and tonic SCS are believed to treat neuropathic pain via distinct pain pathways, the effectiveness of these modalities on brain activity in vivo has not been investigated. This study compared neuronal firing patterns in the brain after nonlinear burst and tonic SCS in a rat model of painful radiculopathy. METHODS Neuronal activity was recorded in the ACC or S1 before and after nonlinear burst or tonic SCS on day 7 following painful cervical nerve root compression (NRC) or sham surgery. The amplitude of nonlinear burst SCS was set at 60% and 90% motor threshold to investigate the effect of lower amplitude SCS on brain activity. Neuronal activity was recorded during and immediately following light brush and noxious pinch of the paw. Change in neuron firing was measured as the percent change in spikes post-SCS relative to pre-SCS baseline. RESULTS ACC activity decreases during brush after 60% nonlinear burst compared to tonic (p < 0.05) after NRC and compared to 90% nonlinear burst (p < 0.04) and pre-SCS baseline (p < 0.03) after sham. ACC neuron activity decreases (p < 0.01) during pinch after 60% and 90% nonlinear burst compared to tonic for NRC. The 60% of nonlinear burst decreases (p < 0.02) ACC firing during pinch in both groups compared to baseline. In NRC S1 neurons, tonic SCS decreases (p < 0.01) firing from baseline during light brush; 60% nonlinear burst decreases (p < 0.01) firing from baseline during brush and pinch. CONCLUSIONS Nonlinear burst SCS reduces firing in the ACC from a painful stimulus; a lower amplitude nonlinear burst appears to have the greatest effect. Tonic and nonlinear burst SCS may have comparable effects in S1.
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Affiliation(s)
| | | | - Patrisia De Anda
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Sonia Kartha
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Beth A Winkelstein
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
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12
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Ross J, Margetts M, Bojak I, Nicks R, Avitabile D, Coombes S. Brain-wave equation incorporating axodendritic connectivity. Phys Rev E 2020; 101:022411. [PMID: 32168690 DOI: 10.1103/physreve.101.022411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 01/28/2020] [Indexed: 06/10/2023]
Abstract
We introduce an integral model of a two-dimensional neural field that includes a third dimension representing space along a dendritic tree that can incorporate realistic patterns of axodendritic connectivity. For natural choices of this connectivity we show how to construct an equivalent brain-wave partial differential equation that allows for efficient numerical simulation of the model. This is used to highlight the effects that passive dendritic properties can have on the speed and shape of large scale traveling cortical waves.
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Affiliation(s)
- James Ross
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Michelle Margetts
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Ingo Bojak
- School of Psychology and Clinical Language Sciences, University of Reading, Reading RG6 6AL, United Kingdom
| | - Rachel Nicks
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Daniele Avitabile
- Department of Mathematics, Vrije Universiteit (VU University Amsterdam), Faculteit der Exacte Wetenschappen, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands
- Mathneuro Team, Inria Sophia Antipolis, 2004 Rue des Lucioles, 06902 Sophia Antipolis, Cedex, France
| | - Stephen Coombes
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, United Kingdom
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Lurie DJ, Kessler D, Bassett DS, Betzel RF, Breakspear M, Kheilholz S, Kucyi A, Liégeois R, Lindquist MA, McIntosh AR, Poldrack RA, Shine JM, Thompson WH, Bielczyk NZ, Douw L, Kraft D, Miller RL, Muthuraman M, Pasquini L, Razi A, Vidaurre D, Xie H, Calhoun VD. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw Neurosci 2020; 4:30-69. [PMID: 32043043 PMCID: PMC7006871 DOI: 10.1162/netn_a_00116] [Citation(s) in RCA: 268] [Impact Index Per Article: 67.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 11/22/2019] [Indexed: 12/12/2022] Open
Abstract
The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.
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Affiliation(s)
- Daniel J. Lurie
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Daniel Kessler
- Departments of Statistics and Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Danielle S. Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Richard F. Betzel
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Breakspear
- University of Newcastle, Callaghan, NSW, 2308, Australia
- QIMR Berghofer, Brisbane, Australia
| | - Shella Kheilholz
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Aaron Kucyi
- Department of Neurology and Neurological Sciences, Stanford University, Stanford CA, USA
| | - Raphaël Liégeois
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | | | - Anthony Randal McIntosh
- Rotman Research Institute - Baycrest Centre, Toronto, Canada
- Department of Psychology, University of Toronto, Toronto, Canada
| | | | - James M. Shine
- Brain and Mind Centre, University of Sydney, NSW, Australia
| | - William Hedley Thompson
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | | | - Linda Douw
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, The Netherlands
| | - Dominik Kraft
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | | | - Muthuraman Muthuraman
- Biomedical Statistics and Multimodal Signal Processing Unit, Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience, Johannes-Gutenberg-University Hospital, Mainz, Germany
| | - Lorenzo Pasquini
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Adeel Razi
- Monash Institute of Cognitive and Clinical Neurosciences and Monash Biomedical Imaging, Monash University, Clayton, Australia
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Diego Vidaurre
- Wellcome Trust Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, United Kingdom
| | - Hua Xie
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, Georgia, USA
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Gast R, Rose D, Salomon C, Möller HE, Weiskopf N, Knösche TR. PyRates-A Python framework for rate-based neural simulations. PLoS One 2019; 14:e0225900. [PMID: 31841550 PMCID: PMC6913930 DOI: 10.1371/journal.pone.0225900] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 11/14/2019] [Indexed: 12/13/2022] Open
Abstract
In neuroscience, computational modeling has become an important source of insight into brain states and dynamics. A basic requirement for computational modeling studies is the availability of efficient software for setting up models and performing numerical simulations. While many such tools exist for different families of neural models, there is a lack of tools allowing for both a generic model definition and efficiently parallelized simulations. In this work, we present PyRates, a Python framework that provides the means to build a large variety of rate-based neural models. PyRates provides intuitive access to and modification of all mathematical operators in a graph, thus allowing for a highly generic model definition. For computational efficiency and parallelization, the model is translated into a compute graph. Using the example of two different neural models belonging to the family of rate-based population models, we explain the mathematical formalism, software structure and user interfaces of PyRates. We show via numerical simulations that the behavior of the PyRates model implementations is consistent with the literature. Finally, we demonstrate the computational capacities and scalability of PyRates via a number of benchmark simulations of neural networks differing in size and connectivity.
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Affiliation(s)
- Richard Gast
- MEG and Cortical Networks Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
- Nuclear Magnetic Resonance Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
- Neurophysics Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
| | - Daniel Rose
- Neurophysics Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
| | - Christoph Salomon
- MEG and Cortical Networks Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
- Institute for Biomedical Engineering and Informatics, TU Ilmenau, Ilmenau, Thuringia, Germany
| | - Harald E. Möller
- Nuclear Magnetic Resonance Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
| | - Nikolaus Weiskopf
- Neurophysics Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
| | - Thomas R. Knösche
- MEG and Cortical Networks Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
- Institute for Biomedical Engineering and Informatics, TU Ilmenau, Ilmenau, Thuringia, Germany
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15
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Rule ME, Schnoerr D, Hennig MH, Sanguinetti G. Neural field models for latent state inference: Application to large-scale neuronal recordings. PLoS Comput Biol 2019; 15:e1007442. [PMID: 31682604 PMCID: PMC6855563 DOI: 10.1371/journal.pcbi.1007442] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 11/14/2019] [Accepted: 09/27/2019] [Indexed: 11/18/2022] Open
Abstract
Large-scale neural recording methods now allow us to observe large populations of identified single neurons simultaneously, opening a window into neural population dynamics in living organisms. However, distilling such large-scale recordings to build theories of emergent collective dynamics remains a fundamental statistical challenge. The neural field models of Wilson, Cowan, and colleagues remain the mainstay of mathematical population modeling owing to their interpretable, mechanistic parameters and amenability to mathematical analysis. Inspired by recent advances in biochemical modeling, we develop a method based on moment closure to interpret neural field models as latent state-space point-process models, making them amenable to statistical inference. With this approach we can infer the intrinsic states of neurons, such as active and refractory, solely from spiking activity in large populations. After validating this approach with synthetic data, we apply it to high-density recordings of spiking activity in the developing mouse retina. This confirms the essential role of a long lasting refractory state in shaping spatiotemporal properties of neonatal retinal waves. This conceptual and methodological advance opens up new theoretical connections between mathematical theory and point-process state-space models in neural data analysis. Developing statistical tools to connect single-neuron activity to emergent collective dynamics is vital for building interpretable models of neural activity. Neural field models relate single-neuron activity to emergent collective dynamics in neural populations, but integrating them with data remains challenging. Recently, latent state-space models have emerged as a powerful tool for constructing phenomenological models of neural population activity. The advent of high-density multi-electrode array recordings now enables us to examine large-scale collective neural activity. We show that classical neural field approaches can yield latent state-space equations and demonstrate that this enables inference of the intrinsic states of neurons from recorded spike trains in large populations.
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Affiliation(s)
- Michael E. Rule
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
- * E-mail:
| | - David Schnoerr
- Theoretical Systems Biology, Imperial College London, London, United Kingdom
| | - Matthias H. Hennig
- Department of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Guido Sanguinetti
- Department of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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16
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Tian Y, Zhang H, Jiang Y, Li P, Li Y. A Fusion Feature for Enhancing the Performance of Classification in Working Memory Load With Single-Trial Detection. IEEE Trans Neural Syst Rehabil Eng 2019; 27:1985-1993. [DOI: 10.1109/tnsre.2019.2936997] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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17
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Stefanovski L, Triebkorn P, Spiegler A, Diaz-Cortes MA, Solodkin A, Jirsa V, McIntosh AR, Ritter P. Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease. Front Comput Neurosci 2019; 13:54. [PMID: 31456676 PMCID: PMC6700386 DOI: 10.3389/fncom.2019.00054] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 07/22/2019] [Indexed: 12/22/2022] Open
Abstract
Introduction: While the prevalence of neurodegenerative diseases associated with dementia such as Alzheimer's disease (AD) increases, our knowledge on the underlying mechanisms, outcome predictors, or therapeutic targets is limited. In this work, we demonstrate how computational multi-scale brain modeling links phenomena of different scales and therefore identifies potential disease mechanisms leading the way to improved diagnostics and treatment. Methods: The Virtual Brain (TVB; thevirtualbrain.org) neuroinformatics platform allows standardized large-scale structural connectivity-based simulations of whole brain dynamics. We provide proof of concept for a novel approach that quantitatively links the effects of altered molecular pathways onto neuronal population dynamics. As a novelty, we connect chemical compounds measured with positron emission tomography (PET) with neural function in TVB addressing the phenomenon of hyperexcitability in AD related to the protein amyloid beta (Abeta). We construct personalized virtual brains based on an averaged healthy connectome and individual PET derived distributions of Abeta in patients with mild cognitive impairment (MCI, N = 8) and Alzheimer's Disease (AD, N = 10) and in age-matched healthy controls (HC, N = 15) using data from ADNI-3 data base (http://adni.loni.usc.edu). In the personalized virtual brains, individual Abeta burden modulates regional Excitation-Inhibition balance, leading to local hyperexcitation with high Abeta loads. We analyze simulated regional neural activity and electroencephalograms (EEG). Results: Known empirical alterations of EEG in patients with AD compared to HCs were reproduced by simulations. The virtual AD group showed slower frequencies in simulated local field potentials and EEG compared to MCI and HC groups. The heterogeneity of the Abeta load is crucial for the virtual EEG slowing which is absent for control models with homogeneous Abeta distributions. Slowing phenomena primarily affect the network hubs, independent of the spatial distribution of Abeta. Modeling the N-methyl-D-aspartate (NMDA) receptor antagonism of memantine in local population models, reveals potential functional reversibility of the observed large-scale alterations (reflected by EEG slowing) in virtual AD brains. Discussion: We demonstrate how TVB enables the simulation of systems effects caused by pathogenetic molecular candidate mechanisms in human virtual brains.
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Affiliation(s)
- Leon Stefanovski
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neurology, Brain Simulation Section, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Paul Triebkorn
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neurology, Brain Simulation Section, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Andreas Spiegler
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neurology, Brain Simulation Section, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Margarita-Arimatea Diaz-Cortes
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neurology, Brain Simulation Section, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- Institut für Informatik, Freie Universität Berlin, Berlin, Germany
| | - Ana Solodkin
- Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, United States
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | | | - Petra Ritter
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neurology, Brain Simulation Section, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
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18
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Garrett DD, Epp SM, Perry A, Lindenberger U. Local temporal variability reflects functional integration in the human brain. Neuroimage 2018; 183:776-787. [DOI: 10.1016/j.neuroimage.2018.08.019] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 06/27/2018] [Accepted: 08/10/2018] [Indexed: 12/28/2022] Open
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20
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Kang S, Bruyns-Haylett M, Hayashi Y, Zheng Y. Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent. J Vis Exp 2017. [PMID: 29286448 PMCID: PMC5755518 DOI: 10.3791/56447] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
Although electroencephalography (EEG) is widely used as a non-invasive technique for recording neural activities of the brain, our understanding of the neurogenesis of EEG is still very limited. Local field potentials (LFPs) recorded via a multi-laminar microelectrode can provide a more detailed account of simultaneous neural activity across different cortical layers in the neocortex, but the technique is invasive. Combining EEG and LFP measurements in a pre-clinical model can greatly enhance understanding of the neural mechanisms involved in the generation of EEG signals, and facilitate the derivation of a more realistic and biologically accurate mathematical model of EEG. A simple procedure for acquiring concurrent and co-localized EEG and multi-laminar LFP signals in the anesthetized rodent is presented here. We also investigated whether EEG signals were significantly affected by a burr hole drilled in the skull for the insertion of a microelectrode. Our results suggest that the burr hole has a negligible impact on EEG recordings.
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Affiliation(s)
- Sungmin Kang
- School of Biological Sciences, Whiteknights, University of Reading
| | | | - Yurie Hayashi
- School of Biological Sciences, Whiteknights, University of Reading
| | - Ying Zheng
- School of Biological Sciences, Whiteknights, University of Reading;
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21
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Gökçe A, Avitabile D, Coombes S. The Dynamics of Neural Fields on Bounded Domains: An Interface Approach for Dirichlet Boundary Conditions. JOURNAL OF MATHEMATICAL NEUROSCIENCE 2017; 7:12. [PMID: 29075933 PMCID: PMC5658324 DOI: 10.1186/s13408-017-0054-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 10/10/2017] [Indexed: 06/07/2023]
Abstract
Continuum neural field equations model the large-scale spatio-temporal dynamics of interacting neurons on a cortical surface. They have been extensively studied, both analytically and numerically, on bounded as well as unbounded domains. Neural field models do not require the specification of boundary conditions. Relatively little attention has been paid to the imposition of neural activity on the boundary, or to its role in inducing patterned states. Here we redress this imbalance by studying neural field models of Amari type (posed on one- and two-dimensional bounded domains) with Dirichlet boundary conditions. The Amari model has a Heaviside nonlinearity that allows for a description of localised solutions of the neural field with an interface dynamics. We show how to generalise this reduced but exact description by deriving a normal velocity rule for an interface that encapsulates boundary effects. The linear stability analysis of localised states in the interface dynamics is used to understand how spatially extended patterns may develop in the absence and presence of boundary conditions. Theoretical results for pattern formation are shown to be in excellent agreement with simulations of the full neural field model. Furthermore, a numerical scheme for the interface dynamics is introduced and used to probe the way in which a Dirichlet boundary condition can limit the growth of labyrinthine structures.
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Affiliation(s)
- Aytül Gökçe
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD UK
| | - Daniele Avitabile
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD UK
| | - Stephen Coombes
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD UK
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22
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Visser S, Nicks R, Faugeras O, Coombes S. Standing and travelling waves in a spherical brain model: The Nunez model revisited. PHYSICA D. NONLINEAR PHENOMENA 2017; 349:27-45. [PMID: 28626276 PMCID: PMC5421190 DOI: 10.1016/j.physd.2017.02.017] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Revised: 02/27/2017] [Accepted: 02/28/2017] [Indexed: 06/07/2023]
Abstract
The Nunez model for the generation of electroencephalogram (EEG) signals is naturally described as a neural field model on a sphere with space-dependent delays. For simplicity, dynamical realisations of this model either as a damped wave equation or an integro-differential equation, have typically been studied in idealised one dimensional or planar settings. Here we revisit the original Nunez model to specifically address the role of spherical topology on spatio-temporal pattern generation. We do this using a mixture of Turing instability analysis, symmetric bifurcation theory, centre manifold reduction and direct simulations with a bespoke numerical scheme. In particular we examine standing and travelling wave solutions using normal form computation of primary and secondary bifurcations from a steady state. Interestingly, we observe spatio-temporal patterns which have counterparts seen in the EEG patterns of both epileptic and schizophrenic brain conditions.
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Affiliation(s)
- S. Visser
- School of Mathematical Sciences, University of Nottingham, NG7 2RD, UK
- Wellcome Trust Centre for Biomedical Modelling and Analysis, RILD Building, University of Exeter, EX2 5DW, UK
| | - R. Nicks
- School of Mathematical Sciences, University of Nottingham, NG7 2RD, UK
| | - O. Faugeras
- INRIA Sophia Antipolis Mediterannee, 2004 Route Des Lucioles, Sophia Antipolis, 06410, France
| | - S. Coombes
- School of Mathematical Sciences, University of Nottingham, NG7 2RD, UK
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23
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Chu H, Chung CK, Jeong W, Cho KH. Predicting epileptic seizures from scalp EEG based on attractor state analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 143:75-87. [PMID: 28391821 DOI: 10.1016/j.cmpb.2017.03.002] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2016] [Accepted: 03/01/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Epilepsy is the second most common disease of the brain. Epilepsy makes it difficult for patients to live a normal life because it is difficult to predict when seizures will occur. In this regard, if seizures could be predicted a reasonable period of time before their occurrence, epilepsy patients could take precautions against them and improve their safety and quality of life. In this paper, we investigate a novel seizure precursor based on attractor state analysis for seizure prediction. METHODS We analyze the transition process from normal to seizure attractor state and investigate a precursor phenomenon seen before reaching the seizure attractor state. From the result of an analysis, we define a quantified spectral measure in scalp EEG for seizure prediction. From scalp EEG recordings, the Fourier coefficients of six EEG frequency bands are extracted, and the defined spectral measure is computed based on the coefficients for each half-overlapped 20-second-long window. The computed spectral measure is applied to seizure prediction using a low-complexity methodology. RESULTS Within scalp EEG, we identified an early-warning indicator before an epileptic seizure occurs. Getting closer to the bifurcation point that triggers the transition from normal to seizure state, the power spectral density of low frequency bands of the perturbation of an attractor in the EEG, showed a relative increase. A low-complexity seizure prediction algorithm using this feature was evaluated, using ∼583h of scalp EEG in which 143 seizures in 16 patients were recorded. With the test dataset, the proposed method showed high sensitivity (86.67%) with a false prediction rate of 0.367h-1 and average prediction time of 45.3min. CONCLUSIONS A novel seizure prediction method using scalp EEG, based on attractor state analysis, shows potential for application with real epilepsy patients. This is the first study in which the seizure-precursor phenomenon of an epileptic seizure is investigated based on attractor-based analysis of the macroscopic dynamics of the brain. With the scalp EEG, we first propose use of a spectral feature identified for seizure prediction, in which the dynamics of an attractor are excluded, and only the perturbation dynamics from the attractor are considered.
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Affiliation(s)
- Hyunho Chu
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Daejeon, Republic of Korea
| | - Chun Kee Chung
- Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea
| | - Woorim Jeong
- Interdisciplinary Program in Neuroscience, Seoul National University College of Natural Science, Seoul, Republic of Korea
| | - Kwang-Hyun Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Daejeon, Republic of Korea.
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24
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Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size. PLoS Comput Biol 2017; 13:e1005507. [PMID: 28422957 PMCID: PMC5415267 DOI: 10.1371/journal.pcbi.1005507] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Revised: 05/03/2017] [Accepted: 04/07/2017] [Indexed: 11/22/2022] Open
Abstract
Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50–2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations. Understanding the brain requires mathematical models on different spatial scales. On the “microscopic” level of nerve cells, neural spike trains can be well predicted by phenomenological spiking neuron models. On a coarse scale, neural activity can be modeled by phenomenological equations that summarize the total activity of many thousands of neurons. Such population models are widely used to model neuroimaging data such as EEG, MEG or fMRI data. However, it is largely unknown how large-scale models are connected to an underlying microscale model. Linking the scales is vital for a correct description of rapid changes and fluctuations of the population activity, and is crucial for multiscale brain models. The challenge is to treat realistic spiking dynamics as well as fluctuations arising from the finite number of neurons. We obtained such a link by deriving stochastic population equations on the mesoscopic scale of 100–1000 neurons from an underlying microscopic model. These equations can be efficiently integrated and reproduce results of a microscopic simulation while achieving a high speed-up factor. We expect that our novel population theory on the mesoscopic scale will be instrumental for understanding experimental data on information processing in the brain, and ultimately link microscopic and macroscopic activity patterns.
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25
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Akkalkotkar A, Brown KS. An algorithm for separation of mixed sparse and Gaussian sources. PLoS One 2017; 12:e0175775. [PMID: 28414814 PMCID: PMC5393591 DOI: 10.1371/journal.pone.0175775] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Accepted: 03/12/2017] [Indexed: 11/18/2022] Open
Abstract
Independent component analysis (ICA) is a ubiquitous method for decomposing complex signal mixtures into a small set of statistically independent source signals. However, in cases in which the signal mixture consists of both nongaussian and Gaussian sources, the Gaussian sources will not be recoverable by ICA and will pollute estimates of the nongaussian sources. Therefore, it is desirable to have methods for mixed ICA/PCA which can separate mixtures of Gaussian and nongaussian sources. For mixtures of purely Gaussian sources, principal component analysis (PCA) can provide a basis for the Gaussian subspace. We introduce a new method for mixed ICA/PCA which we call Mixed ICA/PCA via Reproducibility Stability (MIPReSt). Our method uses a repeated estimations technique to rank sources by reproducibility, combined with decomposition of multiple subsamplings of the original data matrix. These multiple decompositions allow us to assess component stability as the size of the data matrix changes, which can be used to determinine the dimension of the nongaussian subspace in a mixture. We demonstrate the utility of MIPReSt for signal mixtures consisting of simulated sources and real-word (speech) sources, as well as mixture of unknown composition.
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Affiliation(s)
- Ameya Akkalkotkar
- Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT, United States of America
| | - Kevin Scott Brown
- Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT, United States of America
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States of America
- Departments of Physics, and Marine Sciences, University of Connecticut, Storrs, CT, United States of America
- Institute for Systems Genomics and CT Institute for the Brain & Cognitive Sciences, Storrs, CT, United States of America
- * E-mail:
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26
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Krishnagopal S, Lehnert J, Poel W, Zakharova A, Schöll E. Synchronization patterns: from network motifs to hierarchical networks. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2017; 375:20160216. [PMID: 28115613 PMCID: PMC5311436 DOI: 10.1098/rsta.2016.0216] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/04/2016] [Indexed: 05/12/2023]
Abstract
We investigate complex synchronization patterns such as cluster synchronization and partial amplitude death in networks of coupled Stuart-Landau oscillators with fractal connectivities. The study of fractal or self-similar topology is motivated by the network of neurons in the brain. This fractal property is well represented in hierarchical networks, for which we present three different models. In addition, we introduce an analytical eigensolution method and provide a comprehensive picture of the interplay of network topology and the corresponding network dynamics, thus allowing us to predict the dynamics of arbitrarily large hierarchical networks simply by analysing small network motifs. We also show that oscillation death can be induced in these networks, even if the coupling is symmetric, contrary to previous understanding of oscillation death. Our results show that there is a direct correlation between topology and dynamics: hierarchical networks exhibit the corresponding hierarchical dynamics. This helps bridge the gap between mesoscale motifs and macroscopic networks.This article is part of the themed issue 'Horizons of cybernetical physics'.
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Affiliation(s)
- Sanjukta Krishnagopal
- Institut für Theoretische Physik, Technische Universität Berlin, 10623 Berlin, Germany
- Department of Physics, Birla Institute for Technology and Science Pilani, Pilani, Goa 403726, India
| | - Judith Lehnert
- Institut für Theoretische Physik, Technische Universität Berlin, 10623 Berlin, Germany
| | - Winnie Poel
- Institut für Theoretische Physik, Technische Universität Berlin, 10623 Berlin, Germany
| | - Anna Zakharova
- Institut für Theoretische Physik, Technische Universität Berlin, 10623 Berlin, Germany
| | - Eckehard Schöll
- Institut für Theoretische Physik, Technische Universität Berlin, 10623 Berlin, Germany
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27
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Bezgin G, Solodkin A, Bakker R, Ritter P, McIntosh AR. Mapping complementary features of cross-species structural connectivity to construct realistic "Virtual Brains". Hum Brain Mapp 2017; 38:2080-2093. [PMID: 28054725 DOI: 10.1002/hbm.23506] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Revised: 11/08/2016] [Accepted: 12/20/2016] [Indexed: 11/09/2022] Open
Abstract
Modern systems neuroscience increasingly leans on large-scale multi-lab neuroinformatics initiatives to provide necessary capacity for biologically realistic modeling of primate whole-brain activity. Here, we present a framework to assemble primate brain's biologically plausible anatomical backbone for such modeling initiatives. In this framework, structural connectivity is determined by adding complementary information from invasive macaque axonal tract tracing and non-invasive human diffusion tensor imaging. Both modalities are combined by means of available interspecies registration tools and a newly developed Bayesian probabilistic modeling approach to extract common connectivity evidence. We demonstrate how this novel framework is embedded in the whole-brain simulation platform called The Virtual Brain (TVB). Hum Brain Mapp 38:2080-2093, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Gleb Bezgin
- Rotman Research Institute of Baycrest Centre, University of Toronto, Toronto, Ontario, Canada, M6A 2E1.,McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada, H3A 2B4
| | - Ana Solodkin
- Department of Neurology, University of California, Irvine, 200 Manchester Avenue, Suite 206, Orange, California
| | - Rembrandt Bakker
- Donders Institute for Brain, Cognition and Behaviour, Centre for Neuroscience, Radboud University Nijmegen, Nijmegen, AJ, 6525, the Netherlands.,Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), Jülich Research Centre and JARA, Jülich, 52425, Germany
| | - Petra Ritter
- Department of Neurology, Charite - University Medicine, Berlin, Germany.,Minerva Research Group Brain Modes, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin, Germany.,Berlin School of Mind and Brain & Mind & Brain Institute, Humboldt University, Berlin, Germany
| | - Anthony R McIntosh
- Rotman Research Institute of Baycrest Centre, University of Toronto, Toronto, Ontario, Canada, M6A 2E1.,Department of Psychology, University of Toronto, Toronto, Ontario, Canada
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28
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Garimella HT, Kraft RH. A new computational approach for modeling diffusion tractography in the brain. Neural Regen Res 2017; 12:23-26. [PMID: 28250733 PMCID: PMC5319226 DOI: 10.4103/1673-5374.198967] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Computational models provide additional tools for studying the brain, however, many techniques are currently disconnected from each other. There is a need for new computational approaches that span the range of physics operating in the brain. In this review paper, we offer some new perspectives on how the embedded element method can fill this gap and has the potential to connect a myriad of modeling genre. The embedded element method is a mesh superposition technique used within finite element analysis. This method allows for the incorporation of axonal fiber tracts to be explicitly represented. Here, we explore the use of the approach beyond its original goal of predicting axonal strain in brain injury. We explore the potential application of the embedded element method in areas of electrophysiology, neurodegeneration, neuropharmacology and mechanobiology. We conclude that this method has the potential to provide us with an integrated computational framework that can assist in developing improved diagnostic tools and regeneration technologies.
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Affiliation(s)
- Harsha T Garimella
- Department of Mechanical and Nuclear Engineering, Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Reuben H Kraft
- Department of Mechanical and Nuclear Engineering, Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, USA
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29
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Bruyns-Haylett M, Luo J, Kennerley AJ, Harris S, Boorman L, Milne E, Vautrelle N, Hayashi Y, Whalley BJ, Jones M, Berwick J, Riera J, Zheng Y. The neurogenesis of P1 and N1: A concurrent EEG/LFP study. Neuroimage 2016; 146:575-588. [PMID: 27646129 PMCID: PMC5312787 DOI: 10.1016/j.neuroimage.2016.09.034] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Revised: 08/19/2016] [Accepted: 09/15/2016] [Indexed: 10/29/2022] Open
Abstract
It is generally recognised that event related potentials (ERPs) of electroencephalogram (EEG) primarily reflect summed post-synaptic activity of the local pyramidal neural population(s). However, it is still not understood how the positive and negative deflections (e.g. P1, N1 etc) observed in ERP recordings are related to the underlying excitatory and inhibitory post-synaptic activity. We investigated the neurogenesis of P1 and N1 in ERPs by pharmacologically manipulating inhibitory post-synaptic activity in the somatosensory cortex of rodent, and concurrently recording EEG and local field potentials (LFPs). We found that the P1 wave in the ERP and LFP of the supragranular layers is determined solely by the excitatory post-synaptic activity of the local pyramidal neural population, as is the initial segment of the N1 wave across cortical depth. The later part of the N1 wave was modulated by inhibitory post-synaptic activity, with its peak and the pulse width increasing as inhibition was reduced. These findings suggest that the temporal delay of inhibition with respect to excitation observed in intracellular recordings is also reflected in extracellular field potentials (FPs), resulting in a temporal window during which only excitatory post-synaptic activity and leak channel activity are recorded in the ERP and evoked LFP time series. Based on these findings, we provide clarification on the interpretation of P1 and N1 in terms of the excitatory and inhibitory post-synaptic activities of the local pyramidal neural population(s).
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Affiliation(s)
- Michael Bruyns-Haylett
- School of Systems Engineering, Whiteknights, University of Reading, Reading RG6 7AY, United Kingdom.
| | - Jingjing Luo
- School of Systems Engineering, Whiteknights, University of Reading, Reading RG6 7AY, United Kingdom.
| | - Aneurin J Kennerley
- Department of Psychology, University of Sheffield, Sheffield S10 2TP, United Kingdom
| | - Sam Harris
- Department of Psychology, University of Sheffield, Sheffield S10 2TP, United Kingdom
| | - Luke Boorman
- Department of Psychology, University of Sheffield, Sheffield S10 2TP, United Kingdom
| | - Elizabeth Milne
- Department of Psychology, University of Sheffield, Sheffield S10 2TP, United Kingdom
| | - Nicolas Vautrelle
- Department of Psychology, University of Sheffield, Sheffield S10 2TP, United Kingdom
| | - Yurie Hayashi
- School of Systems Engineering, Whiteknights, University of Reading, Reading RG6 7AY, United Kingdom
| | - Benjamin J Whalley
- School of Systems Engineering, Whiteknights, University of Reading, Reading RG6 7AY, United Kingdom
| | - Myles Jones
- Department of Psychology, University of Sheffield, Sheffield S10 2TP, United Kingdom
| | - Jason Berwick
- Department of Psychology, University of Sheffield, Sheffield S10 2TP, United Kingdom
| | - Jorge Riera
- Department of Biomedical Engineering, Florida International University, Miami, FL 33174, United States of America
| | - Ying Zheng
- School of Systems Engineering, Whiteknights, University of Reading, Reading RG6 7AY, United Kingdom.
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30
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Finger H, Bönstrup M, Cheng B, Messé A, Hilgetag C, Thomalla G, Gerloff C, König P. Modeling of Large-Scale Functional Brain Networks Based on Structural Connectivity from DTI: Comparison with EEG Derived Phase Coupling Networks and Evaluation of Alternative Methods along the Modeling Path. PLoS Comput Biol 2016; 12:e1005025. [PMID: 27504629 PMCID: PMC4978387 DOI: 10.1371/journal.pcbi.1005025] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 06/17/2016] [Indexed: 11/19/2022] Open
Abstract
In this study, we investigate if phase-locking of fast oscillatory activity relies on the anatomical skeleton and if simple computational models informed by structural connectivity can help further to explain missing links in the structure-function relationship. We use diffusion tensor imaging data and alpha band-limited EEG signal recorded in a group of healthy individuals. Our results show that about 23.4% of the variance in empirical networks of resting-state functional connectivity is explained by the underlying white matter architecture. Simulating functional connectivity using a simple computational model based on the structural connectivity can increase the match to 45.4%. In a second step, we use our modeling framework to explore several technical alternatives along the modeling path. First, we find that an augmentation of homotopic connections in the structural connectivity matrix improves the link to functional connectivity while a correction for fiber distance slightly decreases the performance of the model. Second, a more complex computational model based on Kuramoto oscillators leads to a slight improvement of the model fit. Third, we show that the comparison of modeled and empirical functional connectivity at source level is much more specific for the underlying structural connectivity. However, different source reconstruction algorithms gave comparable results. Of note, as the fourth finding, the model fit was much better if zero-phase lag components were preserved in the empirical functional connectome, indicating a considerable amount of functionally relevant synchrony taking place with near zero or zero-phase lag. The combination of the best performing alternatives at each stage in the pipeline results in a model that explains 54.4% of the variance in the empirical EEG functional connectivity. Our study shows that large-scale brain circuits of fast neural network synchrony strongly rely upon the structural connectome and simple computational models of neural activity can explain missing links in the structure-function relationship. Brain imaging techniques are broadly divided into the two categories of structural and functional imaging. Structural imaging provides information about the static physical connectivity within the brain, while functional imaging provides data about the dynamic ongoing activation of brain areas. Computational models allow to bridge the gap between these two modalities and allow to gain new insights. Specifically, in this study, we use structural data from diffusion tractography recordings to model functional brain connectivity obtained from fast EEG dynamics occurring at the alpha frequency. First, we present a simple reference procedure which consists of several steps to link the structural to the functional empirical data. Second, we systematically compare several alternative methods along the modeling path in order to assess their impact on the overall fit between simulations and empirical data. We explore preprocessing steps of the structural connectivity and different levels of complexity of the computational model. We highlight the importance of source reconstruction and compare commonly used source reconstruction algorithms and metrics to assess functional connectivity. Our results serve as an important orienting frame for the emerging field of brain network modeling.
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Affiliation(s)
- Holger Finger
- Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- * E-mail:
| | - Marlene Bönstrup
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Arnaud Messé
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Claus Hilgetag
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Gerloff
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Peter König
- Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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31
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Reid AT, Hoffstaedter F, Gong G, Laird AR, Fox P, Evans AC, Amunts K, Eickhoff SB. A seed-based cross-modal comparison of brain connectivity measures. Brain Struct Funct 2016; 222:1131-1151. [PMID: 27372336 DOI: 10.1007/s00429-016-1264-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 06/23/2016] [Indexed: 11/24/2022]
Abstract
Human neuroimaging methods have provided a number of means by which the connectivity structure of the human brain can be inferred. For instance, correlations in blood-oxygen-level-dependent (BOLD) signal time series are commonly used to make inferences about "functional connectivity." Correlations across samples in structural morphometric measures, such as voxel-based morphometry (VBM) or cortical thickness (CT), have also been used to estimate connectivity, putatively through mutually trophic effects on connected brain areas. In this study, we have compared seed-based connectivity estimates obtained from four common correlational approaches: resting-state functional connectivity (RS-fMRI), meta-analytic connectivity modeling (MACM), VBM correlations, and CT correlations. We found that the two functional approaches (RS-fMRI and MACM) had the best agreement. While the two structural approaches (CT and VBM) had better-than-random convergence, they were no more similar to each other than to the functional approaches. The degree of correspondence between modalities varied considerably across seed regions, and also depended on the threshold applied to the connectivity distribution. These results demonstrate some degrees of similarity between connectivity inferred from structural and functional covariances, particularly for the most robust functionally connected regions (e.g., the default mode network). However, they also caution that these measures likely capture very different aspects of brain structure and function.
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Affiliation(s)
- Andrew T Reid
- Institute for Neuroscience and Medicine (INM-1), Jülich Research Center, Wilhelm-Johnen-Straße, 52428, Jülich, Germany. .,Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands.
| | - Felix Hoffstaedter
- Institute for Neuroscience and Medicine (INM-1), Jülich Research Center, Wilhelm-Johnen-Straße, 52428, Jülich, Germany.,Department of Clinical Neuroscience and Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - Gaolang Gong
- School of Brain and Cognitive Sciences, National Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Peter Fox
- University of Texas Health Sciences Center at San Antonio, San Antonio, TX, USA.,South Texas Veterans Health Care System, San Antonio, TX, USA
| | - Alan C Evans
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Katrin Amunts
- Institute for Neuroscience and Medicine (INM-1), Jülich Research Center, Wilhelm-Johnen-Straße, 52428, Jülich, Germany.,C. & O. Vogt Institute for Brain Research, Heinrich Heine University, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute for Neuroscience and Medicine (INM-1), Jülich Research Center, Wilhelm-Johnen-Straße, 52428, Jülich, Germany.,Department of Clinical Neuroscience and Medicine, Heinrich Heine University, Düsseldorf, Germany
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32
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Hutchings F, Han CE, Keller SS, Weber B, Taylor PN, Kaiser M. Predicting Surgery Targets in Temporal Lobe Epilepsy through Structural Connectome Based Simulations. PLoS Comput Biol 2015; 11:e1004642. [PMID: 26657566 PMCID: PMC4675531 DOI: 10.1371/journal.pcbi.1004642] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2015] [Accepted: 10/29/2015] [Indexed: 02/03/2023] Open
Abstract
Temporal lobe epilepsy (TLE) is a prevalent neurological disorder resulting in disruptive seizures. In the case of drug resistant epilepsy resective surgery is often considered. This is a procedure hampered by unpredictable success rates, with many patients continuing to have seizures even after surgery. In this study we apply a computational model of epilepsy to patient specific structural connectivity derived from diffusion tensor imaging (DTI) of 22 individuals with left TLE and 39 healthy controls. We validate the model by examining patient-control differences in simulated seizure onset time and network location. We then investigate the potential of the model for surgery prediction by performing in silico surgical resections, removing nodes from patient networks and comparing seizure likelihood post-surgery to pre-surgery simulations. We find that, first, patients tend to transit from non-epileptic to epileptic states more often than controls in the model. Second, regions in the left hemisphere (particularly within temporal and subcortical regions) that are known to be involved in TLE are the most frequent starting points for seizures in patients in the model. In addition, our analysis also implicates regions in the contralateral and frontal locations which may play a role in seizure spreading or surgery resistance. Finally, the model predicts that patient-specific surgery (resection areas chosen on an individual, model-prompted, basis and not following a predefined procedure) may lead to better outcomes than the currently used routine clinical procedure. Taken together this work provides a first step towards patient specific computational modelling of epilepsy surgery in order to inform treatment strategies in individuals. Temporal lobe epilepsy (TLE) is a disorder characterised by unpredictable seizures, where surgical removal of brain tissue is often the final treatment option. In roughly 30% of cases surgery procedures are unsuccessful at preventing future seizures. This paper shows the application of a computational model which uses patient derived brain connectivity to predict the success rates of surgery in people with TLE. We consider the brains of 22 patients as networks, with brain regions as nodes and the white matter connections between them as edges. The brain network is unique to each subject and produced from brain imaging scans of 22 patients and 39 controls. Seizures are simulated before and after surgery, where surgery in the model is the removal of nodes from the network. The model successfully identifies regions known to be involved in TLE, and its predicted success rates for surgery are close to the results found in reality. The model additionally provides patient specific recommendations for surgical procedures, which in simulations show improved results compared to standard surgery in every case. This is a first step towards designing personalised surgery procedures in order to improve surgery success rates.
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Affiliation(s)
- Frances Hutchings
- Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
- * E-mail:
| | - Cheol E. Han
- Department of Biomedical Engineering, Korea University, Seoul, Republic of Korea
- Department of Brain Cognitive Sciences, Seoul National University, Seoul, Republic of Korea
| | - Simon S. Keller
- Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Bernd Weber
- Center for Economics and Neuroscience, University of Bonn, Bonn, Germany
- Department of Epileptology, University of Bonn, Bonn, Germany
| | - Peter N. Taylor
- Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
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33
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Sanz-Leon P, Knock SA, Spiegler A, Jirsa VK. Mathematical framework for large-scale brain network modeling in The Virtual Brain. Neuroimage 2015; 111:385-430. [PMID: 25592995 DOI: 10.1016/j.neuroimage.2015.01.002] [Citation(s) in RCA: 157] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2014] [Revised: 12/29/2014] [Accepted: 01/01/2015] [Indexed: 12/19/2022] Open
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34
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Dahlem MA, Schmidt B, Bojak I, Boie S, Kneer F, Hadjikhani N, Kurths J. Cortical hot spots and labyrinths: why cortical neuromodulation for episodic migraine with aura should be personalized. Front Comput Neurosci 2015; 9:29. [PMID: 25798103 PMCID: PMC4350394 DOI: 10.3389/fncom.2015.00029] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2014] [Accepted: 02/18/2015] [Indexed: 12/26/2022] Open
Abstract
Stimulation protocols for medical devices should be rationally designed. For episodic migraine with aura we outline model-based design strategies toward preventive and acute therapies using stereotactic cortical neuromodulation. To this end, we regard a localized spreading depression (SD) wave segment as a central element in migraine pathophysiology. To describe nucleation and propagation features of the SD wave segment, we define the new concepts of cortical hot spots and labyrinths, respectively. In particular, we firstly focus exclusively on curvature-induced dynamical properties by studying a generic reaction-diffusion model of SD on the folded cortical surface. This surface is described with increasing level of details, including finally personalized simulations using patient's magnetic resonance imaging (MRI) scanner readings. At this stage, the only relevant factor that can modulate nucleation and propagation paths is the Gaussian curvature, which has the advantage of being rather readily accessible by MRI. We conclude with discussing further anatomical factors, such as areal, laminar, and cellular heterogeneity, that in addition to and in relation to Gaussian curvature determine the generalized concept of cortical hot spots and labyrinths as target structures for neuromodulation. Our numerical simulations suggest that these target structures are like fingerprints, they are individual features of each migraine sufferer. The goal in the future will be to provide individualized neural tissue simulations. These simulations should predict the clinical data and therefore can also serve as a test bed for exploring stereotactic cortical neuromodulation.
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Affiliation(s)
- Markus A Dahlem
- Department of Physics, Humboldt-Universität zu Berlin Berlin, Germany ; Department of Biological Physik, Max Planck Institute for the Physics of Complex Systems Dresden, Germany
| | - Bernd Schmidt
- Department of Physics, Humboldt-Universität zu Berlin Berlin, Germany
| | - Ingo Bojak
- Cybernetics Research Group, School of Systems Engineering, University of Reading Reading, UK
| | - Sebastian Boie
- Department of Mathematics, The University of Auckland Auckland, New Zealand
| | - Frederike Kneer
- Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin Berlin, Germany
| | - Nouchine Hadjikhani
- Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital Charlestown, MA, USA
| | - Jürgen Kurths
- Department of Physics, Humboldt-Universität zu Berlin Berlin, Germany ; Potsdam Institute for Climate Impact Research Potsdam, Germany ; Institute for Complex Systems and Mathematical Biology, University of Aberdeen Aberdeen, UK
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35
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Bojak I, Stoyanov ZV, Liley DTJ. Emergence of spatially heterogeneous burst suppression in a neural field model of electrocortical activity. Front Syst Neurosci 2015; 9:18. [PMID: 25767438 PMCID: PMC4341547 DOI: 10.3389/fnsys.2015.00018] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2014] [Accepted: 02/02/2015] [Indexed: 11/17/2022] Open
Abstract
Burst suppression in the electroencephalogram (EEG) is a well-described phenomenon that occurs during deep anesthesia, as well as in a variety of congenital and acquired brain insults. Classically it is thought of as spatially synchronous, quasi-periodic bursts of high amplitude EEG separated by low amplitude activity. However, its characterization as a “global brain state” has been challenged by recent results obtained with intracranial electrocortigraphy. Not only does it appear that burst suppression activity is highly asynchronous across cortex, but also that it may occur in isolated regions of circumscribed spatial extent. Here we outline a realistic neural field model for burst suppression by adding a slow process of synaptic resource depletion and recovery, which is able to reproduce qualitatively the empirically observed features during general anesthesia at the whole cortex level. Simulations reveal heterogeneous bursting over the model cortex and complex spatiotemporal dynamics during simulated anesthetic action, and provide forward predictions of neuroimaging signals for subsequent empirical comparisons and more detailed characterization. Because burst suppression corresponds to a dynamical end-point of brain activity, theoretically accounting for its spatiotemporal emergence will vitally contribute to efforts aimed at clarifying whether a common physiological trajectory is induced by the actions of general anesthetic agents. We have taken a first step in this direction by showing that a neural field model can qualitatively match recent experimental data that indicate spatial differentiation of burst suppression activity across cortex.
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Affiliation(s)
- Ingo Bojak
- Systems Neuroscience Research Group, School of Systems Engineering, University of Reading Reading, UK
| | - Zhivko V Stoyanov
- Systems Neuroscience Research Group, School of Systems Engineering, University of Reading Reading, UK
| | - David T J Liley
- Brain and Psychological Sciences Research Centre, School of Health Sciences, Swinburne University of Technology Hawthorn, VIC, Australia
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36
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Role of white-matter pathways in coordinating alpha oscillations in resting visual cortex. Neuroimage 2015; 106:328-39. [DOI: 10.1016/j.neuroimage.2014.10.057] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2014] [Revised: 10/21/2014] [Accepted: 10/26/2014] [Indexed: 11/18/2022] Open
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37
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Bielczyk NZ, Buitelaar JK, Glennon JC, Tiesinga PHE. Circuit to construct mapping: a mathematical tool for assisting the diagnosis and treatment in major depressive disorder. Front Psychiatry 2015; 6:29. [PMID: 25767450 PMCID: PMC4341511 DOI: 10.3389/fpsyt.2015.00029] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2014] [Accepted: 02/11/2015] [Indexed: 12/20/2022] Open
Abstract
Major depressive disorder (MDD) is a serious condition with a lifetime prevalence exceeding 16% worldwide. MDD is a heterogeneous disorder that involves multiple behavioral symptoms on the one hand and multiple neuronal circuits on the other hand. In this review, we integrate the literature on cognitive and physiological biomarkers of MDD with the insights derived from mathematical models of brain networks, especially models that can be used for fMRI datasets. We refer to the recent NIH research domain criteria initiative, in which a concept of "constructs" as functional units of mental disorders is introduced. Constructs are biomarkers present at multiple levels of brain functioning - cognition, genetics, brain anatomy, and neurophysiology. In this review, we propose a new approach which we called circuit to construct mapping (CCM), which aims to characterize causal relations between the underlying network dynamics (as the cause) and the constructs referring to the clinical symptoms of MDD (as the effect). CCM involves extracting diagnostic categories from behavioral data, linking circuits that are causal to these categories with use of clinical neuroimaging data, and modeling the dynamics of the emerging circuits with attractor dynamics in order to provide new, neuroimaging-related biomarkers for MDD. The CCM approach optimizes the clinical diagnosis and patient stratification. It also addresses the recent demand for linking circuits to behavior, and provides a new insight into clinical treatment by investigating the dynamics of neuronal circuits underneath cognitive dimensions of MDD. CCM can serve as a new regime toward personalized medicine, assisting the diagnosis and treatment of MDD.
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Affiliation(s)
- Natalia Z Bielczyk
- Donders Institute for Brain, Cognition and Behavior , Nijmegen , Netherlands ; Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre , Nijmegen , Netherlands
| | - Jan K Buitelaar
- Donders Institute for Brain, Cognition and Behavior , Nijmegen , Netherlands ; Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre , Nijmegen , Netherlands
| | - Jeffrey C Glennon
- Donders Institute for Brain, Cognition and Behavior , Nijmegen , Netherlands ; Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre , Nijmegen , Netherlands
| | - Paul H E Tiesinga
- Donders Institute for Brain, Cognition and Behavior , Nijmegen , Netherlands ; Department of Neuroinformatics, Radboud University Nijmegen , Nijmegen , Netherlands
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Fiore VG, Mannella F, Mirolli M, Latagliata EC, Valzania A, Cabib S, Dolan RJ, Puglisi-Allegra S, Baldassarre G. Corticolimbic catecholamines in stress: a computational model of the appraisal of controllability. Brain Struct Funct 2014; 220:1339-53. [PMID: 24578177 PMCID: PMC4409646 DOI: 10.1007/s00429-014-0727-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2013] [Accepted: 02/04/2014] [Indexed: 01/20/2023]
Abstract
Appraisal of a stressful situation and the possibility to control or avoid it is thought to involve frontal-cortical mechanisms. The precise mechanism underlying this appraisal and its translation into effective stress coping (the regulation of physiological and behavioural responses) are poorly understood. Here, we propose a computational model which involves tuning motivational arousal to the appraised stressing condition. The model provides a causal explanation of the shift from active to passive coping strategies, i.e. from a condition characterised by high motivational arousal, required to deal with a situation appraised as stressful, to a condition characterised by emotional and motivational withdrawal, required when the stressful situation is appraised as uncontrollable/unavoidable. The model is motivated by results acquired via microdialysis recordings in rats and highlights the presence of two competing circuits dominated by different areas of the ventromedial prefrontal cortex: these are shown having opposite effects on several subcortical areas, affecting dopamine outflow in the striatum, and therefore controlling motivation. We start by reviewing published data supporting structure and functioning of the neural model and present the computational model itself with its essential neural mechanisms. Finally, we show the results of a new experiment, involving the condition of repeated inescapable stress, which validate most of the model's predictions.
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Affiliation(s)
- Vincenzo G. Fiore
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, UCL, 12 Queen Square, London, WC1N 3BG UK
| | - Francesco Mannella
- Laboratory of Computational Embodied Neuroscience, Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche (LOCEN-ISTC-CNR), Via San Martino della Battaglia 44, 00185 Rome, Italy
| | - Marco Mirolli
- Laboratory of Computational Embodied Neuroscience, Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche (LOCEN-ISTC-CNR), Via San Martino della Battaglia 44, 00185 Rome, Italy
| | - Emanuele Claudio Latagliata
- Dipartimento di Psicologia and Centro Daniel Bovet, Sapienza Università di Roma, Via dei Marsi 78, 00183 Rome, Italy
- Fondazione Santa Lucia, IRCCS, Via Ardeatina 306, 00142 Rome, Italy
| | - Alessandro Valzania
- Dipartimento di Psicologia and Centro Daniel Bovet, Sapienza Università di Roma, Via dei Marsi 78, 00183 Rome, Italy
- Fondazione Santa Lucia, IRCCS, Via Ardeatina 306, 00142 Rome, Italy
| | - Simona Cabib
- Dipartimento di Psicologia and Centro Daniel Bovet, Sapienza Università di Roma, Via dei Marsi 78, 00183 Rome, Italy
- Fondazione Santa Lucia, IRCCS, Via Ardeatina 306, 00142 Rome, Italy
| | - Raymond J. Dolan
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, UCL, 12 Queen Square, London, WC1N 3BG UK
| | - Stefano Puglisi-Allegra
- Dipartimento di Psicologia and Centro Daniel Bovet, Sapienza Università di Roma, Via dei Marsi 78, 00183 Rome, Italy
- Fondazione Santa Lucia, IRCCS, Via Ardeatina 306, 00142 Rome, Italy
| | - Gianluca Baldassarre
- Laboratory of Computational Embodied Neuroscience, Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche (LOCEN-ISTC-CNR), Via San Martino della Battaglia 44, 00185 Rome, Italy
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Petersen S, Zimmermann U, Schmidt C, Schwabe L, Warkentin M, Teipel SJ. Linking a neural mass model with a 3D model of the human brain to reproduce EEG signals. BIOMED ENG-BIOMED TE 2014; 59:231-40. [PMID: 24515994 DOI: 10.1515/bmt-2013-0062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2013] [Accepted: 01/13/2014] [Indexed: 11/15/2022]
Abstract
Electroencephalography (EEG) is often employed to measure electrical activity in the living human brain. Simulation studies can help unravel how the brain electrical activity pattern generates the EEG signal, still a widely unresolved question. This article describes a method to simulate brain electrical activity by using neuronal populations of a neural mass model. Implementing these populations in a finite element model of the head offers the opportunity to investigate the influence of each group of neurons to the scalp potential. This model is based on structural magnetic resonance imaging data to specify tissue composition, and diffusion tensor imaging data to model local anisotropy. We simulated the EEG signals of five neuronal populations generating α waves in the visual cortex. Our results indicate that radially oriented sources dominate over tangential sources in the generation of the scalp signal. Investigating the influence of anisotropic conductivity, we found small differences in topography and phase and larger ones for the potential amplitude compared with an isotropic conductivity distribution. The outcome of this article is a fast method based on superposition of sources for simulating time-dependent EEG signals, which can be used for further studies of neurodegenerative diseases.
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40
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van Putten MJAM, Zandt BJ. Neural mass modeling for predicting seizures. Clin Neurophysiol 2013; 125:867-8. [PMID: 24326320 DOI: 10.1016/j.clinph.2013.11.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2013] [Revised: 11/18/2013] [Accepted: 11/19/2013] [Indexed: 12/01/2022]
Affiliation(s)
- Michel J A M van Putten
- Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente and Clinical Neurophysiology group, MIRA-Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands.
| | - Bas-Jan Zandt
- Neuroimaging group, MIRA-Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands.
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41
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Wang P, Knösche TR. A realistic neural mass model of the cortex with laminar-specific connections and synaptic plasticity - evaluation with auditory habituation. PLoS One 2013; 8:e77876. [PMID: 24205009 PMCID: PMC3813749 DOI: 10.1371/journal.pone.0077876] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2013] [Accepted: 09/05/2013] [Indexed: 11/18/2022] Open
Abstract
In this work we propose a biologically realistic local cortical circuit model (LCCM), based on neural masses, that incorporates important aspects of the functional organization of the brain that have not been covered by previous models: (1) activity dependent plasticity of excitatory synaptic couplings via depleting and recycling of neurotransmitters and (2) realistic inter-laminar dynamics via laminar-specific distribution of and connections between neural populations. The potential of the LCCM was demonstrated by accounting for the process of auditory habituation. The model parameters were specified using Bayesian inference. It was found that: (1) besides the major serial excitatory information pathway (layer 4 to layer 2/3 to layer 5/6), there exists a parallel "short-cut" pathway (layer 4 to layer 5/6), (2) the excitatory signal flow from the pyramidal cells to the inhibitory interneurons seems to be more intra-laminar while, in contrast, the inhibitory signal flow from inhibitory interneurons to the pyramidal cells seems to be both intra- and inter-laminar, and (3) the habituation rates of the connections are unsymmetrical: forward connections (from layer 4 to layer 2/3) are more strongly habituated than backward connections (from Layer 5/6 to layer 4). Our evaluation demonstrates that the novel features of the LCCM are of crucial importance for mechanistic explanations of brain function. The incorporation of these features into a mass model makes them applicable to modeling based on macroscopic data (like EEG or MEG), which are usually available in human experiments. Our LCCM is therefore a valuable building block for future realistic models of human cognitive function.
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Affiliation(s)
- Peng Wang
- Max Planck Institute for Human Cognitive and Brain Sciences, MEG and Cortical Networks, Leipzig, Germany
| | - Thomas R. Knösche
- Max Planck Institute for Human Cognitive and Brain Sciences, MEG and Cortical Networks, Leipzig, Germany
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42
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Spiegler A, Jirsa V. Systematic approximations of neural fields through networks of neural masses in the virtual brain. Neuroimage 2013; 83:704-25. [PMID: 23774395 DOI: 10.1016/j.neuroimage.2013.06.018] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Revised: 05/03/2013] [Accepted: 06/03/2013] [Indexed: 11/30/2022] Open
Abstract
Full brain network models comprise a large-scale connectivity (the connectome) and neural mass models as the network's nodes. Neural mass models absorb implicitly a variety of properties in their constant parameters to achieve a reduction in complexity. In situations, where the local network connectivity undergoes major changes, such as in development or epilepsy, it becomes crucial to model local connectivity explicitly. This leads naturally to a description of neural fields on folded cortical sheets with local and global connectivities. The numerical approximation of neural fields in biologically realistic situations as addressed in Virtual Brain simulations (see http://thevirtualbrain.org/app/ (version 1.0)) is challenging and requires a thorough evaluation if the Virtual Brain approach is to be adapted for systematic studies of disease and disorders. Here we analyze the sampling problem of neural fields for arbitrary dimensions and provide explicit results for one, two and three dimensions relevant to realistically folded cortical surfaces. We characterize (i) the error due to sampling of spatial distribution functions; (ii) useful sampling parameter ranges in the context of encephalographic (EEG, MEG, ECoG and functional MRI) signals; (iii) guidelines for choosing the right spatial distribution function for given anatomical and geometrical constraints.
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Affiliation(s)
- A Spiegler
- Institut de Neurosciences des Systèmes, UMR INSERM 1106, Aix-Marseille Université, Faculté de Médecine, 27, Boulevard Jean Moulin, 13005 Marseille, France.
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Sanz Leon P, Knock SA, Woodman MM, Domide L, Mersmann J, McIntosh AR, Jirsa V. The Virtual Brain: a simulator of primate brain network dynamics. Front Neuroinform 2013; 7:10. [PMID: 23781198 PMCID: PMC3678125 DOI: 10.3389/fninf.2013.00010] [Citation(s) in RCA: 205] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2013] [Accepted: 05/22/2013] [Indexed: 01/21/2023] Open
Abstract
We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.
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Affiliation(s)
- Paula Sanz Leon
- Institut de Neurosciences des Systèmes, Aix Marseille Université Marseille, France
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44
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Modolo J, Thomas AW, Legros A. Neural mass modeling of power-line magnetic fields effects on brain activity. Front Comput Neurosci 2013; 7:34. [PMID: 23596412 PMCID: PMC3622877 DOI: 10.3389/fncom.2013.00034] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2012] [Accepted: 03/25/2013] [Indexed: 11/13/2022] Open
Abstract
Neural mass models are an appropriate framework to study brain activity, combining a high degree of biological realism while being mathematically tractable. These models have been used, with a certain success, to simulate brain electric (electroencephalography, EEG) and metabolic (functional magnetic resonance imaging, fMRI) activity. However, concrete applications of neural mass models have remained limited to date. Motivated by experimental results obtained in humans, we propose in this paper a neural mass model designed to study the interaction between power-line magnetic fields (MFs) (60 Hz in North America) and brain activity. The model includes pyramidal cells; dendrite-projecting, slow GABAergic neurons; soma-projecting, fast GABAergic neurons; and glutamatergic interneurons. A simple phenomenological model of interaction between the induced electric field and neuron membranes is also considered, along with a model of post-synaptic calcium concentration and associated changes in synaptic weights Simulated EEG signals are produced in a simple protocol, both in the absence and presence of a 60 Hz MF. These results are discussed based on results obtained previously in humans. Notably, results highlight that (1) EEG alpha (8-12 Hz) power can be modulated by weak membrane depolarizations induced by the exposure; (2) the level of input noise has a significant impact on EEG power modulation; and (3) the threshold value in MF flux density resulting in a significant effect on the EEG depends on the type of neuronal populations modulated by the MF exposure. Results obtained from the model shed new light on the effects of power-line MFs on brain activity, and will provide guidance in future human experiments. This may represent a valuable contribution to international regulation agencies setting guidelines on MF values to which the general public and workers can be exposed.
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Affiliation(s)
- J Modolo
- Human Threshold Research Group, Lawson Health Research Institute London, ON, Canada ; Department of Medical Biophysics, Western University London, ON, Canada ; Department of Medical Imaging, Western University London, ON, Canada
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45
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Taylor PN, Goodfellow M, Wang Y, Baier G. Towards a large-scale model of patient-specific epileptic spike-wave discharges. BIOLOGICAL CYBERNETICS 2013; 107:83-94. [PMID: 23132433 DOI: 10.1007/s00422-012-0534-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2012] [Accepted: 10/17/2012] [Indexed: 06/01/2023]
Abstract
Clinical electroencephalographic (EEG) recordings of the transition into generalised epileptic seizures show a sudden onset of spike-wave dynamics from a low-amplitude irregular background. In addition, non-trivial and variable spatio-temporal dynamics are widely reported in combined EEG/fMRI studies on the scale of the whole cortex. It is unknown whether these characteristics can be accounted for in a large-scale mathematical model with fixed heterogeneous long-range connectivities. Here, we develop a modelling framework with which to investigate such EEG features. We show that a neural field model composed of a few coupled compartments can serve as a low-dimensional prototype for the transition between irregular background dynamics and spike-wave activity. This prototype then serves as a node in a large-scale network with long-range connectivities derived from human diffusion-tensor imaging data. We examine multivariate properties in 42 clinical EEG seizure recordings from 10 patients diagnosed with typical absence epilepsy and 50 simulated seizures from the large-scale model using 10 DTI connectivity sets from humans. The model can reproduce the clinical feature of stereotypy where seizures are more similar within a patient than between patients, essentially creating a patient-specific fingerprint. We propose the approach as a feasible technique for the investigation of patient-specific large-scale epileptic features in space and time.
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Affiliation(s)
- Peter Neal Taylor
- Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester M1 7DN, UK.
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46
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Barrès V, Simons A, Arbib M. Synthetic event-related potentials: A computational bridge between neurolinguistic models and experiments. Neural Netw 2013. [DOI: 10.1016/j.neunet.2012.09.021] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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Ritter P, Schirner M, McIntosh AR, Jirsa VK. The virtual brain integrates computational modeling and multimodal neuroimaging. Brain Connect 2013; 3:121-45. [PMID: 23442172 PMCID: PMC3696923 DOI: 10.1089/brain.2012.0120] [Citation(s) in RCA: 143] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Brain function is thought to emerge from the interactions among neuronal populations. Apart from traditional efforts to reproduce brain dynamics from the micro- to macroscopic scales, complementary approaches develop phenomenological models of lower complexity. Such macroscopic models typically generate only a few selected-ideally functionally relevant-aspects of the brain dynamics. Importantly, they often allow an understanding of the underlying mechanisms beyond computational reproduction. Adding detail to these models will widen their ability to reproduce a broader range of dynamic features of the brain. For instance, such models allow for the exploration of consequences of focal and distributed pathological changes in the system, enabling us to identify and develop approaches to counteract those unfavorable processes. Toward this end, The Virtual Brain (TVB) ( www.thevirtualbrain.org ), a neuroinformatics platform with a brain simulator that incorporates a range of neuronal models and dynamics at its core, has been developed. This integrated framework allows the model-based simulation, analysis, and inference of neurophysiological mechanisms over several brain scales that underlie the generation of macroscopic neuroimaging signals. In this article, we describe how TVB works, and we present the first proof of concept.
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Affiliation(s)
- Petra Ritter
- Minerva Research Group Brain Modes, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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48
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Abstract
Modern imaging methods allow a non-invasive assessment of both structural and functional brain connectivity. This has lead to the identification of disease-related alterations affecting functional connectivity. The mechanism of how such alterations in functional connectivity arise in a structured network of interacting neural populations is as yet poorly understood. Here we use a modeling approach to explore the way in which this can arise and to highlight the important role that local population dynamics can have in shaping emergent spatial functional connectivity patterns. The local dynamics for a neural population is taken to be of the Wilson–Cowan type, whilst the structural connectivity patterns used, describing long-range anatomical connections, cover both realistic scenarios (from the CoComac database) and idealized ones that allow for more detailed theoretical study. We have calculated graph–theoretic measures of functional network topology from numerical simulations of model networks. The effect of the form of local dynamics on the observed network state is quantified by examining the correlation between structural and functional connectivity. We document a profound and systematic dependence of the simulated functional connectivity patterns on the parameters controlling the dynamics. Importantly, we show that a weakly coupled oscillator theory explaining these correlations and their variation across parameter space can be developed. This theoretical development provides a novel way to characterize the mechanisms for the breakdown of functional connectivity in diseases through changes in local dynamics.
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Affiliation(s)
- Jaroslav Hlinka
- Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodarenskou vezi 271/2, 182 07 Prague 8, Czech Republic.
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49
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Zhang Y, Yan B, Wang M, Hu J, Lu H, Li P. Linking brain behavior to underlying cellular mechanisms via large-scale brain modeling and simulation. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2012.05.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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50
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Baldassarre G, Mannella F, Fiore VG, Redgrave P, Gurney K, Mirolli M. Intrinsically motivated action-outcome learning and goal-based action recall: a system-level bio-constrained computational model. Neural Netw 2012; 41:168-87. [PMID: 23098753 DOI: 10.1016/j.neunet.2012.09.015] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2012] [Revised: 06/21/2012] [Accepted: 09/25/2012] [Indexed: 10/27/2022]
Abstract
Reinforcement (trial-and-error) learning in animals is driven by a multitude of processes. Most animals have evolved several sophisticated systems of 'extrinsic motivations' (EMs) that guide them to acquire behaviours allowing them to maintain their bodies, defend against threat, and reproduce. Animals have also evolved various systems of 'intrinsic motivations' (IMs) that allow them to acquire actions in the absence of extrinsic rewards. These actions are used later to pursue such rewards when they become available. Intrinsic motivations have been studied in Psychology for many decades and their biological substrates are now being elucidated by neuroscientists. In the last two decades, investigators in computational modelling, robotics and machine learning have proposed various mechanisms that capture certain aspects of IMs. However, we still lack models of IMs that attempt to integrate all key aspects of intrinsically motivated learning and behaviour while taking into account the relevant neurobiological constraints. This paper proposes a bio-constrained system-level model that contributes a major step towards this integration. The model focusses on three processes related to IMs and on the neural mechanisms underlying them: (a) the acquisition of action-outcome associations (internal models of the agent-environment interaction) driven by phasic dopamine signals caused by sudden, unexpected changes in the environment; (b) the transient focussing of visual gaze and actions on salient portions of the environment; (c) the subsequent recall of actions to pursue extrinsic rewards based on goal-directed reactivation of the representations of their outcomes. The tests of the model, including a series of selective lesions, show how the focussing processes lead to a faster learning of action-outcome associations, and how these associations can be recruited for accomplishing goal-directed behaviours. The model, together with the background knowledge reviewed in the paper, represents a framework that can be used to guide the design and interpretation of empirical experiments on IMs, and to computationally validate and further develop theories on them.
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Affiliation(s)
- Gianluca Baldassarre
- Laboratory of Computational Embodied Neuroscience, Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche (LOCEN-ISTC-CNR), Via San Martino della Battaglia 44, I-00185 Roma, Italy.
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