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Courson J, Quoy M, Timofeeva Y, Manos T. An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control. Front Comput Neurosci 2024; 18:1360009. [PMID: 38468870 PMCID: PMC10925689 DOI: 10.3389/fncom.2024.1360009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 02/08/2024] [Indexed: 03/13/2024] Open
Abstract
Mean-field models have been developed to replicate key features of epileptic seizure dynamics. However, the precise mechanisms and the role of the brain area responsible for seizure onset and propagation remain incompletely understood. In this study, we employ computational methods within The Virtual Brain framework and the Epileptor model to explore how the location and connectivity of an Epileptogenic Zone (EZ) in a mouse brain are related to focal seizures (seizures that start in one brain area and may or may not remain localized), with a specific focus on the hippocampal region known for its association with epileptic seizures. We then devise computational strategies to confine seizures (prevent widespread propagation), simulating medical-like treatments such as tissue resection and the application of an anti-seizure drugs or neurostimulation to suppress hyperexcitability. Through selectively removing (blocking) specific connections informed by the structural connectome and graph network measurements or by locally reducing outgoing connection weights of EZ areas, we demonstrate that seizures can be kept constrained around the EZ region. We successfully identified the minimal connections necessary to prevent widespread seizures, with a particular focus on minimizing surgical or medical intervention while simultaneously preserving the original structural connectivity and maximizing brain functionality.
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Affiliation(s)
- Juliette Courson
- ETIS Lab, ENSEA, CNRS, UMR8051, CY Cergy-Paris University, Cergy, France
- Laboratoire de Physique Théorique et Modélisation, UMR 8089, CY Cergy Paris Université, CNRS, Cergy-Pontoise, France
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Mathias Quoy
- ETIS Lab, ENSEA, CNRS, UMR8051, CY Cergy-Paris University, Cergy, France
- IPAL CNRS Singapore, Singapore, Singapore
| | - Yulia Timofeeva
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Thanos Manos
- ETIS Lab, ENSEA, CNRS, UMR8051, CY Cergy-Paris University, Cergy, France
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Cabrera-Álvarez J, Sánchez-Claros J, Carrasco-Gómez M, del Cerro-León A, Gómez-Ariza CJ, Maestú F, Mirasso CR, Susi G. Corrigendum: Understanding the effects of cortical gyrification in tACS: insights from experiments and computational models. Front Neurosci 2023; 17:1329826. [PMID: 38046655 PMCID: PMC10691734 DOI: 10.3389/fnins.2023.1329826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 12/05/2023] Open
Abstract
[This corrects the article DOI: 10.3389/fnins.2023.1223950.].
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Affiliation(s)
- Jesús Cabrera-Álvarez
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Jaime Sánchez-Claros
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, UIB-CSIC), Campus UIB, Palma de Mallorca, Spain
| | - Martín Carrasco-Gómez
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | - Alberto del Cerro-León
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | | | - Fernando Maestú
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Claudio R. Mirasso
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, UIB-CSIC), Campus UIB, Palma de Mallorca, Spain
| | - Gianluca Susi
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Structure of Matter, Thermal Physics and Electronics, School of Physics, Complutense University of Madrid, Madrid, Spain
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Cabrera-Álvarez J, Sánchez-Claros J, Carrasco-Gómez M, del Cerro-León A, Gómez-Ariza CJ, Maestú F, Mirasso CR, Susi G. Understanding the effects of cortical gyrification in tACS: insights from experiments and computational models. Front Neurosci 2023; 17:1223950. [PMID: 37655010 PMCID: PMC10467425 DOI: 10.3389/fnins.2023.1223950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/25/2023] [Indexed: 09/02/2023] Open
Abstract
The alpha rhythm is often associated with relaxed wakefulness or idling and is altered by various factors. Abnormalities in the alpha rhythm have been linked to several neurological and psychiatric disorders, including Alzheimer's disease. Transcranial alternating current stimulation (tACS) has been proposed as a potential tool to restore a disrupted alpha rhythm in the brain by stimulating at the individual alpha frequency (IAF), although some research has produced contradictory results. In this study, we applied an IAF-tACS protocol over parieto-occipital areas to a sample of healthy subjects and measured its effects over the power spectra. Additionally, we used computational models to get a deeper understanding of the results observed in the experiment. Both experimental and numerical results showed an increase in alpha power of 8.02% with respect to the sham condition in a widespread set of regions in the cortex, excluding some expected parietal regions. This result could be partially explained by taking into account the orientation of the electric field with respect to the columnar structures of the cortex, showing that the gyrification in parietal regions could generate effects in opposite directions (hyper-/depolarization) at the same time in specific brain regions. Additionally, we used a network model of spiking neuronal populations to explore the effects that these opposite polarities could have on neural activity, and we found that the best predictor of alpha power was the average of the normal components of the electric field. To sum up, our study sheds light on the mechanisms underlying tACS brain activity modulation, using both empirical and computational approaches. Non-invasive brain stimulation techniques hold promise for treating brain disorders, but further research is needed to fully understand and control their effects on brain dynamics and cognition. Our findings contribute to this growing body of research and provide a foundation for future studies aimed at optimizing the use of non-invasive brain stimulation in clinical settings.
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Affiliation(s)
- Jesús Cabrera-Álvarez
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Jaime Sánchez-Claros
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, UIB-CSIC), Campus UIB, Palma de Mallorca, Spain
| | - Martín Carrasco-Gómez
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | - Alberto del Cerro-León
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | | | - Fernando Maestú
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Claudio R. Mirasso
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, UIB-CSIC), Campus UIB, Palma de Mallorca, Spain
| | - Gianluca Susi
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Structure of Matter, Thermal Physics and Electronics, School of Physics, Complutense University of Madrid, Madrid, Spain
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van der Vlag M, Woodman M, Fousek J, Diaz-Pier S, Pérez Martín A, Jirsa V, Morrison A. RateML: A Code Generation Tool for Brain Network Models. Front Netw Physiol 2022; 2:826345. [PMID: 36926112 PMCID: PMC10013028 DOI: 10.3389/fnetp.2022.826345] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022]
Abstract
Whole brain network models are now an established tool in scientific and clinical research, however their use in a larger workflow still adds significant informatics complexity. We propose a tool, RateML, that enables users to generate such models from a succinct declarative description, in which the mathematics of the model are described without specifying how their simulation should be implemented. RateML builds on NeuroML's Low Entropy Model Specification (LEMS), an XML based language for specifying models of dynamical systems, allowing descriptions of neural mass and discretized neural field models, as implemented by the Virtual Brain (TVB) simulator: the end user describes their model's mathematics once and generates and runs code for different languages, targeting both CPUs for fast single simulations and GPUs for parallel ensemble simulations. High performance parallel simulations are crucial for tuning many parameters of a model to empirical data such as functional magnetic resonance imaging (fMRI), with reasonable execution times on small or modest hardware resources. Specifically, while RateML can generate Python model code, it enables generation of Compute Unified Device Architecture C++ code for NVIDIA GPUs. When a CUDA implementation of a model is generated, a tailored model driver class is produced, enabling the user to tweak the driver by hand and perform the parameter sweep. The model and driver can be executed on any compute capable NVIDIA GPU with a high degree of parallelization, either locally or in a compute cluster environment. The results reported in this manuscript show that with the CUDA code generated by RateML, it is possible to explore thousands of parameter combinations with a single Graphics Processing Unit for different models, substantially reducing parameter exploration times and resource usage for the brain network models, in turn accelerating the research workflow itself. This provides a new tool to create efficient and broader parameter fitting workflows, support studies on larger cohorts, and derive more robust and statistically relevant conclusions about brain dynamics.
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Affiliation(s)
- Michiel van der Vlag
- Simulation and Data Lab Neuroscience, Institute for Advanced Simulation, Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, JARA, Jülich, Germany
| | - Marmaduke Woodman
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Jan Fousek
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Sandra Diaz-Pier
- Simulation and Data Lab Neuroscience, Institute for Advanced Simulation, Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, JARA, Jülich, Germany
| | - Aarón Pérez Martín
- Simulation and Data Lab Neuroscience, Institute for Advanced Simulation, Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, JARA, Jülich, Germany
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Abigail Morrison
- Simulation and Data Lab Neuroscience, Institute for Advanced Simulation, Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, JARA, Jülich, Germany.,Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain, Jülich, Germany.,Computer Science 3-Software Engineering, RWTH Aachen University, Aachen, Germany
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Allegra Mascaro AL, Falotico E, Petkoski S, Pasquini M, Vannucci L, Tort-Colet N, Conti E, Resta F, Spalletti C, Ramalingasetty ST, von Arnim A, Formento E, Angelidis E, Blixhavn CH, Leergaard TB, Caleo M, Destexhe A, Ijspeert A, Micera S, Laschi C, Jirsa V, Gewaltig MO, Pavone FS. Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience. Front Syst Neurosci 2020; 14:31. [PMID: 32733210 PMCID: PMC7359878 DOI: 10.3389/fnsys.2020.00031] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 05/08/2020] [Indexed: 01/22/2023] Open
Abstract
Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.
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Affiliation(s)
- Anna Letizia Allegra Mascaro
- Neuroscience Institute, National Research Council, Pisa, Italy.,European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy
| | - Egidio Falotico
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Spase Petkoski
- Aix-Marseille Université, Inserm, INS UMR_S 1106, Marseille, France
| | - Maria Pasquini
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Lorenzo Vannucci
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Núria Tort-Colet
- Paris-Saclay University, Institute of Neuroscience, CNRS, Gif-sur-Yvette, France
| | - Emilia Conti
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
| | - Francesco Resta
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
| | | | | | | | - Emanuele Formento
- Bertarelli Foundation Chair in Translational NeuroEngineering, Institute of Bioengineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Emmanouil Angelidis
- Fortiss GmbH, Munich, Germany.,Chair of Robotics, Artificial Intelligence and Embedded Systems, Department of Informatics, Technical University of Munich, Munich, Germany
| | | | | | - Matteo Caleo
- Neuroscience Institute, National Research Council, Pisa, Italy.,Department of Biomedical Sciences, University of Padua, Padua, Italy
| | - Alain Destexhe
- Paris-Saclay University, Institute of Neuroscience, CNRS, Gif-sur-Yvette, France
| | - Auke Ijspeert
- Biorobotics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Silvestro Micera
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy.,Bertarelli Foundation Chair in Translational NeuroEngineering, Institute of Bioengineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Cecilia Laschi
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Viktor Jirsa
- Aix-Marseille Université, Inserm, INS UMR_S 1106, Marseille, France
| | - Marc-Oliver Gewaltig
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Francesco S Pavone
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
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Abstract
See Lytton (doi:10.1093/awx018) for a scientific commentary on this article.Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions.
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Affiliation(s)
- Timothée Proix
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.,Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, 13005 Marseille, France
| | - Maxime Guye
- Aix-Marseille Université, Centre de Résonance Magnétique et Biologique et Médicale (CRMBM, UMR CNRS-AMU 7339), Medical School of Marseille, 13005, Marseille, France.,Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, CEMEREM, Pôle d'Imagerie Médicale, CHU, 13005, Marseille, France
| | - Viktor K Jirsa
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
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Saeedghalati M, Abbassian A. Modeling spatio-temporal dynamics of network damage and network recovery. Front Comput Neurosci 2015; 9:130. [PMID: 26557071 PMCID: PMC4614320 DOI: 10.3389/fncom.2015.00130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Accepted: 10/07/2015] [Indexed: 11/24/2022] Open
Abstract
How networks endure damage is a central issue in neural network research. In this paper, we study the slow and fast dynamics of network damage and compare the results for two simple but very different models of recurrent and feed forward neural network. What we find is that a slower degree of network damage leads to a better chance of recovery in both types of network architecture. This is in accord with many experimental findings on the damage inflicted by strokes and by slowly growing tumors. Here, based on simulation results, we explain the seemingly paradoxical observation that disability caused by lesions, affecting large portions of tissue, may be less severe than the disability caused by smaller lesions, depending on the speed of lesion growth.
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Affiliation(s)
| | - Abdolhosein Abbassian
- BioMath, School of Mathematics, Institute for Research in Fundamental Sciences (IPM) Tehran, Iran
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