Mathematical framework for large-scale brain network modeling in The Virtual Brain.
Identifieur interne : 002B58 ( Main/Exploration ); précédent : 002B57; suivant : 002B59Mathematical framework for large-scale brain network modeling in The Virtual Brain.
Auteurs : Paula Sanz-Leon [France] ; Stuart A. Knock [Australie] ; Andreas Spiegler [France] ; Viktor K. Jirsa [France]Source :
- NeuroImage [ 1095-9572 ] ; 2015.
Descripteurs français
- KwdFr :
- MESH :
- anatomie et histologie : Encéphale, Réseau nerveux.
- physiologie : Encéphale, Réseau nerveux.
- Humains, Modèles neurologiques, Neuroimagerie fonctionnelle.
English descriptors
- KwdEn :
- MESH :
- anatomy & histology : Brain, Nerve Net.
- methods : Functional Neuroimaging.
- physiology : Brain, Nerve Net.
- Humans, Models, Neurological.
Abstract
In this article, we describe the mathematical framework of the computational model at the core of the tool The Virtual Brain (TVB), designed to simulate collective whole brain dynamics by virtualizing brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. Here, a consistent notation for the generalized BNM is given, so that in this form the equations represent a direct link between the mathematical description of BNMs and the components of the numerical implementation in TVB. Finally, we made a summary of the forward models implemented for mapping simulated neural activity (EEG, MEG, sterotactic electroencephalogram (sEEG), fMRI), identifying their advantages and limitations.
DOI: 10.1016/j.neuroimage.2015.01.002
PubMed: 25592995
Affiliations:
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Electronic address: paula.sanz-leon@univ-amu.fr.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Institut de Neurosciences des Systèmes, INSERM UMR 1106, Aix-Marseille Université, Marseille</wicri:regionArea><placeName><region type="region">Provence-Alpes-Côte d'Azur</region><region type="old region">Provence-Alpes-Côte d'Azur</region><settlement type="city">Marseille</settlement><settlement type="city">Marseille</settlement></placeName><orgName type="university">Université d'Aix-Marseille</orgName></affiliation></author><author><name sortKey="Knock, Stuart A" sort="Knock, Stuart A" uniqKey="Knock S" first="Stuart A" last="Knock">Stuart A. Knock</name><affiliation wicri:level="1"><nlm:affiliation>Systems Neuroscience Group, Queensland Institute of Medical Research Berghofer, Brisbane, Australia. 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Electronic address: viktor.jirsa@univ-amu.fr.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Institut de Neurosciences des Systèmes, INSERM UMR 1106, Aix-Marseille Université, Marseille</wicri:regionArea><placeName><region type="region">Provence-Alpes-Côte d'Azur</region><region type="old region">Provence-Alpes-Côte d'Azur</region><settlement type="city">Marseille</settlement><settlement type="city">Marseille</settlement></placeName><orgName type="university">Université d'Aix-Marseille</orgName></affiliation></author></analytic><series><title level="j">NeuroImage</title><idno type="eISSN">1095-9572</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Brain (anatomy & histology)</term><term>Brain (physiology)</term><term>Functional Neuroimaging (methods)</term><term>Humans</term><term>Models, Neurological</term><term>Nerve Net (anatomy & histology)</term><term>Nerve Net (physiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Encéphale (anatomie et histologie)</term><term>Encéphale (physiologie)</term><term>Humains</term><term>Modèles neurologiques</term><term>Neuroimagerie fonctionnelle ()</term><term>Réseau nerveux (anatomie et histologie)</term><term>Réseau nerveux (physiologie)</term></keywords><keywords scheme="MESH" qualifier="anatomie et histologie" xml:lang="fr"><term>Encéphale</term><term>Réseau nerveux</term></keywords><keywords scheme="MESH" qualifier="anatomy & histology" xml:lang="en"><term>Brain</term><term>Nerve Net</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Functional Neuroimaging</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Encéphale</term><term>Réseau nerveux</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Brain</term><term>Nerve Net</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term><term>Models, Neurological</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Humains</term><term>Modèles neurologiques</term><term>Neuroimagerie fonctionnelle</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this article, we describe the mathematical framework of the computational model at the core of the tool The Virtual Brain (TVB), designed to simulate collective whole brain dynamics by virtualizing brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. Here, a consistent notation for the generalized BNM is given, so that in this form the equations represent a direct link between the mathematical description of BNMs and the components of the numerical implementation in TVB. Finally, we made a summary of the forward models implemented for mapping simulated neural activity (EEG, MEG, sterotactic electroencephalogram (sEEG), fMRI), identifying their advantages and limitations.</div></front></TEI><affiliations><list><country><li>Australie</li><li>France</li></country><region><li>Provence-Alpes-Côte d'Azur</li></region><settlement><li>Marseille</li></settlement><orgName><li>Université d'Aix-Marseille</li></orgName></list><tree><country name="France"><region name="Provence-Alpes-Côte d'Azur"><name sortKey="Sanz Leon, Paula" sort="Sanz Leon, Paula" uniqKey="Sanz Leon P" first="Paula" last="Sanz-Leon">Paula Sanz-Leon</name></region><name sortKey="Jirsa, Viktor K" sort="Jirsa, Viktor K" uniqKey="Jirsa V" first="Viktor K" last="Jirsa">Viktor K. Jirsa</name><name sortKey="Spiegler, Andreas" sort="Spiegler, Andreas" uniqKey="Spiegler A" first="Andreas" last="Spiegler">Andreas Spiegler</name></country><country name="Australie"><noRegion><name sortKey="Knock, Stuart A" sort="Knock, Stuart A" uniqKey="Knock S" first="Stuart A" last="Knock">Stuart A. Knock</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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