Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based Neuronal Equations

Lade...
Vorschaubild
Dateien
Sadeghi_2-2xz8mz81nmgs5.pdf
Sadeghi_2-2xz8mz81nmgs5.pdfGröße: 3.22 MBDownloads: 433
Datum
2020
Autor:innen
Sadeghi, Sadjad
Gerchen, Martin F.
Hass, Joachim
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Gold
Sammlungen
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
Frontiers in Neuroscience. Frontiers Research Foundation. 2020, 14, 593867. ISSN 1662-4548. eISSN 1662-453X. Available under: doi: 10.3389/fnins.2020.593867
Zusammenfassung

Dynamic causal modeling (DCM) is an analysis technique that has been successfully used to infer about directed connectivity between brain regions based on imaging data such as functional magnetic resonance imaging (fMRI). Most variants of DCM for fMRI rely on a simple bilinear differential equation for neural activation, making it difficult to interpret the results in terms of local neural dynamics. In this work, we introduce a modification to DCM for fMRI by replacing the bilinear equation with a non-linear Wilson-Cowan based equation and use Bayesian Model Comparison (BMC) to show that this modification improves the model evidences. Improved model evidence of the non-linear model is shown for our empirical data (imitation of facial expressions) and validated by synthetic data as well as an empirical test dataset (attention to visual motion) used in previous foundational papers. For our empirical data, we conduct the analysis for a group of 42 healthy participants who performed an imitation task, activating regions putatively containing the human mirror neuron system (MNS). In this regard, we build 540 models as one family for comparing the standard bilinear with the modified Wilson-Cowan models on the family-level. Using this modification, we can interpret the sigmoid transfer function as an averaged f-I curve of many neurons in a single region with a sigmoidal format. In this way, we can make a direct inference from the macroscopic model to detailed microscopic models. The new DCM variant shows superior model evidence on all tested data sets.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
150 Psychologie
Schlagwörter
dynamical causal modeling, fMRI, Bayesian model selection, Wilson-Cowan equation, effective connectivity, mirror neuron system
Konferenz
Rezension
undefined / . - undefined, undefined
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Datensätze
Zitieren
ISO 690SADEGHI, Sadjad, Daniela MIER, Martin F. GERCHEN, Stephanie N. L. SCHMIDT, Joachim HASS, 2020. Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based Neuronal Equations. In: Frontiers in Neuroscience. Frontiers Research Foundation. 2020, 14, 593867. ISSN 1662-4548. eISSN 1662-453X. Available under: doi: 10.3389/fnins.2020.593867
BibTex
@article{Sadeghi2020-11-27Dynam-52183,
  year={2020},
  doi={10.3389/fnins.2020.593867},
  title={Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based Neuronal Equations},
  volume={14},
  issn={1662-4548},
  journal={Frontiers in Neuroscience},
  author={Sadeghi, Sadjad and Mier, Daniela and Gerchen, Martin F. and Schmidt, Stephanie N. L. and Hass, Joachim},
  note={Article Number: 593867}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/52183">
    <dc:contributor>Mier, Daniela</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/>
    <dcterms:abstract xml:lang="eng">Dynamic causal modeling (DCM) is an analysis technique that has been successfully used to infer about directed connectivity between brain regions based on imaging data such as functional magnetic resonance imaging (fMRI). Most variants of DCM for fMRI rely on a simple bilinear differential equation for neural activation, making it difficult to interpret the results in terms of local neural dynamics. In this work, we introduce a modification to DCM for fMRI by replacing the bilinear equation with a non-linear Wilson-Cowan based equation and use Bayesian Model Comparison (BMC) to show that this modification improves the model evidences. Improved model evidence of the non-linear model is shown for our empirical data (imitation of facial expressions) and validated by synthetic data as well as an empirical test dataset (attention to visual motion) used in previous foundational papers. For our empirical data, we conduct the analysis for a group of 42 healthy participants who performed an imitation task, activating regions putatively containing the human mirror neuron system (MNS). In this regard, we build 540 models as one family for comparing the standard bilinear with the modified Wilson-Cowan models on the family-level. Using this modification, we can interpret the sigmoid transfer function as an averaged f-I curve of many neurons in a single region with a sigmoidal format. In this way, we can make a direct inference from the macroscopic model to detailed microscopic models. The new DCM variant shows superior model evidence on all tested data sets.</dcterms:abstract>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:creator>Schmidt, Stephanie N. L.</dc:creator>
    <dc:creator>Gerchen, Martin F.</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-12-21T09:21:28Z</dc:date>
    <dc:contributor>Hass, Joachim</dc:contributor>
    <dcterms:title>Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based Neuronal Equations</dcterms:title>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-12-21T09:21:28Z</dcterms:available>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/>
    <dc:contributor>Schmidt, Stephanie N. L.</dc:contributor>
    <dc:language>eng</dc:language>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/52183/1/Sadeghi_2-2xz8mz81nmgs5.pdf"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Mier, Daniela</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/52183"/>
    <dc:creator>Sadeghi, Sadjad</dc:creator>
    <dc:creator>Hass, Joachim</dc:creator>
    <dcterms:issued>2020-11-27</dcterms:issued>
    <dc:rights>Attribution 4.0 International</dc:rights>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/>
    <dc:contributor>Sadeghi, Sadjad</dc:contributor>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/52183/1/Sadeghi_2-2xz8mz81nmgs5.pdf"/>
    <dc:contributor>Gerchen, Martin F.</dc:contributor>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Ja
Diese Publikation teilen