Joint independent component analysis for simultaneous EEG–fMRI: Principle and simulation
An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD–fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible t...
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| Published in | International journal of psychophysiology Vol. 67; no. 3; pp. 212 - 221 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
01.03.2008
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-8760 1872-7697 |
| DOI | 10.1016/j.ijpsycho.2007.05.016 |
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| Abstract | An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD–fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG–fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain. |
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| AbstractList | An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD-fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG-fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain. An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD-fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG-fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain.An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD-fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG-fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain. |
| Author | Nordby, Helge Hugdahl, Kenneth Moosmann, Matthias Eichele, Tom Calhoun, Vince D. |
| AuthorAffiliation | 2 Haukeland University Hospital, Bergen, Norway 1 Department of Biological and Medical Psychology, University of Bergen, Norway 3 MIND Institute, Albuquerque, New Mexico 5 Dept. of Psychiatry, Yale University School of Medicine, New Haven, Connecticut 4 Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico |
| AuthorAffiliation_xml | – name: 5 Dept. of Psychiatry, Yale University School of Medicine, New Haven, Connecticut – name: 1 Department of Biological and Medical Psychology, University of Bergen, Norway – name: 3 MIND Institute, Albuquerque, New Mexico – name: 2 Haukeland University Hospital, Bergen, Norway – name: 4 Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico |
| Author_xml | – sequence: 1 givenname: Matthias surname: Moosmann fullname: Moosmann, Matthias email: moosmann@gmail.com organization: Department of Biological and Medical Psychology, University of Bergen, Jonas Lies Vei 91, 5011 Bergen, Norway – sequence: 2 givenname: Tom surname: Eichele fullname: Eichele, Tom organization: Department of Biological and Medical Psychology, University of Bergen, Jonas Lies Vei 91, 5011 Bergen, Norway – sequence: 3 givenname: Helge surname: Nordby fullname: Nordby, Helge organization: Department of Biological and Medical Psychology, University of Bergen, Jonas Lies Vei 91, 5011 Bergen, Norway – sequence: 4 givenname: Kenneth surname: Hugdahl fullname: Hugdahl, Kenneth organization: Department of Biological and Medical Psychology, University of Bergen, Jonas Lies Vei 91, 5011 Bergen, Norway – sequence: 5 givenname: Vince D. surname: Calhoun fullname: Calhoun, Vince D. organization: MIND Institute, Albuquerque, New Mexico, United States |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/17688965$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Algorithms Brain Mapping - instrumentation Brain Mapping - methods Cerebral Cortex - blood supply Cerebral Cortex - metabolism Cerebrovascular Circulation Computer Simulation Data fusion EEG-fMRI Electroencephalography - instrumentation ERP Humans ICA Magnetic Resonance Imaging - instrumentation Modelling Oxygen - metabolism Principal Component Analysis Reproducibility of Results Signal Processing, Computer-Assisted Simulation |
| Title | Joint independent component analysis for simultaneous EEG–fMRI: Principle and simulation |
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