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Reliability of Dynamic Causal Modeling using the Statistical Parametric Mapping Toolbox
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Author(s): Pegah T. Hosseini (Institute of Sound and Vibration Research, University of Southampton, Southampton, UK), Shouyan Wang (Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China), Julie Brinton (Auditory Implant Service, University of Southampton, Southampton, UK), Steven Bell (Institute of Sound and Vibration Research, University of Southampton, Southampton, UK)and David M. Simpson (Institute of Sound and Vibration Research, University of Southampton, Southampton, UK)
Copyright: 2014
Volume: 3
Issue: 2
Pages: 16
Source title:
International Journal of System Dynamics Applications (IJSDA)
Editor(s)-in-Chief: Ahmad Taher Azar (College of Computer & Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt)
DOI: 10.4018/ijsda.2014040101
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Abstract
Dynamic causal modeling (DCM) is a recently developed approach for effective connectivity measurement in the brain. It has attracted considerable attention in recent years and quite widespread used to investigate brain connectivity in response to different tasks as well as auditory, visual, and somatosensory stimulation. This method uses complex algorithms, and currently the only implementation available is the Statistical Parametric Mapping (SPM8) toolbox with functionality for use on EEG and fMRI. The objective of the current work is to test the robustness of the toolbox when applied to EEG, by comparing results obtained from various versions of the software and operating systems when using identical datasets. Contrary to expectations, it was found that estimated connectivities were not consistent between different operating systems, the version of SPM8, or the version of MATLAB being used. The exact cause of this problem is not clear, but may relate to the high number of parameters in the model. Caution is thus recommended when interpreting the results of DCM estimated with the SPM8 software.
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