Reliability of Dynamic Causal Modeling using the Statistical Parametric Mapping Toolbox

Pegah T. Hosseini, Shouyan Wang, Julie Brinton, Steven Bell and David M. Simpson
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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
David M. Simpson: Institute of Sound and Vibration Research, University of Southampton, Southampton, UK

International Journal of System Dynamics Applications (IJSDA), 2014, vol. 3, issue 2, 1-16

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.

Date: 2014
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