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Reliability of dynamic causal modeling using the statistical parametric mapping toolbox

Reliability of dynamic causal modeling using the statistical parametric mapping toolbox
Reliability of dynamic causal modeling using the statistical parametric mapping toolbox
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.
dynamic causal modelling, effective connectivity, matlab, statistical parametric mapping spm8, robustness
2160-9772
1-16
Hosseini, Pegah T.
47511a4b-5adc-4e93-9d2a-46e3016c87fb
Wang, Shouyan
fa12f1bf-cac9-4118-abdd-9d52f235b05c
Brinton, Julie
573e7087-d630-44ad-82f6-d75f808e2538
Bell, Steven
91de0801-d2b7-44ba-8e8e-523e672aed8a
Simpson, David M.
53674880-f381-4cc9-8505-6a97eeac3c2a
Hosseini, Pegah T.
47511a4b-5adc-4e93-9d2a-46e3016c87fb
Wang, Shouyan
fa12f1bf-cac9-4118-abdd-9d52f235b05c
Brinton, Julie
573e7087-d630-44ad-82f6-d75f808e2538
Bell, Steven
91de0801-d2b7-44ba-8e8e-523e672aed8a
Simpson, David M.
53674880-f381-4cc9-8505-6a97eeac3c2a

Hosseini, Pegah T., Wang, Shouyan, Brinton, Julie, Bell, Steven and Simpson, David M. (2014) Reliability of dynamic causal modeling using the statistical parametric mapping toolbox. International Journal of System Dynamics Applications, 3 (2), 1-16. (doi:10.4018/ijsda.2014040101).

Record type: Article

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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Paper IJSDA 2013 v7 - Accepted Manuscript
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More information

e-pub ahead of print date: April 2014
Published date: April 2014
Keywords: dynamic causal modelling, effective connectivity, matlab, statistical parametric mapping spm8, robustness
Organisations: Inst. Sound & Vibration Research

Identifiers

Local EPrints ID: 369490
URI: http://eprints.soton.ac.uk/id/eprint/369490
ISSN: 2160-9772
PURE UUID: 1e16f2d8-0efb-4ab0-8b67-9f3bd93810d6

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Date deposited: 29 Sep 2014 10:23
Last modified: 12 Jun 2019 16:30

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Contributors

Author: Pegah T. Hosseini
Author: Shouyan Wang
Author: Julie Brinton
Author: Steven Bell

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