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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
Published on: June 30, 2018
Dynamic causal modeling: a generative model of slice timing in fMRI
Stefan J Kiebel1, Stefan Klöppel, Nikolaus Weiskopf
1The Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK. skiebel@fil.ion.ucl.ac.uk
Neuroimage
|December 13, 2006
Summary
Dynamic causal modeling (DCM) for functional magnetic resonance imaging (fMRI) now accounts for slice timing differences. This enhanced DCM provides more accurate brain network connectivity insights, especially with longer repetition times.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Systems Neuroscience
Background:
- Functional magnetic resonance imaging (fMRI) data analysis often uses Dynamic Causal Modeling (DCM) to infer brain network effective connectivity.
- Standard DCM assumes simultaneous slice acquisition, which is not true for echo-planar imaging (EPI) sequences.
- Existing DCM methods struggle with long repetition times (TR) common in fMRI studies due to unaddressed slice timing differences.
Purpose of the Study:
- To extend Dynamic Causal Modeling (DCM) to incorporate slice timing information from functional magnetic resonance imaging (fMRI) data.
- To evaluate the performance of the extended DCM against the original model, particularly with significant slice timing variations.
- To compare the efficacy of modeling slice timing within DCM versus applying slice-timing correction prior to analysis.
Main Methods:
- Developed an extended Dynamic Causal Modeling (DCM) framework that explicitly includes slice timing parameters.
- Utilized synthetic fMRI data with varying slice timing differences to test the accuracy of the extended DCM.
- Performed model comparisons between the extended DCM and the original DCM, as well as contrasting with pre-analysis slice-timing correction.
Main Results:
- The extended DCM accurately estimates effective connectivity parameters (veridical posterior means) even with substantial slice timing differences.
- Model comparisons demonstrate that the extended DCM generally outperforms the original DCM.
- Analysis of real fMRI data confirms the significance of incorporating slice timing parameters directly into the DCM.
Conclusions:
- Incorporating slice timing into Dynamic Causal Modeling (DCM) is crucial for accurate effective connectivity analysis in fMRI.
- The extended DCM provides a more robust and accurate method for analyzing fMRI data, especially when using longer repetition times.
- Directly modeling slice timing within DCM is superior to applying slice-timing correction as a preprocessing step.
