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Granger causality analysis of fMRI BOLD signals is invariant to hemodynamic convolution but not downsampling
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posted on 2023-06-08, 16:09 authored by Anil SethAnil Seth, Paul Chorley, Lionel BarnettLionel BarnettGranger causality is a method for identifying directed functional connectivity based on time series analysis of precedence and predictability. The method has been applied widely in neuroscience, however its application to functional MRI data has been particularly controversial, largely because of the suspicion that Granger causal inferences might be easily confounded by inter-regional differences in the hemodynamic response function. Here, we show both theoretically and in a range of simulations, that Granger causal inferences are in fact robust to a wide variety of changes in hemodynamic response properties, including notably their time-to-peak. However, when these changes are accompanied by severe downsampling, and/or exces- sive measurement noise, as is typical for current fMRI data, incorrect inferences can still be drawn. Our results have important implications for the ongoing debate about lag-based analyses of functional connectivity. Our methods, which include detailed spiking neuronal models coupled to biophysically realistic hemodynamic observation models, provide an important ‘analysis-agnostic’ platform for evaluating functional and effective connectivity methods.
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Publication status
- Published
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- Published version
Journal
NeuroImageISSN
1053-8119Publisher
ElsevierExternal DOI
Volume
65Page range
540-555Department affiliated with
- Informatics Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2013-10-20First Compliant Deposit (FCD) Date
2013-10-18Usage metrics
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