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Granger causality analysis of fMRI BOLD signals is invariant to hemodynamic convolution but not downsampling

journal contribution
posted on 2023-06-08, 16:09 authored by Anil SethAnil Seth, Paul Chorley, Lionel BarnettLionel Barnett
Granger 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.

History

Publication status

  • Published

File Version

  • Published version

Journal

NeuroImage

ISSN

1053-8119

Publisher

Elsevier

Volume

65

Page range

540-555

Department affiliated with

  • Informatics Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2013-10-20

First Compliant Deposit (FCD) Date

2013-10-18

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