Technical NoteThe danger of systematic bias in group-level FMRI-lag-based causality estimation
Highlights
► Group-level FMRI simulations can be useful to test methods such as Granger causality. ► Simulation results need careful evaluation and interpretation. ► There is ample evidence of haemodynamic variability across regions and voxels. ► Lag-based FMRI causality analysis may be biassed by such variation. ► This confound should be considered when reporting lag-based results.
Section snippets
Likelihood of a true direction given a detected direction
When the main conclusions are drawn using Table 6 (in Schippers et al., 2011), it is effectively assumed that if the differential haemodynamic lag between two brain areas is unknown, then on average (across all subjects in a study), it is zero. This arises because the results are pooled across the full (negative and positive) range of potential differential haemodynamic lags. In reality, a given pair of brain regions will have a specific population-average differential haemodynamic lag, so in
Which null to use for causality p-values
We are also concerned that the claim of controlling false positives (first quotation in the Abstract) is in danger of being misunderstood. The false positive evaluations only considered the specific null hypothesis of complete independence between two areas. In practice, most applications of lag-based causality estimation pre-suppose that two timeseries are correlated.2
Conclusion
There is ample evidence of haemodynamic variability—between neighbouring voxels within a functional region, between different functional regions, over different cognitive conditions, and between different subjects (e.g., see discussions and references in Miezin et al., 2000 and Bandettini, 19993
Acknowledgments
We are very grateful to Christian Keysers and Alard Roebroeck for helpful discussions.
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