Hi -
I've been using TBSS/randomise to explore the the association between cognition and white matter structure. I've noticed that when I put two highly correlated variables into the model (e.g., immediate and delayed recall abilities - which unsurprisingly have an R value between 0.6 and 0.8) that I often get surprising amounts of significant voxels.
For instance - with 162 FA maps from subjects of varying ages.
If delayed recall, age and gender are modeled in randomise the corrected t-stat map for delayed recall is entirely insignificant.
If immediate recall, age and gender are modeled in randomise the corrected t-stat map for immediate recall is entirely insignificant
However,
If both immediate and delayed recall, age and gender are modeled in randomise numerous voxels in both the immediate and delayed recall t-stat maps become significant
FYI - age and gender typically have significant voxels in this sample.
I recognize that interpreting what immediate recall means when one "controls for" delayed recall is quite ambiguous, so I am not suggesting this is a "good" model. However, I was curious about the randomise results. Does randomise produce accurate t/p-values if two correlated covariates are included in the model? Should I not be including more than one continuos covariate when using randomise? Should I believe these somewhat surprising results?
Thanks for the help.
Sincerely,
Paul Borghesani
University of Washington
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