Using the GUI (7): Hypothesis testing and Display results
The following session will show you how to perform hypothesis testings and how to visualize the result.
Click Linear Contrast and load LMMmap and its correspondent FixMap to continue.
Click Model Fitting to visualize the R^2, adjusted-R^2, AIC, BIC, Log-Likelihood Ratio and Deviance from the model fitting result.
Click ANOVA to perform F-test on the main effect and/or interaction of the fixed effects. The result of parametric test will be displayed first, then followed by multiple comparison correction or non-parametric statistics (currently nested under multiple comparison correction tab).
For multiple comparison correction, many options are included in iMap4: FDR, Bonferroni Correction, Random Field Theory, Bootstrap Spatial Clustering and Permutation. Bootstrap Spatial Clustering method is the default option, as its familywise error rate (FWER) has already been validated by our team.
You could also perform the multiple comparison correction on the TFCE map instead of the original statistic values (Smith and Nichols, 2009).
Please note: for Bootstrap Spatial Clustering method, if you have a between-subject variable (e.g., gender), you should input it into the bootgroup in the following pop-up window.
Then wait a few minutes for the resampling to finish...
Save the result
Please note: saving could be slow, be patient if it seems nothing happens.
And you can plot the result after control for multiple comparison problem! Below is the new result with a different colormap. As you can see below the result after MCC, many small significant areas are actually false positive (Type I error).
The resulting statistic values and masks are in Matlab Structure format: StatMap (before MCC) or StatMap_c (after MCC). They are saved in the same directory with LMMmap, while the figures are saved under a new folder in .eps format. If a background file is included in display option, the same statistic map will be displayed overlaying on top of the background file.
