  For random field theory based multiple comparison corrections In brain
imaging, it is often necessary to compute the distribution of the supremum of a
random field. Unfortunately, computing the distribution of the supremum of the
random field is not easy and requires satisfying many distributional
assumptions that may not be true in real data. Thus, there is a need to come up
with a different framework that does not use the traditional statistical
hypothesis testing paradigm that requires to compute p-values. With this as a
motivation, we can use a different approach called the logistic regression that
does not require computing the p-value and still be able to localize the
regions of brain network differences. Unlike other discriminant and
classification techniques that tried to classify preselected feature vectors,
the method here does not require any preselected feature vectors and performs
the classification at each edge level.
