Introduction to logistic regression
This provides a practical solution for neuroimaging researchers facing distributional assumption challenges in multiple comparison corrections.
The paper addresses the difficulty of computing supremum distributions for random field theory in brain imaging by proposing logistic regression as an alternative framework that avoids p-value computation, enabling localization of brain network differences without requiring preselected feature vectors.
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.