Figure 2.

A block diagram of the computational steps for evaluation of the MI performance metric. Available independent datasets of an fMRI experiment (y ⁽¹⁾,…,y ^(M)) are preprocessed and then split into two crossvalidation groups for training and validation. Using the training datasets a SPM, s^t, or a label map, c^t, is estimated. The MI between this estimated map and the validation datasets' time‐series is then calculated as an evaluation metric for the analysis technique applied to the training set. The fixed parameters d _y and d _x are distance metrics, respectively, between the validation datasets' high‐dimensional time‐series and the lower dimensional SPM/label map spaces, that allow the MI to be calculated. They are used together with a value of k in a k‐nearest‐neighbor‐based estimate of the MI (see text).