Fig 4.

Feature weight matrix. This heatmap demonstrates the relative influence of each feature on the overall clustering attempt. Darker cells depict higher values of influence as calculated by the scalar product between the feature vector and the principal component vector multiplied by the explained variance of the respective principal component. By performing this operation, the relative importance of certain types of features and imaging sequences can be understood. Feature names describe the attribute that they measure in the following way: ImagingSequence_FeatureType_Metric.