In my research I've been using a method called multivariate distance matrix regression, which is a multivariate methods that regresses a Gower transformed dista

In my research I've been using a method called multivariate distance matrix regression, which is a multivariate methods that regresses a Gower transformed distance matrix onto a set of predictors. In this technique, one must first choose a distance metric. I have been choosing the Manhattan distance (city-block distance) for my brain imaging data variables (volumes, connectivity correlations values, etc) because it is somewhat less vulnerable to outliers than Euclidean distance or the Pearson's distance, and somewhat better in terms massively multivariate data. I am wondering whether HN gurus have any suggestions in terms of finding the optimal distance metric?