Before revision:  We also show that this optimal value of regularization is highly dataset-dependent, and varies as a function of condition, parcellation,  and the number of frames used to compute the functional connectomes .
After revision:  We also show that this optimal value of regularization is highly dataset-dependent, and varies as a function of condition, parcellation, and the number of frames used to compute the FCs. We demonstrate that a universally fixed regularization does not fully uncover the potential of geodesic distance on individual fingerprinting, and indeed could severely diminish it. Thus, an optimal regularization must be estimated on each dataset to uncover the most differentiable across-subject and reproducible within-subject geodesic distances between FCs. The resulting pairwise geodesic distances at the optimal regularization level constitute a very reliable quantification of differences between subjects .