Toward Optimized and Predictive Connectomics at Scale

Probabilistic MRI diffusion tractography is a sophisticated technique to investigate structural connectomes, but its steep computational cost prevents application to broader research and clinical settings. Major speedup can be achieved by reducing the number of tractography streamlines. To ensure this does not degrade connectome quality, we calculate the identifiability of connectomes between test and retest MRI as a proxy for information content. We find that reducing streamline count by up to two orders of magnitude from prevailing levels in literature has no significant impact on identifiability. Incidentally, we also observe that Jaccard similarity is more effective than Pearson correlation in achieving identifiability.