Probabilistic mapping of brain connections without random sampling

For two decades, most tractography has mapped brain connections by sampling millions of pathways probabilistically. Sampling makes results non-deterministic and costly to store. We show that it is unnecessary: modeling tractography as an absorbing Markov chain gives the expected result of infinitely many streamlines. This approach is more reliable than streamline-based tractography across test--retest scans. It readily maps tracts in clinical-grade data and neonates, around tumors and along small pathways. MPAT is available as open-source software at https://github.com/rokemlab/mpat .