Probabilistic mapping of brain connections without random sampling

Modeling probabilistic brain tractography as an absorbing Markov chain yields the expected connectivity of infinitely many streamlines, avoiding random sampling while staying deterministic.

• Why it matters: Sampling-based tractography is costly and non-deterministic, limiting reproducibility and storage, especially for large datasets and clinical-grade imaging.
• What they did: The method treats tractography as an absorbing Markov chain and computes the expected streamline outcome without random draws, then compares reliability across test–retest scans.
• The result: The Markov-chain approach is more reliable than streamline-based tractography across test–retest scans and can map tracts in clinical-grade data and neonates, including around tumors and along small pathways.

AI summary of the abstract · may contain errors · not medical advice · read the paper.