Detecting microstructural deviations in individuals with deep diffusion MRI tractometry

  • Maxime Chamberland
  • Sila Genc
  • Derek K. Jones

Nature Computational Science is a Transformative Journal; authors can publish using the traditional publishing route OR via immediate gold Open Access.

Our Open Access option complies with funder and institutional requirements.


  • An analysis of GPS pedestrian traces shows that (1) people increasingly deviate from the shortest path when the distance between origin and destination increases and that (2) chosen paths are statistically different when origin and destination are swapped. Ultimately, this can explain the observed human attitude in selecting different paths upon return trips.

    • Christian Bongiorno
    • Yulun Zhou
    • Carlo Ratti
  • An algorithmic approach is developed to analyze large-scale patient safety data and remove the confounders of reporting trajectory and drug inference. Such an approach can be effectively used to investigate demographic disparities of drug safety and to identify at-risk patients during a pandemic.

    • Xiang Zhang
    • Marissa Sumathipala
    • Marinka Zitnik
    Article Open Access
  • The authors show that accurate bootstrap confidence limits on inferred evolutionary relationships of species can be estimated by bootstrapping a collection of little samples of very long sequence alignments. Little bootstraps take a fraction of computer time and memory compared to the standard bootstrap, enabling big data analytics on personal computers.

    • Sudip Sharma
    • Sudhir Kumar
    Brief Communication
  • The authors demonstrate how neural systems can encode cognitive functions, and use the proposed model to train robust, scalable deep neural networks that are explainable and capable of symbolic reasoning and domain generalization.

    • Paul J. Blazek
    • Milo M. Lin
    • Mobile-phone data reveal a cognitive strategy in human navigation and motivate the development of a new route planning model, with potential implications for traffic forecasting and transportation planning.

      • Laura Alessandretti
      News & Views
    • Finding a parameter that can accurately identify the order–disorder phase transition, especially for complex physical systems with high-dimensional configurational space, is a challenging task. Recent work proposes a machine learning approach to effectively tackle this challenge.

      • Evert van Nieuwenburg
      News & Views
    • An efficient parallelization technique for tensor network contraction, developed by a careful balance between memory requirement and computational time, speeds up classical simulation of quantum computers.

      • Jordi Tura
      News & Views
    • A framework called EPICS predicts microbial community structures by estimating effective pairwise interactions in an efficient and scalable way.

      • Boyang Ji
      • Markus J. Herrgård
      • Jens Nielsen
      News & Views
    • A new study uses longitudinal mobility data to identify how individuals behave at different stages of the COVID pandemic, elucidating benefits and challenges of using this type of data for decision-making by epidemiologists and policy-makers.

      • Nishant Kishore
      News & Views