AFQ-Insight: Statistical learning for tractometry data ====================================================== AFQ-Insight is a Python library for statistical learning of tractometry data. Tractometry assesses the tissue properties of the major white matter connections between different brain regions. AFQ-Insight inter-operates with the results of tractometry produced by the `pyAFQ `_ software library. However, you can also use the output of other tractometry pipelines if you convert them into the format produced by pyAFQ. .. toctree:: :maxdepth: 3 :hidden: install user_guide auto_examples/index getting_help api contributing AFQ-Insight on GitHub `Getting started `_ ------------------------------------- See the `getting started `_ page for installation and basic usage instructions. `User Guide `_ ------------------------------- See the `user guide `_ for further information on how to use *AFQ-Insight*. `API Documentation `_ ------------------------------- See the `API Documentation `_ for detailed documentation of the API. `Examples `_ -------------------------------------- And look at the `example gallery `_ for a set of introductory examples. Citing *AFQ-Insight* -------------------- If you use *AFQ-Insight* in a scientific publication, we would appreciate citations. Please see our `citation instructions `_ for the latest reference and a bibtex entry. Acknowledgements ---------------- *AFQ-Insight* development was supported through a grant from the `Gordon and Betty Moore Foundation `_ and from the `Alfred P. Sloan Foundation `_ to the `University of Washington eScience Institute `_, NIH Collaborative Research in Computational Neuroscience grant R01EB027585-01 through the National Institute of Biomedical Imaging and Bioengineering to Eleftherios Garyfallidis (Indiana University) and Ariel Rokem (University of Washington) and NIH grant RF1MH121868 (PIs: Ariel Rokem, Jason Yeatman and Noah Simon) from the National Institute of Mental Health and the BRAIN Initiative Informatics program.