BRAIN project (Cox)

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Continuing our testing developments, we have extended the regression testing to cover more of the AFNI codebase. Testing is now carried out automatically with every commit pushed to GitHub, to ensure that the code is (a) compilable, and (b) passes the sequence of program tests. Considering that AFNI comprises over 1 million lines of source code, some of which dates to the 1980s, continual testing has become an important part of keeping our software alive and kicking hopefully for many years to come. This test infrastructure, implemented in a containerized- and cloud-environment has undergone iterations, with feedback from members of the neuroimaging community and from the AFNI team, in order to be a robust solution, and convenient for use by team members. We continue to develop the way in which we package AFNI to make it easier to install across all platforms, including container and cloud contexts. One significant part of this effort has been to use the Cmake (cross-platform make) build tool and to specify the projects dependencies using the conda package manager. Significant efforts to trim down the size of the AFNI containers were made in the last year, by refactoring the code and by eliminating dependence on the very sizable X11 graphics libraries in all purely computational contexts. In addition, several libraries that were included in the AFNI source and binaries were completely removed, as they were used to support features that are obsolete (e.g., support for the outdated MINC-1 data format). Slimming down also makes it simpler for other pipeline developers (e.g., fMRIPrep) to use components from AFNI in their products. We worked directly with the Fitlins team at Stanford (also partly funded by the BRAIN Initiative) to include part of AFNI's computational core in their implementation of the BIDS Statsmodel. While Fitlins creates a general interface for fitting statistical models in neuroimaging, our work under the hood creates a bridge to AFNI's 3dREMLfit tool, providing all the careful decisions in neuroimaging statistical modelling and improvements to algorithmic efficiency that come along with it. Our own fMRI analysis pipeline, afni_proc.py, creates a quality control HTML page, allowing a research to assess many of the details that need to be considered, packaged in the convenient interface of a single interactive webpage.