skiftiTools: An R package for reading, writing, analysing, and visualising, tract-based spatial statistics (TBSS) derived diffusion MR images

skiftiTools processes three- and four-dimensional neuroimaging data, facilitating advanced statistical modelling with voxelwise data in any software of choice. Tract-Based Spatial Statistics (TBSS) is a conventionally used tool to make statistical calculations in voxel space for brain imaging data. While pre-existing software packages provide support for general linear model based statistics, there is a clear need for more sophisticated modeling. skiftiTools writes subject-per-volume NIfTI files as tab-separated value ASCII files, which are easily readable by most commonly used statistical tools such as R language (RStudio), SPSS, SAS, and GraphPad Prism. This facilitates a wide range of voxel-level statistical analyses from TBSS data, including estimation of standardised effect sizes, clustering, dimensionality reduction, non-linear and machine learning predictive modelling, which we showcase in this article using FinnBrain and developing Human Connectome Project diffusion MRI data. After statistical processing, the resulting ASCII data can then be read again for visualization. The package supports NIfTI image format, tab-separated ASCII format, and its own stand-alone format for efficient disk usage. It is open source (https://github.com/haanme/skiftiTools), built on R-language and has easy installation from Rs CRAN package repository. In addition, we provide basic functions available in Docker containers for further platform independence. HighlightsO_LIThe skiftiTools R package is an open-source, user-friendly interface for analysing voxelwise diffusion tensor imaging (DTI) data following tract-based spatial statistics (TBSS) processing C_LIO_LIIt supports reading, writing, visualization, mathematical operations, and data manipulation and thus allows comprehensive conventional and advanced statistics, including machine learning C_LIO_LIskiftiTools bridges a critical gap between statistical tools in R and voxelwise neuroimaging data - including comparable means to perform multiple comparison corrections and much needed possibility to use non-linear statistics C_LI