Permutation testing for non-imaging data using FSL randomise

The <i>randomise_non_imaging</i> script is designed to take advantage of the functionalities of FSL randomise (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Randomise/) to perform GLM-based non-parametric permutation testing using non-imaging data. This can be done fairly easily with other programs, but using randomise could be convenient to FSL users, who are accustomed to creating the necessary input files. <br>How to make it work<br><b>System requirements:</b>This scripts requires FSL (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL) as well as python (including the numpy (http://www.numpy.org/) and nipy (http://nipy.org/) packages), and is meant to be used in Linux systems. The necessary python packages can be easily obtained by installing Anaconda (https://www.continuum.io/downloads).<br><br><b>Add alias and route to .bashrc:</b>After unzipping <i>randomise_non_imaging.zip</i>, we recommend adding an alias to the user's .bashrc as an easy way to call the script from any terminal. The path to the folder containing the <i>parameter2nifti.py</i> script should also be specified as <i>route_NIR </i>in the .bashrc:<br><i>alias randomise_non_imaging='bash <b>/full/path/to/your/folder/</b>randomise_non_imaging.sh'<br></i><i>export route_NIR=<b>/full/path/to/your/folder/</b></i><br><b>Input files:</b>Three basic input files are required: 1. Dependent variable matrix: text file containing the variables to be tested, consisting of one column per variable and one row for each observation. This is equivalent to the image input in randomise – and will in fact be converted to image format so it can be fed into the program 2. Design matrix (<i>.mat</i>) 3. Contrast matrix (<i>.con</i>)<br>Some options require additional input files: 1. F tests: requires <i>.fts </i>files (with the same root name as the design and contrast files) 2. Block permutation: requires exchangeability block labels <i>.grp</i> file (with the same root name as the design and contrast files)<br><b>Output (text) files:</b><br>1. P value file: named <i>(output)_p_all_contrasts</i>2. Stats file: named <i>(output)_stat_all_contrasts</i>3. F test p value file: <i>(output)_p_F_test</i>4. F test stats file: <i>(output)_Fstat</i>5. Corrected p value file: <i>(output)_corrp_all_contrasts</i>6. Corrected F test p value file: <i>(output)_corrp_F_test</i><br><b>Usage instructions are given here:</b> <i>https://cjneurolab.org/2017/07/21/permutation-testing-for-non-imaging-data-using-fsl-randomise/</i><br>