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. <div><br></div><div><div><u>How to make it work</u></div><div><br></div><div><b>System requirements:</b></div><div>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></div></div><div><br></div><div><div><b>Add alias and route to .bashrc:</b></div><div>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:</div><div><br></div><div><i>alias randomise_non_imaging='bash <b>/full/path/to/your/folder/</b>randomise_non_imaging.sh'<br></i></div><div><i>export route_NIR=<b>/full/path/to/your/folder/</b></i></div></div><div><br></div><div><b>Input files:</b></div><div><div>Three basic input files are required:</div><div>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</div><div>2. Design matrix (<i>.mat</i>) </div><div>3. Contrast matrix (<i>.con</i>)</div><div><br></div><div><div>Some options require additional input files:</div><div>1. F tests: requires <i>.fts </i>files (with the same root name as the design and contrast files)</div><div>2. Block permutation: requires exchangeability block labels <i>.grp</i> file (with the same root name as the design and contrast files)</div></div><div><br></div><div><b>Output (text) files:</b><br></div><div><div>1. P value file: named <i>(output)_p_all_contrasts</i></div><div>2. Stats file: named <i>(output)_stat_all_contrasts</i></div><div>3. F test p value file: <i>(output)_p_F_test</i></div><div>4. F test stats file: <i>(output)_Fstat</i></div><div>5. Corrected p value file: <i>(output)_corrp_all_contrasts</i></div><div>6. Corrected F test p value file: <i>(output)_corrp_F_test</i></div></div><div><br></div><div><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></div></div