Threshold-free network-based statistics

<i>TFNBS algorithm. </i>Initially, the raw F statistics matrix <i>Mstat</i> (1) is thresholded at a series of steps <i>h</i> (2). The step interval <i>dh</i> was defined as a hundredth of the maximum value in <i>Mstat</i>. At each thresholding step, possible connected components are identified (3). The value of each matrix element belonging to a connected component is replaced by the component’s topological size (number of connections) raised to the power <i>E</i>, multiplied by the component’s height (equal to the current threshold) raised to the power <i>H</i> (3). The matrices obtained at each step are subsequently summed, giving the final TFNBS score for every network edge (4). Statistical significance is established through permutation testing (5). At each permutation, group membership is shuffled across subjects, and the steps above are repeated. Raw statistics are obtained from the whole connectivity matrix at each permutation, thus preserving topological dependencies among connections. Whole-connectome FWE-corrected p-values are obtained by comparing each connection’s TFNBS score with the null distribution of maximal connectome-wise scores at each permutation

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