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{{{#!wiki caution
'''FSLNets is now in Python!'''

FSLNets has been re-written in Python, and is installed as part of FSL 6.0.7 and newer. The Python FSLNets interface is very similar to the MATLAB version - head to https://open.win.ox.ac.uk/pages/fsl/analysis-course-practicals/fslnets/ for an overview on its use.

'''This''' page contains information on installing the original MATLAB-based version of FSLNets.
}}}

= Overview =

FSLNets v0.6 is a set of simple MATLAB scripts for carrying out basic network modelling from (typically FMRI) timeseries data. This beta-version package requires you to have various other software than just FSL, such as MATLAB (or Octave), and for now is not bundled as part of FSL. It has only been tested with MATLAB and Octave running on Linux/Mac.

Version 0.6 now contains an interactive web-based netmat display function {{{nets_netweb}}} (and also has a new minor improvements in existing functions).  For an example of {{{nets_netweb}}} see [[http://www.fmrib.ox.ac.uk/~steve/ftp/fslnets_webnetexample/index.html|here]].

The main thing you will feed into FSLNets network modelling is N timecourses from S subjects' datasets - i.e., timeseries from N network nodes. For display purposes you will also need the spatial maps associated with the nodes (one map per node). For example, a good way to get these timeseries and spatial maps is to use [[Melodic|MELODIC group-ICA]] with a dimensionality of N, to get the group-level spatial maps, and then use [[DualRegression|dual regression]] to generate S subject-specific versions of the N timecourses. Alternatively, you might have used a set of template images or ROIs from another study, to feed into the dual regression.

Now you are ready to compute a ''network matrix'' for each subject, which in general will be an NxN matrix of connection strengths.  The simplest and most common approach is just to use "full" correlation, giving an NxN matrix of correlation coefficients. Or, you might want to estimate the partial correlation matrix, which should do a better job of only estimating the ''direct'' network connections than the full correlation does. Once you have estimated a network matrix for each subject, you can then test these matrices across subjects, for example, testing each matrix element for a two-group subject difference, or feeding the whole matrices into multivariate discriminant analysis.

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= Installing FSLNets =

Requirements:
 * [[http://www.fmrib.ox.ac.uk/fsl|FSL]]
 * MATLAB or Octave:
   * For MATLAB, you will need the official MATLAB toolboxes:
     * Statistics (you must have this one)
     * Bioinformatics (if you want to use MATLAB's SVM)
     * Signal Processing (if you want to see timeseries spectra)
   * For Octave, you will need Octave version 3.8.0 or later, as well as toolboxes:
     * control (v2.6.2 or greater)
     * general (v1.3.4 or greater)
     * signal (v1.3.0 or greater)
     * statistics (v1.2.3 or greater)
 * LIBSVM (setup for MATLAB or Octave - if you want to use the LIBSVM implementation of SVM for netmat-based classification)
 * [[http://www.cs.ubc.ca/~schmidtm/Software/L1precision.zip|L1precision]] free third-party MATLAB toolbox (to estimate L1-norm regularised partial correlation matrices)
 * [[http://www.cs.helsinki.fi/u/ahyvarin/code/pwcausal/|Pairwise causal]] free third-party MATLAB toolbox (if you want to look at causal directionalities)

Setup FSLNets:
 * Unpack [[http://www.fmrib.ox.ac.uk/~steve/ftp/fslnets.tar.gz|FSLNets]] with {{{tar xvfz fslnets.tar.gz}}} (or {{{tar xvf fslnets.tar}}} if your browser has already uncompressed the file).
 * See the top of the file {{{nets_examples.m}}}, which you can edit to point to the paths of the various additional toolboxes listed above.

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= Running FSLNets =

Take a copy of {{{nets_examples.m}}} (for example {{{cp nets_examples.m my_nets.m}}} ) and edit this new file with your own settings. To run inside MATLAB, just copy individual lines, one at a time, into MATLAB from your new file, or, once it is ready, just type {{{my_nets}}} in MATLAB.

The file {{{nets_examples.m}}} is fairly well documented, with each stage of the analysis hopefully fairly clear and easy.

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CategoryOther [[CategoryFSLNets]]