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- %%% FSLNets - simple network matrix estimation and applications
- %%% FMRIB Analysis Group
- %%% Copyright (C) 2012-2014 University of Oxford
- %%% See documentation at www.fmrib.ox.ac.uk/fsl
- %%% change the following paths according to your local setup
- addpath /home/fs0/steve/NETWORKS/FSLNets % wherever you've put this package
- addpath /home/fs0/steve/matlab/L1precision % L1precision toolbox
- addpath /home/fs0/steve/matlab/pwling % pairwise causality toolbox
- addpath(sprintf('%s/etc/matlab',getenv('FSLDIR'))) % you don't need to edit this if FSL is setup already
- %%% setup the names of the directories containing your group-ICA and dualreg outputs
- group_maps='your-group-ICA.ica/melodic_IC'; % spatial maps 4D NIFTI file, e.g. from group-ICA
- %%% you must have already run the following (outside MATLAB), to create summary pictures of the maps in the NIFTI file:
- %%% slices_summary <group_maps> 4 $FSLDIR/data/standard/MNI152_T1_2mm <group_maps>.sum
- ts_dir='groupICA.dr'; % dual regression output directory, containing all subjects' timeseries
- %%% load timeseries data from the dual regression output directory
- ts=nets_load(ts_dir,3,1);
- %%% arg2 is the TR (in seconds)
- %%% arg3 controls variance normalisation: 0=none, 1=normalise whole subject stddev, 2=normalise each separate timeseries from each subject
- ts_spectra=nets_spectra(ts); % have a look at mean timeseries spectra
- %%% cleanup and remove bad nodes' timeseries (whichever is not listed in ts.DD is *BAD*).
- ts.DD=[1 2 3 4 5 6 7 9 11 14 15 17 18]; % list the good nodes in your group-ICA output (counting starts at 1, not 0)
- % ts.UNK=[10]; optionally setup a list of unknown components (where you're unsure of good vs bad)
- ts=nets_tsclean(ts,1); % regress the bad nodes out of the good, and then remove the bad nodes' timeseries (1=aggressive, 0=unaggressive (just delete bad)).
- % For partial-correlation netmats, if you are going to do nets_tsclean, then it *probably* makes sense to:
- % a) do the cleanup aggressively,
- % b) denote any "unknown" nodes as bad nodes - i.e. list them in ts.DD and not in ts.UNK
- % (for discussion on this, see Griffanti NeuroImage 2014.)
- nets_nodepics(ts,group_maps); % quick views of the good and bad components
- ts_spectra=nets_spectra(ts); % have a look at mean spectra after this cleanup
- %%% create various kinds of network matrices and optionally convert correlations to z-stats.
- %%% here's various examples - you might only generate/use one of these.
- %%% the output has one row per subject; within each row, the net matrix is unwrapped into 1D.
- %%% the r2z transformation estimates an empirical correction for autocorrelation in the data.
- netmats0= nets_netmats(ts,0,'cov'); % covariance (with variances on diagonal)
- netmats0a= nets_netmats(ts,0,'amp'); % amplitudes only - no correlations (just the diagonal)
- netmats1= nets_netmats(ts,1,'corr'); % full correlation (normalised covariances)
- netmats2= nets_netmats(ts,1,'icov'); % partial correlation
- netmats3= nets_netmats(ts,1,'icov',10); % L1-regularised partial, with lambda=10
- netmats5= nets_netmats(ts,1,'ridgep'); % Ridge Regression partial, with rho=0.1
- netmats11= nets_netmats(ts,0,'pwling'); % Hyvarinen's pairwise causality measure
- %%% view of consistency of netmats across subjects; returns t-test Z values as a network matrix
- %%% second argument (0 or 1) determines whether to display the Z matrix and a consistency scatter plot
- %%% third argument (optional) groups runs together; e.g. setting this to 4 means each group of 4 runs were from the same subject
- [Znet1,Mnet1]=nets_groupmean(netmats1,1); % test whichever netmat you're interested in; returns Z values from one-group t-test and group-mean netmat
- [Znet5,Mnet5]=nets_groupmean(netmats5,1); % test whichever netmat you're interested in; returns Z values from one-group t-test and group-mean netmat
- %%% view hierarchical clustering of nodes
- %%% arg1 is shown below the diagonal (and drives the clustering/hierarchy); arg2 is shown above diagonal
- nets_hierarchy(Znet1,Znet5,ts.DD,group_maps);
- %%% view interactive netmat web-based display
- nets_netweb(Znet1,Znet5,ts.DD,group_maps,'netweb');
- %%% cross-subject GLM, with inference in randomise (assuming you already have the GLM design.mat and design.con files).
- %%% arg4 determines whether to view the corrected-p-values, with non-significant entries removed above the diagonal.
- [p_uncorrected,p_corrected]=nets_glm(netmats1,'design.mat','design.con',1); % returns matrices of 1-p
- %%% OR - GLM, but with pre-masking that tests only the connections that are strong on average across all subjects.
- %%% change the "8" to a different tstat threshold to make this sparser or less sparse.
- %netmats=netmats3; [grotH,grotP,grotCI,grotSTATS]=ttest(netmats); netmats(:,abs(grotSTATS.tstat)<8)=0;
- %[p_uncorrected,p_corrected]=nets_glm(netmats,'design.mat','design.con',1);
- %%% view 6 most significant edges from this GLM
- nets_edgepics(ts,group_maps,Znet1,reshape(p_corrected(1,:),ts.Nnodes,ts.Nnodes),6);
- %%% simple cross-subject multivariate discriminant analyses, for just two-group cases.
- %%% arg1 is whichever netmats you want to test.
- %%% arg2 is the size of first group of subjects; set to 0 if you have two groups with paired subjects.
- %%% arg3 determines which LDA method to use (help nets_lda to see list of options)
- [lda_percentages]=nets_lda(netmats3,36,1)
- %%% create boxplots for the two groups for a network-matrix-element of interest (e.g., selected from GLM output)
- %%% arg3 = matrix row number, i.e. the first component of interest (from the DD list)
- %%% arg4 = matrix column number, i.e. the second component of interest (from the DD list)
- %%% arg5 = size of the first group (set to -1 for paired groups)
- nets_boxplots(ts,netmats3,1,7,36);
- %print('-depsc',sprintf('boxplot-%d-%d.eps',IC1,IC2)); % example syntax for printing to file
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