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+%% This is an example script to run CAP analysis without the GUI
+% In this script, we assume that the user is interested in performing a
+% seed-free analysis with one population of subjects
+
+
+
+%% 1. Loading the data files
+
+% Data: cell array, each cell of size n_TP x n_masked_voxels
+TC =
+
+% Mask: n_voxels x 1 logical vector
+mask =
+
+% Header: the header (obtained by spm_vol) of one NIFTI file with proper
+% data dimension and .mat information
+brain_info =
+
+% Framewise displacement: a n_TP x n_subj matrix with framewise
+% displacement information
+FD =
+
+
+
+%% 2. Specifying the main parameters
+
+% Threshold of FD above which to scrub out the frame and also the t-1 and
+% t+1 frames (if you want another scrubbing setting, directly edit the
+% code)
+Tmot = 0.5;
+
+% Percentage of positive-valued voxels to retain for clustering
+Pp = 100;
+
+% Percentage of negative-valued voxels to retain for clustering
+Pn = 100;
+
+% Number of repetitions of the K-means clustering algorithm
+n_rep = 50;
+
+% Percentage of frames to use in each fold of consensus clustering
+Pcc = 80;
+
+% Number of folds we run consensus clustering for
+N = 50;
+
+
+
+%% 3. Selecting the frames to analyse    
+
+% Xon will contain the retained frames, and Indices will tag the time
+% points associated to these frames, for each subject (it contains a
+% subfield for retained frames and a subfield for scrubbed frames)
+% Note that since this is a seed-free analysis, we do not need any
+% seed-related information to be given as argument
+[Xon,~,Indices] = CAP_find_activity_SeedFree(TC,FD,Tmot); 
+
+
+
+%% 4. Consensus clustering (if wished to determine the optimum K)
+
+% This specifies the range of values over which to perform consensus
+% clustering: if you want to run parallel consensus clustering processes,
+% you should feed in different ranges to each call of the function
+K_range = 2:50;
+
+% Have each of these run in a separate process on the server =)
+[Consensus] = CAP_ConsensusClustering(Xon,K_range,'items',Pcc/100,N,'correlation');
+
+% Calculates the quality metrics
+[~,Qual] = ComputeClusteringQuality(Consensus,[]);
+
+% Qual should be inspected to determine the best cluster number(s)
+
+% You should fill this with the actual value 
+K_opt = 
+
+
+
+%% 5. Clustering into CAPs
+
+[CAP,~,~,idx] = Run_Clustering(cell2mat(Xon),...
+        K_opt,mask,brain_info,Pp,Pn,n_rep,[],SeedType);
+
+    
+
+%% 6. Computing metrics
+
+% The TR of your data in seconds
+TR = 
+
+[ExpressionMap,Counts,Entries,Avg_Duration,Duration,TransitionProbabilities,...
+    From_Baseline,To_Baseline,Baseline_resilience,Resilience,Betweenness,...
+    InDegree,OutDegree,SubjectEntries] = Compute_Metrics_simpler(idx,...
+    Indices.kept.active,Indices.scrubbedandactive,K_opt,TR);
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