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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,87 @@ | ||
| % pop_roi_statsplot() - Generate p-values from FC null distributions and plots them. | ||
| % | ||
| % Inputs: | ||
| % EEG - EEGLAB dataset with ROI activity computed. | ||
| % | ||
| % Optional inputs: | ||
| % 'measure' - [cell] Cell of strings corresponding to methods. | ||
| % 'CS' : Cross spectrum | ||
| % 'aCOH' : Coherence | ||
| % 'cCOH' : (complex-valued) coherency | ||
| % 'iCOH' : absolute value of the imaginary part of coherency | ||
| % 'wPLI' : Weighted Phase Lag Index | ||
| % 'PDC' : Partial directed coherence | ||
| % 'TRPDC' : Time-reversed partial directed coherence | ||
| % 'DTF' : Directed transfer entropy | ||
| % 'TRDTF' : Time-reversed directed transfer entropy | ||
| % 'MIM' : Multivariate Interaction Measure for each ROI | ||
| % 'MIC' : Maximized Imaginary Coherency for each ROI | ||
| % 'freqrange' - [min max] frequency range in Hz. Default is to plot broadband power. | ||
|
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| function EEG = pop_roi_statsplot(EEG, varargin) | ||
|
|
||
| if nargin < 2 | ||
| help roi_connstats; | ||
| return | ||
| end | ||
|
|
||
| if ~isfield(EEG, 'roi') || ~isfield(EEG.roi, 'source_roi_data') | ||
| error('Cannot find ROI data - compute ROI data first'); | ||
| end | ||
|
|
||
| % decode input parameters | ||
| % ----------------------- | ||
| g = finputcheck(varargin, { | ||
| 'measure' 'string' { } ''; | ||
| 'freqrange' 'real' { } []}, 'pop_roi_statsplot'); | ||
| if ischar(g), error(g); end | ||
| S = EEG.roi; | ||
|
|
||
| if isempty(g.measure) | ||
| error('You must define a measure to plot'); | ||
| end | ||
|
|
||
| % extract frequency indices | ||
| if ~isempty(g.freqrange) | ||
| try | ||
| frq_inds = find(S.freqs >= g.freqrange(1) & S.freqs < g.freqrange(2)); | ||
| title = sprintf('%1.1f-%1.1f Hz frequency band', g.freqrange(1), g.freqrange(2)); | ||
| catch | ||
| frq_inds = find(S.freqs == g.freqrange(1)); % if a single frequency is passed | ||
| title = sprintf('%1.1f Hz', g.freqrange(1)); | ||
| end | ||
| else | ||
| frq_inds = 1:length(S.freqs); | ||
| title = 'broadband'; | ||
| end | ||
|
|
||
| % select frequency or frequency band | ||
| if length(frq_inds) > 1 | ||
| matrix = squeeze(mean(S.(g.measure)(frq_inds, :, :, :))); | ||
| else | ||
| matrix = squeeze(S.(g.measure)(frq_inds, :, :, :)); | ||
| end | ||
|
|
||
| % generate p-values by comparing the true FC (first shuffle) to null distribution | ||
| netFC = squeeze(mean(matrix, 2)); | ||
| FC_pn = sum(netFC(:, 1) < netFC(:, 2:end), 2)./(size(matrix, 3) - 1); | ||
|
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| % use FDR-correction for multiple comparison's correction | ||
| alpha = 0.05; % add this as a parameter? | ||
| [p_fdr, ~] = fdr(FC_pn, alpha); | ||
| FC_pn(FC_pn > p_fdr) = 1; | ||
|
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||
| % plot | ||
| load cm17; | ||
| load cortex; | ||
| FC_pn(FC_pn==0) = 1 / (size(netFC, 2) - 1); % 1 / nshuf | ||
| data = -log10(FC_pn); | ||
| try | ||
| allplots_cortex_BS(cortex_highres, data, [min(data) max(data)], cm17a ,'-log(p)', 0.3); | ||
| h = textsc(title, 'title'); | ||
| set(h, 'fontsize', 20); | ||
| catch | ||
| warning('There are no "significant" p-values to be plotted. These are the p-values:') | ||
| disp(FC_pn) | ||
| end | ||
| end |
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