SPECT_ICA_Project/Step2_Vince_Script.m at main · amrithah93/SPECT_ICA_Project · GitHub

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%% ------------------------------------------------------------

%%used this for the main figure plotting, I used Step2_3 for calling
%%variables for the log plots
%  Step 0: Setup
% ------------------------------------------------------------
cd '/Users/amrithah/Desktop/CalhounLab/Dissertation/Projects/Chapter6_Paper_5_SPECT Template/Scripts/sc-ICA'
a = spm_read_vols(spm_vol('SPECT_SZ_ICA_Output_Rest_MOO_ICAR_group_loading_coeff_.nii'));

hcind = 1:76;
szind = 77:213;

nSub  = size(a,1);
nComp = size(a,2);   % number of components directly from data

%% ------------------------------------------------------------
%  Step 1: Build full connectivity matrices for each subject
% ------------------------------------------------------------
out_mat = zeros(nSub, nComp, nComp);

for j = 1:nSub
    vec = icatb_mat2vec(a(j,:)' * a(j,:));   % vectorized connectivity
    mat = icatb_vec2mat(vec);                % convert to square matrix
    out_mat(j,:,:) = mat;                    % store
end

out_clean = out_mat;

%% ------------------------------------------------------------
%  Step 2 and 3: Prepare x and y axis tick labels for domains/subdomains
% ------------------------------------------------------------
tickLabels = labels;   % keep if still valid; otherwise replace with your original labels

[uniqueCats, ~, idx] = unique(tickLabels, 'stable');
tickPositions = arrayfun(@(u) find(idx == u, 1, 'first'), 1:numel(uniqueCats));

%% ------------------------------------------------------------
%  Step 4: Compute group means
% ------------------------------------------------------------
HC_mean   = squeeze(mean(out_clean(hcind,:,:)));
SZ_mean   = squeeze(mean(out_clean(szind,:,:)));
Diff_mean = HC_mean - SZ_mean;

%%
% Step 5: Reorder within domain for the 3 matrices
% labels is a 68×1 cell array of strings
% We need numeric indices 1:68

idx = 1:length(labels);   % numeric indices

HC_mean_final   = HC_mean(idx, idx);
SZ_mean_final   = SZ_mean(idx, idx);
Diff_mean_final = Diff_mean(idx, idx);

%% ------------------------------------------------------------
%  Step 5: Plot all three matrices
% --- Plot 1 ---
plotMatrix(HC_mean_final, [-.6 .6], 'Average Control CoM', tickPositions, uniqueCats);
colorbar;
set(gca, 'FontSize', 11);
save('Control_CoM_Matrix_Final.fig');
movefile('*.fig', '/Users/amrithah/Desktop/CalhounLab/Dissertation/Projects/Paper_5_SPECT Template/Figures');
%%
% --- Plot 2 ---
plotMatrix(SZ_mean_final, [-.6 .6], 'Average Patient CoM', tickPositions, uniqueCats);
colorbar;
set(gca, 'FontSize', 11);
save('Patient_CoM_Matrix_Final.fig');
movefile('*.fig', '/Users/amrithah/Desktop/CalhounLab/Dissertation/Projects/Chapter6_Paper_5_SPECT Template/Figures');
%%  --- Plot 3 ---
%% GROUP DIFFERENCES WITH UPDATED FLATTENED MATRIX
figure;
imagesc(meandiff);  %adapted from Step2_3_flatt script since that had a better flattening script
axis square; colormap(whitejet); caxis([-0.4 0.4]); colorbar;
title('Average SC‑ICA Difference (Control – Patients) Co-Modulation Matrix, Flattened Matrix');
set(gca, 'FontSize', 11);
xticks(tickPositions);
yticks(tickPositions);
xticklabels(uniqueCats);
yticklabels(uniqueCats);
xtickangle(90);
ax = gca;
save('GroupDiff_CoM_Matrix_Final.fig');
movefile('*.fig', '/Users/amrithah/Desktop/CalhounLab/Dissertation/Projects/Chapter6_Paper_5_SPECT Template/Figures');
%% ------------------------------------------------------------
%  Nested function for plotting (updated to save .fig)
% ------------------------------------------------------------
function plotMatrix(M, clim, ttl, tickPositions, uniqueCats)

    figure;
    imagesc(M, clim);
    colormap whitejet;
    axis image;
    colorbar;
    title(ttl);

    xticks(tickPositions);
    yticks(tickPositions);
    xticklabels(uniqueCats);
    yticklabels(uniqueCats);
    xtickangle(90);

    % ---- Save figure as .fig ----
    fname = [regexprep(ttl, '\s+', '_') '.fig'];
    savefig(gcf, fname);

end