imported>Psych 204: Created page with '%% Analysis % rgb color code for labels in ITK-Snap (match bar colors with labels) % 0 0 0 0 0 0 0 "Clear Label" % 1 85 36 255 1 1…'

2009-12-03T19:22:55Z

Created page with '%% Analysis % rgb color code for labels in ITK-Snap (match bar colors with labels) % 0 0 0 0 0 0 0 "Clear Label" % 1 85 36 255 1 1…'

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%% Analysis

% rgb color code for labels in ITK-Snap (match bar colors with labels)

% 0 0 0 0 0 0 0 "Clear Label"
% 1 85 36 255 1 1 1 "Label 1"
% 2 7 81 255 1 1 1 "Label 2"
% 3 35 252 255 1 1 1 "Label 3"
% 4 0 255 145 1 1 1 "Label 4"
% 5 87 255 21 1 1 1 "Label 5"
% 6 224 255 10 1 1 1 "Label 6"
% 7 231 178 0 1 1 1 "Label 7"
% 8 228 96 0 1 1 1 "Label 8"
% 9 255 2 0 1 1 1 "Label 9"
% 10 252 255 249 1 1 1 "Label 10"
%

% Specify data sets with regular voxel size and double voxel size

dat = reg_vox_10; % 1x1x1 voxel size, matrix for each subject
dat_sum = sum(reg_vox_10,4); % 1x1x1 voxel size, summed matrix across subjects

dat2 = MNIeach_ten; % 2x2x2 voxel size, matrix for each subject
dat2_sum = sum(MNIeach_ten,4); % 2x2x2 voxel size, summed matrix across subjects
dat_rep = MNI_NAcc_rep; % SPM's representative subject NAc mask

%% 1x1x1 voxels

% Size - finds the # of voxels in each subjects mask then calculates the average and the s.e.

for k = 1:size(dat,4); % 2x2x2
indx = find(dat(:,:,:,k));
subj_vox_count(k) = length(indx);

end

mean_nvox = mean(subj_vox_count);
se_nvox = std(subj_vox_count)/(size(dat,4)^.5);

%% PLOTS

forhist = [];

for i=1:9
indx = find(dat_sum==i);
forhist(i) = length(indx);
end
forhist = forhist';

% ITK Snap Label colors
mycolors = [255 2 0; 228 96 0; 231 178 0; 224 255 10; 87 255 21; 0 255 145; 35 252 255; 7 81 255; 85 36 255]./255;

% Histogram
figure(1), h = bar(forhist)
colormap(mycolors);
xlabel('number of subjects overlap/ NAc voxel')
ylabel('number of NAc voxels')
title('Subject overlap in NAc voxels (1 x 1 x 1 mm)')
ch = get(h,'Children'); %getting a handle on the kids
fvd = get(ch,'Faces');
fvcd = get(ch,'FaceVertexCData');
[zs, izs] = sortrows(forhist,1);
for j = 1:length(forhist)
row = izs(j);
fvcd(fvd(row,:)) = j;
end
set(ch,'FaceVertexCData',fvcd)

clear h ch fvd fvcd zs izs j

% Cumulative Histogram
c_elements = cumsum(forhist);
figure(2), h = bar(c_elements)
colormap(mycolors);
xlabel('number of subjects overlap/ NAc voxel')
ylabel('number of NAc voxels')
title('Cumulative Histogram of subject overlap in NAc voxels (1 x 1 x 1 mm)')
ch = get(h,'Children'); %getting a handle on the kids
fvd = get(ch,'Faces');
fvcd = get(ch,'FaceVertexCData');
[zs, izs] = sortrows(c_elements,1);
for j = 1:length(c_elements)
row = izs(j);
fvcd(fvd(row,:)) = j;
end
set(ch,'FaceVertexCData',fvcd)

clear h ch fvd fvcd zs izs j

% proportional histogram (rhist)
rhist_bars = forhist./sum(forhist);
figure(3), h = bar(rhist_bars);
colormap(mycolors);
xlabel('number of subjects overlap/ NAc voxel')
ylabel('proportion of total NAc voxels')
title('Relative histogram of subject overlap in NAc voxels (1 x 1 x 1 mm)')
ch = get(h,'Children'); %getting a handle on the kids
fvd = get(ch,'Faces');
fvcd = get(ch,'FaceVertexCData');
[zs, izs] = sortrows(rhist_bars,1);
for j = 1:length(rhist_bars)
row = izs(j);
fvcd(fvd(row,:)) = j;
end
set(ch,'FaceVertexCData',fvcd)

clear h ch fvd fvcd zs izs j c_elements forhist i indx indx2 k temp

%% 2x2x2 voxels

% Size - finds the # of voxels in each subjects mask then calculates the average and the s.e.

for k = 1:size(dat2,4); % 2x2x2
indx = find(dat2(:,:,:,k));
subj_vox_count2(k) = length(indx);

end

mean_nvox2 = mean(subj_vox_count2);
se_nvox2 = std(subj_vox_count2)/(size(dat2,4)^.5);

%% PLOTS

forhist = [];

for i=1:9
indx = find(dat_sum==i);
forhist(i) = length(indx);
end
forhist = forhist';

% ITK Snap Label colors
mycolors = [255 2 0; 228 96 0; 231 178 0; 224 255 10; 87 255 21; 0 255 145; 35 252 255; 7 81 255; 85 36 255]./255;

% Histogram
figure(4), h = bar(forhist)
colormap(mycolors);
xlabel('number of subjects overlap/ NAc voxel')
ylabel('number of NAc voxels')
title('Subject overlap in NAc voxels (2 x 2 x 2 mm)')
ch = get(h,'Children'); %getting a handle on the kids
fvd = get(ch,'Faces');
fvcd = get(ch,'FaceVertexCData');
[zs, izs] = sortrows(forhist,1);
for j = 1:length(forhist)
row = izs(j);
fvcd(fvd(row,:)) = j;
end
set(ch,'FaceVertexCData',fvcd)

clear h ch fvd fvcd zs izs j

% Cumulative Histogram
c_elements = cumsum(forhist);
figure(5), h = bar(c_elements)
colormap(mycolors);
xlabel('number of subjects overlap/ NAc voxel')
ylabel('number of NAc voxels')
title('Cumulative Histogram of subject overlap in NAc voxels (2 x 2 x 2 mm)')
ch = get(h,'Children'); %getting a handle on the kids
fvd = get(ch,'Faces');
fvcd = get(ch,'FaceVertexCData');
[zs, izs] = sortrows(c_elements,1);
for j = 1:length(c_elements)
row = izs(j);
fvcd(fvd(row,:)) = j;
end
set(ch,'FaceVertexCData',fvcd)

clear h ch fvd fvcd zs izs j

% proportional histogram (rhist)
rhist_bars2 = forhist./sum(forhist);
figure(6), h = bar(rhist_bars2);
colormap(mycolors);
xlabel('number of subjects overlap/ NAc voxel')
ylabel('proportion of total NAc voxels')
title('Relative histogram of subject overlap in NAc voxels (2 x 2 x 2 mm)')
ch = get(h,'Children'); %getting a handle on the kids
fvd = get(ch,'Faces');
fvcd = get(ch,'FaceVertexCData');
[zs, izs] = sortrows(rhist_bars2,1);
for j = 1:length(rhist_bars2)
row = izs(j);
fvcd(fvd(row,:)) = j;
end
set(ch,'FaceVertexCData',fvcd)

%% Compare to single subject rep NAc

in_indx = find(dat_rep); % # of NAc voxels in Colin mask
in_rep_mask = dat_sum(in_indx);
out_indx = find(dat_rep==0);
out_temp = dat_sum(out_indx);
out_indx2 = find(out_temp); % # of NAc voxels out of Colin mask
out_rep_mask = out_temp(out_indx2);

mean_in_mask = mean(in_rep_mask); % mean subject overlap/NAc voxel within Colin mask
mean_out_mask = mean(out_rep_mask); % mean subject overlap/NAc voxel outside of mask

clear h ch fvd fvcd zs izs j c_elements forhist i indx indx2 k temp

% plot relative hist for reg and double size voxels next to each other

% figure(7)
% ax(1) = subplot(1,2,1);
% rgb = imread('ngc6543a.jpg');
% image(rgb); title('RGB image')
% ax(2) = subplot(122);
% im = mean(rgb,3);
% image(im); title('Intensity Heat Map')
% colormap(hot(256))
% linkaxes(ax,'xy')
% axis(ax,'image')

% proportional histogram (rhist)

figure(7)
subplot(121), h = bar(rhist_bars);
colormap(mycolors);
xlabel('number of subjects overlap/ NAc voxel')
ylabel('proportion of total NAc voxels')
title('1 x 1 x 1 mm voxels ')
ch = get(h,'Children'); %getting a handle on the kids
fvd = get(ch,'Faces');
fvcd = get(ch,'FaceVertexCData');
[zs, izs] = sortrows(rhist_bars,1);
for j = 1:length(rhist_bars)
row = izs(j);
fvcd(fvd(row,:)) = j;
end
set(ch,'FaceVertexCData',fvcd)
subplot(122),h = bar(rhist_bars2);
colormap(mycolors);
title('2 x 2 x 2 mm voxels')
ch = get(h,'Children'); %getting a handle on the kids
fvd = get(ch,'Faces');
fvcd = get(ch,'FaceVertexCData');
[zs, izs] = sortrows(rhist_bars2,1);
for j = 1:length(rhist_bars2)
row = izs(j);
fvcd(fvd(row,:)) = j;
end
set(ch,'FaceVertexCData',fvcd)

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imported>Psych 204: Created page with '%% Analysis % rgb color code for labels in ITK-Snap (match bar colors with labels) % 0 0 0 0 0 0 0 "Clear Label" % 1 85 36 255 1 1…'