SPECT_ICA_Project/Step5_Spatial_Component_Visualization_Scripts.m at main · amrithah93/SPECT_ICA_Project · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
%%  Spatial_Component_Visualization_FDR_Corrected.m to view these sorted components from the loading parameter analysis
%previous scripts cleaned p and q values, labelled per NM 2.2 domain and
%subdomain, then sorted by domain and subdomain for visualization purposes
%Load SPECT_CoM_Workspace
% get into the right path first
cd '/Users/amrithah/Desktop/CalhounLab/Dissertation/Projects/Chapter6_Paper_5_SPECT Template/Scripts/sc-ICA'
%% Plot significant q values from bar plot from the SPECT SZ analysis here
%all 23 components plotted together
% Ordered list of components, q vals that were significant
comps = sig_idx_q;

% Build file list
files = cell(1, numel(comps));
for i = 1:numel(comps)
    files{i} = sprintf('SPECT_SZ_ICA_Output_Rest_MOO_ICAR_group_component_ica_.nii,%d', comps(i));
end

% Display all components in ONE montage figure
icatb_image_viewer(files, ...
    'display_type','montage', ...
    'structfile', fullfile(fileparts(which('gift.m')), ...
    'icatb_templates','shrunk_single_subj_T1.nii'), ...
    'threshold', 5.0, ...
    'slices_in_mm', (-60:8:60), ...
    'convert_to_zscores', 'yes', ...
    'image_values', 'positive', ...
    'iscomposite','yes');

title ('All Domain and Subdomain Components Displayed (FDR Corrected): Loading Parameter Group Differences Plotted ');
set(gca, 'FontSize', 13);

% Save the figure
savefig('AllComponents_Ordered_FDR_Corrected.fig');
movefile('*.fig', '/Users/amrithah/Desktop/CalhounLab/Dissertation/Projects/Chapter6_Paper_5_SPECT Template/Figures');
%% create additional figure with all 68 components displayed from Round 1 of blind ICA
% now put them all in one figure
%Run both run 1 and run 2 scripts at the same time so colors match between
%components for plotting
%Plot from Round 1
%go to appropriate directory
cd  '/Users/amrithah/Desktop/CalhounLab/Dissertation/Projects/Chapter6_Paper_5_SPECT Template/SPECT Template Creation/Blind_ICA_Results/Round 2/SPECT_Secondary_Blind_ICA_Round2_Results/SPECT_Template_Blind_ICA_Dataset2_5000_subj_sbm_results_Round1'
%Use reordered indexing to plot the components for Round 1 component visualization, else it will call the old indexing
% Build file list, but use the order from the spreadsheet for round 1
%create variables from sheet for round 1 and round 2 components (within
%domain ordering does NOT matter for this, so just pull from spreadsheet)
%do ordering for Round 1 and Round 2 components
%copy paste variables from the template ordering
% index components
%set random seed so colors stay consistent when plotting Run 1 and run 2
%have to list out the components like this, else it won't plot properly
%color wise in GIFT
%%create colormaps for all components...
numComp = length(files); % number of components
colorLen = 64;
baseColors = lines(numComp); % or any palette you like
cmap = [];
for i = 1:numComp
    cmap = [cmap; repmat(baseColors(i,:), colorLen, 1)];
end

%list these as in the order of run 1 in the ranked corrs spreadsheet (will
%be same in run 2 so that the colors match)
%%%pass colormap to the display function:
files = {
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,2',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,5',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,15',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,55',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,66',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,72',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,85',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,90',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,97',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,3',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,12',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,16',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,18',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,41',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,54',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,57',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,68',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,73',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,52',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,60',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,98',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,9',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,22',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,34',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,36',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,45',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,51',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,75',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,76',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,89',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,92',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,19',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,49',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,20',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,47',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,81',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,83',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,23',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,14',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,17',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,30',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,56',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,61',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,74',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,78',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,87',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,88',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,91',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,94',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,6',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,7',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,10',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,25',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,33',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,50',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,53',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,80',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,84',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,13',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,24',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,32',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,42',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,46',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,58',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,62',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,63',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,69',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_group_component_ica_.nii,71',...
};


rng(1); %random seed generator to keep colors consistent b/w two figures
% Display all components in ONE montage figure
icatb_image_viewer(files, ...
    'display_type','montage', ...
    'structfile', fullfile(fileparts(which('gift.m')), ...
    'icatb_templates','shrunk_single_subj_T1.nii'), ...
    'threshold', [3,15], ...
    'slices_in_mm', (-60:8:60), ...
    'convert_to_zscores', 'yes', ...
    'image_values', 'positive', ...
    'iscomposite','yes',...
     'cmap', cmap);

title('SPECT ICA Run 1 (N=5,001)');
set(gca, 'FontSize', 13);

% Save the figure
savefig('AllComponents_Ordered_Round1.fig');
movefile('*.fig', '/Users/amrithah/Desktop/CalhounLab/Dissertation/Projects/Chapter6_Paper_5_SPECT Template/Figures');

% now plot for Round 2
%use the spreadsheet order from round 2; import variables from ranked corrs
%go to appropriate directory
cd '/Users/amrithah/Desktop/CalhounLab/Dissertation/Projects/Chapter6_Paper_5_SPECT Template/GIFT Autolabeller Tool/Round_2_Results/SPECT_Autolabeller_Round2/spatial_correlations_round2_results'
% import run 2; order doesn't matter here since it's just display
cd  '/Users/amrithah/Desktop/CalhounLab/Dissertation/Projects/Chapter6_Paper_5_SPECT Template/SPECT Template Creation/Blind_ICA_Results/Round 2/SPECT_Secondary_Blind_ICA_Round2_Results/SPECT_Template_Blind_ICA_Dataset2_5000_subj_sbm_results_Round2/SPECT_Template_Blind_ICA_Dataset2_5000_subj_sbm_results_Round2'
% Call round 2 run component order from the QC spreadsheet
%use rerun, updated mask run 2 components here:

%list out each component, else colors won't plot properly or match run 1
files = {...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,83',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,30',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,86',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,67',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,81',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,63',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,44',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,26',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,16',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,54',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,23',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,98',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,49',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,47',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,3',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,90',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,59',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,93',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,28',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,55',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,88',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,57',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,69',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,66',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,35',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,34',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,68',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,56',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,10',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,29',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,72',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,11',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,21',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,32',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,71',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,64',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,40',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,19',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,7',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,41',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,82',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,13',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,9',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,92',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,43',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,38',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,89',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,15',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,70',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,78',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,8',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,79',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,6',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,52',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,51',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,4',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,62',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,50',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,48',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,20',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,95',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,61',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,80',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,58',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,73',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,2',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,37',...
'SPECT_Template_Blind_ICA_Dataset2_5000_subj_round2_group_component_ica_.nii,18',...
};

% Display all components in ONE montage figure
rng(1);
icatb_image_viewer(files, ...
    'display_type','montage', ...
    'structfile', fullfile(fileparts(which('gift.m')), ...
    'icatb_templates','shrunk_single_subj_T1.nii'), ...
    'threshold', [3,15], ... %set min and max of 3 and 15
    'slices_in_mm', (-60:8:60), ...
    'convert_to_zscores', 'yes', ...
    'image_values', 'positive', ... %convert to absolute value to have the results all show up
    'iscomposite','yes',...
     'cmap', cmap);

title ('SPECT ICA Run 2 (N=5,000)');
set(gca, 'FontSize', 13);

% Save the figure
savefig('AllComponents_Ordered_Round2.fig');
movefile('*.fig', '/Users/amrithah/Desktop/CalhounLab/Dissertation/Projects/Chapter6_Paper_5_SPECT Template/Figures');
%% separate 23 components from loading parameter analysis into SZ > HC, and HC > SZ
sigcompspos = find((mafdr(p1)<.05).*(stats1.tstat>0)); %these match what's plotted in the colored bar plot
sigcompsneg = find((mafdr(p1)<.05).*(stats1.tstat<0));  %these match what's plotted in the colored bar plot
%%
cd '/Users/amrithah/Desktop/CalhounLab/Dissertation/Projects/Chapter6_Paper_5_SPECT Template/Scripts/sc-ICA'

%first, do SZ > HC map
comps = [2,3,6,32,33,34]; %rename variable to call selected indexes based on bar plot

% Build file list
files = cell(1, numel(comps));
for i = 1:numel(comps)
    files{i} = sprintf('SPECT_SZ_ICA_Output_Rest_MOO_ICAR_group_component_ica_.nii,%d', comps(i));
end

% Display montage
icatb_image_viewer(files, ...
    'display_type','montage', ...
    'structfile', fullfile(fileparts(which('gift.m')), ...
    'icatb_templates','shrunk_single_subj_T1.nii'), ...
    'threshold', [3,15], ...
    'slices_in_mm', (-60:8:60), ...
    'convert_to_zscores', 'yes', ...
    'image_values', 'absolute value', ...
    'iscomposite','yes');

title ('Significant Components with SZ > HC  ');
set(gca, 'FontSize', 13);

savefig('SZ>HC_FDR_Corrected_LoadingParameters.fig');
movefile('*.fig', '/Users/amrithah/Desktop/CalhounLab/Dissertation/Projects/Chapter6_Paper_5_SPECT Template/Figures');
%%  now for HC > SZ
% All non-cerebellar components
cd '/Users/amrithah/Desktop/CalhounLab/Dissertation/Projects/Chapter6_Paper_5_SPECT Template/Scripts/sc-ICA'

comps = [2,3,9,11,14,15,16,17,19,22,23,24,25,32,33,34,46,53,54,56,59,60,68]; %based on bar plot

% Build file list
files = cell(1, numel(comps));
for i = 1:numel(comps)
    files{i} = sprintf('SPECT_SZ_ICA_Output_Rest_MOO_ICAR_group_component_ica_.nii,%d', comps(i));
end

% Display montage
icatb_image_viewer(files, ...
    'display_type','montage', ...
    'structfile', fullfile(fileparts(which('gift.m')), ...
    'icatb_templates','shrunk_single_subj_T1.nii'), ...
    'threshold', [3,15], ...
    'slices_in_mm', (-60:8:60), ...
    'convert_to_zscores', 'yes', ...
    'image_values', 'absolute value', ...
    'iscomposite','yes')

title ('Significant Components with HC > SZ ');
set(gca, 'FontSize', 13);
savefig('HC>SZ_FDR_Corrected_LoadingParameters.fig');
movefile('*.fig', '/Users/amrithah/Desktop/CalhounLab/Dissertation/Projects/Chapter6_Paper_5_SPECT Template/Figures');

%% go to step 6 to make the bar plot figure for the paper