-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathplotBetas.m
318 lines (270 loc) · 10.6 KB
/
plotBetas.m
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
316
317
318
function [sess1 sess2 sess3 sess4] = plotBetas(roiName,grouped,simplify)
subs = {'NLR_HB275', 'NLR_KB218','NLR_GB310','NLR_JB420',...
'NLR_JB423','NLR_GB267','NLR_IB357','NLR_197_BK','NLR_GB387'};
% subs = {'NLR_KB218','NLR_GB310','NLR_GB355','NLR_JB423','NLR_IB357','NLR_HB275'};
% indicates whether we have denoised data for each session 1-4. Each row
% corresponds to index of subject number above. These must match.
% sess = [1 1 1 1;...
% 1 1 1 1;...
% 0 1 1 1;...
% 0 1 0 1;...
% 0 1 0 1;...
% 1 1 1 1];
sess = [1 1 1 1;...
1 1 1 1;...
1 1 1 1;
1 1 0 1;...
0 1 0 1;...
0 1 1 1;...
0 1 0 1;...
1 1 0 1;...
1 1 1 0];
% Sets datatype to original, GLM will be run within this script.
dt = [1];
% create empty arrays to collect betas for each session
sess1 = [];
sess2 = [];
sess3 = [];
sess4 = [];
for ii = 1:length(subs)
size1 = size(sess1); %Checks how many subjects have data for that session
size1 = size1(1); % This will tell us which index to insert data for
size2 = size(sess2); % the current subject we are indexing, there is probably
size2 = size2(1); % a more elegant way to do this.
size3 = size(sess3);
size3 = size3(1);
size4 = size(sess4);
size4 = size4(1);
concatDir = strcat('/mnt/diskArray/projects/LMB_Analysis/',subs{ii},'/concatVistaAligned/concatVista');
cd(concatDir)
if sum(sess(ii,:)) == 4 % Add up sess row to see how many sessions we have
scan = [1 2 3 4 5 6 7 8]; %if there are 4, we have 8 scans to work with
vw = initHiddenInplane(dt,scan,roiName); %initialize hidden inplane
if vw.selectedROI == 1
% % enforce consistent preprocessing / event-related parameters
params = er_defaultParams;
params.detrend = 2; % Quadtratic
params.framePeriod = 2; % TRs
params.glmHRF = 3; % SPM difference of gamas
params.ampType = 'betas';
er_setParams(vw, params,scan,dt);
%% Apply glm separately for each session
mv(1) = mv_init(vw,1,[1 2]);
mv(2) = mv_init(vw,1,[3 4]);
mv(3) = mv_init(vw,1,[5 6]);
mv(4) = mv_init(vw,1,[7 8]);
%get betas
for mi = 1:length(mv)
tempmv(mi) = mv_applyGlm(mv(mi));
% average the betas across voxels
b(mi,:) = mean(tempmv(mi).glm.betas,3);
end
sess1((size1+1),:) = b(1,:); %based on previous size of the data matrix,
sess2((size2+1),:) = b(2,:); % insert data at one below.
sess3((size3+1),:) = b(3,:);
sess4((size4+1),:) = b(4,:);
end
elseif sum(sess(ii,:)) == 3
scan = [1 2 3 4 5 6];
vw = initHiddenInplane(dt,scan,roiName);
if vw.selectedROI == 1
% % enforce consistent preprocessing / event-related parameters
params = er_defaultParams;
params.detrend = 2;
params.framePeriod = 2; %ek -changes detrend to quadratic
params.glmHRF = 3;
params.ampType = 'betas';
er_setParams(vw, params,scan,dt);
%% Apply glm separately for each session
mv(1) = mv_init(vw,1,[1 2]);
mv(2) = mv_init(vw,1,[3 4]);
mv(3) = mv_init(vw,1,[5 6]);
for mi = 1:length(mv)
tempmv(mi) = mv_applyGlm(mv(mi));
% average the betas across voxels
b(mi,:) = mean(tempmv(mi).glm.betas,3);
end
%based on which scan is missing, figure out which session data
%belongs with
if sess(ii,1) == 0
sess2((size2+1),:) = b(1,:);
sess3((size3+1),:) = b(2,:);
sess4((size4+1),:) = b(3,:);
elseif sess(ii,2) == 0
sess1((size1+1),:) = b(1,:);
sess3((size3+1),:) = b(2,:);
sess4((size4+1),:) = b(3,:);
elseif sess(ii,3) == 0
sess1((size1+1),:) = b(1,:);
sess2((size2+1),:) = b(2,:);
sess4((size4+1),:) = b(3,:);
elseif sess(ii,4) == 0
sess1((size1+1),:) = b(1,:);
sess2((size2+1),:) = b(2,:);
sess3((size3+1),:) = b(3,:);
end
end
elseif sum(sess(ii,:)) == 2
scan = [1 2 3 4];
vw = initHiddenInplane(dt,scan,roiName);
if vw.selectedROI == 1
% % enforce consistent preprocessing / event-related parameters
params = er_defaultParams;
params.detrend = 2;
params.framePeriod = 2; %ek -changes detrend to quadratic
params.glmHRF = 3;
params.ampType = 'betas';
er_setParams(vw, params,scan,dt);
%% Apply glm separately for each session
mv(1) = mv_init(vw,1,[1 2]);
mv(2) = mv_init(vw,1,[3 4]);
for mi = 1:length(mv)
tempmv(mi) = mv_applyGlm(mv(mi));
% average the betas across voxels
b(mi,:) = mean(tempmv(mi).glm.betas,3);
end
if sess(ii,1) == 0 && sess(ii,2) == 0
sess3((size3+1),:) = b(1,:);
sess4((size4+1),:) = b(2,:);
elseif sess(ii,1) == 0 && sess(ii,3) == 0
sess2((size2+1),:) = b(1,:);
sess4((size4+1),:) = b(2,:);
elseif sess(ii,1) == 0 && sess(ii,4) == 0
sess2((size2+1),:) = b(1,:);
sess3((size3+1),:) = b(2,:);
elseif sess(ii,2) == 0 && sess(ii,3) == 0
sess1((size1+1),:) = b(1,:);
sess4((size4+1),:) = b(2,:);
elseif sess(ii,2) == 0 && sess(ii,4) == 0
sess1((size1+1),:) = b(1,:);
sess3((size3+1),:) = b(2,:);
elseif sess(ii,3) == 0 && sess(ii,4) == 0
sess1((size1+1),:) = b(1,:);
sess2((size2+1),:) = b(2,:);
end
end
elseif sum(sess(ii,:)) == 1
scan = [1 2];
vw = initHiddenInplane(dt,scan,roiName);
if vw.selectedROI == 1
% % enforce consistent preprocessing / event-related parameters
params = er_defaultParams;
params.detrend = 2;
params.framePeriod = 2; %ek -changes detrend to quadratic
params.glmHRF = 3;
params.ampType = 'betas';
er_setParams(vw, params,scan,dt);
%% Apply glm separately for each session
mv(1) = mv_init(vw,1,[1 2]);
for mi = 1:length(mv)
tempmv(mi) = mv_applyGlm(mv(mi));
% average the betas across voxels
b(mi,:) = mean(tempmv(mi).glm.betas,3);
end
if sess(ii,1) == 0 && sess(ii,2) == 0 && sess(ii,3) == 0
sess4((size4+1),:) = b(1,:);
elseif sess(ii,1) == 0 && sess(ii,2) == 0 && sess(ii,4) == 0
sess3((size3+1),:) = b(1,:);
elseif sess(ii,1) == 0 && sess(ii,3) == 0 && sess(ii,4) == 0
sess2((size2+1),:) = b(1,:);
elseif sess(ii,2) == 0 && sess(ii,3) == 0 && sess(ii,4) == 0
sess1((size1+1),:) = b(1,:);
end
end
end
end
if grouped == 0
f(1,1:8) = mean(sess1(:,1:8));
f(2,1:8) = mean(sess2(:,1:8));
f(3,1:8) = mean(sess3(:,1:8));
f(4,1:8) = mean(sess4(:,1:8));
err(1,1:8) = RepMeasErr(sess1(:,1:8));
err(2,1:8) = RepMeasErr(sess2(:,1:8));
err(3,1:8) = RepMeasErr(sess3(:,1:8));
err(4,1:8) = RepMeasErr(sess4(:,1:8));
figure
hold on
colors = {[.3,.8,.5],[.5,0,.5],[0,.7,.7],[1,.8,.2]};
for j = 1:4
k = 1:8;
x = -0.5 + j + 1/9 * k;
bar(x,f(j,k),'FaceColor',colors{j});
errorbar(x,f(j,k),err(j,k),'.','Color','k')
end
elseif grouped == 1
size1 = size(sess1);
size1 = size1(1);
size2 = size(sess2);
size2 = size2(1);
size3 = size(sess3);
size3 = size3(1);
size4 = size(sess4);
size4 = size4(1);
for a = 1:size1
temp1(a,1) = mean(sess1(a,1:4));
% temp1(a,2) = mean(sess1(a,3:4));
temp1(a,2) = mean(sess1(a,5:6));
temp1(a,3) = mean(sess1(a,7:8));
end
for b = 1:size2
temp2(b,1) = mean(sess2(b,1:4));
% temp2(b,2) = mean(sess2(b,3:4));
temp2(b,2) = mean(sess2(b,5:6));
temp2(b,3) = mean(sess2(b,7:8));
end
for c = 1:size3
temp3(c,1) = mean(sess3(c,1:4));
% temp3(c,2) = mean(sess3(c,3:4));
temp3(c,2) = mean(sess3(c,5:6));
temp3(c,3) = mean(sess3(c,7:8));
end
for d = 1:size4
temp4(d,1) = mean(sess4(d,1:4));
% temp4(d,2) = mean(sess4(d,3:4));
temp4(d,2) = mean(sess4(d,5:6));
temp4(d,3) = mean(sess4(d,7:8));
end
f(1,1:3) = mean(temp1);
f(2,1:3) = mean(temp2);
f(3,1:3) = mean(temp3);
f(4,1:3) = mean(temp4);
err(1,1:3) = RepMeasErr(temp1);
err(2,1:3) = RepMeasErr(temp2);
err(3,1:3) = RepMeasErr(temp3);
err(4,1:3) = RepMeasErr(temp4);
s(1,1:3) = mean(temp2);
s(2,1:3) = mean(temp4);
errs(1,1:3) = RepMeasErr(temp2);
errs(2,1:3) = RepMeasErr(temp4);
figure
hold on
colors = {[.3,.8,.5],[.5,0,.5],[0,.7,.7],[1,.8,.2]};
if simplify == 0
ticklocations = [];
for j = 1:4
k = 1:4;
x = -0.5 + j + 1/4 * k;
ticklocations = [ticklocations x];
bar(x,f(j,k),'FaceColor',colors{j});
errorbar(x,f(j,k),err(j,k),'.','Color','k')
end
set(gca,'xtick',ticklocations,'xticklabels',{'FW','NW','F','O','FW','NW','F','O',...
'FW','NW','F','O','FW','NW','F','O'});
ylabel('Betas')
xlabel('Session')
title(roiName)
elseif simplify == 1
ticklocations = [];
for j = 1:2
k = 1:3;
x = -0.5 + j + 1/4 * k;
ticklocations = [ticklocations x];
bar(x,s(j,k),'FaceColor',colors{j});
errorbar(x,s(j,k),errs(j,k),'.','Color','k')
end
set(gca,'xtick',ticklocations,'xticklabels',{'W','F','O','W','F','O'});
ylabel('Betas')
xlabel('Session')
title('LH Concatenated VWFA')
end
end