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Dear AFNI users-
We are very pleased to announce that the new AFNI Message Board framework is up! Please join us at:
Existing user accounts have been migrated, so returning users can login by requesting a password reset. New users can create accounts, as well, through a standard account creation process. Please note that these setup emails might initially go to spam folders (esp. for NIH users!), so please check those locations in the beginning.
The current Message Board discussion threads have been migrated to the new framework. The current Message Board will remain visible, but read-only, for a little while.
Sincerely, AFNI HQ
We are very pleased to announce that the new AFNI Message Board framework is up! Please join us at:
https://discuss.afni.nimh.nih.gov
Existing user accounts have been migrated, so returning users can login by requesting a password reset. New users can create accounts, as well, through a standard account creation process. Please note that these setup emails might initially go to spam folders (esp. for NIH users!), so please check those locations in the beginning.
The current Message Board discussion threads have been migrated to the new framework. The current Message Board will remain visible, but read-only, for a little while.
Sincerely, AFNI HQ
| May 12, 2006 12:25PM | |
AFNI Community,
I have been using AlphaSim to guard against false positives for a social
cognition study. A coworker has also tried using AlphSim for her study. We
have come across a conundrum in Alpha Sim.
The first dataset is named IA_late_mask+orig*
The second dataset is named MASK.nii (she is more advanced than I)
When I run the command
AlphaSim -iter 1000 -fwhm 8 -rmm 4 -mask IA_late_mask+orig. -pthr .05 -out IAmask8.txt
I get a reasonable table. Even if I wanted a clusterp of .001, I would need 41 voxels.
Or approximately .2% of the brain (41/18395 voxels in mask)
When she runs the command
AlphaSim -iter 1000 -fwhm 8 -rmm 4 -mask MASK.nii -pthr .05 -out NIImask8.txt
She has to have 209 voxels for a clusterp of .001. That is approximately
.7% of the brain. (209/29100 voxels in brain)
As her data is really at a smoothness of 12, I have tried running AlphaSim on both masks for
12mm FWHM. Again, her table does not seem as reasonable as mine does.
Can you tell me why she needs 3 times more voxels to get significant clusters when her brain is only
~50% bigger. Thank you in advance
Sumner
tables for IAmask8.txt
Program: AlphaSim
Author: B. Douglas Ward
Initial Release: 18 June 1997
Latest Revision: 02 December 2002
Data set dimensions:
nx = 64 ny = 64 nz = 35 (voxels)
dx = 4.00 dy = 4.00 dz = 4.20 (mm)
Mask filename = IA_late_mask+orig.
Voxels in mask = 18395
Gaussian filter widths:
sigmax = 3.40 FWHMx = 8.00
sigmay = 3.40 FWHMy = 8.00
sigmaz = 3.40 FWHMz = 8.00
Cluster connection radius: rmm = 4.00
Threshold probability: pthr = 5.000000e-02
Number of Monte Carlo iterations = 1000
Cl Size Frequency Cum Prop p/Voxel Max Freq Alpha
1 135843 0.407472 0.05183968 0 1.000000
2 67702 0.610550 0.04445491 0 1.000000
3 40936 0.733340 0.03709399 0 1.000000
4 29712 0.822464 0.03041783 0 1.000000
5 18218 0.877110 0.02395695 0 1.000000
6 12303 0.914014 0.01900506 0 1.000000
7 8278 0.938845 0.01499212 0 1.000000
8 5693 0.955921 0.01184202 0 1.000000
9 4032 0.968016 0.00936613 0 1.000000
10 2896 0.976702 0.00739342 4 1.000000
11 2131 0.983094 0.00581908 8 0.996000
12 1501 0.987597 0.00454477 22 0.988000
13 1076 0.990824 0.00356559 44 0.966000
14 815 0.993269 0.00280516 72 0.922000
15 572 0.994985 0.00218489 86 0.850000
16 422 0.996251 0.00171846 101 0.764000
17 311 0.997184 0.00135140 100 0.663000
18 247 0.997924 0.00106398 106 0.563000
19 172 0.998440 0.00082229 86 0.457000
20 137 0.998851 0.00064463 80 0.371000
21 89 0.999118 0.00049568 59 0.291000
22 82 0.999364 0.00039407 58 0.232000
23 50 0.999514 0.00029600 35 0.174000
24 49 0.999661 0.00023349 41 0.139000
25 33 0.999760 0.00016956 28 0.098000
26 26 0.999838 0.00012471 21 0.070000
27 10 0.999868 0.00008796 9 0.049000
28 12 0.999904 0.00007328 10 0.040000
29 11 0.999937 0.00005501 10 0.030000
30 5 0.999952 0.00003767 5 0.020000
31 3 0.999961 0.00002952 3 0.015000
32 3 0.999970 0.00002446 3 0.012000
33 4 0.999982 0.00001924 4 0.009000
34 2 0.999988 0.00001207 1 0.005000
35 2 0.999994 0.00000837 2 0.004000
36 0 0.999994 0.00000457 0 0.002000
37 0 0.999994 0.00000457 0 0.002000
38 0 0.999994 0.00000457 0 0.002000
39 0 0.999994 0.00000457 0 0.002000
40 1 0.999997 0.00000457 1 0.002000
41 0 0.999997 0.00000239 0 0.001000
42 0 0.999997 0.00000239 0 0.001000
43 0 0.999997 0.00000239 0 0.001000
44 1 1.000000 0.00000239 1 0.001000
tables for NIImask8.txt
Program: AlphaSim
Author: B. Douglas Ward
Initial Release: 18 June 1997
Latest Revision: 02 December 2002
Data set dimensions:
nx = 41 ny = 48 nz = 35 (voxels)
dx = 4.00 dy = 4.00 dz = 4.00 (mm)
Mask filename = MASK.nii
Voxels in mask = 29100
Gaussian filter widths:
sigmax = 3.40 FWHMx = 8.00
sigmay = 3.40 FWHMy = 8.00
sigmaz = 3.40 FWHMz = 8.00
Cluster connection radius: rmm = 4.00
Threshold probability: pthr = 5.000000e-02
Number of Monte Carlo iterations = 1000
Cl Size Frequency Cum Prop p/Voxel Max Freq Alpha
1 94449 0.372840 0.05200860 0 1.000000
2 37488 0.520825 0.04876293 0 1.000000
3 22043 0.607841 0.04618643 0 1.000000
4 15977 0.670910 0.04391396 0 1.000000
5 11912 0.717933 0.04171780 0 1.000000
6 9351 0.754847 0.03967107 0 1.000000
7 7591 0.784812 0.03774303 0 1.000000
8 6231 0.809409 0.03591701 0 1.000000
9 5106 0.829565 0.03420402 0 1.000000
10 4487 0.847278 0.03262485 0 1.000000
11 3825 0.862377 0.03108292 0 1.000000
12 3293 0.875377 0.02963705 0 1.000000
13 2938 0.886974 0.02827911 0 1.000000
14 2476 0.896748 0.02696660 0 1.000000
15 2310 0.905867 0.02577540 0 1.000000
16 2020 0.913841 0.02458467 0 1.000000
17 1720 0.920631 0.02347402 0 1.000000
18 1567 0.926817 0.02246921 0 1.000000
19 1396 0.932328 0.02149993 0 1.000000
20 1335 0.937597 0.02058845 0 1.000000
21 1166 0.942200 0.01967093 0 1.000000
22 1030 0.946266 0.01882949 0 1.000000
23 967 0.950083 0.01805079 0 1.000000
24 865 0.953498 0.01728650 0 1.000000
25 822 0.956743 0.01657309 0 1.000000
26 720 0.959585 0.01586691 0 1.000000
27 669 0.962226 0.01522361 0 1.000000
28 640 0.964752 0.01460289 0 1.000000
29 605 0.967141 0.01398708 0 1.000000
30 530 0.969233 0.01338416 0 1.000000
31 489 0.971163 0.01283777 0 1.000000
32 471 0.973023 0.01231684 0 1.000000
33 448 0.974791 0.01179890 1 1.000000
34 422 0.976457 0.01129086 0 0.999000
35 354 0.977854 0.01079780 1 0.999000
36 339 0.979192 0.01037203 2 0.998000
37 327 0.980483 0.00995265 1 0.996000
38 282 0.981597 0.00953687 1 0.995000
39 273 0.982674 0.00916863 6 0.994000
40 263 0.983712 0.00880275 4 0.988000
41 239 0.984656 0.00844124 1 0.984000
42 199 0.985441 0.00810450 4 0.983000
43 212 0.986278 0.00781729 6 0.979000
44 189 0.987024 0.00750402 3 0.973000
45 169 0.987691 0.00721825 8 0.970000
46 175 0.988382 0.00695691 9 0.962000
47 170 0.989053 0.00668028 13 0.953000
48 154 0.989661 0.00640571 8 0.940000
49 153 0.990265 0.00615168 12 0.932000
50 128 0.990771 0.00589406 12 0.920000
51 128 0.991276 0.00567412 16 0.908000
52 119 0.991746 0.00544979 15 0.892000
53 93 0.992113 0.00523715 8 0.877000
54 92 0.992476 0.00506777 8 0.869000
55 92 0.992839 0.00489704 16 0.861000
56 101 0.993238 0.00472316 16 0.845000
57 94 0.993609 0.00452880 19 0.829000
58 73 0.993897 0.00434467 18 0.810000
59 71 0.994177 0.00419918 16 0.792000
60 82 0.994501 0.00405522 23 0.776000
61 64 0.994754 0.00388615 7 0.753000
62 75 0.995050 0.00375199 21 0.746000
63 65 0.995306 0.00359220 21 0.725000
64 64 0.995559 0.00345148 20 0.704000
65 52 0.995764 0.00331072 14 0.684000
66 65 0.996021 0.00319457 26 0.670000
67 51 0.996222 0.00304715 19 0.644000
68 42 0.996388 0.00292973 16 0.625000
69 19 0.996463 0.00283158 5 0.609000
70 39 0.996617 0.00278653 18 0.604000
71 33 0.996747 0.00269271 17 0.586000
72 39 0.996901 0.00261220 16 0.569000
73 36 0.997043 0.00251570 18 0.553000
74 43 0.997213 0.00242540 17 0.535000
75 38 0.997363 0.00231605 16 0.518000
76 42 0.997529 0.00221811 24 0.502000
77 20 0.997608 0.00210842 12 0.478000
78 27 0.997714 0.00205550 15 0.466000
79 28 0.997825 0.00198313 14 0.451000
80 25 0.997924 0.00190711 14 0.437000
81 21 0.998006 0.00183839 15 0.423000
82 20 0.998085 0.00177993 12 0.408000
83 19 0.998160 0.00172357 13 0.396000
84 27 0.998267 0.00166938 19 0.383000
85 32 0.998393 0.00159144 19 0.364000
86 22 0.998480 0.00149797 19 0.345000
87 26 0.998583 0.00143296 17 0.326000
88 14 0.998638 0.00135522 10 0.309000
89 19 0.998713 0.00131289 16 0.299000
90 11 0.998756 0.00125478 8 0.283000
91 16 0.998820 0.00122076 10 0.275000
92 13 0.998871 0.00117072 10 0.265000
93 10 0.998910 0.00112962 10 0.255000
94 15 0.998970 0.00109766 15 0.245000
95 14 0.999025 0.00104921 13 0.230000
96 13 0.999076 0.00100351 10 0.217000
97 11 0.999120 0.00096062 8 0.207000
98 6 0.999143 0.00092395 2 0.199000
99 9 0.999179 0.00090375 7 0.197000
100 13 0.999230 0.00087313 11 0.190000
101 11 0.999274 0.00082845 8 0.179000
102 6 0.999297 0.00079027 6 0.171000
103 5 0.999317 0.00076924 5 0.165000
104 7 0.999345 0.00075155 7 0.160000
105 13 0.999396 0.00072653 13 0.153000
106 4 0.999412 0.00067962 4 0.140000
107 11 0.999455 0.00066505 10 0.136000
108 5 0.999475 0.00062460 3 0.126000
109 5 0.999495 0.00060605 5 0.123000
110 8 0.999526 0.00058732 7 0.118000
111 4 0.999542 0.00055708 4 0.111000
112 6 0.999566 0.00054182 4 0.107000
113 5 0.999585 0.00051873 5 0.103000
114 5 0.999605 0.00049931 5 0.098000
115 2 0.999613 0.00047973 1 0.093000
116 3 0.999625 0.00047182 3 0.092000
117 6 0.999649 0.00045986 5 0.089000
118 2 0.999656 0.00043574 2 0.084000
119 4 0.999672 0.00042763 3 0.082000
120 3 0.999684 0.00041127 2 0.079000
121 1 0.999688 0.00039890 1 0.077000
122 2 0.999696 0.00039474 2 0.076000
123 2 0.999704 0.00038636 2 0.074000
124 4 0.999720 0.00037790 4 0.072000
125 1 0.999723 0.00036086 1 0.068000
126 3 0.999735 0.00035656 3 0.067000
127 6 0.999759 0.00034357 6 0.064000
128 2 0.999767 0.00031739 2 0.058000
129 3 0.999779 0.00030859 3 0.056000
130 3 0.999791 0.00029529 3 0.053000
131 1 0.999795 0.00028189 1 0.050000
132 1 0.999798 0.00027739 1 0.049000
133 3 0.999810 0.00027285 2 0.048000
134 1 0.999814 0.00025914 1 0.046000
135 4 0.999830 0.00025454 3 0.045000
136 2 0.999838 0.00023598 2 0.042000
137 1 0.999842 0.00022663 1 0.040000
138 2 0.999850 0.00022192 2 0.039000
139 1 0.999854 0.00021244 1 0.037000
140 1 0.999858 0.00020766 0 0.036000
141 2 0.999865 0.00020285 2 0.036000
142 1 0.999869 0.00019316 1 0.034000
143 0 0.999869 0.00018828 0 0.033000
144 0 0.999869 0.00018828 0 0.033000
145 1 0.999873 0.00018828 1 0.033000
146 2 0.999881 0.00018330 2 0.032000
147 1 0.999885 0.00017326 1 0.030000
148 1 0.999889 0.00016821 1 0.029000
149 0 0.999889 0.00016313 0 0.028000
150 2 0.999897 0.00016313 2 0.028000
151 1 0.999901 0.00015282 1 0.026000
152 1 0.999905 0.00014763 1 0.025000
153 3 0.999917 0.00014241 3 0.024000
154 0 0.999917 0.00012663 0 0.021000
155 2 0.999925 0.00012663 2 0.021000
156 2 0.999932 0.00011598 2 0.019000
157 0 0.999932 0.00010526 0 0.017000
158 2 0.999940 0.00010526 2 0.017000
159 0 0.999940 0.00009440 0 0.015000
160 1 0.999944 0.00009440 1 0.015000
161 0 0.999944 0.00008890 0 0.014000
162 0 0.999944 0.00008890 0 0.014000
163 0 0.999944 0.00008890 0 0.014000
164 2 0.999952 0.00008890 2 0.014000
165 0 0.999952 0.00007763 0 0.012000
166 0 0.999952 0.00007763 0 0.012000
167 0 0.999952 0.00007763 0 0.012000
168 0 0.999952 0.00007763 0 0.012000
169 0 0.999952 0.00007763 0 0.012000
170 1 0.999956 0.00007763 1 0.012000
171 1 0.999960 0.00007179 1 0.011000
172 0 0.999960 0.00006591 0 0.010000
173 0 0.999960 0.00006591 0 0.010000
174 0 0.999960 0.00006591 0 0.010000
175 2 0.999968 0.00006591 2 0.010000
176 0 0.999968 0.00005388 0 0.008000
177 1 0.999972 0.00005388 1 0.008000
178 0 0.999972 0.00004780 0 0.007000
179 1 0.999976 0.00004780 1 0.007000
180 0 0.999976 0.00004165 0 0.006000
181 0 0.999976 0.00004165 0 0.006000
182 0 0.999976 0.00004165 0 0.006000
183 0 0.999976 0.00004165 0 0.006000
184 0 0.999976 0.00004165 0 0.006000
185 0 0.999976 0.00004165 0 0.006000
186 0 0.999976 0.00004165 0 0.006000
187 1 0.999980 0.00004165 1 0.006000
188 0 0.999980 0.00003522 0 0.005000
189 0 0.999980 0.00003522 0 0.005000
190 1 0.999984 0.00003522 1 0.005000
191 0 0.999984 0.00002869 0 0.004000
192 1 0.999987 0.00002869 1 0.004000
193 0 0.999987 0.00002210 0 0.003000
194 0 0.999987 0.00002210 0 0.003000
195 0 0.999987 0.00002210 0 0.003000
196 0 0.999987 0.00002210 0 0.003000
197 0 0.999987 0.00002210 0 0.003000
198 0 0.999987 0.00002210 0 0.003000
199 0 0.999987 0.00002210 0 0.003000
200 0 0.999987 0.00002210 0 0.003000
201 0 0.999987 0.00002210 0 0.003000
202 0 0.999987 0.00002210 0 0.003000
203 0 0.999987 0.00002210 0 0.003000
204 0 0.999987 0.00002210 0 0.003000
205 0 0.999987 0.00002210 0 0.003000
206 1 0.999991 0.00002210 1 0.003000
207 0 0.999991 0.00001502 0 0.002000
208 1 0.999995 0.00001502 1 0.002000
209 0 0.999995 0.00000787 0 0.001000
210 0 0.999995 0.00000787 0 0.001000
211 0 0.999995 0.00000787 0 0.001000
212 0 0.999995 0.00000787 0 0.001000
213 0 0.999995 0.00000787 0 0.001000
214 0 0.999995 0.00000787 0 0.001000
215 0 0.999995 0.00000787 0 0.001000
216 0 0.999995 0.00000787 0 0.001000
217 0 0.999995 0.00000787 0 0.001000
218 0 0.999995 0.00000787 0 0.001000
219 0 0.999995 0.00000787 0 0.001000
220 0 0.999995 0.00000787 0 0.001000
221 0 0.999995 0.00000787 0 0.001000
222 0 0.999995 0.00000787 0 0.001000
223 0 0.999995 0.00000787 0 0.001000
224 0 0.999995 0.00000787 0 0.001000
225 0 0.999995 0.00000787 0 0.001000
226 0 0.999995 0.00000787 0 0.001000
227 0 0.999995 0.00000787 0 0.001000
228 0 0.999995 0.00000787 0 0.001000
229 1 0.999999 0.00000787 1 0.001000
I have been using AlphaSim to guard against false positives for a social
cognition study. A coworker has also tried using AlphSim for her study. We
have come across a conundrum in Alpha Sim.
The first dataset is named IA_late_mask+orig*
The second dataset is named MASK.nii (she is more advanced than I)
When I run the command
AlphaSim -iter 1000 -fwhm 8 -rmm 4 -mask IA_late_mask+orig. -pthr .05 -out IAmask8.txt
I get a reasonable table. Even if I wanted a clusterp of .001, I would need 41 voxels.
Or approximately .2% of the brain (41/18395 voxels in mask)
When she runs the command
AlphaSim -iter 1000 -fwhm 8 -rmm 4 -mask MASK.nii -pthr .05 -out NIImask8.txt
She has to have 209 voxels for a clusterp of .001. That is approximately
.7% of the brain. (209/29100 voxels in brain)
As her data is really at a smoothness of 12, I have tried running AlphaSim on both masks for
12mm FWHM. Again, her table does not seem as reasonable as mine does.
Can you tell me why she needs 3 times more voxels to get significant clusters when her brain is only
~50% bigger. Thank you in advance
Sumner
tables for IAmask8.txt
Program: AlphaSim
Author: B. Douglas Ward
Initial Release: 18 June 1997
Latest Revision: 02 December 2002
Data set dimensions:
nx = 64 ny = 64 nz = 35 (voxels)
dx = 4.00 dy = 4.00 dz = 4.20 (mm)
Mask filename = IA_late_mask+orig.
Voxels in mask = 18395
Gaussian filter widths:
sigmax = 3.40 FWHMx = 8.00
sigmay = 3.40 FWHMy = 8.00
sigmaz = 3.40 FWHMz = 8.00
Cluster connection radius: rmm = 4.00
Threshold probability: pthr = 5.000000e-02
Number of Monte Carlo iterations = 1000
Cl Size Frequency Cum Prop p/Voxel Max Freq Alpha
1 135843 0.407472 0.05183968 0 1.000000
2 67702 0.610550 0.04445491 0 1.000000
3 40936 0.733340 0.03709399 0 1.000000
4 29712 0.822464 0.03041783 0 1.000000
5 18218 0.877110 0.02395695 0 1.000000
6 12303 0.914014 0.01900506 0 1.000000
7 8278 0.938845 0.01499212 0 1.000000
8 5693 0.955921 0.01184202 0 1.000000
9 4032 0.968016 0.00936613 0 1.000000
10 2896 0.976702 0.00739342 4 1.000000
11 2131 0.983094 0.00581908 8 0.996000
12 1501 0.987597 0.00454477 22 0.988000
13 1076 0.990824 0.00356559 44 0.966000
14 815 0.993269 0.00280516 72 0.922000
15 572 0.994985 0.00218489 86 0.850000
16 422 0.996251 0.00171846 101 0.764000
17 311 0.997184 0.00135140 100 0.663000
18 247 0.997924 0.00106398 106 0.563000
19 172 0.998440 0.00082229 86 0.457000
20 137 0.998851 0.00064463 80 0.371000
21 89 0.999118 0.00049568 59 0.291000
22 82 0.999364 0.00039407 58 0.232000
23 50 0.999514 0.00029600 35 0.174000
24 49 0.999661 0.00023349 41 0.139000
25 33 0.999760 0.00016956 28 0.098000
26 26 0.999838 0.00012471 21 0.070000
27 10 0.999868 0.00008796 9 0.049000
28 12 0.999904 0.00007328 10 0.040000
29 11 0.999937 0.00005501 10 0.030000
30 5 0.999952 0.00003767 5 0.020000
31 3 0.999961 0.00002952 3 0.015000
32 3 0.999970 0.00002446 3 0.012000
33 4 0.999982 0.00001924 4 0.009000
34 2 0.999988 0.00001207 1 0.005000
35 2 0.999994 0.00000837 2 0.004000
36 0 0.999994 0.00000457 0 0.002000
37 0 0.999994 0.00000457 0 0.002000
38 0 0.999994 0.00000457 0 0.002000
39 0 0.999994 0.00000457 0 0.002000
40 1 0.999997 0.00000457 1 0.002000
41 0 0.999997 0.00000239 0 0.001000
42 0 0.999997 0.00000239 0 0.001000
43 0 0.999997 0.00000239 0 0.001000
44 1 1.000000 0.00000239 1 0.001000
tables for NIImask8.txt
Program: AlphaSim
Author: B. Douglas Ward
Initial Release: 18 June 1997
Latest Revision: 02 December 2002
Data set dimensions:
nx = 41 ny = 48 nz = 35 (voxels)
dx = 4.00 dy = 4.00 dz = 4.00 (mm)
Mask filename = MASK.nii
Voxels in mask = 29100
Gaussian filter widths:
sigmax = 3.40 FWHMx = 8.00
sigmay = 3.40 FWHMy = 8.00
sigmaz = 3.40 FWHMz = 8.00
Cluster connection radius: rmm = 4.00
Threshold probability: pthr = 5.000000e-02
Number of Monte Carlo iterations = 1000
Cl Size Frequency Cum Prop p/Voxel Max Freq Alpha
1 94449 0.372840 0.05200860 0 1.000000
2 37488 0.520825 0.04876293 0 1.000000
3 22043 0.607841 0.04618643 0 1.000000
4 15977 0.670910 0.04391396 0 1.000000
5 11912 0.717933 0.04171780 0 1.000000
6 9351 0.754847 0.03967107 0 1.000000
7 7591 0.784812 0.03774303 0 1.000000
8 6231 0.809409 0.03591701 0 1.000000
9 5106 0.829565 0.03420402 0 1.000000
10 4487 0.847278 0.03262485 0 1.000000
11 3825 0.862377 0.03108292 0 1.000000
12 3293 0.875377 0.02963705 0 1.000000
13 2938 0.886974 0.02827911 0 1.000000
14 2476 0.896748 0.02696660 0 1.000000
15 2310 0.905867 0.02577540 0 1.000000
16 2020 0.913841 0.02458467 0 1.000000
17 1720 0.920631 0.02347402 0 1.000000
18 1567 0.926817 0.02246921 0 1.000000
19 1396 0.932328 0.02149993 0 1.000000
20 1335 0.937597 0.02058845 0 1.000000
21 1166 0.942200 0.01967093 0 1.000000
22 1030 0.946266 0.01882949 0 1.000000
23 967 0.950083 0.01805079 0 1.000000
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| Subject | Author | Posted |
|---|---|---|
|
Sumner Williams | May 12, 2006 12:25PM |
|
rick reynolds | May 12, 2006 12:58PM |
|
Sumner Williams | May 12, 2006 05:28PM |
