Freesurfer April 2010

freesurfer@nmr.mgh.harvard.edu

102 participants
147 discussions

a 3T MPRAGE subject fails at mri_ca_label
by Guang Zeng 22 Apr '10

22 Apr '10

Hi, there I have a 3T MPRAGE subject which fails at the mri_ca_label step, the recon-all.log is pasted below, Any suggestions to fix this error? Thanks a lot! Guang mri_ca_label -align -nobigventricles norm.mgz transforms/talairach.m3z /usr/local/biotools/freesurfer/freesurfer-v4.5.0/average/RB_all_2008-03-26.gca aseg.auto_noCCseg.mgz renormalizing sequences with structure alignment, equivalent to: -renormalize -renormalize_mean 0.500 -regularize 0.500 not handling expanded ventricles... reading 1 input volumes... reading classifier array from /usr/local/biotools/freesurfer/freesurfer-v4.5.0/average/RB_all_2008-03-26.gca... reading input volume from norm.mgz... average std[0] = 6.9 reading transform from transforms/talairach.m3z... zcat transforms/talairach.m3z Atlas used for the 3D morph was /usr/local/biotools/freesurfer/freesurfer-v4.5.0/average/RB_all_2008-03-26.gca average std = 6.9 using min determinant for regularization = 4.7 0 singular and 0 ill-conditioned covariance matrices regularized labeling volume... renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.15151 (27) mri peak = 0.20145 (24) Left_Lateral_Ventricle (4): linear fit = 0.85 x + 0.0 (15451 voxels, overlap=0.608) Left_Lateral_Ventricle (4): linear fit = 0.85 x + 0.0 (15451 voxels, peak = 23), gca=23.0 gca peak = 0.14982 (20) mri peak = 0.18345 (24) Right_Lateral_Ventricle (43): linear fit = 1.02 x + 0.0 (13803 voxels, overlap=0.564) Right_Lateral_Ventricle (43): linear fit = 1.02 x + 0.0 (13803 voxels, peak = 20), gca=20.5 gca peak = 0.28003 (97) mri peak = 0.12841 (110) Right_Pallidum (52): linear fit = 1.12 x + 0.0 (445 voxels, overlap=0.000) Right_Pallidum (52): linear fit = 1.12 x + 0.0 (445 voxels, peak = 109), gca=109.1 gca peak = 0.18160 (96) mri peak = 0.13582 (111) Left_Pallidum (13): linear fit = 1.15 x + 0.0 (451 voxels, overlap=0.004) Left_Pallidum (13): linear fit = 1.15 x + 0.0 (451 voxels, peak = 110), gca=110.4 gca peak = 0.27536 (62) mri peak = 0.07207 (76) Right_Hippocampus (53): linear fit = 1.15 x + 0.0 (1453 voxels, overlap=0.164) Right_Hippocampus (53): linear fit = 1.15 x + 0.0 (1453 voxels, peak = 71), gca=71.3 gca peak = 0.32745 (63) mri peak = 0.08789 (75) Left_Hippocampus (17): linear fit = 1.15 x + 0.0 (1469 voxels, overlap=0.000) Left_Hippocampus (17): linear fit = 1.15 x + 0.0 (1469 voxels, peak = 72), gca=72.4 gca peak = 0.08597 (105) mri peak = 0.08672 (108) Right_Cerebral_White_Matter (41): linear fit = 1.02 x + 0.0 (29417 voxels, overlap=0.653) Right_Cerebral_White_Matter (41): linear fit = 1.02 x + 0.0 (29417 voxels, peak = 108), gca=107.6 gca peak = 0.09209 (106) mri peak = 0.08520 (109) Left_Cerebral_White_Matter (2): linear fit = 1.05 x + 0.0 (36362 voxels, overlap=0.554) Left_Cerebral_White_Matter (2): linear fit = 1.05 x + 0.0 (36362 voxels, peak = 111), gca=111.3 gca peak = 0.07826 (63) mri peak = 0.02796 (76) Left_Cerebral_Cortex (3): linear fit = 1.20 x + 0.0 (68673 voxels, overlap=0.031) Left_Cerebral_Cortex (3): linear fit = 1.20 x + 0.0 (68673 voxels, peak = 76), gca=75.6 gca peak = 0.08598 (64) mri peak = 0.02607 (77) Right_Cerebral_Cortex (42): linear fit = 1.20 x + 0.0 (68179 voxels, overlap=0.006) Right_Cerebral_Cortex (42): linear fit = 1.20 x + 0.0 (68179 voxels, peak = 77), gca=76.8 gca peak = 0.24164 (71) mri peak = 0.09815 (87) Right_Caudate (50): linear fit = 1.20 x + 0.0 (1249 voxels, overlap=0.000) Right_Caudate (50): linear fit = 1.20 x + 0.0 (1249 voxels, peak = 85), gca=85.2 gca peak = 0.18227 (75) mri peak = 0.08068 (84) Left_Caudate (11): linear fit = 1.08 x + 0.0 (1061 voxels, overlap=0.509) Left_Caudate (11): linear fit = 1.08 x + 0.0 (1061 voxels, peak = 81), gca=80.6 gca peak = 0.10629 (62) mri peak = 0.05013 (71) Left_Cerebellum_Cortex (8): linear fit = 1.15 x + 0.0 (36025 voxels, overlap=0.446) Left_Cerebellum_Cortex (8): linear fit = 1.15 x + 0.0 (36025 voxels, peak = 71), gca=71.3 gca peak = 0.11668 (59) mri peak = 0.05257 (70) Right_Cerebellum_Cortex (47): linear fit = 1.15 x + 0.0 (36255 voxels, overlap=0.425) Right_Cerebellum_Cortex (47): linear fit = 1.15 x + 0.0 (36255 voxels, peak = 68), gca=67.8 gca peak = 0.17849 (88) mri peak = 0.09129 (95) Left_Cerebellum_White_Matter (7): linear fit = 1.08 x + 0.0 (6702 voxels, overlap=0.720) Left_Cerebellum_White_Matter (7): linear fit = 1.08 x + 0.0 (6702 voxels, peak = 95), gca=94.6 gca peak = 0.16819 (86) mri peak = 0.09287 (95) Right_Cerebellum_White_Matter (46): linear fit = 1.10 x + 0.0 (5941 voxels, overlap=0.422) Right_Cerebellum_White_Matter (46): linear fit = 1.10 x + 0.0 (5941 voxels, peak = 95), gca=94.6 gca peak = 0.41688 (64) mri peak = 0.08561 (83) Left_Amygdala (18): linear fit = 1.27 x + 0.0 (553 voxels, overlap=0.000) Left_Amygdala (18): linear fit = 1.27 x + 0.0 (553 voxels, peak = 82), gca=81.6 gca peak = 0.42394 (62) mri peak = 0.10507 (82) Right_Amygdala (54): linear fit = 1.30 x + 0.0 (492 voxels, overlap=0.000) Right_Amygdala (54): linear fit = 1.30 x + 0.0 (492 voxels, peak = 81), gca=80.6 gca peak = 0.10041 (96) mri peak = 0.06936 (96) Left_Thalamus_Proper (10): linear fit = 1.05 x + 0.0 (5098 voxels, overlap=0.799) Left_Thalamus_Proper (10): linear fit = 1.05 x + 0.0 (5098 voxels, peak = 101), gca=100.8 gca peak = 0.13978 (88) mri peak = 0.05876 (102) Right_Thalamus_Proper (49): linear fit = 1.10 x + 0.0 (4698 voxels, overlap=0.529) Right_Thalamus_Proper (49): linear fit = 1.10 x + 0.0 (4698 voxels, peak = 97), gca=96.8 gca peak = 0.08514 (81) mri peak = 0.09350 (92) Left_Putamen (12): linear fit = 1.08 x + 0.0 (1465 voxels, overlap=0.545) Left_Putamen (12): linear fit = 1.08 x + 0.0 (1465 voxels, peak = 87), gca=87.1 gca peak = 0.09624 (82) mri peak = 0.16556 (90) Right_Putamen (51): linear fit = 1.10 x + 0.0 (1124 voxels, overlap=0.363) Right_Putamen (51): linear fit = 1.10 x + 0.0 (1124 voxels, peak = 90), gca=90.2 gca peak = 0.07543 (88) mri peak = 0.05860 (97) Brain_Stem (16): linear fit = 1.12 x + 0.0 (12206 voxels, overlap=0.421) Brain_Stem (16): linear fit = 1.12 x + 0.0 (12206 voxels, peak = 99), gca=99.0 gca peak = 0.12757 (95) mri peak = 0.06982 (110) Right_VentralDC: unreasonable value (111.6/110.0), not in range [80, 110] - rejecting gca peak = 0.17004 (92) mri peak = 0.08107 (112) Left_VentralDC: unreasonable value (108.1/112.0), not in range [80, 110] - rejecting gca peak = 0.21361 (36) mri peak = 0.18754 (25) Third_Ventricle (14): linear fit = 0.80 x + 0.0 (503 voxels, overlap=0.115) Third_Ventricle (14): linear fit = 0.80 x + 0.0 (503 voxels, peak = 29), gca=28.8 gca peak = 0.26069 (23) mri peak = 0.11881 (24) Fourth_Ventricle (15): linear fit = 0.88 x + 0.0 (463 voxels, overlap=0.723) Fourth_Ventricle (15): linear fit = 0.88 x + 0.0 (463 voxels, peak = 20), gca=20.1 gca peak Unknown = 0.94427 ( 0) gca peak Left_Inf_Lat_Vent = 0.31795 (35) gca peak CSF = 0.14367 (38) gca peak Left_Accumbens_area = 0.57033 (70) gca peak Left_VentralDC = 0.17004 (92) gca peak Left_undetermined = 1.00000 (35) gca peak Left_vessel = 0.65201 (62) gca peak Left_choroid_plexus = 0.09084 (48) gca peak Right_Inf_Lat_Vent = 0.31129 (32) gca peak Right_Accumbens_area = 0.30219 (72) gca peak Right_VentralDC = 0.12757 (95) gca peak Right_vessel = 0.83418 (60) gca peak Right_choroid_plexus = 0.10189 (48) gca peak Fifth_Ventricle = 0.72939 (42) gca peak WM_hypointensities = 0.14821 (82) gca peak non_WM_hypointensities = 0.10354 (53) gca peak Optic_Chiasm = 0.34849 (76) not using caudate to estimate GM means estimating mean gm scale to be 1.21 x + 0.0 estimating mean wm scale to be 1.04 x + 0.0 estimating mean csf scale to be 0.89 x + 0.0 Left_Pallidum too bright - rescaling by 0.942 (from 1.150) to 104.0 (was 110.4) Right_Pallidum too bright - rescaling by 0.953 (from 1.125) to 104.0 (was 109.1) saving intensity scales to aseg.auto_noCCseg.label_intensities.txt renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.17125 (23) mri peak = 0.20145 (24) Left_Lateral_Ventricle (4): linear fit = 1.00 x + 0.0 (15451 voxels, overlap=0.669) Left_Lateral_Ventricle (4): linear fit = 1.00 x + 0.0 (15451 voxels, peak = 23), gca=23.0 gca peak = 0.19382 (21) mri peak = 0.18345 (24) Right_Lateral_Ventricle (43): linear fit = 1.00 x + 0.0 (13803 voxels, overlap=0.577) Right_Lateral_Ventricle (43): linear fit = 1.00 x + 0.0 (13803 voxels, peak = 21), gca=21.0 gca peak = 0.22474 (104) mri peak = 0.12841 (110) Right_Pallidum (52): linear fit = 1.05 x + 0.0 (445 voxels, overlap=0.749) Right_Pallidum (52): linear fit = 1.05 x + 0.0 (445 voxels, peak = 109), gca=109.2 gca peak = 0.18148 (104) mri peak = 0.13582 (111) Left_Pallidum (13): linear fit = 1.02 x + 0.0 (451 voxels, overlap=0.768) Left_Pallidum (13): linear fit = 1.02 x + 0.0 (451 voxels, peak = 107), gca=106.6 gca peak = 0.24928 (71) mri peak = 0.07207 (76) Right_Hippocampus (53): linear fit = 1.00 x + 0.0 (1453 voxels, overlap=0.785) Right_Hippocampus (53): linear fit = 1.00 x + 0.0 (1453 voxels, peak = 71), gca=71.0 gca peak = 0.25085 (72) mri peak = 0.08789 (75) Left_Hippocampus (17): linear fit = 0.98 x + 0.0 (1469 voxels, overlap=0.998) Left_Hippocampus (17): linear fit = 0.98 x + 0.0 (1469 voxels, peak = 70), gca=70.2 gca peak = 0.08354 (108) mri peak = 0.08672 (108) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (29417 voxels, overlap=0.739) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (29417 voxels, peak = 108), gca=108.0 gca peak = 0.08793 (111) mri peak = 0.08520 (109) Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (36362 voxels, overlap=0.718) Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (36362 voxels, peak = 111), gca=111.0 gca peak = 0.06468 (76) mri peak = 0.02796 (76) Left_Cerebral_Cortex (3): linear fit = 0.98 x + 0.0 (68673 voxels, overlap=0.222) Left_Cerebral_Cortex (3): linear fit = 0.98 x + 0.0 (68673 voxels, peak = 74), gca=74.1 gca peak = 0.07358 (76) mri peak = 0.02607 (77) Right_Cerebral_Cortex (42): linear fit = 0.98 x + 0.0 (68179 voxels, overlap=0.207) Right_Cerebral_Cortex (42): linear fit = 0.98 x + 0.0 (68179 voxels, peak = 74), gca=74.1 gca peak = 0.23743 (85) mri peak = 0.09815 (87) Right_Caudate (50): linear fit = 0.98 x + 0.0 (1249 voxels, overlap=1.000) Right_Caudate (50): linear fit = 0.98 x + 0.0 (1249 voxels, peak = 83), gca=82.9 gca peak = 0.16079 (80) mri peak = 0.08068 (84) Left_Caudate (11): linear fit = 1.00 x + 0.0 (1061 voxels, overlap=0.832) Left_Caudate (11): linear fit = 1.00 x + 0.0 (1061 voxels, peak = 80), gca=80.0 gca peak = 0.09121 (70) mri peak = 0.05013 (71) Left_Cerebellum_Cortex (8): linear fit = 1.00 x + 0.0 (36025 voxels, overlap=0.968) Left_Cerebellum_Cortex (8): linear fit = 1.00 x + 0.0 (36025 voxels, peak = 70), gca=70.0 gca peak = 0.10783 (68) mri peak = 0.05257 (70) Right_Cerebellum_Cortex (47): linear fit = 1.00 x + 0.0 (36255 voxels, overlap=0.955) Right_Cerebellum_Cortex (47): linear fit = 1.00 x + 0.0 (36255 voxels, peak = 68), gca=68.0 gca peak = 0.17131 (94) mri peak = 0.09129 (95) Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (6702 voxels, overlap=0.990) Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (6702 voxels, peak = 94), gca=94.0 gca peak = 0.15537 (95) mri peak = 0.09287 (95) Right_Cerebellum_White_Matter (46): linear fit = 1.00 x + 0.0 (5941 voxels, overlap=0.995) Right_Cerebellum_White_Matter (46): linear fit = 1.00 x + 0.0 (5941 voxels, peak = 95), gca=95.0 gca peak = 0.30366 (81) mri peak = 0.08561 (83) Left_Amygdala (18): linear fit = 1.00 x + 0.0 (553 voxels, overlap=1.000) Left_Amygdala (18): linear fit = 1.00 x + 0.0 (553 voxels, peak = 81), gca=81.0 gca peak = 0.36454 (81) mri peak = 0.10507 (82) Right_Amygdala (54): linear fit = 1.00 x + 0.0 (492 voxels, overlap=0.776) Right_Amygdala (54): linear fit = 1.00 x + 0.0 (492 voxels, peak = 81), gca=81.0 gca peak = 0.10118 (99) mri peak = 0.06936 (96) Left_Thalamus_Proper (10): linear fit = 1.00 x + 0.0 (5098 voxels, overlap=0.924) Left_Thalamus_Proper (10): linear fit = 1.00 x + 0.0 (5098 voxels, peak = 99), gca=99.0 gca peak = 0.09695 (97) mri peak = 0.05876 (102) Right_Thalamus_Proper (49): linear fit = 1.02 x + 0.0 (4698 voxels, overlap=0.892) Right_Thalamus_Proper (49): linear fit = 1.02 x + 0.0 (4698 voxels, peak = 99), gca=99.4 gca peak = 0.08397 (87) mri peak = 0.09350 (92) Left_Putamen (12): linear fit = 1.00 x + 0.0 (1465 voxels, overlap=0.855) Left_Putamen (12): linear fit = 1.00 x + 0.0 (1465 voxels, peak = 87), gca=87.0 gca peak = 0.08263 (85) mri peak = 0.16556 (90) Right_Putamen (51): linear fit = 1.00 x + 0.0 (1124 voxels, overlap=0.741) Right_Putamen (51): linear fit = 1.00 x + 0.0 (1124 voxels, peak = 85), gca=85.0 gca peak = 0.07503 (99) mri peak = 0.05860 (97) Brain_Stem (16): linear fit = 1.00 x + 0.0 (12206 voxels, overlap=0.801) Brain_Stem (16): linear fit = 1.00 x + 0.0 (12206 voxels, peak = 99), gca=99.0 gca peak = 0.11812 (95) mri peak = 0.06982 (110) Right_VentralDC (60): linear fit = 1.15 x + 0.0 (1457 voxels, overlap=0.011) Right_VentralDC (60): linear fit = 1.15 x + 0.0 (1457 voxels, peak = 109), gca=109.2 gca peak = 0.15960 (92) mri peak = 0.08107 (112) Left_VentralDC: unreasonable value (105.8/112.0), not in range [80, 110] - rejecting gca peak = 0.20402 (30) mri peak = 0.18754 (25) Third_Ventricle (14): linear fit = 0.98 x + 0.0 (503 voxels, overlap=0.845) Third_Ventricle (14): linear fit = 0.98 x + 0.0 (503 voxels, peak = 29), gca=29.2 gca peak = 0.24174 (20) mri peak = 0.11881 (24) Fourth_Ventricle (15): linear fit = 1.02 x + 0.0 (463 voxels, overlap=0.882) Fourth_Ventricle (15): linear fit = 1.02 x + 0.0 (463 voxels, peak = 20), gca=20.5 gca peak Unknown = 0.94427 ( 0) gca peak Left_Inf_Lat_Vent = 0.18244 (40) gca peak CSF = 0.17923 (34) gca peak Left_Accumbens_area = 0.46662 (75) gca peak Left_VentralDC = 0.15960 (92) gca peak Left_undetermined = 1.00000 (35) gca peak Left_vessel = 0.63638 (62) gca peak Left_choroid_plexus = 0.09084 (48) gca peak Right_Inf_Lat_Vent = 0.19452 (37) gca peak Right_Accumbens_area = 0.27742 (86) gca peak Right_vessel = 0.83418 (60) gca peak Right_choroid_plexus = 0.10189 (48) gca peak Fifth_Ventricle = 0.82185 (37) gca peak WM_hypointensities = 0.14997 (85) gca peak non_WM_hypointensities = 0.10331 (55) gca peak Optic_Chiasm = 0.34851 (76) mri_ca_label GCA sequential renormalization: label 28 not consistently computed. not using caudate to estimate GM means setting label Right_VentralDC based on Left_VentralDC = 1.15 x + 0 estimating mean gm scale to be 0.99 x + 0.0 estimating mean wm scale to be 1.00 x + 0.0 estimating mean csf scale to be 1.00 x + 0.0 Left_Pallidum too bright - rescaling by 0.976 (from 1.025) to 104.0 (was 106.6) Right_Pallidum too bright - rescaling by 0.953 (from 1.050) to 104.0 (was 109.2) saving intensity scales to aseg.auto_noCCseg.label_intensities.txt Linux dnode80.mayo.edu 2.6.18-164.11.1.el5 #1 SMP Wed Jan 20 07:32:21 EST 2010 x86_64 x86_64 x86_64 GNU/Linux recon-all exited with ERRORS at Thu Apr 15 22:46:04 CDT 2010 _________________________________________________________________ The New Busy think 9 to 5 is a cute idea. 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freesurfer 4.5.0 EM Registration failure
by bell0368＠umn.edu 22 Apr '10

22 Apr '10

Freesurfers, I have looked at this subject and original data looks good with no motion. The betting also looks good on the brainmask.mgz loaded into tkmedit. Any suggestions on what might be happening? I have attached the relevant log files, except recon-all.log because I thought it might be too big, but I can send it if you want it. Hopefully this is the correct way to submit attachments? Chris Bell University of Minnesota

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Question
by Dave Brohawn 22 Apr '10

22 Apr '10

Hello, While doing a autorecon-all, I experienced an error during the template deformation step. writing output transformation to transforms/talairach_with_skull.lta... registration took 187 minutes and 39 seconds. mri_watershed -T1 -brain_atlas /usr/local/freesurfer/stable4/average/RB_all_withskull_2008-03-26.gca transforms/talairach_with_skull.lta T1.mgz brainmask.auto.mgz Mode: T1 normalized volume Mode: Use the information of atlas (default parms, --help for details) ********************************************************* The input file is T1.mgz The output file is brainmask.auto.mgz If this is incorrect, please exit with CTL-C Weighting the input with atlas information before watershed *************************WATERSHED************************** Sorting... first estimation of the COG coord: x=128 y=93 z=111 r=96 first estimation of the main basin volume: 3776534 voxels Looking for seedpoints 2 found in the cerebellum 10 found in the rest of the brain global maximum in x=146, y=87, z=88, Imax=255 CSF=15, WM_intensity=110, WM_VARIANCE=5 WM_MIN=110, WM_HALF_MIN=110, WM_HALF_MAX=110, WM_MAX=110 preflooding height equal to 10 percent done Analyze main basin size=1213505248 voxels, voxel volume =1.000 = 1213505248 mmm3 = 1213505.248 cm3 done PostAnalyze...Basin Prior 0 basins merged thanks to atlas ***** 0 basin(s) merged in 1 iteration(s) ***** 0 voxel(s) added to the main basin done Weighting the input with prior template ****************TEMPLATE DEFORMATION**************** second estimation of the COG coord: x=125,y=102, z=116, r=8049 iterations GLOBAL CSF_MIN=1, CSF_intensity=2, CSF_MAX=3 , nb = 7803 RIGHT_CER CSF_MIN=1, CSF_intensity=2, CSF_MAX=3 , nb = 378 LEFT_CER CSF_MIN=1, CSF_intensity=2, CSF_MAX=3 , nb = 504 RIGHT_BRAIN CSF_MIN=1, CSF_intensity=2, CSF_MAX=3 , nb = 1809 LEFT_BRAIN CSF_MIN=1, CSF_intensity=2, CSF_MAX=3 , nb = 1152 OTHER CSF_MIN=1, CSF_intensity=2, CSF_MAX=3 , nb = 882 mri_watershed Error: GLOBAL region of the brain empty ! Linux seymour 2.6.18-128.7.1.el5 #1 SMP Mon Aug 24 08:20:55 EDT 2009 i686 i686 i386 GNU/Linux Here is my info. FREESURFER_HOME: /usr/local/freesurfer/stable4 Build stamp: freesurfer-Linux-centos4-stable-v4.5.0-20100311 RedHat release: CentOS release 5.3 (Final) Kernel info: Linux 2.6.18-128.7.1.el5 i686 NMR Center info (/space/freesurfer exists): machine: seymour SUBJECTS_DIR: /space/seymour/1/users/MTH_Study/ PWD: /space/newdata/1/siemens ssh seymour setenv SUBJECTS_DIR /space/seymour/1/users/MTH_Study/ cd /space/newdata/1/siemens --------------------------------------------------------------------- Please include the following additional information in your report: 1) subject name 2) the entire command-line executed 3) the error message generated 4) optionally include the subject's /script/recon-all.log See also: http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting Please write when you can, and thanks, Dave

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labels for Brodmann areas
by Jooman Han 22 Apr '10

22 Apr '10

Hi all, I want to make labels for Brodmann area 9, 24 and 46. I was wondering if there are corresponding labels in the labels generated by recon-all. Any suggestion to label those areas? Thanks! Jooman

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Starting out on Freesurfer: Beginner Questions
by Stefan Du Plessis 22 Apr '10

22 Apr '10

Dear all I am a young researcher just starting on fMRI/ freesurfer analysis in Cape Town. I got a good start after a Course the MGH guys gave us a few months back. Ran 53 subjects, T1 MPRAGE 3T Allegra Siemens on our local cluster (8 core nehalem X 300) (www.chpc.ac.za) : at one subject per node, took 12 -13 hrs on them, all recon-ed without a hitch as far as I can tell with tkmedit within 2 weeks. I have a few problems though in my initial "get feet warm" analysis: First off I just wanted to run by you guys my stats analysis: I wanted to start with a very basic ROI using four different predefined regions: (Right and left) Medial Orbital Frontal Cortex, Lateral Frontal Cortex, Caudate and Globus Pallidus. I took the mean cortical thickness of one group and did a unpaired t-test two-tailed t-test with the other group in R (Which I have also started to use BTW).(13 in OCD group; 15 in control group) I also did a non-parametric Wilcoxon test in the same way as I inspected the data on box-charts in R and they looked a little suspicious to me. The t-tests were all insignificant. The Wilcoxon turned up as 0.054 on the Right MOFC which is a bit dicey but nevertheless right on the money as a colleague working independently on the DTI found some good results in the Right Uncinate. The recruiting is still ongoing though and the numbers will increase, so maybe that will sway the P values one way or the other. Next I want to run a regression analysis in qdec using their ybocs scales(OCD severity): cortical thickness on the one arm and the score on the other. Am I doing this part right? Is there a simpler or more effective way? Comments or concerns... Also: I found that the parcelation for the MOFC and LOFC are spot on as far as I can tell from inspecting the scans in tkmedit. The segmentation of the Caudate looks okay, but the Globus Pallidus is a bit off. Sometimes it includes bits of the (?)anterior commisure and surrounding white matter (?CS tracts). Is it a bit pale for this type of analysis? (Also, I might be asking a bit much, but has anyone tried and measure the pars interna and externa of the Globus Pallidus?) Any suggestions? Thanks again everyone for a fantastic course and an even more brilliant software package. regards Dr Stefan du Plessis CUBIC South Africa

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questions about: fix bad talairachs tranforms
by liyari5018 22 Apr '10

22 Apr '10

Hi freesurfer experts: 1\ when I finished the first step of recon-all(-autorecon1), check with tkregister2, do i need to check each slice or I just need to check few key anatomicals (Anterior / posterior commissures, the temporal lobes in the coronal plane, and the midline cut)? I think a lot of work to do so, and difficult to control. Is that necessary to do this for every subject? 2\ when I finished fixing bad output from the talairachs registration, click the SAVE REG button, and I should rerun : recon-all -autoreon1 -subjectid subjectname or next command ?

2 1

skull stripping error?
by Wenger, Elisabeth 22 Apr '10

22 Apr '10

Dear freesurfer specialists, we just ran a sample of 91 subjects (with 3 timepoints), and in 5 of them the following problem occured: freesurfer exited with error in autorecon1 during skull stripping. The protocol looks like this: #-------------------------------------------- #@# Skull Stripping Thu Sep 17 18:13:23 CEST 2009 /Applications/freesurfer/subjects/1115is05B/mri \n mri_em_register -skull nu.mgz /Applications/freesurfer/average/RB_all_withskull_2008-03-26.gca transforms/talairach_with_skull.lta \n aligning to atlas containing skull, setting unknown_nbr_spacing = 5 reading 1 input volumes... logging results to talairach_with_skull.log reading '/Applications/freesurfer/average/RB_all_withskull_2008-03-26.gca'... average std = 23.1 using min determinant for regularization = 53.4 0 singular and 5702 ill-conditioned covariance matrices regularized reading 'nu.mgz'... freeing gibbs priors...done. bounding unknown intensity as < 20.2 or > 943.7 total sample mean = 92.0 (1443 zeros) ************************************************ spacing=8, using 3481 sample points, tol=1.00e-05... ************************************************ register_mri: find_optimal_transform find_optimal_transform: nsamples 3481, passno 0, spacing 8 GCAhistoScaleImageIntensities: could not find wm peak resetting wm mean[0]: 117 --> 126 resetting gm mean[0]: 74 --> 74 input volume #1 is the most T1-like using real data threshold=9.0 skull bounding box = (51, 10, 25) --> (205, 255, 237) using (102, 92, 131) as brain centroid... mean wm in atlas = 126, using box (83,62,105) --> (120, 122,157) to find MRI wm before smoothing, mri peak at 0 after smoothing, mri peak at 0, scaling input intensities by inf Darwin LIP-bxp-164-3.local 9.6.0 Darwin Kernel Version 9.6.0: Mon Nov 24 17:37:00 PST 2008; root:xnu-1228.9.59~1/RELEASE_I386 i386 recon-all exited with ERRORS at Thu Sep 17 18:14:19 CEST 2009 We are pretty much freesurfer beginners and don't have a clue what kind of mistake this could be. We couldn't find any obvious differnces to our other subjects which ran fine. I also attach the whole recon-all.log file, just in case a problem occured on a previous stage we didn't recognize. We used a MacPro3,1 and Freesurfer version 4.2.0. Thank you for your help! Best, Elisabeth and Genia

2 2

extract time series on surface
by GREGORY R KIRK 21 Apr '10

21 Apr '10

Hi, i want to extract epi time series on the surface, seems if i want every last drop of accuracy from bbregister i will need to run bbregister --mov epi_i_.nii --bold \ --s fbirn-anat-101.v4 \ --init-fsl --reg register_i_.dat on every epi volume in the time series and then mri_vol2surf --mov epi_i_.nii \ --reg register_i_.dat \ --projfrac 0.5 --interp nearest \ --hemi lh --o lh.epi_i_.mgh or else if i just use the linear reg (flirt or something ) to the first epi and use the bbreg transform to the first for all the others i might as well just be doing linear registration, i.e. losing the power of bbreg on all the others. then of course you have the problem of manually? checking all 300 or whatever registrations for the time series X number of subjects, or some auto method to check for time series outliers as a way to see what registrations may have gone of the track, but thats another story or am i missing something ? thanks greg

1 0

other color map for a volume in freeview..
by Gonzalo Rojas Costa 21 Apr '10

21 Apr '10

Hi: Freeview only has grayscale, heat and jet in the color maps for a volume... How can I specify another color map ?... Sincerely, Gonzalo Rojas Costa

3 3

Define ROI in individual surface based on area around a Talairach coordinate?
by Dickinson, Annelise (NIH/NIMH) [F] 21 Apr '10

21 Apr '10

Hi all, Is there perhaps a tksurfer tutorial for how to define and/or draw an ROI on an individual's surface by dilating out from a given Talairach coordinate? Freesurfer's volume registration is excellent and we'd like to use the correspondence to those normalized Talairach coordinates. Any insights or suggestions would be greatly appreciated! Thank you so much, hope you are having a good week! Best, Elise ***************************************************** Annelise Dickinson Post-Bac IRTA Laboratory of Brain and Cognition, NIMH/NIH/DHHS Building 10, Room 4C212 10 CENTER DR MSC 1366 BETHESDA MD 20892-1366 ***************************************************** Phone: (301) 435-4941 FAX: (301) 402-0921 E-Mail : dickinsona(a)mail.nih.gov

1 0

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Freesurfer April 2010