Freesurfer

freesurfer@nmr.mgh.harvard.edu

19 participants
30132 discussions

Error in the smooth2 stage
by Jonathan Winkle 23 Apr '10

23 Apr '10

Hey Mailing List, I'm getting the following error in the smooth2 stage of reconstruction: #-------------------------------------------- #@# Smooth2 lh Wed Apr 21 17:22:08 EDT 2010 mris_smooth -n 3 -nw ../surf/lh.white ../surf/lh.smoothwm /mindhive/desimonelab/structurals/monkeys/muli/scripts smoothing for 3 iterations MRISread(../surf/lh.white): could not open file No such file or directory mris_smooth: could not read surface file ../surf/lh.white No such file or directory Linux ba2 2.6.31-20-generic #57-Ubuntu SMP Mon Feb 8 09:02:26 UTC 2010 x86_64 GNU/Linux recon-all exited with ERRORS at Wed Apr 21 17:22:08 EDT 2010 ----------------------------------------------- The lh.white file has not been created yet, so I'm not sure why it's trying to access it. Any help would be much appreciated. Thanks! -Jonathan -- Jonathan Winkle McGovern Institute for Brain Research Massachusetts Institute of Technology

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WM segmentation error...
by Gonzalo Rojas 23 Apr '10

23 Apr '10

Hi: I got a WM segmentation error such as that I show in the attached image... The segmentation error include many coronal slices in size (more than 15)... How can I correct it ?... Besides, I got a very big mesh defect (vertices=21320, convex hull=9913) that freesurfer couldn't correct: could that defect be produced by that WM segmentation error?... If you want, I could send you more additional files... Sincerely, Gonzalo Rojas Costa

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opseq2 efficiency vs VRF
by raij＠nmr.mgh.harvard.edu 22 Apr '10

22 Apr '10

Hi Doug and other Freesurfers, We are trying to balance the power of different event-related fMRI runs (each consisting of a different experimental condition with different interstimulus intervals) using the optseq2 definition for efficiency. Our outcome metric is the BOLD % signal change amplitude - we expect different runs to show different amplitudes. We use a FIR model. Because of the variable under investigation (interstimulus interval) we must have different number of stimuli in each run. We are uncertain as to which optseq2 efficiency measure more accurately suits this situation. I know you typically prefer "VRF" over "Efficiency". However, is VRF better suited for reflecting the power of statistical contrast (for example for t-test and F-values) as opposed to "raw" BOLD % signal change? In other words, would "Efficiency" more accurately reflect the balance for just the BOLD % signal change? The ultimate goal is to make sure that changes in BOLD % signal change amplitude are not due to the different number of stimuli across runs. Thanks! Best regards, Tommi --- Tommi Raij, M.D., Ph.D. MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging Bldg 149, 13th St Charlestown, MA 02129 U.S.A.

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Post-doctoral training in the Neuroscience of Schizophrenia
by Brian Roach 22 Apr '10

22 Apr '10

University of California San Francisco Three post-doctoral fellowships in translational neuroscience of schizophrenia. Sponsor(s): NIMH Application Date(s): Beginning April 1, 2010 The NIMH-funded T32 Training Grant (Neurobiological mechanisms underlying the symptoms and course of schizophrenia) at the University of California in San Francisco is now accepting applications for post-doctoral fellowships from recent PhDs, MDs, and MD/PhDs. Trainees will work in labs studying the neurobiological mechanisms of the symptoms of schizophrenia and its neuro-developmental and neuro-degenerative course. The core T32 faculty are basic neuroscientists and psychiatrists, working in genetics, brain imaging, electrophysiology, and neuroplasticity. They are: Steve Batki, William Byerley, Benjamin Cheyette, Allison Doupe, Judith Ford, Steven Hamilton, Daniel Mathalon, John Rubenstein, Susan Voglmaier, Sophia Vinogradov, and Mark von Zastrow. T32 Trainees will have extended experience in a laboratory, leading to the submission of research papers and grant proposals. Trainees will be dual-mentored with Research and Career Mentors to guide them both formally and informally, through learning neurobiological methods, producing a body of data, presenting data at national meetings, writing and publishing papers, preparing grant proposals, and attending local and national workshops on launching and maintaining successful careers in biological psychiatry. We seek applications from ethnically diverse scientists who have strong academic credentials and US citizenship or permanent residence. NIH rules for T32 trainees state, "The individual to be trained must be a citizen or a noncitizen national of the United States or have been lawfully admitted for permanent residence by the time of award. Individuals who have been lawfully admitted for permanent residence must have a currently valid Alien Registration Receipt Card (I-551) or other legal verification of such status." Potential applicants are welcome to contact any of the core faculty members. An application form is attached. Additional information can be found by visiting our website (http://psych.ucsf.edu/t32/neuro_scz)

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WM segmentation error...
by Gonzalo Rojas 22 Apr '10

22 Apr '10

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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. Combine multiple calendars with Hotmail. http://www.windowslive.com/campaign/thenewbusy?tile=multicalendar&ocid=PID2…

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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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