Freesurfer

freesurfer@nmr.mgh.harvard.edu

17 participants
30086 discussions

Cluster size
by johanna feiler 26 Apr '10

26 Apr '10

Hello, I got the same concern as miranda with trying to determine the number of vertices in my significant regions. I tried the 'Map Label to Subjects' options way, but I'm not sure where I can find the information Nick is mentioning (the text that says 'Found 389 points in source label.'). What do you mean with options? Is it in the subjects' individual label files? What if I want this information for the group and not for the individual subjects? Thanks for your help! Johanna Feiler >>>>>>>>>>> Miranda, I'll assume for now that what you mean by 'cluster' is not a cluster in the sense of clusters produced by a cluster-based correction for multiple comparisons, which was only very recently introduced to qdec (actually, just a script generator is produced). But rather, I think you mean the areas of significance that remain after you have either manually adjusted the threshold or just left the thresholds at their defaults. If this is what you mean by cluster-size, there is no way now in qdec to get the size of this 'cluster', outside of drawing a label around it, and then click on the 'Map Label to Subjects' options, and looking for the text that says 'Found 389 points in source label.' (where in this example, the label had 389 points/vertices). If you want a bit more exact way, you can use tksurfer: tksurfer fsaverage lh inflated -overlay /path/to/qdec/sig.mgh to load the sig.mgh file (from your qdec contrast dir of interest), then adjust the thresholds (View->Configure->Overlay), then click on the 'cluster' you want (the blob), then click the 'Custom Fill' icon (a paint bucket), then click 'Up to functional values below threshold', click Fill, and it will fill that 'cluster' and the terminal window will show you the number of vertices filled. I'm hoping to add this 'fill' feature to qdec at some point, to give a label creation option other than drawing. Nick On Tue, 2009-01-13 at 16:43 -0600, nels5...(a)umn.edu wrote: > Hello, > > I am wondering if there is an easy way to determine the size of a cluster > of significant voxels resulting from a QDEC analysis? I have created a > label and mapped the label to all of the subjects, but didn't know if there > was a way to extract information about cluster size. > > Thanks, > > Miranda ___________________________________________________________ GRATIS: Movie-Flat mit über 300 Top-Videos. Für WEB.DE Nutzer dauerhaft kostenlos! Jetzt freischalten unter http://movieflat.web.de

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WM segmentation error...
by Gonzalo Rojas Costa 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 ?... Could that WM segmentation error produce a very big mesh defect (vertices=21320, convex hull=9913)... If you want, I could send you more additional files... Sincerely, Gonzalo Rojas Costa

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7T MPRAGE
by Jose Luis Cantero Lorente 23 Apr '10

23 Apr '10

Dear Freesurfers, we are acquiring human T1-MPRAGE images (0.5 mm resolution) in a 7T siemen scanner (32-channel head coil). Unfortunately, 7T MPRAGE acquisitions typically present inhomogeneities leading to a broad range of segmentation failures in Freesurfer. Has anyone run automatic segmentation of 7T MPRAGE sequences by using Freesurfer successfully? If so, I would be highly interested to knowing specific parameters of 7T MPRAGE sequences to avoid these problems with Freesurfer. Thanks a lot in advance. Best, Jose --- Jose L. Cantero, Ph.D. Laboratory of Functional Neuroscience Department of Physiology, Anatomy, and Cell Biology University Pablo de Olavide Ctra. de Utrera, Km.1 41013 - Sevilla - Spain - Phone: +34 954 977433 Fax: +34 954 349151 Email: jlcanlor(a)upo.es http://www.upo.es/neuroaging/en/

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Reconstructing surfaces of post-resection brains
by Jed Singer 23 Apr '10

23 Apr '10

Hello! I've been using Freesurfer to build maps of the cortical surfaces of surgical epilepsy patients who have volunteered to participate as research subjects. It works great, except when these patients have had previous resections. In these cases, recon-all fails; I know there are ways that people get around this when working with brains that have lesions (manually filling in white matter, e.g.) but is it possible to get a reconstruction of the brain as it actually is? For example, in the case of someone who has previously had an anterior temporal lobectomy, I would love to end up with a model that does not have an anterior temporal lobe. I'm sorry if this is an obvious question - I've been digging through the archives of this list and furiously Googling and I've only found descriptions of how to manually fill in holes (due to lesions or caused by bad segmentations) in intact brains. Thanks in advance for any pointers, Jed

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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

1 0

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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