did you try setting dist to .01 or something very small? Note that we don't register individual surfaces to each other for a reason - the inverse variance weighting in the warp functional is critical to it being stable and accurate

cheers
Bruce

On Fri, 6 Nov 2015, Razlighi, Qolamreza R. wrote:

Dear Bruce, I did set the DIAG and ran it again with no luck. Below is the
output and attached please find the result.

Best[IMAGE]

ray@athens-b8-e8-56-47-e6-54:~\> tcsh
[athens-b8-e8-56-47-e6-54:~] ray% 
[athens-b8-e8-56-47-e6-54:~] ray% setenv DIAG 0x04040
[athens-b8-e8-56-47-e6-54:~] ray% 
[athens-b8-e8-56-47-e6-54:~/Data/P00001613/FreeSurferClean] ray% cd surf/
[athens-b8-e8-56-47-e6-54:P00001613/FreeSurferClean/surf] ray% 
[athens-b8-e8-56-47-e6-54:P00001613/FreeSurferClean/surf] ray% mris_register
-1 -parea 0 -curv -dist .25 lh.sphere
../../../P00001639/FreeSurferClean/surf/lh.sphere lh.sphere_dis25_parea0.reg
treating target as a single subject's surface...
using l_parea = 0.000
using smoothwm curvature for final alignment
l_dist = 0.250
$Id: mris_register.c,v 1.59 2011/03/02 00:04:33 nicks Exp $
  $Id: mrisurf.c,v 1.693.2.7 2013/05/12 22:28:01 nicks Exp $
reading surface from lh.sphere...
reading spherical surface
../../../P00001639/FreeSurferClean/surf/lh.sphere...
curvature mean = -0.000, std = 1.000
computing parameterization for surface
../../../P00001639/FreeSurferClean/surf/lh.inflated.H...
curvature mean = 0.000, std = 0.566
computing parameterization for surface
../../../P00001639/FreeSurferClean/surf/lh.sulc...
curvature mean = -0.030, std = 0.282
computing parameterization for surface
../../../P00001639/FreeSurferClean/surf/lh.smoothwm...
MRISregister() -------
max_passes = 4 
min_degrees = 0.500000 
max_degrees = 64.000000 
nangles = 8 
tol=5.0e-01, sigma=0.0, host=athen, nav=1024, nbrs=1, l_extern=10000.000,
l_nlarea=1.000, l_corr=1.000, l_dist=0.250
using quadratic fit line minimization
complete_dist_mat 0
rms 0
smooth_averages 0
remove_neg 0
ico_order 0
which_surface 0
target_radius 0.000000
nfields 0
scale 0.000000
desired_rms_height -1.000000
momentum 0.950000
nbhd_size -10
max_nbrs 10
niterations 25
nsurfaces 0
SURFACES 3
flags 16 (10)
use curv 16
no sulc 0
no rigid align 0
mris->nsize 1
mris->hemisphere 0
randomSeed 0

--------------------
tol=5.0e-01, sigma=0.0, host=athen, nav=1024, nbrs=1, l_extern=10000.000,
l_nlarea=1.000, l_corr=1.000, l_dist=0.250
using quadratic fit line minimization
1 Reading lh.sulc
curvature mean = 0.000, std = 0.583
reading precomputed curvature from lh.sulc

blurring surfaces with sigma=4.00...
done.
curvature mean = 0.044, std = 0.847
curvature mean = 0.022, std = 0.852
finding optimal rigid alignment
Starting MRISrigidBodyAlignGlobal()
000: dt: 0.000, sse: 135379.3 (0.279, 21.6, 0.412, 0.853), neg: 0
(%0.00:%0.00), avgs: 1024
scanning 64.00 degree nbhd, min sse = 95165.92
(+64.00, +64.00, -64.00), min @ (0.00, 0.00, 0.00) = 95165.9   
scanning 32.00 degree nbhd, min sse = 95165.92
(+32.00, +32.00, -32.00), min @ (8.00, 0.00, 0.00) = 90765.7   
  d=32.00 min @ (8.00, 0.00, 0.00) sse = 90765.7, tmin=1.1635
min sse = 90765.70 at (8.00, 0.00, 0.00)
001: dt: 0.000, sse: 130979.1 (0.279, 21.6, 0.412, 0.833), neg: 0
(%0.00:%0.00), avgs: 1024
scanning 16.00 degree nbhd, min sse = 90765.70
(+16.00, +16.00, -16.00), min @ (-4.00, -4.00, 0.00) = 87371.6   
  d=16.00 min @ (-4.00, -4.00, 0.00) sse = 87371.6, tmin=1.7380
min sse = 87371.58 at (-4.00, -4.00, 0.00)
002: dt: 0.000, sse: 127584.9 (0.279, 21.6, 0.412, 0.817), neg: 0
(%0.00:%0.00), avgs: 1024
scanning 8.00 degree nbhd, min sse = 87371.58
(+8.00, +8.00, -8.00), min @ (-2.00, 0.00, 0.00) = 86754.0   
  d=8.00 min @ (-2.00, 0.00, 0.00) sse = 86754.0, tmin=2.3264
min sse = 86754.03 at (-2.00, 0.00, 0.00)
003: dt: 0.000, sse: 126967.4 (0.279, 21.6, 0.412, 0.814), neg: 0
(%0.00:%0.00), avgs: 1024
scanning 4.00 degree nbhd, min sse = 86754.03
(+4.00, +4.00, -4.00), min @ (1.00, 1.00, 0.00) = 86449.9   
  d=4.00 min @ (1.00, 1.00, 0.00) sse = 86449.9, tmin=2.9298
min sse = 86449.91 at (1.00, 1.00, 0.00)
004: dt: 0.000, sse: 126663.3 (0.279, 21.6, 0.412, 0.813), neg: 0
(%0.00:%0.00), avgs: 1024
scanning 2.00 degree nbhd, min sse = 86449.91
(+2.00, +2.00, -2.00), min @ (0.00, -0.50, 0.50) = 86436.5   
  d=2.00 min @ (0.00, -0.50, 0.50) sse = 86436.5, tmin=3.5333
min sse = 86436.46 at (0.00, -0.50, 0.50)
005: dt: 0.000, sse: 126649.8 (0.279, 21.6, 0.412, 0.813), neg: 0
(%0.00:%0.00), avgs: 1024
scanning 1.00 degree nbhd, min sse = 86436.45
(+1.00, +1.00, -1.00), min @ (0.00, 0.25, -0.25) = 86423.4   
  d=1.00 min @ (0.00, 0.25, -0.25) sse = 86423.4, tmin=4.1242
min sse = 86423.41 at (0.00, 0.25, -0.25)
006: dt: 0.000, sse: 126636.8 (0.279, 21.6, 0.412, 0.813), neg: 0
(%0.00:%0.00), avgs: 1024
scanning 0.50 degree nbhd, min sse = 86423.41
(+0.50, +0.50, -0.50), min @ (0.00, 0.00, 0.00) = 86423.4   
MRISrigidBodyAlignGlobal() done   4.71 min
tol=5.0e-01, sigma=4.0, host=athen, nav=1024, nbrs=1, l_extern=10000.000,
l_nlarea=1.000, l_corr=1.000, l_dist=0.250
using quadratic fit line minimization
nlarea/corr = 1.000
integrating with navgs=1024 and tol=5.002e-01
007: dt: 241.975, sse: 109215.9 (0.308, 23.5, 0.429, 0.709), neg: 48
(%0.00:%0.01), avgs: 1024
008: dt: 117.533, sse: 99575.6 (0.298, 23.7, 0.432, 0.651), neg: 50
(%0.00:%0.01), avgs: 1024
009: dt: 136.508, sse: 95352.8 (0.305, 24.1, 0.436, 0.620), neg: 101
(%0.00:%0.02), avgs: 1024
010: dt: 105.985, sse: 92867.4 (0.303, 24.8, 0.442, 0.597), neg: 176
(%0.01:%0.03), avgs: 1024
011: dt: 149.343, sse: 90631.3 (0.311, 25.4, 0.448, 0.573), neg: 334
(%0.01:%0.06), avgs: 1024
012: dt: 85.796, sse: 89225.0 (0.312, 25.9, 0.453, 0.557), neg: 439
(%0.02:%0.08), avgs: 1024
013: dt: 177.745, sse: 87895.2 (0.321, 26.7, 0.460, 0.536), neg: 690
(%0.04:%0.13), avgs: 1024
014: dt: 66.370, sse: 87281.5 (0.322, 27.0, 0.463, 0.527), neg: 782
(%0.05:%0.14), avgs: 1024
015: dt: 236.440, sse: 86602.8 (0.331, 27.8, 0.472, 0.508), neg: 1155
(%0.08:%0.22), avgs: 1024
016: dt: 69.671, sse: 86419.9 (0.332, 28.0, 0.474, 0.502), neg: 1213
(%0.09:%0.23), avgs: 1024
integrating with navgs=256 and tol=2.505e-01
017: dt: 122.246, sse: 82435.7 (0.347, 29.5, 0.492, 0.436), neg: 2026
(%0.16:%0.43), avgs: 256
018: dt: 69.015, sse: 81795.8 (0.350, 29.8, 0.499, 0.417), neg: 1971
(%0.12:%0.42), avgs: 256
019: dt: 36.896, sse: 81619.9 (0.353, 30.1, 0.502, 0.407), neg: 2059
(%0.12:%0.44), avgs: 256
integrating with navgs=64 and tol=1.260e-01
020: dt: 33.532, sse: 80387.5 (0.362, 30.7, 0.513, 0.370), neg: 2300
(%0.10:%0.49), avgs: 64
021: dt: 9.860, sse: 80325.9 (0.364, 30.9, 0.516, 0.362), neg: 2391
(%0.10:%0.51), avgs: 64
integrating with navgs=16 and tol=6.442e-02
022: dt: 6.761, sse: 80080.1 (0.368, 30.9, 0.520, 0.348), neg: 1935
(%0.07:%0.41), avgs: 16
023: dt: 0.929, sse: 80074.6 (0.368, 31.0, 0.521, 0.347), neg: 1928
(%0.06:%0.40), avgs: 16
integrating with navgs=4 and tol=3.494e-02
024: dt: 1.444, sse: 80034.9 (0.370, 31.0, 0.522, 0.343), neg: 1734
(%0.06:%0.35), avgs: 4
025: dt: 0.556, sse: 80028.6 (0.370, 31.0, 0.523, 0.341), neg: 1762
(%0.05:%0.36), avgs: 4
integrating with navgs=1 and tol=2.210e-02
026: dt: 0.066, sse: 80027.9 (0.370, 31.0, 0.523, 0.341), neg: 1753
(%0.05:%0.36), avgs: 1
integrating with navgs=0 and tol=1.562e-02
027: dt: 0.050, sse: 80016.6 (0.370, 31.0, 0.523, 0.341), neg: 1716
(%0.05:%0.33), avgs: 0

blurring surfaces with sigma=2.00...
done.
curvature mean = 0.035, std = 0.927
curvature mean = 0.011, std = 0.928
tol=5.0e-01, sigma=2.0, host=athen, nav=1024, nbrs=1, l_extern=10000.000,
l_nlarea=1.000, l_corr=1.000, l_dist=0.250
using quadratic fit line minimization
nlarea/corr = 1.000
integrating with navgs=1024 and tol=5.002e-01
028: dt: 137.600, sse: 83361.7 (0.371, 31.1, 0.524, 0.373), neg: 1570
(%0.04:%0.30), avgs: 1024
integrating with navgs=256 and tol=2.505e-01
029: dt: 16.658, sse: 83344.4 (0.372, 31.1, 0.526, 0.370), neg: 1484
(%0.03:%0.28), avgs: 256
integrating with navgs=64 and tol=1.260e-01
030: dt: 10.485, sse: 83245.6 (0.375, 31.4, 0.530, 0.359), neg: 1316
(%0.03:%0.23), avgs: 64
integrating with navgs=16 and tol=6.442e-02
031: dt: 4.712, sse: 83112.1 (0.379, 31.6, 0.534, 0.346), neg: 1194
(%0.02:%0.19), avgs: 16
032: dt: 0.845, sse: 83109.5 (0.380, 31.6, 0.535, 0.344), neg: 1176
(%0.02:%0.19), avgs: 16
integrating with navgs=4 and tol=3.494e-02
033: dt: 0.700, sse: 83081.6 (0.381, 31.6, 0.536, 0.340), neg: 1147
(%0.02:%0.18), avgs: 4
integrating with navgs=1 and tol=2.210e-02
034: dt: 0.517, sse: 83064.4 (0.382, 31.7, 0.536, 0.338), neg: 1204
(%0.02:%0.19), avgs: 1
integrating with navgs=0 and tol=1.562e-02
035: dt: 0.041, sse: 83050.5 (0.382, 31.7, 0.537, 0.337), neg: 1173
(%0.02:%0.18), avgs: 0
036: dt: 0.042, sse: 83039.7 (0.382, 31.7, 0.537, 0.337), neg: 1171
(%0.02:%0.18), avgs: 0

blurring surfaces with sigma=1.00...
done.
curvature mean = 0.033, std = 0.955
curvature mean = 0.005, std = 0.964
tol=5.0e-01, sigma=1.0, host=athen, nav=1024, nbrs=1, l_extern=10000.000,
l_nlarea=1.000, l_corr=1.000, l_dist=0.250
using quadratic fit line minimization
nlarea/corr = 1.000
integrating with navgs=1024 and tol=5.002e-01
037: dt: 115.451, sse: 87076.6 (0.383, 31.7, 0.537, 0.378), neg: 1086
(%0.02:%0.16), avgs: 1024
integrating with navgs=256 and tol=2.505e-01
038: dt: 0.037, sse: 87076.2 (0.383, 31.7, 0.537, 0.378), neg: 1087
(%0.02:%0.16), avgs: 256
integrating with navgs=64 and tol=1.260e-01
039: dt: 6.000, sse: 87067.2 (0.385, 31.9, 0.540, 0.372), neg: 1155
(%0.02:%0.17), avgs: 64
integrating with navgs=16 and tol=6.442e-02
040: dt: 0.771, sse: 87047.6 (0.385, 32.0, 0.540, 0.370), neg: 1166
(%0.02:%0.17), avgs: 16
integrating with navgs=4 and tol=3.494e-02
041: dt: 0.926, sse: 86979.7 (0.387, 32.1, 0.542, 0.365), neg: 1169
(%0.02:%0.17), avgs: 4
042: dt: 0.550, sse: 86964.0 (0.388, 32.2, 0.543, 0.362), neg: 1182
(%0.02:%0.17), avgs: 4
integrating with navgs=1 and tol=2.210e-02
043: dt: 0.255, sse: 86957.1 (0.389, 32.2, 0.544, 0.360), neg: 1203
(%0.02:%0.18), avgs: 1
integrating with navgs=0 and tol=1.562e-02
044: dt: 0.035, sse: 86943.6 (0.389, 32.2, 0.544, 0.359), neg: 1219
(%0.02:%0.17), avgs: 0

blurring surfaces with sigma=0.50...
done.
curvature mean = 0.033, std = 0.965
curvature mean = 0.001, std = 0.983
tol=5.0e-01, sigma=0.5, host=athen, nav=1024, nbrs=1, l_extern=10000.000,
l_nlarea=1.000, l_corr=1.000, l_dist=0.250
using quadratic fit line minimization
nlarea/corr = 1.000
integrating with navgs=1024 and tol=5.002e-01
045: dt: 39.869, sse: 89122.8 (0.389, 32.2, 0.544, 0.381), neg: 1239
(%0.02:%0.18), avgs: 1024
integrating with navgs=256 and tol=2.505e-01
046: dt: 0.040, sse: 89122.7 (0.389, 32.2, 0.544, 0.381), neg: 1239
(%0.02:%0.18), avgs: 256
integrating with navgs=64 and tol=1.260e-01
047: dt: 0.015, sse: 89122.7 (0.389, 32.2, 0.544, 0.381), neg: 1239
(%0.02:%0.18), avgs: 64
integrating with navgs=16 and tol=6.442e-02
048: dt: 0.763, sse: 89114.0 (0.390, 32.3, 0.545, 0.379), neg: 1259
(%0.02:%0.18), avgs: 16
integrating with navgs=4 and tol=3.494e-02
049: dt: 0.531, sse: 89093.1 (0.391, 32.4, 0.546, 0.376), neg: 1286
(%0.02:%0.18), avgs: 4
integrating with navgs=1 and tol=2.210e-02
050: dt: 0.280, sse: 89084.5 (0.392, 32.4, 0.547, 0.374), neg: 1315
(%0.02:%0.19), avgs: 1
integrating with navgs=0 and tol=1.562e-02
051: dt: 0.128, sse: 89052.0 (0.393, 32.5, 0.547, 0.372), neg: 1300
(%0.04:%0.18), avgs: 0
052: dt: 0.032, sse: 89024.1 (0.392, 32.5, 0.547, 0.372), neg: 1329
(%0.02:%0.18), avgs: 0
053: dt: 0.033, sse: 89014.9 (0.393, 32.5, 0.547, 0.371), neg: 1377
(%0.02:%0.19), avgs: 0
2 Reading smoothwm
curvature mean = -0.027, std = 0.269
calculating curvature of smoothwm surface
tol=1.0e+00, sigma=0.5, host=athen, nav=1024, nbrs=1, l_extern=10000.000,
l_nlarea=1.000, l_corr=0.050, l_spring=0.500, l_dist=0.250
using quadratic fit line minimization

blurring surfaces with sigma=4.00...
done.
curvature mean = 0.089, std = 0.338
curvature mean = 0.065, std = 0.378
tol=1.0e+00, sigma=4.0, host=athen, nav=1024, nbrs=1, l_extern=10000.000,
l_nlarea=1.000, l_corr=0.050, l_spring=0.500, l_dist=0.250
using quadratic fit line minimization
nlarea/corr = 20.000
integrating with navgs=1024 and tol=1.000e+00
054: dt: 3.299, sse: 745272.2 (0.384, 31.8, 0.538, 0.745), neg: 1168
(%0.04:%0.21), avgs: 1024
055: dt: 2.605, sse: 738110.4 (0.379, 31.4, 0.532, 0.748), neg: 740
(%0.01:%0.11), avgs: 1024
integrating with navgs=256 and tol=5.010e-01
056: dt: 2.945, sse: 732954.1 (0.377, 31.2, 0.529, 0.751), neg: 774
(%0.02:%0.13), avgs: 256
057: dt: 2.657, sse: 728543.9 (0.374, 30.9, 0.526, 0.753), neg: 511
(%0.01:%0.08), avgs: 256
058: dt: 2.797, sse: 724908.4 (0.373, 30.8, 0.523, 0.756), neg: 551
(%0.01:%0.09), avgs: 256
059: dt: 2.740, sse: 721596.7 (0.371, 30.6, 0.521, 0.758), neg: 404
(%0.00:%0.06), avgs: 256
integrating with navgs=64 and tol=2.519e-01
060: dt: 0.737, sse: 720241.4 (0.370, 30.4, 0.520, 0.758), neg: 307
(%0.00:%0.05), avgs: 64
integrating with navgs=16 and tol=1.288e-01
061: dt: 6.542, sse: 716147.8 (0.370, 30.3, 0.518, 0.758), neg: 559
(%0.01:%0.07), avgs: 16
062: dt: 1.625, sse: 712606.1 (0.367, 29.9, 0.514, 0.757), neg: 151
(%0.00:%0.02), avgs: 16
063: dt: 11.631, sse: 707697.2 (0.368, 29.9, 0.513, 0.757), neg: 580
(%0.02:%0.06), avgs: 16
064: dt: 1.462, sse: 703515.8 (0.365, 29.3, 0.508, 0.756), neg: 88
(%0.00:%0.01), avgs: 16
065: dt: 29.856, sse: 694749.9 (0.368, 29.3, 0.505, 0.756), neg: 702
(%0.03:%0.07), avgs: 16
066: dt: 1.308, sse: 688816.4 (0.363, 28.4, 0.499, 0.756), neg: 56
(%0.00:%0.01), avgs: 16
067: dt: 25.204, sse: 684304.4 (0.364, 28.4, 0.496, 0.756), neg: 526
(%0.02:%0.06), avgs: 16
068: dt: 1.261, sse: 680584.3 (0.361, 27.8, 0.492, 0.756), neg: 60
(%0.00:%0.01), avgs: 16
069: dt: 10.120, sse: 679053.8 (0.362, 27.8, 0.492, 0.756), neg: 168
(%0.00:%0.01), avgs: 16
070: dt: 1.667, sse: 677616.7 (0.361, 27.6, 0.490, 0.756), neg: 51
(%0.00:%0.00), avgs: 16
071: dt: 6.724, sse: 676628.9 (0.361, 27.6, 0.490, 0.756), neg: 124
(%0.00:%0.01), avgs: 16
072: dt: 1.678, sse: 675656.8 (0.360, 27.5, 0.489, 0.756), neg: 52
(%0.00:%0.01), avgs: 16
073: dt: 8.216, sse: 674570.9 (0.361, 27.5, 0.488, 0.757), neg: 133
(%0.00:%0.01), avgs: 16
074: dt: 1.694, sse: 673512.5 (0.360, 27.3, 0.487, 0.757), neg: 53
(%0.00:%0.01), avgs: 16
075: dt: 1.764, sse: 673109.5 (0.360, 27.3, 0.487, 0.757), neg: 50
(%0.00:%0.00), avgs: 16
integrating with navgs=4 and tol=6.988e-02
076: dt: 22.423, sse: 670854.9 (0.362, 27.2, 0.485, 0.742), neg: 200
(%0.00:%0.02), avgs: 4
077: dt: 1.661, sse: 669613.8 (0.361, 27.0, 0.484, 0.743), neg: 95
(%0.00:%0.01), avgs: 4
078: dt: 4.100, sse: 668786.9 (0.361, 27.0, 0.483, 0.744), neg: 92
(%0.00:%0.01), avgs: 4
079: dt: 3.000, sse: 668304.8 (0.361, 27.0, 0.483, 0.744), neg: 102
(%0.00:%0.01), avgs: 4
080: dt: 1.848, sse: 667791.1 (0.361, 26.9, 0.482, 0.744), neg: 77
(%0.00:%0.01), avgs: 4
081: dt: 5.400, sse: 667120.4 (0.361, 26.9, 0.482, 0.745), neg: 105
(%0.00:%0.01), avgs: 4
082: dt: 1.967, sse: 666614.0 (0.361, 26.8, 0.481, 0.745), neg: 73
(%0.00:%0.01), avgs: 4
083: dt: 4.462, sse: 666103.5 (0.361, 26.8, 0.481, 0.745), neg: 103
(%0.00:%0.01), avgs: 4
084: dt: 2.154, sse: 665621.2 (0.361, 26.8, 0.481, 0.745), neg: 76
(%0.00:%0.01), avgs: 4
085: dt: 2.615, sse: 665218.2 (0.361, 26.7, 0.480, 0.745), neg: 74
(%0.00:%0.01), avgs: 4
integrating with navgs=1 and tol=4.419e-02
086: dt: 2.667, sse: 664951.9 (0.361, 26.7, 0.480, 0.744), neg: 76
(%0.00:%0.01), avgs: 1
integrating with navgs=0 and tol=3.125e-02
087: dt: 0.506, sse: 664875.2 (0.361, 26.7, 0.480, 0.743), neg: 76
(%0.00:%0.01), avgs: 0

blurring surfaces with sigma=2.00...
done.
curvature mean = 0.090, std = 0.502
curvature mean = 0.020, std = 0.575
tol=1.0e+00, sigma=2.0, host=athen, nav=1024, nbrs=1, l_extern=10000.000,
l_nlarea=1.000, l_corr=0.050, l_spring=0.500, l_dist=0.250
using quadratic fit line minimization
nlarea/corr = 20.000
integrating with navgs=1024 and tol=1.000e+00
088: dt: 6.568, sse: 665480.4 (0.359, 26.7, 0.479, 0.937), neg: 270
(%0.02:%0.04), avgs: 1024
integrating with navgs=256 and tol=5.010e-01
089: dt: 1.744, sse: 664408.7 (0.359, 26.6, 0.478, 0.938), neg: 58
(%0.00:%0.01), avgs: 256
integrating with navgs=64 and tol=2.519e-01
090: dt: 6.774, sse: 663527.8 (0.360, 26.6, 0.478, 0.941), neg: 186
(%0.01:%0.03), avgs: 64
integrating with navgs=16 and tol=1.288e-01
091: dt: 1.450, sse: 662806.6 (0.359, 26.5, 0.477, 0.939), neg: 45
(%0.00:%0.00), avgs: 16
integrating with navgs=4 and tol=6.988e-02
092: dt: 6.720, sse: 662316.9 (0.359, 26.4, 0.477, 0.920), neg: 63
(%0.00:%0.01), avgs: 4
093: dt: 4.200, sse: 662053.0 (0.360, 26.5, 0.477, 0.913), neg: 85
(%0.00:%0.01), avgs: 4
integrating with navgs=1 and tol=4.419e-02
094: dt: 0.903, sse: 661962.4 (0.360, 26.5, 0.477, 0.910), neg: 79
(%0.00:%0.01), avgs: 1
integrating with navgs=0 and tol=3.125e-02
095: dt: 0.110, sse: 661952.2 (0.360, 26.5, 0.477, 0.909), neg: 73
(%0.00:%0.01), avgs: 0

blurring surfaces with sigma=1.00...
done.
curvature mean = 0.091, std = 0.611
curvature mean = 0.012, std = 0.719
tol=1.0e+00, sigma=1.0, host=athen, nav=1024, nbrs=1, l_extern=10000.000,
l_nlarea=1.000, l_corr=0.050, l_spring=0.500, l_dist=0.250
using quadratic fit line minimization
nlarea/corr = 20.000
integrating with navgs=1024 and tol=1.000e+00
096: dt: 8.627, sse: 661315.6 (0.359, 26.4, 0.476, 1.011), neg: 227
(%0.02:%0.04), avgs: 1024
integrating with navgs=256 and tol=5.010e-01
097: dt: 1.786, sse: 660256.5 (0.358, 26.3, 0.475, 1.012), neg: 66
(%0.00:%0.01), avgs: 256
integrating with navgs=64 and tol=2.519e-01
098: dt: 5.077, sse: 659704.2 (0.359, 26.3, 0.474, 1.011), neg: 149
(%0.01:%0.02), avgs: 64
integrating with navgs=16 and tol=1.288e-01
099: dt: 1.431, sse: 659144.8 (0.358, 26.2, 0.474, 1.009), neg: 44
(%0.00:%0.00), avgs: 16
integrating with navgs=4 and tol=6.988e-02
100: dt: 2.714, sse: 658944.9 (0.358, 26.2, 0.474, 0.996), neg: 47
(%0.00:%0.00), avgs: 4
integrating with navgs=1 and tol=4.419e-02
101: dt: 0.075, sse: 658940.8 (0.358, 26.2, 0.474, 0.995), neg: 47
(%0.00:%0.00), avgs: 1
integrating with navgs=0 and tol=3.125e-02
102: dt: 0.054, sse: 658935.4 (0.358, 26.2, 0.474, 0.994), neg: 47
(%0.00:%0.00), avgs: 0

blurring surfaces with sigma=0.50...
done.
curvature mean = 0.092, std = 0.682
curvature mean = 0.004, std = 0.828
tol=1.0e+00, sigma=0.5, host=athen, nav=1024, nbrs=1, l_extern=10000.000,
l_nlarea=1.000, l_corr=0.050, l_spring=0.500, l_dist=0.250
using quadratic fit line minimization
nlarea/corr = 20.000
integrating with navgs=1024 and tol=1.000e+00
103: dt: 27.636, sse: 656331.2 (0.358, 26.1, 0.472, 1.092), neg: 165
(%0.01:%0.03), avgs: 1024
integrating with navgs=256 and tol=5.010e-01
104: dt: 2.000, sse: 654661.2 (0.357, 25.9, 0.470, 1.083), neg: 82
(%0.00:%0.01), avgs: 256
integrating with navgs=64 and tol=2.519e-01
105: dt: 3.105, sse: 653928.0 (0.357, 25.8, 0.469, 1.076), neg: 67
(%0.00:%0.01), avgs: 64
integrating with navgs=16 and tol=1.288e-01
106: dt: 2.292, sse: 653418.9 (0.357, 25.8, 0.469, 1.066), neg: 53
(%0.00:%0.01), avgs: 16
integrating with navgs=4 and tol=6.988e-02
107: dt: 1.047, sse: 653125.0 (0.357, 25.8, 0.469, 1.057), neg: 35
(%0.00:%0.00), avgs: 4
integrating with navgs=1 and tol=4.419e-02
108: dt: 0.724, sse: 653027.9 (0.357, 25.8, 0.469, 1.047), neg: 36
(%0.00:%0.00), avgs: 1
integrating with navgs=0 and tol=3.125e-02
109: dt: 0.064, sse: 653009.2 (0.357, 25.8, 0.469, 1.044), neg: 34
(%0.00:%0.00), avgs: 0

Removing remaining folds...
tol=1.0e-01, sigma=0.5, host=athen, nav=64, nbrs=1, l_extern=10000.000,
l_nlarea=100.000, l_corr=0.001, l_spring=0.005, l_dist=0.002
using quadratic fit line minimization
nlarea/corr = 199999.984
integrating with navgs=64 and tol=2.519e-02
110: dt: 5.514, sse: 187681.1 (0.355, 25.8, 0.466, 1.062), neg: 85
(%0.00:%0.01), avgs: 64
111: dt: 1.735, sse: 184602.0 (0.355, 25.7, 0.466, 1.064), neg: 16
(%0.00:%0.00), avgs: 64
112: dt: 2.802, sse: 182518.2 (0.354, 25.7, 0.466, 1.068), neg: 16
(%0.00:%0.00), avgs: 64
113: dt: 3.973, sse: 180638.1 (0.355, 25.8, 0.466, 1.072), neg: 16
(%0.00:%0.00), avgs: 64
114: dt: 2.388, sse: 179649.5 (0.355, 25.8, 0.467, 1.075), neg: 20
(%0.00:%0.00), avgs: 64
115: dt: 3.088, sse: 178962.5 (0.355, 25.9, 0.467, 1.079), neg: 28
(%0.00:%0.00), avgs: 64
116: dt: 2.775, sse: 178633.9 (0.356, 26.0, 0.468, 1.082), neg: 36
(%0.00:%0.00), avgs: 64
117: dt: 0.831, sse: 178379.4 (0.356, 26.0, 0.468, 1.083), neg: 37
(%0.00:%0.00), avgs: 64
118: dt: 2.731, sse: 178093.2 (0.356, 26.1, 0.469, 1.087), neg: 52
(%0.00:%0.00), avgs: 64
119: dt: 0.606, sse: 177994.0 (0.356, 26.1, 0.469, 1.088), neg: 46
(%0.00:%0.00), avgs: 64
120: dt: 0.250, sse: 177987.2 (0.356, 26.1, 0.469, 1.088), neg: 48
(%0.00:%0.00), avgs: 64
integrating with navgs=16 and tol=1.288e-02
121: dt: 0.230, sse: 177851.4 (0.357, 26.1, 0.470, 1.089), neg: 46
(%0.00:%0.00), avgs: 16
122: dt: 0.031, sse: 177844.3 (0.357, 26.1, 0.470, 1.089), neg: 45
(%0.00:%0.00), avgs: 16
integrating with navgs=4 and tol=6.988e-03
123: dt: 0.013, sse: 177801.5 (0.357, 26.1, 0.470, 1.089), neg: 38
(%0.00:%0.00), avgs: 4
124: dt: 0.014, sse: 177784.2 (0.357, 26.1, 0.470, 1.089), neg: 32
(%0.00:%0.00), avgs: 4
125: dt: 0.014, sse: 177779.8 (0.357, 26.1, 0.470, 1.089), neg: 31
(%0.00:%0.00), avgs: 4
integrating with navgs=1 and tol=4.419e-03
126: dt: 0.000, sse: 177779.8 (0.357, 26.1, 0.470, 1.089), neg: 31
(%0.00:%0.00), avgs: 1
integrating with navgs=0 and tol=3.125e-03
127: dt: 0.000, sse: 177746.4 (0.357, 26.1, 0.470, 1.089), neg: 30
(%0.00:%0.00), avgs: 0
128: dt: 0.000, sse: 177734.2 (0.357, 26.1, 0.470, 1.089), neg: 28
(%0.00:%0.00), avgs: 0
129: dt: 0.000, sse: 177734.2 (0.357, 26.1, 0.470, 1.089), neg: 28
(%0.00:%0.00), avgs: 0
registration took 0.25 hours
MRISregister() return, current seed 0
expanding nbhd size to 1
writing registered surface to lh.sphere_dis25_parea0.reg...
registration took 0.25 hours
[athens-b8-e8-56-47-e6-54:P00001613/FreeSurferClean/surf] ray% 

 
-- 
Ray Razlighi, Ph.D.
Assistant Professor
Quantitative Neuroimaging Laboratory
Division of Cognitive Neuroscience
Department of Neurology
Columbia University

Alt: razli...@gmail.com
Office Phone: 212-342-1352
Office Fax: 212-342-1838
Website: http://www.columbia.edu/cu/qnl/

On Nov 6, 2015, at 2:16 PM, Bruce Fischl <fis...@nmr.mgh.harvard.edu> wrote:

      Hi Ray

      try doing:

      setenv DIAG 0x04040

      then run it again and send me the output

      Bruce

      On Fri, 6 Nov 2015, Razlighi, Qolamreza R. wrote:

            Dear Bruce, Thank again for your kind help. I’m
            running version 5.1.0. 
            Below is the output of the command.
            I also ran it with parea=0 but almost no change in
            the result (see the
            attachment-1)
            I also plot the sluci map for comparison (see the
            attachment-2) but still
            not satisfactory.[IMAGE][IMAGE] 
            Best
            ~/Data/P00001613/FreeSurferClean/surf\>
            mris_register -1 -curv -dist .25
            lh.sphere
            ../../../P00001639/FreeSurferClean/surf/lh.sphere
            lh.sphere_dis25.reg
            treating target as a single subject's surface...
            using smoothwm curvature for final alignment
            l_dist = 0.250
            $Id: mris_register.c,v 1.59 2011/03/02 00:04:33
            nicks Exp $
              $Id: mrisurf.c,v 1.693.2.7 2013/05/12 22:28:01
            nicks Exp $
            reading surface from lh.sphere...
            reading spherical surface
            ../../../P00001639/FreeSurferClean/surf/lh.sphere...
            curvature mean = -0.000, std = 1.000
            computing parameterization for surface
            ../../../P00001639/FreeSurferClean/surf/lh.inflated.H...
            curvature mean = 0.000, std = 0.566
            computing parameterization for surface
            ../../../P00001639/FreeSurferClean/surf/lh.sulc...
            curvature mean = -0.030, std = 0.282
            computing parameterization for surface
            ../../../P00001639/FreeSurferClean/surf/lh.smoothwm...
            MRISregister() -------
            max_passes = 4 
            min_degrees = 0.500000 
            max_degrees = 64.000000 
            nangles = 8 
            tol=5.0e-01, sigma=0.0, host=unkno, nav=1024,
            nbrs=1, l_extern=10000.000,
            l_parea=0.100, l_nlarea=1.000, l_corr=1.000,
            l_dist=0.250
            using quadratic fit line minimization
            complete_dist_mat 0
            rms 0
            smooth_averages 0
            remove_neg 0
            ico_order 0
            which_surface 0
            target_radius 0.000000
            nfields 0
            scale 0.000000
            desired_rms_height -1.000000
            momentum 0.950000
            nbhd_size -10
            max_nbrs 10
            niterations 25
            nsurfaces 0
            SURFACES 3
            flags 16 (10)
            use curv 16
            no sulc 0
            no rigid align 0
            mris->nsize 1
            mris->hemisphere 0
            randomSeed 0
            --------------------
            tol=5.0e-01, sigma=0.0, host=unkno, nav=1024,
            nbrs=1, l_extern=10000.000,
            l_parea=0.100, l_nlarea=1.000, l_corr=1.000,
            l_dist=0.250
            using quadratic fit line minimization
            1 Reading lh.sulc
            curvature mean = 0.000, std = 0.583
            curvature mean = 0.044, std = 0.847
            curvature mean = 0.022, std = 0.852
            Starting MRISrigidBodyAlignGlobal()
              d=32.00 min @ (8.00, 0.00, 0.00) sse = 90765.7,
            tmin=0.9987
              d=16.00 min @ (-4.00, -4.00, 0.00) sse = 87371.6,
            tmin=1.5086
              d=8.00 min @ (-2.00, 0.00, 0.00) sse = 86754.0,
            tmin=2.0493
              d=4.00 min @ (1.00, 1.00, 0.00) sse = 86449.9,
            tmin=2.5976
              d=2.00 min @ (0.00, -0.50, 0.50) sse = 86436.5,
            tmin=3.1563
              d=1.00 min @ (0.00, 0.25, -0.25) sse = 86423.4,
            tmin=3.6949
            MRISrigidBodyAlignGlobal() done   4.24 min
            curvature mean = 0.036, std = 0.923
            curvature mean = 0.010, std = 0.931
            curvature mean = 0.036, std = 0.951
            curvature mean = 0.004, std = 0.967
            curvature mean = 0.036, std = 0.962
            curvature mean = 0.001, std = 0.984
            2 Reading smoothwm
            curvature mean = -0.027, std = 0.269
            tol=1.0e+00, sigma=0.5, host=unkno, nav=1024,
            nbrs=1, l_extern=10000.000,
            l_parea=0.100, l_nlarea=1.000, l_corr=0.050,
            l_spring=0.500, l_dist=0.250
            using quadratic fit line minimization
            curvature mean = 0.090, std = 0.337
            curvature mean = 0.059, std = 0.386
            curvature mean = 0.091, std = 0.507
            curvature mean = 0.020, std = 0.580
            curvature mean = 0.091, std = 0.618
            curvature mean = 0.011, std = 0.723
            curvature mean = 0.093, std = 0.681
            curvature mean = 0.004, std = 0.828
            MRISregister() return, current seed 0
            expanding nbhd size to 1
            writing registered surface to lh.sphere_dis25.reg...
            -- 
            Ray Razlighi, Ph.D.
            Assistant Professor
            Quantitative Neuroimaging Laboratory
            Division of Cognitive Neuroscience
            Department of Neurology
            Columbia University
            Alt: razli...@gmail.com
            Office Phone: 212-342-1352
            Office Fax: 212-342-1838
            Website: http://www.columbia.edu/cu/qnl/
            On Nov 4, 2015, at 7:30 PM, Bruce Fischl
            <fis...@nmr.mgh.harvard.edu> wrote:

                 Hi Ray

                 what version of FS are you using? Can you send
            me the output of
                 the command? The distance term will prevent the
            curvatures from
                 deforming too much. You can set it much smaller
            and see what
                 happens if you want. There may also be an area
            constraint.
                 Trying using -parea 0 also (or something small)

                 cheers
                 Bruce

                 On Wed, 4 Nov 2015, Razlighi, Qolamreza R.
            wrote:

                       Hi Bruce,
                       Thanks for reply. I thought after
            registering, the
                       source surface's curvature map should be
            very
                       similar to the target one. I don't see
            that here.
                       The registered surface has pretty much
            the same
                       curvature map only slightly shifted.
                       Am I missing something here?

                       Best

                       On Nov 4, 2015, at 5:56 PM, Bruce Fischl
                       <fis...@nmr.mgh.harvard.edu>
                       wrote:

                             Hi Ray

                             that sounds right. The way I
            visualize
                             these is using nmovie (which I
            think we
                             include in our distribution) and
                             flipping back and forth between the
                             different images showing the
            different
                             surfaces/curv maps.

                             Which inaccuracy are you referring
            to?

                             cheers
                             Bruce

                             On Wed, 4 Nov 2015, Razlighi,
            Qolamreza
                             R. wrote:

                                   Dear Bruce,
                                   I did run it again with
                                   option (-dist 0.25); however
                                   the result did not
                                   change much (See the
                                   attachment). These results
                                   do not seem right to me and
                                   I think I’m running the
                                   command correctly (see the
                                   first email). However,
                                   I’m not sure I’m visualizing
                                   the curvature maps
                                   correctly. I assume the
                                   lh.sphere.reg has the same
                                   vertices and facets as
                                   lh.sphere but slightly
                                   displaced in space to match
                                   the curvature and sulci map
                                   of the source
                                   surface to target surface.
                                   Therefore, I visualize the
                                   lh.sphere.reg by
                                   pulling the same lh.curv
                                   file from the source image.
                                   If this is wrong,
                                   please let me know how can I
                                   correctly visualize the
                                   lh.sphere.reg,
                                   otherwise I have no idea why
                                   this surface based
                                   registration produce such
                                   inaccurate results. Any
                                   comments or suggestion is
                                   greatly appreciated.
                                   Best
                                   [IMAGE]
                                   --
                                   Ray Razlighi, Ph.D.
                                   Assistant Professor
                                   Quantitative Neuroimaging
                                   Laboratory
                                   Division of Cognitive
                                   Neuroscience
                                   Department of Neurology
                                   Columbia University
                                   Alt: razli...@gmail.com
                                   Office Phone: 212-342-1352
                                   Office Fax: 212-342-1838
                                   Website:
                                   http://www.columbia.edu/cu/qnl/
                                   On Nov 3, 2015, at 3:47 PM,
                                   Bruce Fischl
                                   <fis...@nmr.mgh.harvard.edu>
                                   wrote:

                                       Hi Ray

                                       the -1 means that the
                                   target is a single surface
                                   and not an
                                       atlas, but the
                                   registration is still
                                   nonlinear. The variances
                                       will all be 1 so you may
                                   have to play with the
                                   weights in the
                                       energy functional. We
                                   don't do this very much and
                                   it probably
                                       defaults to quite rigid.
                                   Try reducing the weight on
                                   the metric
                                       preservation term (e.g.
                                   -dist .25) if you want it to
                                   be more
                                       nonlinear

                                       cheers
                                       Bruce

                                       On Tue, 3 Nov 2015,
                                   Razlighi, Qolamreza R.
                                   wrote:

                                             Hi Guys,
                                             I read in the
                                   sidenote here
          (https://surfer.nmr.mgh.harvard.edu/fswiki/SurfaceRegAndTemplates

                                   )
                                             that
                                             inter-subject
                                   surface base registration
                                   using
                                             mris_register and
                                   -1 flag
                                             performs a sort of
                                   rigid registration. So I
                                   tried it
                                             between two of my
                                             subjects with the
                                   command below
                                             mris_register -1
                                   -curv
                                             
P00001639/FreeSurferClean/surf/lh.sphere
                                             
P00001639/FreeSurferClean/surf/lh.sphere
                                             lh.sphere3.reg
                                             and got the
                                   results (see the
                                   attachment). It is
                                             clear that the
                                   registration
                                             output is just a
                                   shifted version of the
                                   source.
                                             Having this
                                   confirmed I want
                                             to know if there
                                   is any way to force the
                                             mris_register to
                                   perform a complete
                                             non-linear surface
                                   based registration for
                                             inter-subjects
                                   registration the
                                             same way it does
                                   for template.
                                             I have to mention
                                   that I visualize the
                                             lh.sphere3.reg
                                   using freeview and
                                             loaded the sane
                                   lh.curv on that surface. I
                                   hope I’m
                                             not doing anything
                                             stupid.

                                             [IMAGE]
                                             Best
                                             --
                                             Ray Razlighi,
                                   Ph.D.
                                             Assistant
                                   Professor
                                             Quantitative
                                   Neuroimaging Laboratory
                                             Division of
                                   Cognitive Neuroscience
                                             Department of
                                   Neurology
                                             Columbia
                                   University
                                             Alt:
                                   razli...@gmail.com
                                             Office Phone:
                                   212-342-1352
                                             Office Fax:
                                   212-342-1838
                                             Website:
                                   http://www.columbia.edu/cu/qnl/

                                       
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                 The information in this e-mail is intended only
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                 to whom it is
                 addressed. If you believe this e-mail was sent
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                 and the e-mail
                 contains patient information, please contact
            the Partners
                 Compliance HelpLine at
                 http://www.partners.org/complianceline . If the
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                 to you in error
                 but does not contain patient information,
            please contact the
                 sender and properly
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      _______________________________________________
      Freesurfer mailing list
      Freesurfer@nmr.mgh.harvard.edu
      https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer


      The information in this e-mail is intended only for the person
      to whom it is
      addressed. If you believe this e-mail was sent to you in error
      and the e-mail
      contains patient information, please contact the Partners
      Compliance HelpLine at
      http://www.partners.org/complianceline . If the e-mail was sent
      to you in error
      but does not contain patient information, please contact the
      sender and properly
      dispose of the e-mail.



_______________________________________________
Freesurfer mailing list
Freesurfer@nmr.mgh.harvard.edu
https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer


The information in this e-mail is intended only for the person to whom it is
addressed. If you believe this e-mail was sent to you in error and the e-mail
contains patient information, please contact the Partners Compliance HelpLine at
http://www.partners.org/complianceline . If the e-mail was sent to you in error
but does not contain patient information, please contact the sender and properly
dispose of the e-mail.

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