Freesurfer June 2009

freesurfer@nmr.mgh.harvard.edu

94 participants
140 discussions

do I have to include age as covariate in qdec analysis?
by 류철형 25 Jun '09

25 Jun '09

Dear all I'm trying to compare the cortical thickness between the neurodegenerative disease patient group and normal controls. To match the age, I selected subjects from both groups and finally included age-matched groups into freesurfer thickness analysis (similar mean age, same age distribution from 40-73, 70 normal controls and 91 patients) In 1st qdec analysis, direct comparison between two groups without age covariate showed thickness of temporoparietal cortex of patient group was smaller than that of normal controls (uncorrected P<0.001 or FDR corrected P<0.05). However, when the age was included in the model as a covariate, the significant surface vertices almostly disappearred and some unexpected vertices emerged as significant, in which mean thickness were not so much different between two groups. My question is... 1) Is there any possibility that disease-related cortical thinning area is quite similar to the age related cortical thinning area and resulted in the disappearance of significant vertices? 2) Do I have to include age covariate in that model even if I clearly matched the age distribution? 3) The area "does the average thickness differ between normal and patient?" was just same as the area "does the thickness--age correlation differ between normal and patient?", except for the color of blobs (e.g. red -> blue, blue -> red). Then, what do those question mean? >From Lyoo

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averaging fMRI data across multiple scan sessions
by Krishna Srihasam 25 Jun '09

25 Jun '09

Hi, We had collected a lot of data in our first few scan sessions trying different TR/TE/voxel size/REPs combinations in order to get the optimal values. Now, I would like to do a joint analysis of all those scans. Is it possible ? regards, Krishna

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Errors in qdec/Analyze
by Toh Hock Wong 25 Jun '09

25 Jun '09

Hi to all, I have been having troubles with qdec and several error messages appear. The first message appeared when clicking 'analyze'. It doesn't matter if i use one or two discrete plus one or two continuos factors, i get the same error message. What does it mean by codno=1e+08? Creating output directory /Applications/freesurfer/subjects/qdec/ lh_postcentral_thickness Loading y from /Applications/freesurfer/subjects/qdec/ lh_postcentral_thickness/y.mgh Matrix condition is 1e+08 ERROR: matrix is ill-conditioned or badly scaled, condno = 1e+08 Possible problem with experimental design: Check for duplicate entries and/or lack of range of continuous variables within a class. Another error message that appeared earlier when i include age, gender, diagnosis(epilepsy/nonepileptic) and ln_postcentral_thickness is as follow: $Id: mri_glmfit.c,v 1.138.2.3 2008/08/27 13:33:05 greve Exp $ cwd /Applications/freesurfer cmdline mri_glmfit --y /Applications/freesurfer/subjects/qdec/ lh_postcentral_thickness/y.mgh --fsgd /Applications/freesurfer/ subjects/qdec/lh_postcentral_thickness/qdec.fsgd dods --glmdir / Applications/freesurfer/subjects/qdec/lh_postcentral_thickness --surf fsaverage lh --label /Applications/freesurfer/subjects/fsaverage/label/ lh.cortex.label --C /Applications/freesurfer/subjects/qdec/ lh_postcentral_thickness/contrasts/lh-Avg-Intercept-thickness.mat --C / Applications/freesurfer/subjects/qdec/lh_postcentral_thickness/ contrasts/lh-Avg-thickness-age-Cor.mat --C /Applications/freesurfer/ subjects/qdec/lh_postcentral_thickness/contrasts/lh-Avg-thickness- lh_postcentral_thickness-Cor.mat --C /Applications/freesurfer/subjects/ qdec/lh_postcentral_thickness/contrasts/lh-Diff-Male-Female-Intercept- thickness.mat --C /Applications/freesurfer/subjects/qdec/ lh_postcentral_thickness/contrasts/lh-Diff-Male-Female-Cor-thickness- age.mat --C /Applications/freesurfer/subjects/qdec/ lh_postcentral_thickness/contrasts/lh-Diff-Male-Female-Cor-thickness- lh_postcentral_thickness.mat --C /Applications/freesurfer/subjects/ qdec/lh_postcentral_thickness/contrasts/lh-Diff-nonepileptic-epilepsy- Intercept-thickness.mat --C /Applications/freesurfer/subjects/qdec/ lh_postcentral_thickness/contrasts/lh-Diff-nonepileptic-epilepsy-Cor- thickness-age.mat --C /Applications/freesurfer/subjects/qdec/ lh_postcentral_thickness/contrasts/lh-Diff-nonepileptic-epilepsy-Cor- thickness-lh_postcentral_thickness.mat --C /Applications/freesurfer/ subjects/qdec/lh_postcentral_thickness/contrasts/lh-X-gender-diagnosis- Intercept-thickness.mat --C /Applications/freesurfer/subjects/qdec/ lh_postcentral_thicknesss/contrasts/lh-X-gender-diagnosis-Cor- thickness-age.mat --C /Applications/freesurfer/subjects/qdec/ lh_postcentral_thickness/contrasts/lh-X-gender-diagnosis-Cor-thickness- lh_postcentral_thickness.mat Can the above error occur because one of the two groups has only one subject (epilepsy and female). I have also attached fsgd1 generated by freesurfer as below: GroupDescriptorFile 1 Title lh_postcentral_thickness MeasurementName thickness Class genderMale-diagnosisnonepileptic Class genderFemale-diagnosisnonepileptic Class genderMale-diagnosisepilepsy Class genderFemale-diagnosisepilepsy Variables age lh_postcentral_thickness Input RCoward genderMale-diagnosisnonepileptic 11.000000 2.112000 Input SCoward genderFemale-diagnosisnonepileptic 13.000000 1.979000 Input JConigrave genderMale-diagnosisnonepileptic 10.000000 2.470000 Input AConigrave genderFemale-diagnosisnonepileptic 5.000000 2.268000 Input KBleasel genderFemale-diagnosisnonepileptic 9.000000 2.292000 Input MBleasel genderFemale-diagnosisnonepileptic 16.000000 2.181000 Input CMcintosh genderFemale-diagnosisnonepileptic 15.000000 2.107000 Input OJohanssen genderMale-diagnosisnonepileptic 6.000000 2.033000 Input MJohanssen genderFemale-diagnosisnonepileptic 6.000000 2.037000 Input GHerbert genderFemale-diagnosisnonepileptic 4.000000 2.209000 Input JRathmell genderMale-diagnosisnonepileptic 11.000000 2.287000 Input ARathmell genderMale-diagnosisnonepileptic 14.000000 2.223000 Input ZTucker genderFemale-diagnosisepilepsy 11.000000 2.415000 Your advice and tips will be most appreciated! Toh Wong (Sydney, Australia)

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RE: fdr thresholds in tksurfer compared to Nichol's function
by Lars M. Rimol 25 Jun '09

25 Jun '09

Hi, Please disregard my previous mail. I was a little too trigger happy..., the 1.928 threshold set by tksurfer is too high for any vertex to survive so the two methods DO concur. I should actually look at the maps and not just the output... The reason I asked is that I'm combining images to obtain an overall FDR, either for both hemispheres or for several comparisons. I don't see how this can be done in tksurfer so I'm using Nichols' function, which works just fine. I just don't know how to get an FDR threshold when no vertex has a p-value that survives - but tksurfer obviously produces such a threshold. How do I obtain that threshold? (Or is it simply set just higher than the highest -log(p-value) in the image, for display purposes? It seems always to be just above.) thanks (and apologies for the confusion), LMR From: larilin(a)gmail.com To: freesurfer(a)nmr.mgh.harvard.edu Subject: fdr thresholds in tksurfer compared to Nichol's function Date: Thu, 25 Jun 2009 14:07:08 +0200 Hi, I've used the fdr function in tksurfer and compared the results to Tom Nichol's matlab function (http://www.sph.umich.edu/~nichols/FDR/) I use p = 10.^-abs(pfile), where "pfile" is 'sig.mgh'. I find that the p-threshold (pID, assuming positive dependence) is usually exactly the same as the one in tksurfer (probably because you use the same algorithm..?). But in some cases where pID is empty, which should mean that there is no suprathreshold vertex, I do get a threshold in tksurfer. Here's an example: For one contrast where pID was returned empty in Nichol's matlab function, I got a threshold of 1.927741 in tksurfer. Is this supposed to happen? The output is: MRISfdr2vwth(rate=0.05000, sign = 0, 1, only marked = 0) = 1.927741 surfer: MRISfdr2vwth with rate 0.05 and sign 0 returned threshold 1.927741 Thank you! yours, LMR

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[Fwd: Re: [Freesurfer] setting of fdr thresholds in qdec]
by Michael Harms 25 Jun '09

25 Jun '09

Lars, I had a similar question regarding the setting of the FDR p-value threshold back in Dec 2008 (search "fdr Harms" in the Archive), but for some reason, I see that Nick's reply to me didn't make it into the archive, even though he cc'ed the FS list. So, I'm forwarding Nick's response below, which I believe answers your question. cheers, -Mike H. -------- Forwarded Message -------- From: Nick Schmansky <nicks(a)nmr.mgh.harvard.edu> To: Michael Harms <mharms(a)conte.wustl.edu> Cc: Freesurfer Mailing List <freesurfer(a)nmr.mgh.harvard.edu> Subject: Re: [Freesurfer] setting of fdr thresholds in qdec Date: Tue, 16 Dec 2008 18:06:24 -0500 Mike, I've attached the code that operates the "Set Using FDR" button in qdec. First have a look at the routine SetSurfaceScalarsColorsUsingFDR in vtkQdecWindow.cxx, which calls our FDR library and returns a threshold. Then, it calls the routine SetSurfaceScalarsColors after setting mid = threshold + 1.5, and max = threshold + 2.25. from there, SetSurfaceScalarsColors just checks min, mid and max against the current values (from prior run) and updates them if they differ. so basically, the 'algorithm' for mid and max appears to be just adding 1.5 and 2.25 to the threshold. I wasn't aware of this, or understand why (other than the need to set it to something!), so if you have an alternative, I'd like to hear. Also attached are mri2.c and sig.c, which contains the core fdr2vwth routine. It appears that "if no values meet the FDR criterion, then a value slightly smaller than the smallest p is returned." I've updated the code to print to the terminal the number of vertices having a P < the fdr min, although it seems to always be a large number of vertices because the fdr min is a positive value, and a typical result has both positive and negative P values. Nick On Thu, 2008-12-11 at 17:09 -0600, Michael Harms wrote: > Hello, > I'm curious, what algorithm is used for setting the "mid" and "max" > threshold values in QDEC when using the "Set Using FDR" button? > > And, what does the algorithm do for setting the "min" threshold when no > single p-value is significant enough to satisfy the necessary inequality > for the specified FDR Rate? > In that case, is the resulting "min" just set to the -log10(p) of the > most significant p-value? > > If easy to implement, it would be handy if the output to the terminal > reported the number of vertices with P < than the FDR determined "min" > threshold. > > cheers, > Mike H. > > _______________________________________________ Freesurfer mailing list Freesurfer(a)nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

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RES: [Freesurfer] volume group analysis
by ppj＠netfilter.com.br 25 Jun '09

25 Jun '09

To make analysis of volumetric data export volumetric data to csv and use your statistical package. -- Pedro Paulo Jr E71 Nokia -----Msg original----- De: Anita di Iasio Enviada: 25/06/2009 06:04:38 Para: freesurfer(a)nmr.mgh.harvard.edu Assunto: [Freesurfer] volume group analysis Hi all, I have 2 groups of subjects and I want to do a volume group analysis to compare volumes of subcortical structures. I have to run my analysis with the command-line methods but I don't know the correct flags to use with the commands (mris_preproc and mri_glmfit). Can you help me? Can you give me some suggestion? thank you in adavanced, Anita _______________________________________________ Freesurfer mailing list Freesurfer(a)nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

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Re: [Freesurfer] RE: fdr thresholds in tksurfer compared to Nichol's function
by jorge luis 25 Jun '09

25 Jun '09

Use the fast_fdrthresh matlab function provided in the fsfast/toolbox directory in freesurfer. It gives the same results as tksurfer. Cheers Jorge Luis Bernal Rusiel Phd Student Laboratory of Functional Neuroscience. University of Pablo de Olavide. Seville. Spain --- El jue, 25/6/09, Lars M. Rimol <larilin(a)gmail.com> escribió: > De: Lars M. Rimol <larilin(a)gmail.com> > Asunto: [Freesurfer] RE: fdr thresholds in tksurfer compared to Nichol's function > Para: "FS maling list" <freesurfer(a)nmr.mgh.harvard.edu> > Fecha: jueves, 25 junio, 2009 4:18 > > > > #yiv1972015953 .hmmessage P > { > margin:0px;padding:0px;} > #yiv1972015953 { > font-size:10pt;font-family:Verdana;} > > > > > > > #yiv1972015953 .ExternalClass .EC_hmmessage P > {padding:0px;} > #yiv1972015953 .ExternalClass body.EC_hmmessage > {font-size:10pt;font-family:Verdana;} > > Hi, > > Please disregard my previous mail. I was a > little too trigger happy..., the 1.928 threshold set by > tksurfer is too high for any vertex to survive so the > two methods DO concur. I should actually look at > the maps and not just the output... > > > The reason I asked is that I'm combining images to > obtain an overall FDR, either for both hemispheres or for > several comparisons. I don't see how this can be done in > tksurfer so I'm using Nichols' function, which > works just fine. I just don't know how to get > an FDR threshold when no vertex has a p-value that > survives - but tksurfer obviously produces such a > threshold. How do I obtain that threshold? > > (Or is it simply set just higher than the highest > -log(p-value) in the image, for display purposes? It seems > always to be just above.) > > > > thanks (and apologies for the confusion), > LMR > > > > > From: larilin(a)gmail.com > To: freesurfer(a)nmr.mgh.harvard.edu > Subject: fdr thresholds in tksurfer compared to > Nichol's function > Date: Thu, 25 Jun 2009 14:07:08 +0200 > > > > #yiv1972015953 .ExternalClass .EC_hmmessage P > {padding:0px;} > #yiv1972015953 .ExternalClass body.EC_hmmessage > {font-size:10pt;font-family:Verdana;} > > > Hi, > > I've used the fdr function in tksurfer and compared the > results to Tom Nichol's matlab function (http://www.sph.umich.edu/~nichols/FDR/) > I use p = 10.^-abs(pfile), where "pfile" is > 'sig.mgh'. > > I find that the p-threshold (pID, assuming positive > dependence) is usually exactly the same as the one in > tksurfer (probably because you use the same algorithm..?). > > But in some cases where pID is empty, which > should mean that there is no suprathreshold vertex, I do get > a threshold in tksurfer. Here's an example: > > For one contrast where pID was returned empty in > Nichol's matlab function, I got a threshold of > 1.927741 in tksurfer. > > Is this supposed to happen? > > > The output is: > > > MRISfdr2vwth(rate=0.05000, sign = 0, 1, only marked = 0) = > 1.927741 > > surfer: MRISfdr2vwth with rate 0.05 and sign 0 > returned threshold 1.927741 > > > > > Thank you! > > yours, > LMR > > > > > > > > > > > -----Adjunto en línea a continuación----- > > _______________________________________________ > Freesurfer mailing list > Freesurfer(a)nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

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fdr thresholds in tksurfer compared to Nichol's function
by Lars M. Rimol 25 Jun '09

25 Jun '09

Hi, I've used the fdr function in tksurfer and compared the results to Tom Nichol's matlab function (http://www.sph.umich.edu/~nichols/FDR/) I use p = 10.^-abs(pfile), where "pfile" is 'sig.mgh'. I find that the p-threshold (pID, assuming positive dependence) is usually exactly the same as the one in tksurfer (probably because you use the same algorithm..?). But in some cases where pID is empty, which should mean that there is no suprathreshold vertex, I do get a threshold in tksurfer. Here's an example: For one contrast where pID was returned empty in Nichol's matlab function, I got a threshold of 1.927741 in tksurfer. Is this supposed to happen? The output is: MRISfdr2vwth(rate=0.05000, sign = 0, 1, only marked = 0) = 1.927741 surfer: MRISfdr2vwth with rate 0.05 and sign 0 returned threshold 1.927741 Thank you! yours, LMR

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File problem
by Vina Goghari 25 Jun '09

25 Jun '09

Hello, I used freesurfer table programs to concat files for all my subjects for all the parcellations. For the LH only, for some reason I am seeing one column of values at the very end that are not associated with a label and it is not present for all the subjects. I also saw it for the volume file. Any ideas why just for LH and what it is? Thanks! Vina lh.aparc.a2005s.thickness lh_G_cingulate-Isthmus_thickness lh_G_cingulate-Main_part_thickness lh_G_cuneus_thickness lh_G_frontal_inf-Opercular_part_thickness lh_G_frontal_inf-Orbital_part_thickness lh_G_frontal_inf-Triangular_part_thickness lh_G_frontal_middle_thickness lh_G_frontal_superior_thickness lh_G_frontomarginal_thickness lh_G_insular_long_thickness lh_G_insular_short_thickness lh_G_and_S_occipital_inferior_thickness lh_G_occipital_middle_thickness lh_G_occipital_superior_thickness lh_G_occipit-temp_lat-Or_fusiform_thickness lh_G_occipit-temp_med-Lingual_part_thickness lh_G_occipit-temp_med-Parahippocampal_part_thickness lh_G_orbital_thickness lh_G_paracentral_thickness lh_G_parietal_inferior-Angular_part_thickness lh_G_parietal_inferior-Supramarginal_part_thickness lh_G_parietal_superior_thickness lh_G_postcentral_thickness lh_G_precentral_thickness lh_G_precuneus_thickness lh_G_rectus_thickness lh_G_subcallosal_thickness lh_G_subcentral_thickness lh_G_temporal_inferior_thickness lh_G_temporal_middle_thickness lh_G_temp_sup-G_temp_transv_and_interm_S_thickness lh_G_temp_sup-Lateral_aspect_thickness lh_G_temp_sup-Planum_polare_thickness lh_G_temp_sup-Planum_tempolare_thickness lh_G_and_S_transverse_frontopolar_thickness lh_Lat_Fissure-ant_sgt-ramus_horizontal_thickness lh_Lat_Fissure-ant_sgt-ramus_vertical_thickness lh_Lat_Fissure-post_sgt_thickness lh_Medial_wall_thickness lh_Pole_occipital_thickness lh_Pole_temporal_thickness lh_S_calcarine_thickness lh_S_central_thickness lh_S_central_insula_thickness lh_S_cingulate-Main_part_and_Intracingulate_thickness lh_S_cingulate-Marginalis_part_thickness lh_S_circular_insula_anterior_thickness lh_S_circular_insula_inferior_thickness lh_S_circular_insula_superior_thickness lh_S_collateral_transverse_ant_thickness lh_S_collateral_transverse_post_thickness lh_S_frontal_inferior_thickness lh_S_frontal_middle_thickness lh_S_frontal_superior_thickness lh_S_frontomarginal_thickness lh_S_intermedius_primus-Jensen_thickness lh_S_intraparietal-and_Parietal_transverse_thickness lh_S_occipital_anterior_thickness lh_S_occipital_middle_and_Lunatus_thickness lh_S_occipital_superior_and_transversalis_thickness lh_S_occipito-temporal_lateral_thickness lh_S_occipito-temporal_medial_and_S_Lingual_thickness lh_S_orbital-H_shapped_thickness lh_S_orbital_lateral_thickness lh_S_orbital_medial-Or_olfactory_thickness lh_S_paracentral_thickness lh_S_parieto_occipital_thickness lh_S_pericallosal_thickness lh_S_postcentral_thickness lh_S_precentral-Inferior-part_thickness lh_S_precentral-Superior-part_thickness lh_S_subcentral_post_thickness lh_S_suborbital_thickness lh_S_subparietal_thickness lh_S_temporal_inferior_thickness lh_S_temporal_superior_thickness lh_S_temporal_transverse_thickness MERIT_001 2.617 3.095 1.773 3.009 2.841 3.111 2.935 3.098 2.459 3.402 3.847 2.356 2.166 1.735 2.655 1.83 3.009 2.856 2.637 2.868 3.063 2.313 2.393 3.01 2.765 2.701 2.876 2.91 3.016 3.273 2.492 3.219 3.949 2.813 2.302 2.845 2.569 2.629 1.175 1.62 3.521 1.645 1.816 3.367 2.729 2.164 3.273 2.83 2.778 3.628 1.902 2.453 2.438 2.675 2.184 2.384 2.277 2.464 2.232 2.184 2.743 2.182 2.464 2.624 1.891 2.349 2.242 2.486 2.113 2.399 2.576 2.352 2.722 2.502 2.639 2.706 2.42 MERIT_002 2.339 2.87 1.8 2.76 2.375 2.066 2.325 2.741 2.373 3.37 3.583 2.431 2.629 2.426 2.459 1.963 3.034 2.407 2.159 2.519 2.536 2.313 2.012 2.265 2.622 2.457 3.17 2.752 2.71 2.873 2.496 2.782 3.148 2.509 1.968 2.417 2.11 2.327 1.075 1.953 2.729 1.859 1.779 3.948 2.553 2.141 2.702 2.669 2.669 2.852 1.898 2.042 1.999 2.193 2.091 2.08 2.128 2.248 1.906 2.309 2.421 2.358 2.26 2.022 2.233 2.071 2.179 2.266 2.087 2.21 2.175 2.77 2.136 2.312 2.286 2.384 2.033 MERIT_003 2.845 2.995 1.862 2.62 2.692 2.491 2.481 2.751 3.223 2.825 3.615 2.31 2.749 2.021 2.738 1.997 2.964 2.577 2.284 2.522 2.457 2.186 1.914 2.507 2.267 2.8 3.451 2.667 3.034 3.064 2.224 2.78 3.13 2.315 2.621 2.68 2.401 2.313 0.951 1.769 3.383 1.718 1.868 2.687 2.355 2.127 2.544 2.552 2.372 2.365 1.692 2.055 2.202 2.059 2.018 2.12 1.829 2.101 1.86 1.987 2.458 2.103 2.352 1.866 2.157 1.872 1.773 2.557 1.746 2.228 1.89 2.063 2.081 2.055 2.387 2.154 2.282 MERIT_004 2.485 3.313 1.768 3.212 3.241 2.838 2.88 3.016 3.079 3.229 3.784 2.433 2.626 1.884 3.084 2.273 3.522 3.062 2.509 2.98 2.942 2.551 2.214 2.815 2.722 3.05 3.488 3.015 3.301 3.455 2.935 3.209 3.855 2.893 2.579 2.434 3.361 2.537 1.014 1.829 3.72 1.937 2.097 3.608 2.622 2.339 2.967 3.047 2.741 2.524 2.197 2.399 2.339 2.665 2.4 2.521 2.262 2.44 2.028 2.24 2.615 2.54 2.723 2.302 1.916 2.811 2.431 2.452 2.118 2.587 2.471 2.937 2.727 2.467 2.786 2.556 2.614 MERIT_005 2.989 2.652 1.873 2.654 2.859 2.575 2.457 2.787 2.262 2.953 3.672 2.344 2.319 2.211 2.395 1.973 3.438 2.699 2.069 2.441 2.463 2.243 1.976 2.339 2.2 2.789 2.955 2.431 2.62 2.869 1.992 2.515 3.235 2.158 2.413 2.164 1.966 2.012 1.189 2.298 3.116 1.676 1.526 3.648 2.338 1.792 2.902 2.604 2.378 2.372 1.949 2.203 2.333 2.228 1.872 2.152 1.927 1.973 2.123 2.058 2.192 2.303 2.705 2.26 2.169 2.274 2.003 2.156 1.904 2.319 2.082 2.468 2.471 3.01 2.219 2.355 2.186 1.678 MERIT_006 2.173 2.986 2.128 3.22 3.519 3.077 2.945 3.135 3.067 3.587 3.425 2.893 3.093 2.642 3 2.155 3.126 2.95 2.519 3.252 2.851 2.916 2.188 2.93 2.828 2.593 3.054 3.002 2.988 3.473 3.221 3.416 3.445 2.953 2.786 3.18 2.845 2.516 1.084 1.959 3.583 1.871 1.909 4.077 2.475 1.785 2.744 2.752 2.757 2.655 2.649 2.433 2.501 2.644 2.325 2.416 2.455 2.549 2.313 2.416 2.511 2.63 2.772 2.714 1.711 2.387 2.299 2.582 1.995 2.507 2.307 2.494 2.53 2.49 2.333 2.786 2.644 2.738 MERIT_007 2.525 3.246 2.082 3.185 3.338 3.215 3.186 3.414 2.583 3.684 4.245 2.494 2.329 2.565 3.154 2.4 3.075 2.772 2.505 2.872 3.045 2.811 2.294 3.06 3.002 2.582 3.172 2.769 2.906 3.278 2.463 3.098 3.638 2.818 2.761 2.976 2.698 2.874 1.066 2.024 3.231 2.12 1.893 3.419 2.877 2.64 3.459 2.963 3.037 3.837 2.454 2.413 2.296 2.69 2.339 2.694 2.306 2.44 2.408 2.264 2.757 2.474 2.789 2.384 1.98 1.952 2.573 2.422 2.38 2.646 2.608 2.98 2.438 2.556 2.697 2.755 2.683 2.446 MERIT_008 2.229 3.049 2.167 2.882 3.108 2.968 2.944 3.247 2.871 3.302 3.84 2.79 2.823 2.215 2.973 2.051 3.217 2.93 2.422 3.186 2.989 2.787 2.275 2.986 2.68 2.804 3.771 2.617 3.047 3.308 2.317 3.502 3.738 3.034 3.079 2.372 2.324 2.681 1.11 1.683 2.804 1.959 1.875 3.461 2.82 2.339 3.374 3.016 2.922 2.767 1.892 2.446 2.637 2.544 2.543 2.496 2.404 2.419 2.116 2.379 2.42 2.543 2.758 2.239 2.24 2.835 2.162 2.09 2.238 2.64 2.738 3.025 3.217 2.61 3.018 2.616 2.516 MERIT_009 2.323 3.001 2.066 3.007 3.344 2.893 2.902 3.072 2.913 3.355 3.875 2.404 2.684 2.451 2.791 1.999 2.768 2.841 2.568 2.888 2.849 2.591 2.245 2.78 2.778 3.011 3.382 2.846 2.983 3.221 2.839 3.175 3.337 2.698 2.779 2.286 2.479 2.416 1.158 2.246 3.116 1.862 1.871 3.713 2.692 2.389 3.043 2.784 2.638 2.58 1.825 2.504 2.472 2.59 2.358 2.289 2.206 2.237 2.003 2.31 2.249 2.667 2.481 2.753 2.05 2.521 2.333 2.413 2.189 2.576 2.629 3.413 2.47 2.681 2.779 2.671 2.529 2.659 MERIT_010 2.001 2.852 1.674 2.337 2.559 2.496 2.902 2.938 2.349 3.089 3.736 2.323 2.432 1.967 2.781 2.13 2.998 2.731 2.362 2.758 2.496 2.606 2.139 2.688 2.627 2.845 2.974 2.584 2.814 3.144 2.341 3.069 3.407 2.573 2.875 2.037 2.429 2.2 0.98 1.891 3.424 1.558 1.698 2.556 2.475 2.203 2.487 2.694 2.457 2.389 2.268 2.104 2.415 2.462 2 2.361 1.879 2.101 2.067 2.317 2.276 2.436 2.854 2.347 2.025 2.478 1.921 2.629 2.068 2.387 2.184 2.21 2.539 2.227 2.773 2.296 2.195 MERIT_011 2.925 2.871 1.782 2.624 2.826 2.803 2.823 2.938 2.945 2.973 3.38 2.391 2.869 2.369 2.813 2.021 2.979 2.572 2.249 3.015 2.756 2.729 2.31 2.638 2.583 2.525 3.024 2.772 3.069 3.246 2.609 3.356 3.37 2.66 2.613 1.964 2.023 2.191 0.969 1.97 3.434 1.86 1.644 3.109 2.391 2.335 2.232 2.678 2.488 2.558 2.113 2.389 2.316 2.528 2.089 2.489 2.181 2.409 2.026 2.366 2.418 2.132 2.678 2.247 2.021 1.997 2.205 2.078 2.112 2.422 2.326 1.779 2.339 2.376 2.703 2.607 2.54 2.914 MERIT_012 2.215 3.052 1.741 2.777 3.044 2.946 2.792 2.925 2.652 3.318 3.728 2.591 2.214 1.961 2.801 1.978 3 2.698 2.59 3.027 2.829 2.617 2.399 3.009 2.543 2.599 2.965 2.747 2.999 3.054 2.48 2.952 3.354 2.997 2.534 2.591 2.607 2.487 1.199 1.613 3.5 1.877 2.082 3.841 2.415 2.152 2.953 2.688 2.483 2.656 2.196 2.147 2.202 2.528 1.949 2.396 2.255 2.125 2.116 2.196 2.413 2.375 2.709 2.24 1.854 2.559 2.187 2.581 2.206 2.514 2.596 2.055 2.326 2.387 2.351 2.551 2.477 2.09 MERIT_013 2.755 2.88 1.718 2.984 3.815 2.711 2.739 2.973 2.749 3.052 3.364 2.466 2.363 2.088 2.513 1.867 2.746 2.682 2.419 2.761 2.812 2.407 2.34 2.854 2.497 2.36 3.182 2.83 2.96 3.162 2.362 3.058 3.844 2.821 2.236 2.425 2.14 2.296 0.985 1.89 3.523 1.774 1.784 3.478 2.582 2.098 2.76 2.541 2.568 2.368 2.025 2.147 2.152 2.508 2.089 2.017 1.955 2.214 2.082 2.182 2.282 2.104 2.626 2.496 1.59 2.699 2.126 2.453 2.151 2.396 2.36 3.024 2.17 2.148 2.405 2.834 2.366 1.941 MERIT_014 2.095 2.953 1.829 2.978 3.423 2.981 3.067 3.064 2.954 3.072 3.467 2.558 2.792 2.308 2.939 2.203 3.087 3.034 2.402 2.923 2.946 2.607 2.37 3.04 2.717 2.691 3.217 2.647 3.132 3.297 2.439 3.367 3.31 2.615 2.931 2.569 2.844 2.254 0.988 1.907 3.815 1.703 1.952 3.232 2.594 2.216 2.914 2.552 2.493 2.793 2.147 2.294 2.57 2.51 2.41 2.85 2.333 2.432 2.231 2.182 2.735 2.433 2.811 2.51 2.028 2.739 2.451 2.549 2.232 2.595 2.49 2.399 2.616 2.51 2.446 2.618 2.638 1.933 MERIT_015 2.441 3.037 1.797 2.788 2.956 2.644 2.816 3.074 2.543 3.373 3.845 2.552 2.634 2.212 2.903 2.023 2.796 2.794 2.239 2.622 2.587 2.601 2.083 2.892 2.419 2.572 3.226 2.938 2.952 2.999 2.861 2.949 3.696 2.865 2.335 2.679 2.403 2.316 1.132 1.749 3.388 1.783 1.808 3.32 2.674 2.184 3.045 2.891 2.56 3.319 2.014 2.278 2.367 2.576 2.297 2.324 1.942 2.441 2.313 1.983 2.552 2.426 2.776 2.073 1.758 2.351 2.268 2.59 1.909 2.29 2.403 3.263 3.381 2.142 2.222 2.346 2.402 2.611 MERIT_016 2.511 2.933 1.907 2.836 2.654 2.987 2.803 2.936 2.738 3.146 3.67 2.078 2.509 2.42 2.583 1.959 3.126 2.579 2.571 2.577 2.648 2.607 2.411 2.85 2.566 2.756 3.1 2.566 2.898 3.192 2.226 2.912 3.586 2.717 3.06 2.17 2.054 2.241 1.291 1.651 3.056 1.753 1.756 3.232 2.357 2.147 2.688 2.594 2.638 2.771 2.055 2.235 2.286 2.525 2.437 2.68 2.02 2.302 2.168 2.122 2.44 2.141 2.515 2.081 1.875 2.23 1.971 2.322 2.026 2.338 2.516 2 2.094 2.34 2.447 2.716 2.315 2.317 MERIT_017 2.204 3.05 1.85 2.806 2.377 2.522 2.786 2.973 2.266 3.474 4.007 2.227 2.506 2.11 2.895 2.142 2.63 2.812 2.324 2.911 2.903 2.384 2.307 2.753 2.633 2.732 3.469 2.807 3.027 3.042 2.737 2.992 2.966 2.492 2.593 2.094 2.315 2.434 0.836 1.978 3.549 1.929 1.824 3.759 2.547 2.096 2.662 2.86 2.505 2.21 1.985 2.291 2.465 2.519 2.261 2.679 2.11 2.154 2.251 2.382 2.484 2.777 2.634 2.302 2.219 2.759 2.103 2.784 2.07 2.345 2.581 2.822 2.598 2.105 2.676 2.359 3.144 MERIT_018 2.319 3.342 1.701 2.952 3.181 2.884 3.176 3.293 2.672 3.754 3.744 2.424 2.698 2.02 3.168 2.006 3.425 2.88 2.504 2.955 2.999 2.436 2.288 2.859 2.701 2.988 3.597 2.861 3.111 3.402 2.523 3.267 4.063 2.694 3.079 1.781 2.667 2.426 1.146 1.954 3.946 1.803 1.713 3.076 2.728 2.06 3.147 2.826 2.697 2.859 2.207 2.43 2.526 2.56 2.593 2.556 2.276 2.614 2.18 2.306 2.677 2.408 2.688 2.585 2.304 2.621 2.188 2.703 2.011 2.523 2.498 3.442 2.979 2.882 2.205 2.915 2.717 2.402 MERIT_019 2.942 2.666 2.043 2.754 2.887 2.75 2.959 2.803 2.545 3.086 3.573 2.438 2.524 2.031 2.847 1.869 3.162 2.67 2.469 2.485 2.574 2.552 2.414 2.802 2.623 2.99 3.461 2.85 2.901 3.15 2.414 2.984 3.604 2.476 2.686 2.228 2.273 2.175 0.975 1.799 3.209 1.827 1.712 3.827 2.366 2.101 2.812 2.785 2.326 2.515 1.685 2.251 2.259 2.289 2.201 2.011 1.888 2.079 1.769 1.862 2.474 2.321 2.48 2.474 2.057 1.777 2.205 2.159 1.926 2.268 2.236 2.478 2.543 2.101 2.033 2.574 2.288 1.984 MERIT_020 2.305 3.13 1.879 2.938 3.255 2.756 2.923 2.978 2.771 3.499 3.787 2.282 2.514 2.252 2.871 1.974 3.002 2.981 2.548 2.958 2.88 2.745 2.265 3.121 2.81 2.946 3.323 2.963 3.077 3.127 2.899 3.008 3.765 2.358 2.798 1.929 2.124 2.217 1.004 1.791 3.386 1.725 2.231 3.086 2.536 2.462 3.062 2.885 2.538 2.783 1.784 2.401 2.235 2.645 2.301 3.191 2.394 2.481 1.779 2.075 2.44 2.293 2.561 2.051 2.051 2.733 2.159 2.675 2.195 2.619 2.708 2.57 2.317 2.453 2.447 2.388 2.397 MERIT_021 2.861 3.056 1.698 2.873 3.267 2.848 2.772 3.046 3.287 3.406 3.765 2.387 2.099 1.923 2.704 1.985 3.275 3.017 2.294 2.512 2.561 2.447 2.027 2.701 2.586 2.945 3.524 2.591 3.052 3.022 2.207 3.183 3.775 2.357 2.851 2.794 2.314 2.128 0.992 1.731 3.651 1.706 1.673 2.26 2.669 2.158 2.829 2.59 2.422 2.323 2.024 2.201 2.416 2.424 2.528 2.161 2.017 2.309 2.196 1.969 2.505 2.196 2.677 3.229 2.441 1.985 2.079 2.456 1.735 2.486 2.262 2.341 2.711 2.29 2.53 2.708 2.436 2.048 MERIT_022 2.304 2.928 1.804 2.928 3.071 2.992 2.872 2.946 2.495 3.004 3.605 2.693 2.49 2.131 2.798 2.133 2.985 2.749 2.373 2.887 2.661 2.634 2.051 2.802 2.614 2.457 3.59 2.379 2.93 3.034 2.407 3.139 3.562 2.528 2.595 2.592 2.525 2.389 1.02 2.022 3.361 1.792 1.783 3.644 2.529 2.169 2.871 2.571 2.674 2.84 2.038 2.312 2.344 2.491 2.215 2.532 2.241 2.298 2.101 2.231 2.725 2.568 2.623 2.23 2.032 2.046 2.204 2.552 2.12 2.555 2.31 1.573 2.317 2.59 2.504 2.532 2.493 2.205 MERIT_023 2.596 2.972 1.85 3.075 2.968 2.875 3.071 3.128 2.452 3.015 3.987 2.674 2.863 2.23 2.88 1.932 2.905 2.806 2.526 2.949 2.956 2.686 2.277 2.823 2.732 2.895 2.733 2.773 3.343 3.078 2.769 3.084 3.466 2.741 3.028 2.226 3.367 2.512 0.996 1.943 3.512 1.887 1.766 3.495 2.618 2.245 2.947 2.899 2.635 3.376 2.26 2.513 2.434 2.669 2.64 2.85 2.398 2.401 2.245 2.203 2.583 2.235 2.861 2.513 2.133 2.734 2.31 2.571 2.144 2.69 2.443 2.71 2.606 2.535 3.027 2.526 2.274 MERIT_024 2.462 3.276 1.939 2.832 2.873 2.526 3.001 3.179 2.491 3.854 3.836 2.466 2.825 2.505 2.743 2.126 3.349 2.703 2.581 3.014 2.814 2.613 2.509 2.894 2.662 3.137 3.693 2.782 3.156 3.139 2.815 3.375 3.58 2.54 2.766 2.648 2.239 2.546 1.047 1.947 3.842 1.97 1.961 3.614 2.672 2.087 2.884 2.977 2.678 2.692 2.012 2.257 2.5 2.546 2.377 2.461 2.24 2.263 1.949 2.219 2.667 2.222 2.49 2.158 2.568 1.9 2.265 2.899 2.266 2.538 2.246 2.826 2.457 2.957 2.383 2.612 2.503 2.154 MERIT_025 2.315 2.64 1.487 2.288 1.953 2.214 2.458 2.728 2.674 2.664 3.619 2.012 2.607 2.213 2.193 1.828 2.536 2.719 1.836 2.561 2.629 2.194 2.022 2.48 2.195 2.977 3.203 2.516 2.917 2.692 1.833 2.663 2.825 2.604 2.418 2.169 2.244 2.165 0.985 1.616 3.272 1.789 1.559 3.436 2.494 1.928 2.628 2.57 2.172 3.245 1.743 2.151 1.713 2.082 2.052 2.194 1.978 2.019 1.931 2.106 2.328 2.167 2.653 2.258 2.486 2.246 1.757 2.113 2.096 2.521 1.938 2.442 2.278 2.932 2.165 2.51 2.357 2.547 MERIT_026 2.662 3.049 2.047 3.296 3.545 3.429 3.203 3.372 2.725 3.579 3.671 2.322 2.778 2.208 2.445 1.858 3.12 3.062 2.455 3.093 3.099 2.75 2.221 2.997 2.752 3.176 3.891 2.953 2.979 3.342 3.017 3.258 3.535 2.743 2.946 3.568 2.42 2.345 0.991 1.88 3.827 1.795 1.628 3.7 2.871 2.133 3.05 3.321 2.528 2.988 1.864 2.596 2.495 2.642 2.485 2.654 2.168 2.614 1.988 2.267 2.735 2.075 2.847 2.611 2.235 2.771 2.213 2.263 2.18 2.568 2.565 2.776 2.366 3.068 2.447 2.672 2.517 3.04 MERIT_027 2.451 3.161 1.73 2.781 2.754 2.519 2.763 3.213 2.782 3.186 3.82 2.397 2.755 2.273 2.857 1.924 3.186 2.7 2.092 2.702 2.994 2.454 2.357 2.508 2.555 2.625 3.198 3.014 3.254 3.122 2.896 3.025 3.441 2.628 2.93 2.567 2.622 2.695 1.054 1.762 3.033 1.826 1.695 2.962 2.639 2.423 2.798 2.943 2.557 2.78 2.034 2.438 2.167 2.657 2.258 2.594 2.226 2.596 2.172 2.463 2.637 2.571 2.649 2.229 2.042 2.319 2.464 2.839 2.252 2.803 2.247 2.914 2.511 2.529 2.788 2.775 2.652 MERIT_028 2.179 3.303 1.869 3.293 3.464 3.038 2.965 3.143 3.177 3.681 3.49 2.452 2.724 2.417 2.976 2.181 3.08 2.955 2.569 3.094 2.93 2.833 2.467 3.033 2.778 2.431 3.285 3.219 3.014 3.338 2.853 3.49 3.544 2.854 2.579 3.034 2.629 2.342 1.049 1.832 3.598 2.102 2.129 4.277 2.826 2.203 2.929 2.981 2.847 3.207 1.879 2.459 2.55 2.782 2.632 2.229 2.289 2.455 2.307 2.426 2.677 2.382 2.681 2.506 2.056 2.672 2.365 2.749 2.285 2.742 2.541 3.019 2.47 2.207 2.644 2.751 2.622 2.283 MERIT_029 2.901 3.249 2.137 3.066 3.076 2.904 3.048 3.303 3.048 3.228 4.054 2.327 2.772 2.295 3.128 2.201 3.221 3.269 2.497 3.306 3.154 2.738 2.331 3.157 2.951 2.828 4.156 3.253 2.822 3.316 2.594 3.359 3.342 2.753 2.739 2.332 2.432 2.522 1.137 1.978 3.443 2.052 2.107 3.341 2.782 2.34 3.174 2.842 2.794 2.545 1.955 2.329 2.269 2.678 2.6 2.888 2.294 2.424 2.468 2.445 2.598 2.689 2.93 2.472 2.375 2.688 2.534 2.168 2.319 2.68 2.522 2.945 3.005 2.462 2.629 2.704 2.565 MERIT_030 2.029 2.834 1.759 2.711 2.841 2.608 2.686 3.11 2.06 3.216 3.405 2.485 2.539 1.98 2.954 2.059 3.015 2.504 2.604 2.663 2.934 2.477 2.253 2.569 2.613 2.437 3.042 2.754 2.881 3.207 2.69 3.137 3.532 2.849 2.467 2.06 2.191 2.627 0.939 1.649 3.199 1.793 1.895 3.701 2.619 2.192 3.23 2.857 2.485 2.872 2.011 2.309 2.087 2.436 1.934 2.379 2.321 2.591 2.175 2.159 2.64 2.625 2.382 2.163 1.823 2.942 2.267 2.192 2.432 2.548 2.496 3.232 2.307 2.571 2.94 2.611 2.844 MERIT_031 2.708 3.011 1.804 2.535 3.067 2.478 2.39 2.808 2.203 3.479 3.824 2.264 2.951 2.097 2.695 1.866 2.646 2.494 2.27 2.665 2.532 2.426 2.088 2.66 2.405 2.507 3.461 2.759 2.951 2.829 2.329 2.738 3.282 2.563 2.405 2.484 1.917 2.208 1.077 2.281 3.305 1.903 1.978 3.683 2.534 2.068 2.593 2.785 2.596 3.017 2.065 1.904 1.99 2.389 2.316 2.262 2.063 1.948 2.346 2.196 2.714 2.118 2.411 2.143 2.456 2.373 1.925 2.821 2.146 2.208 2.408 2.601 2.248 1.801 1.986 2.572 2.226 2.697 MERIT_032 1.989 2.845 1.691 2.755 2.903 2.841 3.02 3.094 2.387 3.633 3.697 2.726 2.737 2.019 2.678 1.896 2.704 2.78 2.037 2.822 3.016 2.241 1.961 2.687 2.479 2.557 3.201 2.837 2.793 3.005 2.729 2.744 2.797 2.73 3.033 2.014 2.73 2.24 1.05 1.775 2.935 1.741 1.724 3.081 2.649 2.518 3.108 2.967 2.661 3.16 1.944 2.304 2.5 2.594 2.878 2.415 1.921 2.116 2.04 2.097 2.805 2.2 2.642 2.57 2.372 2.3 2.067 1.852 1.981 2.63 2.583 2.656 1.875 2.306 2.696 2.456 2.734 MERIT_033 1.605 2.901 1.763 2.089 2.342 2.259 2.578 2.87 2.297 3.178 3.334 2.385 2.542 2.12 2.769 2.043 2.536 2.217 2.276 2.773 2.742 2.474 2.134 2.214 2.76 2.464 3.248 2.402 2.722 2.846 2.379 2.512 2.685 2.71 2.544 1.907 2.983 2.426 0.956 2.131 3.153 1.904 1.7 2.759 2.529 2.14 2.61 2.528 2.584 2.144 2.163 2.216 2.47 2.382 2.83 2.85 2.415 2.387 2.192 2.081 2.542 2.422 2.519 2.736 2.363 2.38 2.056 2.304 2.226 2.328 2.393 3.423 2.722 2.163 2.766 2.683 2.497 2.385 MERIT_034 2.631 2.944 1.877 2.642 3.095 2.612 2.675 2.84 2.789 3.272 3.984 2.184 2.432 2.17 2.821 1.992 3.337 2.869 2.2 2.704 2.533 2.476 2.324 2.968 2.575 3.013 3.493 2.628 2.905 2.886 2.437 3.231 3.318 2.575 2.731 2.509 1.899 2.212 0.966 1.683 3.707 1.794 2.023 3.417 2.474 2.014 3 2.479 2.503 2.25 1.751 2.042 2.28 2.31 1.953 1.947 2.083 2.097 1.868 1.979 2.264 2.438 2.477 2.282 2.273 2.266 2.22 2.319 1.96 2.139 2.167 1.745 2.188 2.437 2.403 2.244 2.129 MERIT_035 2.197 2.871 1.437 2.547 2.646 2.57 2.523 2.634 2.696 3.061 3.769 1.808 2.383 2.049 2.491 1.617 2.875 2.531 1.445 2.272 2.394 2.085 1.619 1.789 2.028 2.427 3.233 2.254 2.428 2.729 2.342 2.537 2.977 1.806 2.654 2.267 2.353 2.106 1.105 1.869 3.534 1.478 1.281 3.226 2.741 1.989 3.083 2.87 2.596 2.735 1.687 2.192 2.34 2.426 2.517 1.816 1.666 1.843 1.803 1.778 2.109 2.13 2.505 2.176 1.783 1.803 1.827 2.507 1.866 2.443 1.747 2.855 2.639 2.483 2.18 2.45 2.248 2.341 MERIT_036 2.546 2.849 1.853 2.78 2.813 2.916 2.817 3.072 3.355 3.163 3.96 2.604 2.466 2.217 2.742 2.188 3.1 3.069 2.526 2.799 2.993 2.556 2.497 2.791 2.508 2.719 3.284 2.851 3.024 3.038 2.46 3.006 3.393 2.408 3.113 2.01 2.426 2.199 1.108 1.988 3.524 1.883 1.815 4.063 2.502 2.159 2.962 2.759 2.58 2.831 2.012 2.24 2.268 2.475 2.537 2.06 2.305 2.522 2.072 2.158 2.447 2.354 2.968 1.913 2.203 2.93 2.282 2.543 2.208 2.5 2.613 3.281 2.7 2.075 2.372 2.463 2.492 MERIT_037 2.514 3.186 1.815 2.667 3.116 2.688 2.847 3.185 3.083 3.169 3.778 2.466 2.203 1.866 2.561 1.918 3.044 2.929 2.37 2.544 2.774 2.29 2.258 2.923 2.58 2.723 3.327 2.644 3.122 3.223 2.793 3.029 3.121 2.785 2.56 2.495 2.133 2.564 1.087 1.54 3.709 1.676 1.895 3.345 2.771 2.101 2.663 2.928 2.517 2.527 2.076 2.222 2.429 2.523 2.288 2.454 2.146 2.481 2.016 2.064 2.374 2.546 2.752 2.389 1.857 3.243 2.286 2.337 1.985 2.487 2.303 2.015 2.773 2.573 2.619 2.505 2.612 MERIT_038 2.219 2.96 1.897 3.018 2.878 2.692 2.921 3.046 3.093 3.65 3.687 2.314 2.496 2.196 2.466 1.921 2.648 2.822 2.466 2.753 2.944 2.425 2.251 2.712 2.584 2.999 3.416 2.944 2.708 3.101 2.525 3.243 3.538 2.759 2.882 2.477 2.269 2.366 0.941 1.806 3.686 1.911 1.924 3.61 2.526 2.342 2.925 2.897 2.641 2.429 1.796 2.365 2.498 2.716 2.371 2.355 2.279 2.246 1.873 2.231 2.702 2.257 2.851 2.514 2.15 2.286 2.004 2.524 2.267 2.522 2.402 2.574 2.789 2.865 2.391 2.506 2.446 2.275 MERIT_039 2.855 3.08 1.59 2.893 3.353 3.029 2.888 3.018 3.015 3.151 3.663 2.078 2.182 1.737 2.62 1.82 2.85 2.721 2.189 2.715 2.838 2.602 2.083 2.768 2.647 2.579 3.167 2.584 2.995 3.095 2.485 3.245 3.353 2.48 2.172 2.555 2.322 2.316 1.067 1.715 3.657 1.695 1.835 3.386 2.66 2.176 3.09 2.665 2.471 2.734 1.661 2.393 2.508 2.58 2.567 2.361 2.095 2.258 1.981 2.022 2.627 2.168 2.701 2.387 2.3 1.868 2.122 2.171 2.006 2.518 2.057 2.672 2.301 2.579 2.193 2.72 2.469 2.019 MERIT_040 2.135 3.073 1.745 3.104 3.131 3.097 3.094 3.01 2.775 2.865 2.477 2.759 2.119 1.779 3.048 1.865 1.994 3.086 2.566 2.885 3.098 2.524 2.729 3.011 2.562 2.621 3.247 3.39 2.962 3.37 2.763 3.254 2.598 2.967 2.965 2.394 2.687 2.629 0.974 1.59 2.259 1.807 2.042 2.833 2.731 2.325 2.994 2.695 2.708 3.046 2.181 2.654 2.547 2.613 2.397 2.46 2.143 2.605 2.229 2.284 2.892 1.868 2.738 2.54 2.126 2.293 2.234 2.209 2.321 2.63 2.405 3.89 2.491 2.36 2.617 2.71 2.773 MERIT_041 2.038 2.831 1.917 3.018 3.178 2.647 2.829 2.991 2.545 3.336 3.914 2.492 2.558 2.137 2.823 2.175 2.998 2.959 2.457 2.957 2.796 2.615 2.267 2.785 2.481 2.99 3.219 2.678 3.062 3.178 2.807 3.305 3.555 2.745 2.757 2.232 3.085 2.451 1.09 1.825 3.614 1.864 1.898 3.927 2.703 2.08 3.461 3.086 2.487 2.75 1.958 2.391 2.197 2.504 2.339 3.472 2.121 2.333 1.967 2.076 2.362 2.132 2.352 2.275 2.264 2.515 2.201 1.985 2.151 2.321 2.483 2.834 2.126 2.417 2.178 2.285 2.398 2.188 MERIT_042 2.611 3.225 1.748 3.256 2.377 2.518 2.544 3.109 2.295 3.951 4.351 2.31 2.532 2.37 2.72 1.996 3.047 2.452 2.345 2.781 2.732 2.38 2.386 2.672 2.485 2.663 3.403 3.222 2.976 3.275 2.921 3.59 3.499 2.713 2.829 2.216 2.849 2.674 1.045 1.938 3.533 1.916 1.807 3.333 2.854 2.007 3.375 2.854 2.807 2.734 2.238 2.304 1.962 2.695 2.377 2.616 2.123 2.245 2.266 2.122 2.845 2.555 2.677 2.256 2.345 2.676 2.247 2.416 2.261 2.5 2.69 3.123 2.524 2.343 2.716 2.625 2.524 MERIT_043 2.456 2.942 1.864 2.672 2.927 2.575 2.536 2.635 2.327 3.102 3.431 2 2.525 2.37 2.907 2.059 2.974 2.601 2.15 2.463 2.552 2.357 2.039 2.552 2.402 2.159 3.392 2.442 2.793 2.903 2.338 2.958 3.422 2.594 2.544 2.405 1.96 2.335 0.932 2.017 3.373 1.975 1.852 2.855 2.396 2.008 2.678 2.875 2.35 3.078 1.734 2.046 1.928 2.18 1.903 1.931 1.985 2.266 1.845 2.015 2.252 2.262 2.336 2.123 2.11 2.167 2.107 2.442 1.906 2.222 2.284 1.949 2.327 2.237 2.346 2.067 2.383 2.257 MERIT_044 2.594 2.783 1.833 2.788 2.883 2.596 2.47 2.914 2.817 2.71 3.865 2.475 2.758 2.245 2.741 2.019 3.144 2.623 2.347 2.849 2.738 2.514 2.183 2.744 2.607 2.212 2.749 2.509 2.726 2.876 2.34 2.881 3.28 2.435 2.599 2.286 2.292 2.294 0.945 2.207 3.197 1.803 1.837 3.044 2.533 2.19 2.523 2.484 2.453 2.317 2.329 1.949 1.95 2.413 1.792 3.714 2.203 2.319 1.949 2.034 2.565 2.192 2.359 1.431 2.226 2.428 2.239 2.12 2.153 2.277 2.384 2.983 2.454 2.21 2.358 2.25 2.334 2.601 MERIT_045 2.464 2.771 2.012 2.628 2.717 2.726 2.676 2.712 2.934 3.459 3.529 2.259 2.657 2.562 2.693 2.143 2.572 2.645 2.198 2.803 2.722 2.531 2.215 2.78 2.736 2.246 3.086 2.54 2.727 2.996 2.735 2.927 3.506 2.4 2.529 2.141 2.273 2.317 0.993 2.121 2.816 2.001 2.024 4.313 2.398 2.163 2.597 2.671 2.222 2.633 1.868 2.154 2.321 2.31 1.958 2.009 2.089 2.193 1.76 2.082 2.383 2.116 2.247 2.1 1.98 2.429 2.21 1.991 2.213 2.245 2.3 2.145 2.182 2.301 2.306 2.35 2.475 MERIT_046 3.066 3.059 1.902 2.826 2.94 2.738 2.715 2.836 2.903 2.931 3.683 2.536 2.675 2.4 2.963 2.26 2.608 2.85 2.476 2.92 2.644 2.569 2.35 2.891 2.629 2.739 3.426 2.555 2.606 3.006 2.628 3.117 3.672 2.549 2.617 2.252 2.075 2.227 0.813 2.123 3.032 1.996 1.978 2.722 2.406 2.183 2.612 2.72 2.342 2.27 1.9 2.093 2.261 2.252 2.011 2.076 2.073 2.372 2.032 1.969 2.232 2.308 2.263 2.281 2.34 1.789 2.249 2.62 2.1 2.164 2.192 2.295 2.13 2.335 2.284 2.183 2.274 2.046 MERIT_047 1.982 2.748 1.887 2.282 2.389 2.255 2.451 2.578 2.45 2.465 3.108 2.399 2.312 2.455 2.841 1.994 2.772 2.355 2.249 2.653 2.408 2.648 2.187 2.424 2.629 2.264 3.742 2.554 2.735 3.095 2.187 2.669 3.243 2.579 2.299 1.835 2.264 2.276 0.877 2.11 2.896 1.837 1.716 2.336 2.167 2.104 2.509 2.311 2.234 2.698 1.894 2.012 2.264 2.298 1.873 1.954 1.969 1.969 1.941 2.278 2.063 2.301 2.214 1.788 1.885 2.187 2.119 2.018 1.99 2.094 2.094 2.536 2.723 2.057 2.239 2.214 2.572 MERIT_048 1.156 3.286 2.015 2.897 3.144 2.766 2.881 3.071 2.462 3.458 3.789 2.671 2.872 2.587 2.901 2.223 3.027 2.778 2.566 2.953 2.989 2.619 2.432 3.065 2.668 2.722 3.463 3.138 3.091 3.263 2.799 3.44 3.664 2.859 2.834 2.115 2.393 2.904 1.133 2.326 2.603 2.006 2.083 3.192 2.672 2.474 3.01 2.971 2.736 2.427 2.265 2.341 2.152 2.592 2.307 2.159 2.323 2.492 2.153 2.499 2.778 2.44 2.611 2.211 2.151 2.729 2.499 2.078 2.356 2.618 2.547 2.224 2.388 2.268 2.543 2.471 2.624 2.436 MERIT_049 2.714 2.952 1.897 2.772 2.902 2.882 2.637 2.893 2.637 2.922 3.774 2.378 2.647 2.436 3.035 2.129 2.877 2.639 2.303 2.808 2.916 2.543 2.422 2.834 2.865 2.349 3.156 2.863 2.842 2.983 2.552 3.291 3.038 2.482 2.577 2.502 2.245 2.355 0.954 2.112 3.554 1.961 1.934 2.941 2.403 2.353 2.727 2.678 2.537 2.759 1.985 2.259 2.142 2.348 1.9 2.273 2.152 2.255 2.067 2.063 2.548 2.477 2.71 2.087 1.91 2.504 2.293 1.856 2.235 2.468 2.244 2.899 2.935 2.524 2.487 2.288 2.317 2.55 MERIT_050 2.896 2.89 2.069 2.831 2.966 2.968 3.018 2.917 2.669 3.064 3.781 2.335 2.813 2.507 3.066 2.184 3.156 2.707 2.475 2.903 2.903 2.732 2.464 3.012 2.793 2.32 3.001 3.078 2.949 3.151 2.307 3.264 3.603 2.713 2.715 1.728 2.374 2.292 0.979 2.295 2.924 2.087 2.018 2.968 2.337 2.14 2.653 2.648 2.411 2.77 2.2 2.13 2.252 2.248 2.319 2.239 2.271 2.159 2.325 2.023 2.429 2.262 2.804 2.415 2.25 2.678 2.232 2.257 2.107 2.415 2.425 2.294 2.418 2.732 2.248 2.34 2.164 MERIT_051 2.022 2.782 1.916 2.456 2.839 2.562 2.575 2.601 2.541 2.891 3.451 2.036 2.423 2.195 2.354 1.886 3.108 2.516 2.409 2.515 2.467 2.366 2.067 2.726 2.517 2.11 2.702 2.711 2.567 2.897 2.446 2.948 3.234 2.365 2.138 2.064 2.16 2.122 0.812 1.903 3.127 1.797 1.909 2.46 2.231 1.823 2.543 2.734 2.17 2.134 1.767 2.062 1.881 2.126 1.793 1.976 1.85 1.745 1.758 2.063 1.732 2.085 2.238 1.91 2.155 1.935 2.125 2.003 1.888 2.314 2.086 2.89 2.192 2.229 2.168 2.039 2.195 2.056 MERIT_052 2.619 2.584 1.904 2.683 2.876 2.6 2.399 2.703 2.923 3.103 3.697 2.011 2.579 2.314 2.608 2.031 2.883 2.68 2.333 2.675 2.603 2.367 1.985 2.602 2.588 2.344 3.044 2.682 2.551 2.961 2.622 3.202 3.178 2.757 2.263 2.151 2.191 2.33 0.671 1.976 3.329 2.003 1.959 3.174 2.289 2.128 2.883 2.531 2.465 2.769 1.973 2.155 2.105 2.228 1.943 1.639 2.012 1.968 1.72 2.023 1.912 2.152 2.453 1.971 2.323 2.222 2.271 1.825 2.074 2.381 2.258 2.365 2.306 2.188 2.156 2.234 2.272 MERIT_053 2.178 2.451 1.796 2.442 2.476 2.285 2.383 2.391 2.283 3.094 3.355 2.15 2.401 2.461 2.754 1.904 3.565 2.555 2.182 2.44 2.394 2.215 1.942 2.528 2.325 2.223 2.89 2.507 3.009 2.954 2.079 3.19 3.293 2.201 2.282 1.743 2.031 1.835 0.927 1.988 3.197 1.747 1.829 3.665 2.064 1.753 2.568 2.375 2.241 2.522 2.333 1.832 1.866 2.027 1.836 2.059 1.788 2.198 1.978 2.201 2.531 2.349 2.523 2.08 1.966 1.846 2.036 2.135 1.712 2.069 2 1.917 2.152 2.015 2.24 2.764 2.379 1.896 MERIT_054 2.162 2.807 1.796 2.575 2.772 2.462 2.494 2.861 2.356 3.028 3.499 2.373 2.625 2.187 2.657 2.036 3.148 2.437 2.347 2.779 2.784 2.408 2.101 2.869 2.316 2.027 3.157 2.601 2.657 2.936 2.247 2.912 3.271 2.546 2.06 2.04 1.974 2.351 0.946 2.192 2.845 1.814 1.93 3.157 2.271 2.182 2.463 2.602 2.307 2.398 1.961 2.042 1.937 2.215 1.665 1.947 2.035 2.491 2.043 1.957 2.443 2.282 2.141 1.737 1.622 3.273 1.973 2.024 2.048 2.109 2.333 3.048 2.534 2.514 2.064 2.295 2.318 2.131 MERIT_055 2.61 2.691 2.037 2.563 2.585 2.221 2.407 2.646 2.442 2.859 3.39 2.087 2.495 2.081 2.701 1.916 2.828 2.295 2.257 2.506 2.59 2.321 1.945 2.531 2.442 2.322 3.233 2.547 2.586 2.9 2.233 2.947 3.316 2.509 2.086 1.802 2.04 2.227 0.904 1.995 2.794 1.797 1.733 2.5 2.112 2.064 2.464 2.506 2.308 1.964 2.05 1.998 2.054 2.217 1.831 2.034 2.007 2.018 1.869 1.891 1.837 2.019 2.098 1.706 2.13 2.261 1.871 2.033 1.924 2.101 2.056 2.508 2.21 2.567 1.989 2.099 2.223 2.157 MERIT_056 2.248 2.78 1.932 2.78 2.66 2.411 2.498 2.714 2.674 3.108 3.337 2.476 2.696 2.2 3.032 2.015 3.315 2.602 2.254 2.751 2.598 2.482 2.196 2.764 2.747 2.369 2.954 2.486 2.877 3.084 2.386 3.198 3.24 2.432 2.437 1.907 2.124 2.119 0.933 2.042 3.551 1.744 1.991 3.342 2.297 2.13 2.807 2.525 2.395 2.811 2.263 2.21 2.167 2.371 1.961 2.45 2.085 2.34 2.066 2.051 2.389 2.513 2.34 2.249 2.332 2.411 2.14 2.134 2.087 2.156 2.329 2.166 2.561 2.18 2.487 2.386 2.075 MERIT_057 3.033 3.042 1.724 2.769 2.86 2.82 2.729 2.879 2.643 3.312 3.502 2.498 2.584 2.225 2.682 2.071 2.906 2.755 2.258 2.801 2.819 2.483 2.284 2.983 2.575 2.387 3.19 2.971 3.124 3.137 2.656 3.338 3.428 2.677 2.502 2.042 1.939 2.339 1.069 1.991 3.053 1.975 2.029 3.036 2.462 2.354 2.506 2.622 2.561 2.972 2.008 2.228 2.093 2.31 1.902 2.294 2.024 2.027 1.855 1.937 2.06 2.272 2.483 2.008 1.97 2.317 2.291 2.018 2.098 2.305 2.351 2.612 2.226 2.096 2.189 2.071 2.432 2.639 MERIT_058 1.999 2.941 1.929 2.881 2.913 2.67 2.75 2.84 2.824 2.819 3.237 2.203 2.575 2.284 2.604 1.965 2.609 2.637 2.319 2.755 2.757 2.596 2.455 2.979 2.555 2.367 2.963 2.853 2.731 2.989 2.401 3.127 3.428 2.4 2.473 2.683 2.692 2.353 0.833 2.088 2.984 1.757 2.019 2.595 2.328 2.112 2.738 2.347 2.308 2.274 1.981 2.35 2.17 2.381 1.935 1.924 2.054 2.047 2.119 2.056 1.874 2.211 2.311 1.941 1.908 2.335 2.258 2.374 2.09 2.354 2.35 3.137 2.352 2.552 2.229 2.068 2.326 2.461 MERIT_059 2.606 3.451 1.861 3.073 3.082 2.994 2.803 2.998 3.289 3.389 3.848 2.696 2.898 2.658 2.976 1.915 3.063 2.982 2.415 3.112 2.887 2.554 2.219 2.863 2.873 2.839 3.407 3.161 3.302 3.485 2.548 3.542 4.077 2.866 2.751 2.198 2.965 2.351 1.003 2.188 3.678 1.964 2.086 3.682 2.663 2.121 2.891 3.341 2.642 3.065 2.059 2.256 2.257 2.53 2.285 2.291 2.178 1.931 2.148 2.015 2.479 2.579 2.833 2.178 2.613 2.276 2.241 2.525 2.131 2.556 2.201 2.345 2.42 2.473 2.708 2.698 2.456 MERIT_060 2.1 2.698 1.757 2.674 2.626 2.464 2.574 2.672 2.563 2.419 3.019 2.087 2.462 2.166 2.664 2.091 2.59 2.47 2.306 2.617 2.28 2.397 2.034 2.406 2.463 2.813 3.199 2.469 2.461 2.791 2.16 2.669 2.938 2.135 2.409 1.994 1.811 2.069 0.769 2.12 3.101 1.968 1.885 3.027 2.361 2.099 2.146 2.424 2.235 1.968 1.786 1.924 2.167 2.381 2 2.313 2.013 1.846 1.581 1.835 1.933 1.812 2.505 2.081 2.127 2.075 1.758 2.01 2.032 2.178 2.056 3.14 1.754 2.279 2.121 2.01 2.043 2.149 MERIT_061 1.987 2.958 1.921 2.56 2.395 2.583 2.526 2.717 2.815 2.847 3.137 2.435 2.867 2.415 2.692 2.24 3.109 2.714 2.366 2.808 2.718 2.483 2.246 2.81 2.583 2.257 2.843 2.439 3.041 3.03 2.37 3.035 3.457 2.568 2.638 2.069 2.198 2.623 0.953 2.073 3.549 1.854 2.015 2.607 2.423 2.158 3.33 2.733 2.449 2.475 2.083 2.307 1.915 2.132 2.318 2.28 2.19 2.437 2.326 2.255 2.484 2.494 2.471 1.695 2.497 2.143 2.249 2.507 2.236 2.169 2.288 2.283 2.324 2.359 2.605 2.472 1.931 MERIT_062 2.753 3.112 2.294 3.149 2.927 2.872 2.64 3.063 2.466 3.108 3.944 2.564 3.017 2.564 3.094 2.306 3.266 2.913 2.694 2.991 2.77 2.709 2.344 3.131 2.835 2.246 3.155 2.845 3.146 3.298 2.667 3.58 3.666 2.849 2.832 1.902 2.334 2.296 1.054 2.444 3.546 2.008 2.128 3.834 2.703 2.494 2.997 2.844 2.522 2.876 2.013 2.222 2.242 2.289 2.083 2.17 2.151 2.295 2.09 2.06 2.441 2.77 2.792 2.172 2.284 2.425 2.245 2.172 2.234 2.327 2.497 2.366 2.424 2.464 2.505 2.482 2.664 MERIT_063 2.412 2.997 2.042 2.783 2.52 2.852 2.724 2.916 2.689 3.133 3.228 2.209 2.628 1.93 2.502 2.213 3.129 2.775 2.196 2.666 2.575 2.468 2.384 2.51 2.576 2.629 3.226 2.694 2.483 2.622 2.428 3.065 3.841 2.406 2.458 1.886 2.027 2.282 0.946 1.97 3.177 2.039 1.879 3.286 2.565 2.047 2.509 2.788 2.57 2.162 1.879 2.254 2.371 2.571 1.963 2.457 2.112 1.974 1.516 1.857 1.897 2.238 2.98 2.326 2.347 2.194 2.22 2.201 2.315 2.296 2.396 2.324 2.429 2.415 2.246 1.835 2.237 2.55 MERIT_064 2.232 2.723 2 2.586 2.555 2.639 2.461 2.732 3.049 2.499 3.44 2.237 2.536 2.026 2.446 2.255 2.875 2.656 2.241 2.877 2.406 2.248 2.053 2.338 2.201 2.367 3.186 2.435 2.37 2.87 2.158 2.789 3.204 2.306 2.814 2.088 2.155 2.17 0.965 2.384 2.898 2.093 1.841 3.247 2.296 2.014 2.396 2.673 2.287 2.259 2.31 2.195 2.666 2.323 2.011 2.73 1.862 2.101 1.816 1.93 2.11 2.238 2.406 2.222 1.915 2.746 1.979 2.825 1.775 2.206 2.268 2.287 2.14 1.974 1.82 2.288 2.133 MERIT_065 2.493 2.964 2.034 2.561 2.566 2.602 2.655 2.822 2.786 2.951 3.65 2.258 2.645 2.174 2.823 2.142 3.051 2.512 2.417 2.795 2.807 2.536 2.336 2.612 2.622 2.459 3.131 2.826 2.503 2.815 2.882 3.021 3.248 2.486 2.489 1.847 2.237 2.334 1.012 2.171 3.339 2.076 1.797 2.94 2.539 2.049 2.665 2.643 2.575 2.692 1.813 1.955 2.062 2.26 1.964 1.91 2.019 2.273 1.884 1.933 2.406 2.2 2.307 1.861 1.806 2.256 2.209 1.92 2.134 2.327 2.156 3.637 2.569 2.042 2.278 2.284 2.266 2.867 MERIT_066 1.938 2.833 1.699 2.661 2.715 2.432 2.51 2.708 2.573 2.912 3.354 2.432 2.667 2.239 2.946 1.875 3.347 2.566 2.25 2.663 2.564 2.307 2.33 2.685 2.481 2.225 2.532 2.498 2.901 2.957 2.621 3.094 3.598 2.466 2.637 2.15 2.075 2.201 0.985 2.096 3.322 1.813 1.897 2.883 2.391 2.16 2.501 2.6 2.439 2.472 2.494 1.99 1.983 2.335 1.874 1.975 2.07 2.057 2.072 2.113 2.316 2.442 2.713 1.849 2.328 2.481 2.265 1.906 1.878 2.413 2.149 2.115 2.182 2.227 2.196 2.259 2.152 MERIT_067 2.375 2.905 1.904 2.71 2.974 2.596 2.719 2.698 2.617 2.823 3.358 2.401 2.554 2.366 2.757 2.076 3.181 2.732 2.441 2.775 2.683 2.372 2.252 2.77 2.54 2.444 3.223 2.7 2.881 3.223 2.523 3.12 3.535 2.491 2.481 2.357 2.278 2.125 1.125 2.271 3.232 2.109 1.933 2.866 2.45 2.24 3.422 2.666 2.403 2.39 2.007 2.113 2.188 2.276 2.228 2.107 2.102 2.158 2.084 2.196 2.179 2.456 2.719 2.181 1.948 2.012 2.213 2.163 2.031 2.355 2.147 2.774 2.345 2.379 2.461 2.477 2.799 MERIT_068 2.533 3.107 1.816 2.73 3.035 2.63 2.78 3.003 2.886 3.306 3.408 2.481 2.899 2.263 3.096 1.835 2.76 2.769 2.566 2.777 2.673 2.566 2.435 3.025 2.72 2.091 2.578 2.754 3.08 3.012 2.5 3.157 3.633 2.579 2.537 1.733 2.262 2.171 0.939 2.191 3.22 1.799 2.05 2.82 2.456 2.339 2.684 2.723 2.356 3.141 2.141 2.154 2.137 2.302 1.871 2.633 2.049 2.335 2.077 2.132 2.533 2.251 2.612 2.201 2.038 2.769 2.121 2.894 1.988 2.315 2.454 2.278 2.374 2.373 2.295 2.37 2.632 MERIT_069 2.01 2.702 1.789 2.462 2.727 2.448 2.452 2.613 2.276 2.747 3.318 2.056 2.311 2.411 2.59 1.929 2.806 2.286 2.133 2.486 2.331 2.458 1.884 2.508 2.558 2.471 2.169 2.313 2.52 2.641 2.425 2.804 2.97 2.295 2.324 2.141 2.307 2.057 0.814 2.007 2.939 1.768 1.887 2.757 2.314 2.303 2.699 2.61 2.225 2.593 1.699 2.156 2.13 2.22 2.041 2.012 2.137 2.104 1.755 1.882 2.207 2.143 2.272 2.049 2.105 2.149 1.973 2.483 2.057 2.35 2.483 1.888 2.272 2.375 2.15 2.276 2.257 2.07 MERIT_070 2.733 2.849 1.941 2.73 2.887 2.824 2.693 2.837 2.9 3.35 3.71 2.313 3.03 2.645 2.719 1.879 2.999 2.847 2.578 2.846 2.825 2.631 2.392 2.88 2.874 2.401 3.025 2.695 2.857 3.08 2.434 3.146 3.461 2.555 2.609 2.262 2.251 2.389 0.818 2.182 3.438 1.988 1.949 3.187 2.326 2.527 2.789 2.994 2.606 2.72 1.904 2.196 2.133 2.384 2.308 2.082 2.139 2.055 2.132 2.348 2.725 2.422 2.503 2.091 2.269 2.276 2.317 2.325 2.208 2.373 2.241 2.353 2.763 2.376 2.58 2.385 2.354 2.338 MERIT_071 2.239 2.892 1.868 2.686 2.828 2.579 2.485 2.651 2.796 3.074 3.501 2.288 2.642 2.254 2.726 1.853 2.847 2.559 2.386 2.575 2.634 2.37 2.217 2.868 2.416 2.396 3.012 2.63 2.923 3.028 2.591 2.919 2.999 2.226 2.384 2.159 2.15 2.19 1.069 2.154 3.059 1.677 2.041 3.154 2.25 1.988 2.696 2.574 2.391 2.686 1.814 2.067 2.089 2.231 2.093 2.067 1.866 2.295 2.095 1.937 2.373 2.142 2.372 2.024 1.942 2.269 2.042 2.456 2.087 2.313 2.172 2.253 1.926 2.045 2.314 2.331 2.314 MERIT_072 2.538 3.057 2.126 2.586 2.308 2.497 2.479 2.99 2.814 3.442 4.026 2.148 2.815 2.783 2.94 2.2 2.928 2.721 2.59 2.763 2.84 2.654 2.401 2.572 2.78 2.48 2.993 2.818 2.862 3.076 2.072 2.953 2.907 2.809 2.635 2.17 2.167 2.503 0.98 2.386 3.274 1.991 1.999 3.669 2.569 2.181 2.734 3.044 2.519 2.083 2.038 2.137 2.267 2.486 2.126 2.038 2.307 1.928 2.221 1.981 2.305 2.098 2.445 2.103 2.363 2.833 2.272 2.183 2.222 2.445 2.365 2.46 2.597 2.43 2.253 2.477 2.401 MERIT_073 2.199 3.044 1.759 2.793 2.7 2.628 2.717 2.733 2.566 3.262 3.647 2.564 2.842 2.371 2.81 2.094 3.19 2.593 2.367 2.732 2.759 2.543 2.128 2.894 2.581 2.649 2.684 2.831 2.88 3.134 2.681 3.25 3.702 2.697 2.336 2.091 2.059 2.341 1.074 2.209 3.446 1.965 1.893 3.499 2.386 2.156 2.628 2.545 2.351 2.767 2.103 2.204 1.966 2.333 1.782 2.107 2.128 2.255 2.024 2.196 2.499 2.37 2.554 2.072 1.922 2.044 2.059 2.163 2.11 2.361 2.582 2.665 2.101 2.374 2.371 2.468 2.479 2.759 MERIT_074 2.925 3.129 1.89 3.034 3.452 2.85 2.842 2.802 2.761 3.185 3.713 2.218 2.779 2.193 2.931 2.21 3.19 2.85 2.595 2.784 2.679 2.45 2.25 2.809 2.559 2.453 2.951 2.836 3.059 2.978 2.578 3.391 3.286 2.344 2.36 2.331 2.502 2.202 1.005 2.111 3.603 2.151 1.966 3.269 2.424 2.186 2.736 2.827 2.56 2.538 2.21 2.214 2.267 2.436 1.867 2.011 1.982 1.916 1.823 1.968 2.144 2.368 2.891 1.992 2.083 1.994 2.134 2.772 1.959 2.344 2.168 2.182 2.604 2.376 2.284 2.362 2.45 2.517 MERIT_075 2.103 2.788 1.679 2.693 3.083 2.536 2.722 2.784 2.447 3.454 3.582 2.299 2.555 2.284 2.651 2.135 2.808 2.607 2.135 2.679 2.672 2.525 2.234 2.783 2.521 2.256 2.802 2.733 2.899 3.002 2.469 3.125 3.401 2.692 2.431 1.89 2.029 2.232 0.808 2.01 3.023 2.014 1.859 3.88 2.274 2.054 2.407 2.516 2.331 2.853 2.191 2.162 2.074 2.465 1.807 2.089 2.066 2.253 2.204 1.915 2.348 2.341 2.438 2.178 1.938 2.271 2.09 2.391 2.114 2.374 2.168 2.654 2.527 2.343 2.289 2.452 2.263 MERIT_076 2.578 2.93 1.821 2.843 2.919 2.725 2.819 2.891 2.703 3.215 3.538 2.336 2.632 2.062 2.983 1.927 3.282 2.824 2.478 2.851 2.709 2.533 2.277 3.182 2.824 2.369 3.166 2.997 3.065 3.261 2.738 3.281 3.486 2.579 2.382 2.204 2.06 2.349 0.977 1.911 3.008 1.858 2.055 2.752 2.422 2.212 3.062 2.699 2.537 2.237 1.884 2.254 2.256 2.448 1.904 2.07 2.021 2.248 2.08 2.184 2.311 2.362 2.859 2.058 2.196 2.801 2.079 2.512 2.162 2.581 2.506 2.621 2.456 2.479 2.33 2.456 2.982 MERIT_077 2.479 2.904 2.082 2.849 2.93 2.942 2.636 2.722 2.999 2.823 3.14 2.318 2.711 2.367 2.879 2.147 2.768 2.949 2.28 2.806 2.705 2.626 2.431 2.623 2.531 2.346 2.777 2.741 2.771 2.86 2.169 3.075 3.038 2.619 2.524 2.262 2.426 2.319 0.952 2.108 2.575 2.051 2.06 3.144 2.349 2.035 2.636 2.777 2.484 2.198 2.088 2.139 2.161 2.345 2.035 2.541 2.09 2.143 1.861 2.052 2.261 2.331 2.381 2.022 1.872 2.13 2.346 2.585 2.189 2.252 2.266 2.951 2.314 1.975 2.206 2.365 2.305 2.098 MERIT_078 2.175 2.854 1.893 2.583 3.067 2.285 2.541 2.764 2.77 3.482 3.428 2.268 2.709 2.351 2.673 1.955 3.44 2.863 2.325 2.633 2.726 2.381 2.241 2.813 2.503 2.823 3.431 2.527 2.851 3.107 2.15 2.994 3.599 2.63 2.203 2.252 2.219 2.251 1.165 1.999 3.76 2.079 1.838 3.001 2.489 2.162 2.539 2.753 2.556 2.431 2.208 1.93 1.777 2.138 1.97 1.975 2.019 2.191 2.012 2.04 2.297 2.477 2.655 1.848 2.292 2.469 2.006 2.446 2.073 2.539 2.202 2.728 2.54 2.568 2.481 2.321 2.327 1.914 MERIT_079 2.557 2.929 2.02 2.702 2.566 2.713 2.678 2.862 2.562 3.182 3.657 2.484 2.766 2.39 2.851 1.977 2.925 2.55 2.559 2.689 2.739 2.393 2.263 3.016 2.553 2.362 2.73 2.61 2.683 2.966 2.389 3.077 3.213 2.543 2.469 2.054 2.23 2.188 0.971 2.201 3.073 1.979 2.163 3.918 2.401 2.089 2.708 2.595 2.345 2.169 2.253 2.126 2.184 2.329 1.745 2.159 2.002 2.203 1.854 2.058 2.29 2.165 2.522 2.045 1.988 2.57 2.12 2.13 2.204 2.293 2.266 1.797 2.235 2.309 2.181 1.982 2.283 2.148 MERIT_080 1.467 2.594 1.668 2.53 2.442 2.249 2.472 2.57 2.606 2.914 3.206 2.334 2.64 2.156 2.792 1.911 3.406 2.565 2.209 2.556 2.699 2.297 2.058 2.628 2.433 2.262 2.599 2.665 2.933 3.098 2.035 2.914 3.51 2.4 2.2 1.523 2.226 2.11 0.799 2.144 3.33 1.836 1.753 2.324 2.202 2.148 2.328 2.507 2.277 2.729 1.871 1.966 1.928 2.211 2.032 2.181 2.013 2.325 2.203 1.899 2.323 2.137 2.65 2.059 2.101 1.996 2.116 1.954 1.995 2.297 2.394 2.336 1.987 2.049 2.253 2.459 2.237 2.281 MERIT_081 2.611 3.087 1.772 2.731 2.668 2.58 2.587 2.695 2.685 3.157 3.634 2.502 2.627 2.37 2.823 1.982 3.453 2.678 2.356 2.615 2.671 2.44 2.263 2.852 2.555 2.087 2.646 2.762 2.79 3.08 2.562 2.875 3.355 2.554 2.213 2.026 2.235 2.403 0.899 2.207 3.211 1.872 1.948 3.29 2.476 2.152 2.869 2.485 2.37 2.486 1.835 2.094 1.96 2.256 1.694 2.009 2.079 2.462 2.161 2.231 2.314 2.296 2.539 2.068 2.204 2.502 2.229 2.683 2.107 2.302 2.307 2.739 2.174 2.366 2.116 2.303 3.09 MERIT_082 2.254 2.764 1.578 2.449 2.744 2.307 2.397 2.633 2.449 2.578 3.23 2.171 2.337 2.008 2.75 1.805 3.208 2.629 1.846 2.421 2.315 2.245 1.932 2.316 2.242 2.276 3.184 2.432 2.817 3.035 1.909 2.815 3.481 2.068 2.235 1.797 2.749 2.075 0.926 2.081 3.35 1.629 1.796 2.463 2.19 1.69 2.745 2.704 2.269 2.305 1.735 1.959 2.075 2.103 1.861 1.814 1.999 2.091 1.723 1.987 2.353 2.116 2.077 2.018 1.852 2.321 2.03 1.851 1.898 2.019 2.227 2.897 2.279 1.807 2.219 2.233 1.871 MERIT_083 3.088 3.075 1.737 2.774 2.835 2.357 2.71 2.896 2.735 2.903 3.923 2.201 2.653 2.164 2.673 1.921 3.053 2.484 2.479 3.043 2.677 2.508 2.327 2.896 2.585 2.456 3.348 2.343 2.97 3.138 2.1 2.989 3.747 2.383 2.669 2.778 2.597 2.114 0.998 1.977 3.294 1.732 1.911 3.5 2.426 1.879 2.782 2.756 2.501 2.591 1.805 2.262 2.252 2.451 2.14 2.203 1.938 1.895 1.697 2.067 2.273 2.432 2.604 2.143 2.379 2.561 2.059 2.428 1.986 2.336 2.328 2.24 2.471 2.808 2.117 2.482 2.355 2.638 MERIT_084 2.224 2.795 1.87 2.78 2.368 2.734 2.698 2.682 2.941 2.956 3.948 2.19 2.463 2.264 2.71 2.026 3.294 2.722 2.317 2.777 2.769 2.418 2.229 2.801 2.605 2.592 3.361 2.824 2.806 2.913 2.362 3.141 3.38 2.382 2.429 1.856 2.316 2.387 1.037 1.991 3.506 1.917 1.748 2.774 2.343 2.094 2.717 2.792 2.503 2.626 2.042 2.096 1.988 2.297 1.979 2.317 2.05 2.194 1.755 1.919 2.507 2.529 2.788 1.735 2.44 2.301 2.128 2.164 2.103 2.38 2.249 2.585 2.464 2.509 2.159 2.13 2.382 2.538 MERIT_085 2.306 2.763 1.875 2.587 2.692 2.56 2.685 2.725 2.633 3.072 3.331 2.393 2.748 2.21 2.83 2.067 3.029 2.634 2.406 2.664 2.734 2.563 2.381 2.817 2.53 2.064 2.744 2.519 2.836 2.96 2.217 3.018 3.115 2.653 2.447 1.781 2.176 2.312 1.04 2.152 3.531 1.913 1.97 3.27 2.336 2.125 2.603 2.628 2.357 2.309 1.776 2.168 2.225 2.396 2.087 1.767 2.081 2.054 2.017 2.118 2.187 2.35 2.194 2.18 2.058 2.523 2.02 1.846 2.228 2.204 2.177 1.943 2.444 2.394 2.341 2.425 2.384 2.244 MERIT_086 2.25 2.888 1.781 2.586 2.816 2.544 2.44 2.533 2.525 3.025 3.591 2.109 2.388 2.085 2.527 1.856 2.911 2.69 2.093 2.374 2.414 2.164 1.985 2.401 2.524 2.355 3.381 2.471 2.605 2.813 2.391 2.921 3.28 2.383 2.106 1.933 2.099 2.107 0.842 1.906 3.324 1.63 1.896 2.034 2.2 2.046 2.637 2.571 2.37 2.344 1.722 1.947 1.792 2.258 1.838 1.841 1.892 1.922 1.679 1.77 1.989 2.331 2.406 1.715 2.275 2.348 1.961 2.141 1.884 2.177 2.111 2.095 2.295 2.066 2.188 2.04 2.174 2.379 MERIT_087 2.614 2.879 1.729 2.629 2.321 2.696 2.512 2.621 2.654 2.974 3.697 2.384 2.482 1.936 2.525 1.811 3.066 2.616 2.252 2.631 2.593 2.39 2.023 2.6 2.409 2.31 2.895 2.597 2.701 2.9 2.408 2.926 3.328 2.419 2.241 2.066 1.885 2.099 0.959 1.932 3.122 1.668 1.879 3.547 2.301 1.966 2.315 2.913 2.249 2.142 1.649 2.067 2.002 2.265 1.896 2.203 1.986 2.018 1.717 2.026 2.026 1.943 2.298 1.973 2.026 1.977 2.224 2.324 1.944 2.259 2.077 2.046 2.389 2.283 2.128 2.209 2.281 2.251 MERIT_088 2.453 2.936 1.719 2.696 2.602 2.781 2.634 2.785 2.783 3.022 3.518 2.185 2.61 2.152 2.574 1.842 2.855 2.759 2.231 2.851 2.717 2.524 2.342 2.767 2.62 2.768 3.465 2.802 2.661 3.253 2.145 3.139 3.545 2.213 2.636 2.411 2.244 2.157 0.926 2.27 3.675 1.823 1.875 2.803 2.373 2.073 2.647 2.39 2.377 2.353 2.053 2.082 2.145 2.224 1.918 2.18 2.005 2.042 2.034 1.937 2.132 2.296 2.228 2.174 2.272 2.068 2.169 2.324 2.108 2.446 2.298 2.04 2.09 2.558 2.648 2.09 2.428 1.933 MERIT_089 2.557 3.209 1.89 2.757 3.011 2.829 2.74 3.066 2.736 3.053 3.922 2.468 2.67 2.229 2.914 2.06 3.264 2.914 2.448 2.754 2.789 2.507 2.231 2.969 2.766 2.444 2.894 2.689 2.971 3.048 2.182 2.892 3.322 2.617 2.455 1.937 2.014 2.218 0.949 2.203 3.262 1.914 1.955 3.903 2.423 2.206 3.051 2.804 2.458 2.737 2.334 2.151 2.058 2.498 2.022 2.41 2.059 2.2 2.041 2.041 2.404 2.751 2.432 1.493 2.243 2.235 2.128 2.559 2.171 2.366 2.435 1.825 2.179 2.136 2.458 2.311 2.517 1.975 MERIT_090 2.367 2.945 1.758 2.733 2.784 2.722 2.602 2.821 2.685 2.961 3.568 2.313 2.547 2.242 2.834 1.992 2.846 2.671 2.284 2.616 2.546 2.433 2.274 2.9 2.624 2.544 3.093 2.774 2.954 3.088 2.56 3.151 3.487 2.4 2.507 2.253 2.395 2.359 0.988 2.06 3.425 1.799 1.925 3.469 2.383 2.095 2.503 2.694 2.561 2.467 2.09 2.246 2.189 2.358 2.053 2.144 1.936 2.114 1.546 1.8 2.089 2.246 2.404 2.052 2.1 1.985 2.067 2.374 1.964 2.292 2.32 2.294 2.611 1.892 2.196 2.476 2.284 2.95

2 1

apply inverse transform
by Martin Chadwick 24 Jun '09

24 Jun '09

Dear freesurfers, I'm new to freesurfer and have a problem using --apply_inverse_transform with the .m3z warp. Any help would be greatly appreciated: I've been running a multivariate fmri decoding study, and have used freesurfer to generate anatomical masks which I've used as ROIs for the multivariate analysis in the native space of each subject. I then took the pattern of informative voxels from each subject as a binary mask, and warped it into standard space using the freesurfer high-dimensional warp (I am looking at subcortical structures, so this level of registration accuracy is quite important) with the following command: mri_convert -ot nifti1 ~/inputimage --apply_transform ~/transforms/talairach.m3z ~/outputimage.img Which seems to have worked fine. I've then performed some overlap analyses on these warped binary images, and created a new binary image representing the between-subject overlap. What I really want to do next is to warp this back into the native space for each subject and use as an ROI for further multivariate analyses. To do this I tried the following command: mri_convert -it nifti1 -ot nifti1 ~/inputimage.img --apply_inverse_transform ~/transforms/talairach/m3z ~/outputimage.img which gave me the following error message: mriBuildVoronoiDiagramFloat: incorrect input type(s) Any ideas on what I'm doing wrong here? Many thanks, Martin

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Freesurfer June 2009