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final update
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@zclhit
zclhit committed Jan 14, 2018
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53 changes: 53 additions & 0 deletions BrainNet/JHU_level3_m2_52/JHU52_level3_new.node
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#jhu 52
27.67 23.2 25.34 1 1 Frontal_L
-28.21 25.58 24.17 1 1 Frontal_R
35.08 -42.2 42.02 1 1 parietal_L
-37.95 -39.27 42.38 1 1 parietal_R
47.35 -24.03 -11.03 1 1 Temporal_L
-49.46 -21.81 -11.8 1 1 Temporal_R
10.88 -15.14 18.13 1 1 Limbic_L
-12.15 -15.48 17.37 1 1 Limbic_R
22.08 -75.10 8.760 1 1 optical_L
-25.04 -73.10 10.15 1 1 optical_R
35.91 5.940 3.410 1 1 Insula_L
-37.65 8.330 1.800 1 1 Insula_R
20.46 3.200 5.520 1 1 BasalGang_L
-22.05 4.620 4.670 1 1 BasalGang_R
10.61 -18.46 8.630 1 1 Thalamus_L
-12.03 -17.64 8.280 1 1 Thalamus_R
14.28 0.3100 -6.310 1 1 BasalForebrain_L
-16.18 1.300 -6.710 1 1 BasalForebrain_R
9.250 -19.02 -7.070 1 1 midbrain_L
-10.60 -19.09 -7.630 1 1 midbrain_R
-21.93 -59.68 -32.92 1 1 cerebellum_L
20.26 -59.68 -32.56 1 1 cerebellum_R
6.790 -27.04 -27.98 1 1 Pons_L
-8.710 -26.86 -28.12 1 1 Pons_R
2.120 -41.64 -51.44 1 1 Medulla_L
-5.640 -40.95 -51.52 1 1 Medulla_R
22.29 15.01 23.93 1 1 AnteriorWM_L
-22.97 15.14 22.37 1 1 AnteriorWM_R
26.80 -33.78 29.34 1 1 PosteriorWM_L
-28.48 -34.25 29.76 1 1 PosteriorWM_R
10.49 -11.37 19.38 1 1 CorpusCallosum_L
-11.07 -11.19 18.45 1 1 CorpusCallosum_R
29.83 -24.93 6.150 1 1 InferiorWM_L
-31.48 -20.92 5.200 1 1 InferiorWM_R
15.47 -13.87 11.59 1 1 LimbicWM_L
-16.74 -13.51 11.74 1 1 LimbicWM_R
15.51 -10.79 12.61 1 1 LatervalVentricle_L
-16.77 -9.380 12.23 1 1 LatervalVentricle_R
-0.8800 -11.54 3.870 1 1 III_ventricle
28.51 28.81 31.43 1 1 FrontSul_L
-29.98 29.32 29.99 1 1 FrontSul_R
40.31 -20.11 52.89 1 1 CentralSul_L
-36.07 -20.00 56.89 1 1 CentralSul_R
51.69 -13.45 5.270 1 1 TempSul_L
-54.19 -10.88 3.120 1 1 TempSul_R
32.04 -47.68 51.70 1 1 parietsul_L
-28.06 -47.03 53.35 1 1 parietsul_R
1.360 5.240 36.99 1 1 CinguSul_L
-2.550 5.190 36.44 1 1 CinguSul_R
17.92 -75.14 29.11 1 1 occiptsul_L
-18.16 -73.11 27.38 1 1 occiptsul_R
-1.230 -44.04 -32.08 1 1 IV_ventricle
31 changes: 31 additions & 0 deletions BrainNet/ROI_coordinate_cal.m
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clc;clear all;close all;
filename = 'G:\所有的文档\DTI神经纤维成像方法\29Male\output\29Male_M2_MNI_level3_type2.img';
[hdr,filetype,fileprefix,machine] = load_nii_hdr(filename);
[img,hdr] = load_nii_img(hdr,filetype,fileprefix,machine);
co_x = zeros(90,1);
co_y = zeros(90,1);
co_z = zeros(90,1);
count = zeros(90,1);
for roi=1:90
for i=1:181
for j=1:217
for k=1:181
if(img(i,j,k)==roi)
co_x(roi)=co_x(roi)+i;
co_y(roi)=co_y(roi)+j;
co_z(roi)=co_z(roi)+k;
count(roi)=count(roi)+1;
end
end
end
end
end
for i=1:90
co_x(i)=co_x(i)/count(i);
co_y(i)=co_y(i)/count(i);
co_z(i)=co_z(i)/count(i);
end
corrdinate=zeros(90,3);
corrdinate(:,1)=co_x-91;
corrdinate(:,2)=co_y-125;
corrdinate(:,3)=co_z-72;
90 changes: 90 additions & 0 deletions BrainNet/new_co.txt
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2.7670000e+01 2.3200000e+01 2.5340000e+01
-2.8210000e+01 2.5580000e+01 2.4170000e+01
3.5080000e+01 -4.2200000e+01 4.2020000e+01
-3.7950000e+01 -3.9270000e+01 4.2380000e+01
4.7350000e+01 -2.4030000e+01 -1.1030000e+01
-4.9460000e+01 -2.1810000e+01 -1.1800000e+01
1.0880000e+01 -1.5140000e+01 1.8130000e+01
-1.2150000e+01 -1.5480000e+01 1.7370000e+01
2.2080000e+01 -7.5100000e+01 8.7600000e+00
-2.5040000e+01 -7.3100000e+01 1.0150000e+01
3.5910000e+01 5.9400000e+00 3.4100000e+00
-3.7650000e+01 8.3300000e+00 1.8000000e+00
2.0460000e+01 3.2000000e+00 5.5200000e+00
-2.2050000e+01 4.6200000e+00 4.6700000e+00
1.0610000e+01 -1.8460000e+01 8.6300000e+00
-1.2030000e+01 -1.7640000e+01 8.2800000e+00
1.4280000e+01 3.1000000e-01 -6.3100000e+00
-1.6180000e+01 1.3000000e+00 -6.7100000e+00
9.2500000e+00 -1.9020000e+01 -7.0700000e+00
-1.0600000e+01 -1.9090000e+01 -7.6300000e+00
-2.1930000e+01 -5.9680000e+01 -3.2920000e+01
2.0260000e+01 -5.9680000e+01 -3.2560000e+01
6.7900000e+00 -2.7040000e+01 -2.7980000e+01
-8.7100000e+00 -2.6860000e+01 -2.8120000e+01
2.1200000e+00 -4.1640000e+01 -5.1440000e+01
-5.6400000e+00 -4.0950000e+01 -5.1520000e+01
2.2290000e+01 1.5010000e+01 2.3930000e+01
-2.2970000e+01 1.5140000e+01 2.2370000e+01
2.6800000e+01 -3.3780000e+01 2.9340000e+01
-2.8480000e+01 -3.4250000e+01 2.9760000e+01
1.0490000e+01 -1.1370000e+01 1.9380000e+01
-1.1070000e+01 -1.1190000e+01 1.8450000e+01
2.9830000e+01 -2.4930000e+01 6.1500000e+00
-3.1480000e+01 -2.0920000e+01 5.2000000e+00
1.5470000e+01 -1.3870000e+01 1.1590000e+01
-1.6740000e+01 -1.3510000e+01 1.1740000e+01
1.5510000e+01 -1.0790000e+01 1.2610000e+01
-1.6770000e+01 -9.3800000e+00 1.2230000e+01
-8.8000000e-01 -1.1540000e+01 3.8700000e+00
2.8510000e+01 2.8810000e+01 3.1430000e+01
-2.9980000e+01 2.9320000e+01 2.9990000e+01
4.0310000e+01 -2.0110000e+01 5.2890000e+01
-3.6070000e+01 -2.0000000e+01 5.6890000e+01
5.1690000e+01 -1.3450000e+01 5.2700000e+00
-5.4190000e+01 -1.0880000e+01 3.1200000e+00
3.2040000e+01 -4.7680000e+01 5.1700000e+01
-2.8060000e+01 -4.7030000e+01 5.3350000e+01
1.3600000e+00 5.2400000e+00 3.6990000e+01
-2.5500000e+00 5.1900000e+00 3.6440000e+01
1.7920000e+01 -7.5140000e+01 2.9110000e+01
-1.8160000e+01 -7.3110000e+01 2.7380000e+01
-1.2300000e+00 -4.4040000e+01 -3.2080000e+01
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
6 changes: 3 additions & 3 deletions Plot_Gretna/hitsz_gretn_PlotNetwork_2GroupCompare.m
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num_G=2;

%分别确定G1与G2的Network Analysis网络参数txt的文件位置
pathname_G1 = 'G:\玄武医院数据\2017_10_01PD_NC_XXG\NCnetwork';
pathname_G2 = 'G:\玄武医院数据\2017_10_01PD_NC_XXG\xiaoxueguanNETWORK';
pathname_G1 = 'G:\玄武医院数据\PD\PD画图\parameter_nc';
pathname_G2 = 'G:\玄武医院数据\PD\PD画图\parameter_pd';
pathname_G3 = 'G:\韩璎课题组数据\SCD-NC\Result_path';
pathname_P='G:\玄武医院数据\2017_10_01PD_NC_XXG\xiaoxueguan_NC';
pathname_P='G:\玄武医院数据\PD\PD画图\p_value';

% 读取G1的数据
dirs_G1=dir([pathname_G1, '\*.txt']);
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102 changes: 102 additions & 0 deletions ROI_volmue_283_MNI_level2_20.m
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%function:统计一个目录下所有分割结果中283个脑区的ROI大小，放到一个txt文档中
clc;clear all;close all;
clc;
clear all;
close all;

load('M2lookuptable.mat'); %para named 'a'

b = a(:,5);
hashset = unique(b);

res = a;
row_num = zeros(21,1);
cnt = 1;
for z =1:283
% flag = 0;
dim = size(hashset);
if(dim(2)~=0)
for j = 1:dim(1)
if isequal(res(z,5),hashset(j))
% flag = 1;
row_num(cnt)=z;
hashset(j)=[];
cnt=cnt+1;
break;
end
end
end
% if(flag==0)
% res(i,:)=[];
% end
end
%after that we got important row num in array row_num

cnt = 21;
for z=283:-1:1
if(z~=row_num(cnt))
res(z,:)=[];
else
cnt=cnt-1;
end
end
%开始进行回溯
path = 'G:\玄武医院数据\韩璎课题组数据\NC\NC_M2_level2_type1';
cd(path)
dirs_G1=dir([path, '\*.*']);
dircell_G1=struct2cell(dirs_G1)' ;
filenames_G1=dircell_G1(3:length(dircell_G1),1);
for m=1:length(filenames_G1)
floder_name = char(filenames_G1(m));
cd(floder_name);
disp(floder_name);
cd('output');
dirs_G2=dir;
dircell_G2=struct2cell(dirs_G2)' ;
filenames_G2=dircell_G2(3:length(dircell_G2),1);
len = length(filenames_G2);
for j = 1:len

filenamesIS = char(filenames_G2(j));
jamar = strfind(filenamesIS,'283Labels_M2_MNI.img');
if length(jamar) ~= 0

[hdr,filetype,fileprefix,machine] = load_nii_hdr(filenamesIS);
[img,hdr] = load_nii_img(hdr,filetype,fileprefix,machine);
%开始回溯步骤
for indexer=1:21
for img_index = 1:283
if isequal(res(indexer,5),a(img_index,5))
if indexer~= 17
index=(img==img_index); %index是图像中对应label number的体素序号
for t=img_index
index1=(img==t);
index=index|index1;
end
img(index)=indexer;
else
index=(img==img_index); %index是图像中对应label number的体素序号
for t=img_index
index1=(img==t);
index=index|index1;
end
img(index)=0;
end
end
end
end
%从1到20排列，因为总共有20个脑区
Lables=unique(img);
for z=1:20
temp=Lables(z); %label number
index=(img==temp); %index是图像中对应label number的体素序号
img(index)=z-1;
end

CC_286_L=make_ana(img); %savemat
savename = strcat(floder_name,'_M2_MNI_level2_type1');
save_untouch_nii(CC_286_L,savename);
cd(path);
end
end
end
4 changes: 2 additions & 2 deletions level5_to_level3_M2_type2.m
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Expand Up @@ -43,7 +43,7 @@
end
end
%开始进行回溯
path = 'G:\玄武医院数据\Image_Out\画图分割结果';
path = 'G:\所有的文档\DTI神经纤维成像方法';
cd(path)
dirs_G1=dir([path, '\*.*']);
dircell_G1=struct2cell(dirs_G1)' ;
Expand All @@ -60,7 +60,7 @@
for j = 1:len

filenamesIS = char(filenames_G2(j));
jamar = strfind(filenamesIS,'283Labels_MNI.img');
jamar = strfind(filenamesIS,'283Labels_M2_MNI.img');
if length(jamar) ~= 0

[hdr,filetype,fileprefix,machine] = load_nii_hdr(filenamesIS);
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34 changes: 34 additions & 0 deletions yecf/coregis_spm12.m
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%-----------------------------------------------------------------------
% Chenfei Ye updated:10/16/2016
% function coregis_spm12(dir_ref,dir_source,dir_other) can run
% intra-subject coregsitering based on SPM12
% Input:
% dir_ref: reference image directory
% dir_source: source image directory
% dir_other: other image dicrectory
% This function will generate the coregistered images
% The default parameters is using normalized mutual information, 12-parametric transformation and trilinear interpolation
% Below are formats of some variables.
% jobs{1}.spm.spatial.coreg.estwrite.ref = {'D:\Subjects\0339350_118_LAUEAR\0339350_118_LAUEAR_150914\scans\301-MPRAGE\oWIPMPRAGESENSE.nii,1'};
% jobs{1}.spm.spatial.coreg.estwrite.source = {'D:\Subjects\0339350_118_LAUEAR\0339350_118_LAUEAR_150914\scans\1201-Skinny_T2AX\WIPSkinnyT2AXSENSEA.nii,1'};
% jobs{1}.spm.spatial.coreg.estwrite.other = {'D:\Subjects\0339350_118_LAUEAR\0339350_118_LAUEAR_150914\scans\1201-Skinny_T2AX\resources\DICOM\files\T2_map.nii,1'};
%-----------------------------------------------------------------------
function coregis_spm12(dir_ref,dir_source,dir_other)
spm_get_defaults;
global defaults
spm_jobman('initcfg');
%% using default parameters in SPM12
jobs{1}.spm.spatial.coreg.estwrite.ref =dir_ref;
jobs{1}.spm.spatial.coreg.estwrite.source =dir_source;
jobs{1}.spm.spatial.coreg.estwrite.other =dir_other;
jobs{1}.spm.spatial.coreg.estwrite.eoptions.cost_fun = 'nmi';
jobs{1}.spm.spatial.coreg.estwrite.eoptions.sep = [4 2];
jobs{1}.spm.spatial.coreg.estwrite.eoptions.tol = [0.02 0.02 0.02 0.001 0.001 0.001 0.01 0.01 0.01 0.001 0.001 0.001];
jobs{1}.spm.spatial.coreg.estwrite.eoptions.fwhm = [7 7];
jobs{1}.spm.spatial.coreg.estwrite.roptions.interp = 1;
jobs{1}.spm.spatial.coreg.estwrite.roptions.wrap = [0 0 0];
jobs{1}.spm.spatial.coreg.estwrite.roptions.mask = 0;
jobs{1}.spm.spatial.coreg.estwrite.roptions.prefix = 'r';

spm_jobman('run',jobs);
clear jobs

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