fsbrain的学习笔记

之前分析皮层数据，基于freesurferformats构建了一套自己的脚本，后来偶然间发现了fsbrain，把这些都封装好了，并且还包括可视化。因此，就抽空来学习学习。

1

subjects_dir = fsbrain::get_optional_data_filepath("subjects_dir");
subjects_list = c("subject1", "subject2");
subject_id = 'subject1';       # for function which use one subject only

1
2
3
4

subjects_dir的截图：
| |
|
|–|–|

可以看出，subject文件夹是经freesurfer处理后的格式化输出。

2

# Read from a simple ASCII subjects file (one subject per line, typically no header):
sjl = read.md.subjects("~/data/study1/subjects.txt", header = FALSE);

# Extract the subject identifiers from a FreeSurfer Group Descriptor file, used for the GLM:
sjl = read.md.subjects.from.fsgd("~/data/study1/subjects.fsgd");

# Use the subject identifier column from a demographics file, i.e., a CSV file containing demographics and other covariates (typically age, gender, site, IQ):
demographics = read.md.demographics("~/data/study1/demogr.csv", header = TRUE);
sjl = demographics$participant_id; # or whatever your subject identifier column is called.
1
2
3
4
5
6
7
8
9

这个就是介绍了几种读取subjectID的方法，有一般的txt格式，有freesurfer的fsgd格式，有csv格式。

3

groupdata_nat = group.morph.native(subjects_dir, subjects_list, "thickness", "lh");
1

group.morph.native函数用法：
group.morph.native( subjects_dir, subjects_list, measure, hemi, format = “curv”, cortex_only = FALSE )

这个函数就很方便了，能循环读取文件夹下所有被试。

length(groupdata_nat[[subject_id]])
> [1] 149244
mean(groupdata_nat[[subject_id]])
> [1] 2.437466
1
2
3
4

4

groupdata_std = group.morph.standard(subjects_dir, subjects_list, "thickness", "lh", fwhm="10");
1

这个就是平滑，使用平滑核。

5

# Load the full hemisphere data, including media wall:
fulldata = group.morph.native(subjects_dir, subjects_list, "thickness", "lh");
mean(fulldata$subject1);

# This time, restrict data to the cerebral cortex (the medial wall vertices will get NA values):
cortexdata = group.morph.native(subjects_dir, subjects_list, "thickness", "lh", cortex_only=TRUE);
mean(cortexdata$subject1, na.rm=TRUE);
1
2
3
4
5
6
7

cortex_only就是将lh.cortex.label传递到函数内，使得内壁等非皮质区域的数值变为0。但是，前提是需要label文件下有cortex.label文件，一般freesurfer都会自动生成。

6

# zipped all the group subjects into one file in the 4th- dimision.
write.group.morph.standard.sf('~/mystudy/group_thickness_lh_fwhm10.mgz', groupdata_std);
#groupdata_std = group.morph.standard(subjects_dir, subjects_list, "thickness", "lh", fwhm="10");  # slow
groupdata_std = group.morph.standard.sf('~/mystudy/group_thickness_lh_fwhm10.mgz');                # fast
1
2
3
4

这个就是为了更快的处理数据，之前groupdata是list格式，现在把他们全部压缩起来，形成数组，这样操作起来更快，变成了freesurfer的mgz格式。

7

grouplabels = group.label(subjects_dir, subjects_list, "cortex.label", hemi='lh');
1

从文件夹中加载label文件。

surface = 'white';
hemi = 'both';
atlas = 'aparc';
region = 'bankssts';

# Create a mask from a region of an annotation:
lh_annot = subject.annot(subjects_dir, subject_id, 'lh', atlas);
rh_annot = subject.annot(subjects_dir, subject_id, 'rh', atlas);
lh_label = label.from.annotdata(lh_annot, region);
rh_label = label.from.annotdata(rh_annot, region);
lh_mask = mask.from.labeldata.for.hemi(lh_label, length(lh_annot$vertices));
rh_mask = mask.from.labeldata.for.hemi(rh_label, length(rh_annot$vertices));

# Edit the mask: add the vertices from another region to it:
region2 = 'medialorbitofrontal';
lh_label2 = label.from.annotdata(lh_annot, region2);
rh_label2 = label.from.annotdata(rh_annot, region2);
lh_mask2 = mask.from.labeldata.for.hemi(lh_label2, length(lh_annot$vertices),
  existing_mask = lh_mask);
rh_mask2 = mask.from.labeldata.for.hemi(rh_label2, length(rh_annot$vertices),
  existing_mask = rh_mask);
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

这个就是加载ROI了。

groupannot = group.annot(subjects_dir, subjects_list, 'lh', 'aparc');
cat(sprintf("The left hemi of subject2 has %d vertices, and vertex 10 is in region '%s'.\n", length(groupannot$subject2$vertices), groupannot$subject2$label_names[10]));
# output: The left hemi of subject2 has 149244 vertices, and vertex 10 is in region 'lateraloccipital'.
1
2
3

这个就是加载ROI名字了。

8

mean_thickness_lh_native = group.morph.agg.native(subjects_dir, subjects_list, "thickness", "lh", agg_fun=mean);
mean_thickness_lh_native;
# output:
#  subject_id hemi measure_name measure_value
#1   subject1   lh    thickness      2.437466
#2   subject2   lh    thickness      2.437466
1
2
3
4
5
6

这个就是高级用法，agg：aggregate，可以添加一些参数直接对他们运用，类似于lapply函数。

mean_thickness_lh_std = group.morph.agg.standard(subjects_dir, subjects_list, "thickness", "lh", fwhm="10", agg_fun=mean);
mean_thickness_lh_std;
# output:
  subject_id hemi measure_name measure_value
1   subject1   lh    thickness       2.32443
2   subject2   lh    thickness       2.32443
1
2
3
4
5
6

agg_nat = group.multimorph.agg.native(subjects_dir, subjects_list, c("thickness", "area"), c("lh", "rh"), agg_fun = mean);
head(agg_nat);
# output:
#  subject_id hemi measure_name measure_value
#1   subject1   lh    thickness     2.4374657
#2   subject2   lh    thickness     2.4374657
#3   subject1   lh         area     0.6690556
#4   subject2   lh         area     0.6690556
#5   subject1   rh    thickness     2.4143047
#6   subject2   rh    thickness     2.4143047
1
2
3
4
5
6
7
8
9
10

这个就是在原始空间进行一些数据的汇总

data_std = group.multimorph.agg.standard(subjects_dir, subjects_list, c("thickness", "area"), c("lh", "rh"), fwhm='10', agg_fun = mean);
head(data_std);
# output:
#  subject_id hemi measure_name measure_value
#1   subject1   lh    thickness     2.3244303
#2   subject2   lh    thickness     2.3244303
1
2
3
4
5
6

这个是在标准空间进行一些数据的汇总

data_std_cortex = group.multimorph.agg.standard(subjects_dir, subjects_list, c("thickness", "area"), c("lh", "rh"), fwhm='10', agg_fun = mean, cortex_only=TRUE,  agg_fun_extra_params=list("na.rm"=TRUE));
head(data_std_cortex);
1
2

这个是屏蔽了内壁以后的数据统计，但是注意的是，na.rm一定是需要的。

ROI查看

atlas = 'aparc';         # or 'aparc.a2009s', or 'aparc.DKTatlas'.
measure = 'thickness';
region_means_native = group.agg.atlas.native(subjects_dir, subjects_list, measure, "lh", atlas, agg_fun = mean);
head(region_means_native[,1:6]);
# output:
#          subject bankssts caudalanteriorcingulate caudalmiddlefrontal   cuneus entorhinal
#subject1 subject1 2.485596                 2.70373            2.591197 1.986978   3.702725
#subject2 subject2 2.485596                 2.70373            2.591197 1.986978   3.702725
1
2
3
4
5
6
7
8

这个是在原始空间查看ROI

region_means_std = group.agg.atlas.standard(subjects_dir, subjects_list, measure, "lh", atlas, fwhm = '10', agg_fun = mean);
head(region_means_std[1:5]);
# output:
#          subject bankssts caudalanteriorcingulate caudalmiddlefrontal   cuneus
#subject1 subject1 2.583408                2.780666            2.594696 2.018783
#subject2 subject2 2.583408                2.780666            2.594696 2.018783
1
2
3
4
5
6

这个是在标准空间查看ROI

地图集

FreeSurfer 根据您的受试者的三个不同的脑图谱计算脑表面分区：Desikan-Killiany 图集（‘aparc’ 文件，Desikan等人，2006），Destrieux 图集（‘aparc.a2009s’ 文件，Destrieux等al. , 2010) 和 DKT 或 Mindboggle 40 地图集（‘aparc.DKTatlas40’ 文件）

surface = 'white';
hemi = 'both';
atlas = 'aparc';  # one of 'aparc', 'aparc.a2009s', or 'aparc.DKTatlas40'.
region = 'bankssts';

# Create a label from a region of an annotation or atlas:
lh_annot = subject.annot(subjects_dir, subject_id, 'lh', atlas);
rh_annot = subject.annot(subjects_dir, subject_id, 'rh', atlas);
lh_label = label.from.annotdata(lh_annot, region);
rh_label = label.from.annotdata(rh_annot, region);
1
2
3
4
5
6
7
8
9
10

结果映射

hemi = "lh"               # 'lh' or 'rh'
atlas = "aparc"           # an atlas, e.g., 'aparc', 'aparc.a2009s', 'aparc.DKTatlas'

# Some directory where we can find fsaverage. This can be omitted if FREESURFER_HOME or SUBJECTS_DIR is set, the function will find fsaverage in there by default. Also see the function download_fsaverage().
template_subjects_dir = "~/software/freesurfer/subjects";    

region_value_list = list("bankssts"=0.9, "precuneus"=0.7, "postcentral"=0.8, "lingual"=0.6);

ret = fsbrain::write.region.values.fsaverage(hemi, atlas, region_value_list, output_file="/tmp/lh_spread.mgz", template_subjects_dir=template_subjects_dir, show_freeview_tip=TRUE);
1
2
3
4
5
6
7
8
9

可视化

vis.subject.annot(subjects_dir, 'subject1', 'aparc', 'both', views=c('si'));
1

可视化标签

surface = 'white';
hemi = 'both';
label = 'cortex.label';
vis.subject.label(subjects_dir, subject_id, label, hemi);
1
2
3
4

可视化ROI

atlas = 'aparc';
template_subject = 'fsaverage';
# Some directory where we can find the template_subject. This can be omitted if FREESURFER_HOME or SUBJECTS_DIR is set and the template subject is in one of them. In that case, the function will find fsaverage in there by default. Also see the function download_fsaverage().
template_subjects_dir = "~/software/freesurfer/subjects";    # adapt to your machine


# For the left hemi, we manually set data values for some regions.
lh_region_value_list = list("bankssts"=0.9, "precuneus"=0.7, "postcentral"=0.8, "lingual"=0.6);

# For the right hemisphere, we do something a little bit more complex: first get all atlas region names:
atlas_region_names = get.atlas.region.names(atlas, template_subjects_dir=template_subjects_dir, template_subject=template_subject);
# As mentioned above, if you have fsaverage in your SUBJECTS_DIR or FREESURFER_HOME is set, you could replace the last line with:
#atlas_region_names = get.atlas.region.names(atlas);

# OK, now that we can check all region names. We will now assign a random value to each region:
rh_region_value_list = rnorm(length(atlas_region_names), 3.0, 1.0);         # create 36 random values with mean 3 and stddev 1
names(rh_region_value_list) = atlas_region_names;                           # use the region names we retrieved earlier

# Now we have region_value_lists for both hemispheres. Time to visualize them:
vis.region.values.on.subject(template_subjects_dir, template_subject, atlas, lh_region_value_list, rh_region_value_list);
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

可视化时候，使用背景

subjects_dir = find.subjectsdir.of("fsaverage")$found_at;
subject_id = 'fsaverage';

lh_demo_cluster_file = system.file("extdata", "lh.clusters_fsaverage.mgz", package = "fsbrain", mustWork = TRUE);
rh_demo_cluster_file = system.file("extdata", "rh.clusters_fsaverage.mgz", package = "fsbrain", mustWork = TRUE);
lh_clust = freesurferformats::read.fs.morph(lh_demo_cluster_file);   # contains a single positive cluster
rh_clust = freesurferformats::read.fs.morph(rh_demo_cluster_file);   # contains two negative clusters
vis.symmetric.data.on.subject(subjects_dir, subject_id, lh_clust, rh_clust, bg="sulc");
1
2
3
4
5
6
7
8

输出

fsbrain.set.default.figsize(1200, 1200);
1

自己改编的代码，以适应dpabisurf的输出

subject_dir = 'Area_dataset/fsaverage5/' # folder generated by dpabisurf
subject_id = read.csv('subject_info.csv')$SubjectID

subject.l = list.files(subject_dir, pattern = "(hemi-L)", full.names = T) # read files
subject.r = list.files(subject_dir, pattern = "(hemi-R)", full.names = T) 

# read function
Group_Morph_Standard = function(subject_file, subject_list){
  # subject_file: L or R, should be defined early
  # subject_list: subject ID, list
  subject_data = list()
  for(i in 1:length(subject_list)){
    subject_data[[subject_list[i]]] = readgii(subject_file[i])[[1]][[1]][ ,1]
  }
  return(subject_data)
}

groupdata_stand = Group_Morph_Standard(subject.l, subject_list=subject_id)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

补充-ubuntu安装fsbrain

安装fsbrain的时候，提示没有magick。
1.先去https://www.imagemagick.org/download/ImageMagick.tar.gz下载文件
2.解压 tar -xzvf ImageMagick-7.0.8-15； 进入目录 cd ImageMagick-7.0.8-15； 执行 ./configure，执行结束后在结尾可以看到已链接到tif
执行 make && make install
3.提示没有libdjvu/ddjvuapi.h no such file or directory。去下载libdjvu.
4.进入djvulibre-master文件夹，输入bash autogen.sh，然后sudo make install
5.重新进入ImageMagick，然后输入 sudo ./configure, sudo make && sudo make install
6.sudo ldconfig /usr/local/lib
这样，就安装好了ImageMagick了.

使用IDE的时候，报错“/lib/x86_64-linux-gnu/libstdc++.so.6”错误，这个是因为旧版本的libstdc占据了环境，新的libstdc没有导入。操作如下：
1.首先寻找，“sudo find / -name “libstdc++.so.6*””
2.可以看到最新的有miniconda的libstdc++.so.30
3.把这个libstdc++.so.30用sudo 复制到/lib/x86_64-linux-gnu/下面
4.删除原来的: sudo rm -rf libstdc++.so.6
5.制作软连接：sudo ln -s libstdc++.so.6.0.30 libstdc++.so.6
OK, enjoy it.

专题补充，fsbrain可视化

基础信息——fsbrain的逻辑
subject_dir 是freesurfer的re-con all运行结束后，自动生成的freesurfer文件夹，里面有fsaverage, fsaverage5, sub-sub001… 等等。
subject_list 是被试的名字，例如sub001, sub002…

lh_annot = subject.annot(subjects_dir, subject_id, ‘lh’, atlas=‘aparc.a2009s’);

lh_annot 是读取了模板以后，所有ROI及其顶点信息（例如75个ROI及12424个顶点）。

lh_label是annot中ROI等于roi的顶点（例如S_orbital_lateral的l_label是430个）。

lh_label = label.from.annotdata(lh_annot, region);

lh_mask是label中，所有label的顶点变为false，其他变为true。

lh_mask = mask.from.labeldata.for.hemi(lh_label, length(lh_annot$vertices));

freesurfer自动生成的freesurfer文件夹下的sub-sub001是在原始空间上，拥有125221
而dpabi的results文件夹下的fsaverage是163842个顶点，fsaverage5是10242。所以其实原始空间处于中等地位，fsaverage是把原始空间拉大了，fsaverage5把原始空间缩小了。

vis.symmetric.data.on.subject(subjects_dir, subject_id, lh_clust, rh_clust, bg="curv_light");
1