vbm8使用手册 中文

http://wenku.baidu.com/view/ea101c30eefdc8d376ee323f.html

vbm5处理流程

http://wenku.baidu.com/view/cd9b5ac20c22590102029d98.html?re=view

vbm8使用手册 英文

静息态论坛的一些讨论：

http://restfmri.net/forum/node/1997全脑体积的影响作为协变量去除掉，但是应该使用原始空间的c1还是使用wc1或者mwc1作为协变量呢？

http://restfmri.net/forum/node/1345 How

to get a mean grey matter map as a mask?

mwc1***.img的是灰质体积wc1***.img的是灰质密度mwc2***.img的是白质体积wc2***.img的是白质密度

c3那个是csf

http://restfmri.net/forum/node/1784 你说的各种segmentation的办法，现在有直接分割，即vbm和spm里面的segmentation，我记得这种segmentation叫unified

segmentation，

http://restfmri.net/forum/node/2014 wc1*(wc2*)，mwc1*(mwc2*)及smwc1*(smwc2*)用于后续计算灰质(白质)密度和体积。其余的都可以删掉。

http://restfmri.net/forum/node/1686 我想加入灰质萎缩做协变量，那么是加入wc1*图像还是mwc1图像？mwc1图像是灰质密度还是灰质体积？谢谢指导

dparsf-A 版本里也有vbm的处理模板。

全脑体积计算软件：volume one和IBASPM-----vbm可以计算

==================================================================

VBM-学习汇总

==================================================================

非常感谢Li老师的耐心指导VBM的学习。我以前误认为：在功能像与结构像进行配准，结构像分割的那部的数据即可用于VBM分析。Dpabi-dparsfA有对VBM的处理功能，非常简单；FSL-VBM；SPM-VBM如下将详细介绍：

prepare your T1-weighted images in

the right format

fslvbm_1_bet -

carry out brain extraction on all T1 images

fslvbm_2_template -

create the study-specific symmetric grey matter

template 生成模板这步：需要将不同组人数不同的弄成等组人数，来产生模板。即：A组15人，B组20人。那生成模板时，B组要选取15人，A组继续使用15人，即等组人数来生成模板。----这也是与SPM-VBM不同的地方：即SPM-VBM使用该程序里默认模板，不需要生成组模板再进行后续分析。

fslvbm_3_proc -

register all the grey matter images to the template, modulate and

smooth them with different kernel sizes and finally runs an initial

GLM analysis for qualitative evaluation

randomise - carry

out voxelwise GLM analysis using permutation testing

SPM-VBM的汇总(基于VBM8-manual的汇总)

大致流程：“preprocessing---> quality

check ---> smoothing---> statistical analysis”

其中，preprocessing主要基于VBM下Estimate and write文件

quality  check主要基于VBM下check data quality文件

Smoothing和statistical analysis主要基于SPM8的工作目录进行。

preprocessing主要基于VBM下Estimate and write文件可以直接用默认文件：在manual中，也详细介绍了非默认的方法。

1.

Estimation Options-对于非儿童被试，都选择默认参数，对于儿童被试需要制作自己的模板customized

Tissue Probability

Maps。关于儿童这块制作，manual后面有详细介绍。

2.

Extended Options-可以选择high-dimensional DARTEL

normalization，而非默认的low—dimensional: SPM default. 即采用DARTEL的方法；对于非脑组织(即头皮之类的)的去除，可以选择light

cleanup或thorough cleanup(彻底的清除)。这对一些脑萎缩的头部处理非常有用。之后，有两种去噪的方法，选择默认的Denoising

(multi-threaded).MRF也是选择默认数值0.15即可，但如填0就表示两种SANLM&MRF两种滤波都不选择操作。

3.

Writing Options 是分别对GM/WM和CSF进行操作。注意最好选择默认的“Modulated

normalized-non linear only”.

该步骤可根据个体脑大小来对比the

absolute amount of tissue. 这步骤很关键，必须选择这个。

但如果不选择“Modulated normalized-non linear

only”的话，“Affine+non-linear”&“global normalization”(这步是后续的统计模型中操作，具体看manual介绍)的组合操作就与“Modulated

normalized-non linear only”

操作相似！或者选择“Affine+non-linear”与numeric

brain volume为covariate。但后面这两组方法，都不及“Modulated

normalized-non linear only”。

“Native space”指在原始脑数据中产生tissue

class images in spatial。该方法可以估计global tissue volumes全脑体积(即GM+WM+CSF)，但由于其失去全脑voxel-wise，故在VBM处理中不适合做。那不做此步VBM中GM/WM和CSF可以选择它“Native

space”(No)，那在“Estimate

and write”里每一名被试生成的“*_seg8.txt”文件里就含有每一名被试的GM、WM、CSF的值，将其相加即为全脑体积，即操作步骤为VBM-data

presentation-calculate raw volume for GM/WM/CSF。-----不过，这步骤并不影响结果，可以选择不做，那不做此步VBM中GM/WM和CSF可以选择它“Native

space”(No)。

“Normalized” 在脑模板中产生tissue class images in spatial。这对于VBM分析非常有用。所以VBM中GM/WM和CSF可以选择它“Normalized”(No)。-----不过，这步骤并不影响结果，可以选择不做。

DARTEL export选择affine的方法。

“bias

corrected image volume”用于移除MRI inhomogeneities和噪音，并将其写进normalized

或

native 空间。这步骤对于quality control和产生所有组的normalized T1 images非常有用，用于display.

“ partial volume effect

(PVE) label image volume”写进”normalized”或“native space“，该步骤作为quality control和将来用image重构surface非常有用.

“Jacobian

determinant”将每个voxel写进normalized space 这步骤在FSL里面也有。选择“Jacobian

determinant”(YES)。

“deformation

fields”----inverse + forward 原始空间和标准空间相互转换，用于后续研究使用。

之后运行，质量监控这步就是选择“wm*”文件查看，对于不合格的数据。就得重新操作，这种操作类似dparsfa里面的reorient，将结构像的调整至原始点。

注意：最后标准化和smooth后会产生一个1.5*1.5*1.5的数据,以进行统计分析时，

a.可将每个被试用于统计分析的数据进行reslice 2*2*2 (结构像一般都是重新采样成这体素大小的，我尝试过采样成1*1*1的结果，统计分析后结果矩阵太大，都无法用rest软件查看结果；而采样成3*3*3的结果时，有时结果跑到脑外去了。)，之后，再用一个“与重采样相同的dimension，相同的voxel，相同的origin的灰质mask或全脑mask(即dimension91*109*91;voxel2*2*2;orgin:46,64,37)”进行统计分析；

b.或者将smooth后会产生一个1.5*1.5*1.5的数据，以Estimation

Options中的Tissue Probability

Maps(TPM，它共有6层，第一层为灰质，第二层为白质...可以用spm里面reg查看，点击图像后，再点击1：6)的第一层灰质为mask进行统计分析，之后再将结果文件重新采样成2*2*2的结果进行查看。

Note:Estimation

Options中的Tissue Probability

Maps(TPM，它共有6层，第一层为灰质，第二层为白质...可以用spm里面reg查看，点击图像后，再点击1：6),我们尝试将TPM的第一层提取出来，之后重新采样了，发现：重新采样后的图像的origin为46,55,46与smooth后会产生一个1.5*1.5*1.5的数据再重采样的orgin46,64,37是不同的。所以不能将TPM第一层提取出来再采样再进行统计分析！

==================================================================

CAT-学习汇总

==================================================================

_______  ___

_______

|  ____/ / _ \ \_

_/

| |___  / /_\ \

| |  Computational Anatomy Toolbox

|____/ /_/  \_\ |_|

CAT12 -

http://www.neuro.uni-jena.de

CAT default file:

D:\Program

Files\MATLAB\R2012a\toolbox\spm12\toolbox\cat12\cat_defaults.m

------------------------------------------------------------------------

Running job #1

------------------------------------------------------------------------

Running 'CAT12: Segmentation'

WARNING: Please note that no additional modules in the batch

can be run

except CAT12 segmentation. Any

dependencies will be broken for

subsequent modules if you

split the job into separate processes.

Running job 1:

.\1_t1_mpr_sag_iso_mww.img,1

.\2_t1_mpr_sag_iso_mww.img,1

.\3_t1_mpr_sag_iso_mww.img,1

.\4_t1_mpr_sag_iso_mww.img,1

.\5_t1_mpr_sag_iso_mww.img,1

.\6_t1_mpr_sag_iso_mww.img,1

.\7_t1_mpr_sag_iso_mww.img,1

.\8_t1_mpr_sag_iso_mww.img,1

.\9_t1_mpr_sag_iso_mww.img,1

.\10_t1_mpr_sag_iso_mww.img,1

WARNING: No background processes possible because your SPM

installation is located in

a folder that contains spaces.

Please set the number of processes in the

GUI

to '0'. In order to split your

job into different processes,

please do not use any spaces

in folder names!

------------------------------------------------------------------------

CAT12 r1092: .\1_t1_mpr_sag_iso_mww.img,1

------------------------------------------------------------------------

SANLM denoising (NCstr=Inf):  32s

APP: Rough bias correction:

Initialize  4s

Estimate background  5s

Initial correction  6s

Refine background  4s

Final correction  5s

Background correction  1s

Final scaling  2s

28s

Coarse affine registration:  4s

Affine registration  8s

SPM preprocessing 1:  105s

SPM preprocessing 2:  80s

Global intensity correction:  20s

SANLM noise correction:  3s

Local adaptive segmentation (LASstr=0.50):

Prepare maps  4s

Prepare partitions  2s

Prepare segments  15s

Estimate local tissue thresholds

41s

SANLM noise correction for LAS

4s

66s

ROI segmentation (partitioning):

Atlas -> subject space

8s

Major structures  4s

Ventricle detection  4s

NO WMH detection (CSF ~7%)

0s

Closing of deep structures

1s

Side alignment  4s

Final corrections  2s

24s

Skull-stripping using graph-cut (gcutstr=0.50):

WM initialisation  4s

GM region growing  1s

GM-CSF region growing  4s

CSF region growing  4s

Ventricle filling  3s

15s

Amap using initial SPM12 segmentations (MRF filter strength

0.06):  21s

AMAP peaks: [CSF,GM,WM]

= [0.42±0.07,0.71±0.10,0.99±0.02]

Final cleanup (gcutstr=0.50):

Level 1 cleanup (ROI estimation)

4s

Level 1 cleanup (brain masking)

2s

Level 2 cleanup (CSF correction)

2s

Level 3 cleanup (CSF/WM PVE)

1s

9s

Internal WMH correction for spatial normalization

(WMHCstr=0.50):  4s

Dartel registration with 1.50 mm:

167s

Write result maps:  55s

ROI estimation:

Data mapping to normalized space

20s

ROI estimation of 'hammers' atlas

4s

ROI estimation of 'neuromorphometrics'

atlas  6s

ROI estimation of 'lpba40' atlas

2s

33s

Quality check:  12s

Print 'Graphics' figure to:

I:\VBM\T1Img-CAT\1_t1_mpr_sag_iso_mww.pdf

------------------------------------------------------------------------

CAT preprocessing takes 10 minute(s) and 35 second(s).

Image Quality Rating (IQR):  85.44% (B)

Segmentations are saved in I:\VBM\T1Img-CAT\1\mri

Reports are saved in I:\VBM\T1Img-CAT\1\report

Labels are saved in I:\VBM\T1Img-CAT\1\label

------------------------------------------------------------------------

------------------------------------------------------------------------

CAT12 r1092: .\2_t1_mpr_sag_iso_mww.img,1

------------------------------------------------------------------------

SANLM denoising (NCstr=Inf):  30s

APP: Rough bias correction:

Initialize  4s

Estimate background  3s

Initial correction  6s

Refine background  4s

Final correction  5s

Background correction  1s

Final scaling  3s

25s

Coarse affine registration:  8s

Affine registration  7s

SPM preprocessing 1:  130s

SPM preprocessing 2:  82s

Global intensity correction:  19s

SANLM noise correction:  3s

Local adaptive segmentation (LASstr=0.50):

Prepare maps  4s

Prepare partitions  2s

Prepare segments  15s

Estimate local tissue thresholds

41s

SANLM noise correction for LAS

4s

66s

ROI segmentation (partitioning):

Atlas -> subject space

8s

Major structures  4s

Ventricle detection  9s

NO WMH detection (CSF ~9%)

0s

Closing of deep structures

1s

Side alignment  4s

Final corrections  2s

29s

Skull-stripping using graph-cut (gcutstr=0.50):

WM initialisation  4s

GM region growing  >>

========================= 上述成两组处理呈现的内容

=========================如下是一起处理后，matlab呈现的内容==================

------------------------------------------------------------------------

Running job #2

------------------------------------------------------------------------

Running 'CAT12: Segmentation'

------------------------------------------------------------------------

CAT12 r1092: .\1_002_t1_mpr_sag_iso_mww.img,1

------------------------------------------------------------------------

SANLM denoising (NCstr=Inf):  31s

APP: Rough bias correction:

Initialize  4s

Estimate background  3s

Initial correction  6s

Refine background  4s

Final correction  5s

Background correction  1s

Final scaling  2s

24s

Coarse affine registration:  4s

Affine registration  7s

SPM preprocessing 1:  99s

SPM preprocessing 2:  82s

Global intensity correction:  19s

SANLM noise correction:  4s

Local adaptive segmentation (LASstr=0.50):

Prepare maps  4s

Prepare partitions  2s

Prepare segments  17s

Estimate local tissue thresholds

42s

SANLM noise correction for LAS

4s

70s

ROI segmentation (partitioning):

Atlas -> subject space

9s

Major structures  5s

Ventricle detection  6s

NO WMH detection (CSF ~8%)

0s

Closing of deep structures

1s

Side alignment  5s

Final corrections  2s

28s

Skull-stripping using graph-cut (gcutstr=0.50):

WM initialisation  4s

GM region growing  2s

GM-CSF region growing  4s

CSF region growing  4s

Ventricle filling  3s

17s

Amap using initial SPM12 segmentations (MRF filter strength

0.05):  23s

AMAP peaks: [CSF,GM,WM]

= [0.42±0.08,0.72±0.10,0.99±0.02]

Final cleanup (gcutstr=0.50):

Level 1 cleanup (ROI estimation)

4s

Level 1 cleanup (brain masking)

2s

Level 2 cleanup (CSF correction)

2s

Level 3 cleanup (CSF/WM PVE)

2s

11s

Internal WMH correction for spatial normalization

(WMHCstr=0.50):  3s

Dartel registration with 1.50 mm:

162s

Write result maps:  55s

ROI estimation:

Data mapping to normalized space

20s

ROI estimation of 'hammers' atlas

4s

ROI estimation of 'neuromorphometrics'

atlas  6s

ROI estimation of 'lpba40' atlas

2s

32s

Quality check:  12s

Print 'Graphics' figure to:

I:\VBM\T1Img-CAT\1_002_t1_mpr_sag_iso_mww.pdf

------------------------------------------------------------------------

CAT preprocessing takes 10 minute(s) and 45 second(s).

Image Quality Rating (IQR):  86.00% (B)

Segmentations are saved in I:\VBM\T1Img-CAT\1\mri

Reports are saved in I:\VBM\T1Img-CAT\1\report

Labels are saved in I:\VBM\T1Img-CAT\1\label

------------------------------------------------------------------------