introduction：
1. feat 是一种基于模型的fmri数据分析方法。
2. feat 首先使用顺手，至少看起来，比spm漂亮多了。 feat是按照正常人的使用方法去设计的。 spm 由于matlab的gui库的限制，诶，不说了。还是自己能力不够啊。不够熟练啊。
3. feat 对于单被试，也就是individual的情形，大概需要5到10分钟，能跑出结果，最后，结果在网页进行显示。直接显示了激活图，显示了时间序列和model的拟合情况。
4. feat是基于glm而设计的，也就是基于一种多元回归的方法。glm的实现，通过film软件包，而film软件包，较之于spm，有一个prewhiten的过程。
5. feat利用flirt进行配准。flirt配准，使用二级配准方式，t2配准到t1，T1再配准到mni152。
guide：
1.在运行feat执行，数据要做这样的预处理：
1.1 结构图像做bet操作，脑壳抽取操作。
2. 选择4D功能图像.nii数据。选择好之后，4d数据的时间维数会自动在total volumes中显示出来。注意一点，就是在feat中，或者说在fsl中，第一个volume的下标值是0，不是1.
3. 然后在data 面板(stab)中，还有几个参数需要设置，delete volumes，也就是最前面有多少volumes需要discard丢弃；
TR(s)；
高通滤波时间 high pass filter cutoff(s)；
directory 输出目录；
4. 在stats 面板(stab)中，建立模型以及相应的限制contrast。将所有model设置完成之后，可以将所有settings保存到setup 文件中，比如design.fsf中。以后，就可以直接通过load这样的setup file，不需要重复设置了。
5.feat的全分析full analysis步骤包括：pre-stats，stats，post-stats。
6.Brain/background threshold 对图像的灰度值进行归一化，然后，背景颜色所占的灰度range。
pre-stat:
去脑壳：By default BET brain extraction is applied to create a brain mask from the first volume in the FMRI data. This is normally better than simple intensity-based thresholding for getting rid of unwanted voxels in FMRI data. Note that here, BET is setup to run in a quite liberal way so that there is very little danger of removing valid brain voxels. If the field-of-view of the image (in any direction) is less than 30mm then BET is turned off by default. Note that, with respect to any structural image(s) used in FEAT registration, you need to have already run BET on those before running FEAT.
空间平滑：去除噪声，
Spatial smoothing is carried out on each volume of the FMRI data set separately. This is intended to reduce noise without reducing valid activation; this is successful as long as the underlying activation area is larger than the extent of the smoothing. Thus if you are looking for very small activation areas then you should maybe reduce smoothing from the default of 5mm, and if you are looking for larger areas, you can increase it, maybe to 10 or even 15mm. To turn off spatial smoothing simply set FWHM to 0.
灰度值归一化：这一步，对于高层分析是有必要的。Intensity normalisation forces every FMRI volume to have the same mean intensity. For each volume it calculates the mean intensity and then scales the intensity across the whole volume so that the global mean becomes a preset constant. This step is normally discouraged - hence is turned off by default. When this step is not carried out, the whole 4D data set is still normalised by a single scaling factor ("grand mean scaling") - each volume is scaled by the same amount. This is so that higher-level analyses are valid.
是否开启MELODIC选项：开启ica选项，可能会检测到一些方法检查不到的伪影等等。ica对于去除结构性噪声，是可以做文章的。
The MELODIC option runs the ICA (Independent Component Analysis) tool in FSL. We recommend that you run this, in order to gain insight into unexpected artefacts or activation in your data.
stats：
Use FILM prewhitening：开启film（fsl的glm实现包）的与白化功能，使得统计结果更有效。
在glm模型中，去除头动协变量：spm中的方法是，将在头动矫正估计得到的参数，作为设计矩阵的协变量，进行回归去除。然而，fsl中，认为这种方法的有效性值得怀疑，所以采用了ica进行头动伪影的识别和去除。
本文转自二郎三郎博客园博客，原文链接：http://www.cnblogs.com/haore147/p/3656837.html，如需转载请自行联系原作者