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关于皮尔逊相关系数 #2
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We use the entire sequence for obtaining functional connectivity. The length of the timesteps has little effect when using the resting-state fMRI. |
文章的意思是说:将大脑分割成为M个ROI之后,对每个ROI求N个被试之间的皮尔逊相关系数,形成一个N*N维的矩阵,不知道我理解的对吗?如果是这样的,那么在求每对被试指定ROI的时间序列之间的皮尔逊相关系数时,来自不同站点的时间序列长度不同,比如:NYU:176 timesteps MaxMun:116 timesteps.是把较长的时间序列都截取为和最短的时间序列相等长度的再计算吗? |
不不不,预处理阶段我们是对每个被试构建一个FC矩阵,无论时间长度是多少,最终都是一个MM的FC矩阵,然后和MDMR一样处理的是这个MM的FC矩阵,后面再针对每个ROI构建N*N的被试距离矩阵。这个过程有点绕,可以看一下MDMR的流程(R写的):https://github.com/podismine/MDMR-based-Brain-Connectome-Analysis 需要的话我们可以提供MDMR的python代码 |
之前确实没有接触过MDMR,如果先构建MM的FC矩阵,那么对于每个ROI来说,都产生一个1*M的向量,是对这些向量再求皮尔逊相关系数吗?是这个意思吗? |
FC预处理:fmri预处理完得到M * T的时间序列(M个ROI,T个timestep),然后在时间T上做皮尔逊系数得到M * M的矩阵 |
明白,谢谢啦~ |
ABIDE数据集不同站点fMRI数据时间序列长短不一,请问作者在求它们之间的皮尔逊相关系数是怎么处理的?
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