请教功能连接里关于回归的问题

您好！ 今天我在pnas文章The maturing architecture of the brain’s default network中看到做脑区之间的功能连接的时候，预处理步骤包括：Functional Connectivity Preprocessing. For rs-fcMRI analyses as previously described (2, 3), several additional preprocessing steps were used to reduce spurious variance unlikely to reflect neuronal activity (e.g., heart rate and respiration). These steps included: (i) a temporal band-pass filter (0.009 Hz < f <0.08 Hz) and spatial smoothing (6 mm full width at half maximum), (ii) regression of six parameters obtained by rigid body head motion correction, (iii) regression of the whole brain signal averaged over the whole brain, (iv) regression of ventricular signal averaged from ventricular region of interest (ROI), and (v) regression of white matter signal averaged from white matter ROI. Regression of first order derivative terms for the whole brain, ventricular, and white matter signals were also included in the correlation preprocessing. 前面这些滤波，回归头动、全脑、白质、脑脊液平均信号都和之前的一样，可是Regression of first order derivative terms for the whole brain, ventricular, and white matter signals were also included in the correlation preprocessing.这一句我不太理解，也不太清楚为什么这么做，有什么好处？怎么样才能实现呢？难道是一阶导数的意思？可是常数求导为0啊？？？ 或者说可能是这个意思，如果是比如说我把全脑信号、白质、脑脊液平均信号提取出来，每一道信号都是180个时间点，first order derivative terms 是不是就是后项减前项？就是matlab中的diff函数，但是这样的话得到的结果只有179个时间点，无法做regress分析，因为原始信号180. 谢谢