Submitted by Duoning on Sun, 05/10/2015 - 17:26
ICA analysi: Each participant’s smoothed, normalized images were concatenated across
time to form a four dimensional matrix using FSL 3.3. This four dimensional matrix was
then analyzed with FSL 4.4 melodic ICA concatenated across participants. This analysis was
limited to output only 25 components for the group. From these components, networks of
interest – salience network (SN), central executive network (CEN), and default mode
network (DMN) – were selected for subsequent analyses using previously validated methods
(Greicius et al., 2004). These components were then binarized using SPM5 in order to create
templates for choosing network components for individuals
看到这篇文章说要先在组水平上得到三个网络模板，然后在拿这个模板和个体水平上的各成分进行匹配，请问在GIFT上是如何实现的，还是说这样的方法只能在FSL上可以？ Selection of the best-fit component. An automated two-step process was then used to select the component in each subject that most
closely matched the default-mode network. First, because functional connectivity networks have been detected in lowfrequency ranges (32), a frequency filter was applied to remove any components in which high-frequency signal (0.1 Hz) constituted 50% or more of the total power in the Fourier spectrum. Next, a template of the default-mode network was used to select the ‘‘best-fit’’ of the remaining low-frequency components in each subject.
先滤波再进行模板匹配，请问这个滤波这一步在GIFT上具体是如何实现的？