April 2014

Dynamic brain connectome analysis toolbox

Dynamic brain connectome (DynamicBC) analysis toolbox is a Matlab toolbox to calculate Dynamic Functional Connectivity (d-FC) and Dynamic Effective Connectivity (d-EC). Sliding window analysis (Bivariate Pearson correlation and Granger causality) and time varying parameter regression method (Flexible Least Squares) are two dynamic analysis strategies for time-variant connectivity analysis in the DynamicBC. Granger causality density/strength (GCD/GCS) and functional connectivity density/strength (FCD/FCS) analysis would be performed in this toolbox. Add DynamicBC's directory to MATLAB's path and enter "DynamicBC" in the command window of MATLAB to enjoy it.

The latest release is DynamicBC2.2_20181112

Manual could also be downloaded here. 

New features of DynamicBC 2.0 release 20180311:
New features of DynamicBC2.2 release 20181112:
--Added a visualization module for connectogram.
--Added a demo for visualization a connectogram.
--Added new feature for cluster number estimation.
--Fixed a bug for clustering the ALFF maps.

 

New features of DynamicBC 2.0 release 20180311:
   --Fixed minor bugs in the Clustering module.

New features of DynamicBC 2.0 release 20171228:
1. Changed the toolbox cover.
2. Added the new module for dynamic intrinsic brain activity (dynamic ALFF).  

 

New features of DynamicBC 1.2 release 20160415: 

Fixed the step bugs when selecting window size.

 

New features of DynamicBC 1.1 release 20140710:
1. Added the new utilties including the ‘Clustering’ and 'Spectrum' for dynamic FC/EC time series.
2. Added the new output of variance of dynamic FC/EC time series.  
 

New features of DynamicBC 1.0 release 20140429: 
This release fixed some minor bugs in dynamic FCD.

关于功能连接结果的解释

 各位老师好:
       我使用静息态的数据做功能连接,利用Voxel-based的方法研究M1区的功能连接,种子点选择左右两侧的M1区,出来的全脑功能连接图取了阈值之后总是会出现两侧的枕叶区域,我们的数据都是闭眼扫静息态的,我查的做运动功能连接的文献里面基本上都没提到枕叶区域,不知道是他们的结果就没枕叶还是他们没报道。在我矫正很严格的情况下枕叶还是存在,不知该如何解释这种情况?是不是比如我只关心和运动相关的脑区,但是出来一些明显和运动无关的脑区可以不用报出来或解释,请指导~~

ICA结果后处理

 张老师你好:
我有两个问题想咨询下

  • 1、 我现在有两组数据,正常对照组和病人组,我使用MICA已经处理完数据,并且已经从正常对照组的成分中找出对应的8个RSN,要比较两组被试某个RSN的差异,我想问下,病人的8个RSN所对应的成分是否就和正常对照组的成分一样(在处理数据时两组设置的成分数一样的),还是两组被试各找各的RSN所对应的成分?