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 DynamicBC_V1.1_20140710.
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.
Resting-State fMRI Data Analysis Toolkit (REST) is a convenient toolkit to calculate Functional Connectivity (FC), Regional Homogeneity (ReHo), Amplitude of Low-Frequency Fluctuation (ALFF), Fractional ALFF (fALFF), Gragner causality, degree centrality, voxel-mirrored homotopic connectivity (VMHC) and perform statistical analysis. You also can use REST to view your data, perform Monte Carlo simulation similar to AlphaSim in AFNI, perform Gaussian random field theory multiple comparison correction like easythresh in FSL, calculate your images, regress out covariates, extract ROI time courses, reslice images, and sort DICOM files. Download a MULTIMEDIA COURSE would be helpful for knowing more about how to use this software. Add REST's directory to MATLAB's path and enter "REST" in the command window of MATLAB to enjoy it.
Citation of REST is:
The latest release is REST_V1.8_130615.
New features of REST V1.8 release 130615:
1. Fixed a bug in temporal correlation of two groups of images in Image Calculator. (Thanks for the report of ZHANG Han)
New features of REST V1.8 release 130303:
When calling Mingrui Xia's BrainNet Viewer (http://www.nitrc.org/projects/bnv/), the default surface template is changed to the smoothed version (BrainMesh_ICBM152_smoothed.nv). The previous default template (BrainMesh_ICBM152.nv) hide more information in the sulcus. If the users want to use BrainMesh_ICBM152.nv as default surface template, please uncomment Line 3740 in rest_sliceviewer: %SurfFileName=[BrainNetViewerPath,filesep,'Data',filesep,'SurfTemplate',filesep,'BrainMesh_ICBM152.nv'];
(After discussion with Mingrui Xia).
New features of REST V1.8 release 130214:
1. This release fixed some minor bugs, will not affect any data analysis.
2. Fixed a bug when using .nii(.gz) files in REST Image Calculator. (WANG Xin-Di)
3. Fixed a bug in using .nii(.gz) files in GCA analyses. (ZANG Zhen-Xiang)
4. Fixed the imresize_old bug of REST Slice Viewer with Matlab 2012b. (YAN Chao-Gan)
New features of REST V1.8 release 121225:
1. Support parallel computing! If you installed the MATLAB parallel computing toolbox, REST can distribute the subjects into different CPU cores. (WANG Xin-Di and YAN Chao-Gan).
2. Algorithm change: (1) Filtering: a separate function for matrix filtering was written. The low cutoff frequency index calculation changed from round (in REST V1.7) to "ceil". E.g., if low cut off corresponded to index 5.1, now it will start from 6 other than 5. This change also applies to ALFF and fALFF calculation. The filtered data changes slightly, about 0.0001. (2) The ALFF generated by the new version is sqrt(2/N) times of the original version. (new version used: 2*abs(fft(x))/N; original version used: sqrt(2*abs(fft(x))^2/N)). This change will not affect group analysis (as each individual scaled the same number), and will not affect mALFF and fALFF calculation as this factor will be normalized. (3) In the calculation of ReHo, the rank will keep as double and no longer converted into uint16, thus created slight difference with REST V1.7. (YAN Chao-Gan)
3. REST Slice Viewer support 4D file display and the maximum and minimum value could be set. (WANG Xin-Di)
4. Gaussian random field (GRF) theory multiple comparison correction (like easythresh in FSL) was supported. The smoothness could be evaluated for GRF correction or AlphaSim correction. (GUI by WANG Xin-Di, algorithm by YAN Chao-Gan)
5. Modules of voxel-mirrored homotopic connectivity (VMHC) (Zuo et al., 2010), Degree Centrality (Buckner et al., 2009) were added. (GUI by WANG Xin-Di, algorithm by YAN Chao-Gan)
6. REST GCA: could handle multiple ROIs (other than 2) in ROI-wise GCA now. Fixed a bug of discordance between the outputs and the description in REST-GCA readme in the pre-release of REST V1.8. (ZANG Zhen-Xiang)
7. rest_readfile.m and rest_writefile: The default format changed to .nii from .img. (WANG Xin-Di)
8. rest_to4d.m: now support one 4d file other than a directory, also support a cell of image filenames. (YAN Chao-Gan)
9. rest_regress_ss.m: add the output of T value. (YAN Chao-Gan)
10. rest_Write4DNIfTI.m: This function was added for write 4D nifti files based on SPM’s nifti function. (YAN Chao-Gan)
11. rest_writefile.m: No longer need to change to RPI before writing. (YAN Chao-Gan)
DPARSFA预处理设置时，reorient after Coreg*这个选项是必须勾的吗，它的作用是什么？还有Global Signal要勾选吗？有人说选有人说不选，我要做的是做术前和术后数据比较的。
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我是第一次处理fMRI数据，对数据做了个单样本T检验，然后用rest slice viewer看图卡阈值，但是color bar的最大值和最小值怎么看起来不正常呢？最大值为307.84，最小值为-50331647.00，一般情况color bar值的范围不都是10以内吗？希望老师给指点一下，谢谢！下面是截图：
我想问一下，由于我的fmri数据太大，转换成hdr/img文件共2400个，所以没有办法一次做detrend和filter，总是out of memory，而且试了很多次大概一次240个对象（120个hdr和120个img）比较合适，那么如果我把它们分开放在几个文件中分别做detrend和filter可以吗？这么多的time points，我应该去除前多少个比较好呢,或者用多少个比较好呢？
when i try to run DPARSFA, i get an error at the reslice stage as follow:
我想请教一下有关degree centrality的问题，之前看过一篇介绍lFCD和gFCD的文章，对于gFCD应该同DPARSFA里的degree centrality相似，是否在设置degree centrality的阈值时选择0.6，就跟计算gFCD完全一样了？我想直接用degree centrality计算lFCD和gFCD不知可否。
The error occured when i running the Dpabi in the test data. I am very appreciated for anyone who can help me. Thank you very much.
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