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)
How could I extract peak voxel time series?
Many thanks in advance.
I have identified DMN using ICA algorithm and I would like to examin effective connectivity using GCA.
As far as I know unlike functional connectivity,effective connectivity is presented as a set of nodes (regions) and edges. How should I specify nodes and edges?How could I obtain path coefficients?
Many thanks in advance.
Cluster 7 Number of voxels: 287 Peak MNI coordinate: -3 -24 51 Peak MNI coordinate region: // Left Cerebrum // Frontal Lobe // Medial Frontal Gyrus // Gray Matter // brodmann area 6 // Paracentral_Lobule_L (aal) Peak intensity: 4.0519 # voxels structure 287
--TOTAL # VOXELS-- 241 Frontal Lobe
155 Right Cerebrum
136 Gray Matter
124 Left Cerebrum
107 Medial Frontal Gyrus
92 White Matter
81 brodmann area 6
64 Cingulum_Mid_R (aal)
57 Supp_Motor_Area_L (aal)
52 Supp_Motor_Area_R (aal)
37 Paracentral_Lobule_L (aal)
36 Cingulum_Mid_L (aal) 35 Paracentral_Lobule_R (aal)
34 brodmann area 31
33 Limbic Lobe
28 Cingulate Gyrus
11 brodmann area 5 10 brodmann area 24 8 Inter-Hemispheric 5 Parietal Lobe 4 Precuneus_R (aal)
Hello DPARSFA researchers,
I'm working with two different subject populations - one which has 31 rfMRI slices and 240 time points and one which has 36 slices and 300 time points. There are a couple of patients who have different numbers of slices as well (e.g., 32 and 33). So far, I've run:
请教：1. 用dpabi做REHO的统计学分析，在统计学计算时没有加BRAIN mask，看图做Alphasim时加Brain mask的得到的结果，与在统计学计算时加BRAIN mask，看图做Alphasim时加Brain mask的得到的结果完全不同，哪个是对的呢？
2. 同一组被试前、中、后，3个时间段比较是否应该用ANCOVA（repeated measure）分析，之后再做两两分析时一定是在ANCOVA有意义的区域才可以，对吗？