The R-fMRI Course V3.0 English Version
(Mostly recorded at the R-fMRI Workshop, July 30~31, 2019)
1. Resting-State fMRI: Current Research, Methodological Issues and Computational Platform 
2. Resting-State fMRI: Applications to Brain Disorders
3. The REST-meta-MDD Project: towards a Neuroimaging Biomarker of Major Depressive Disorder
4. Data Processing of Resting-State fMRI: DPARSF
5. The R-fMRI Maps Project
6. Surface-Based Brain Imaging Analysis and DPABISurf
7. DPABI Animal Data Processing
8. Temporal Dynamic Analysis
9. DPABI: Quality Control, Statistical Analysis and Results Viewing
10. DPABISurf: A Surface-Based Resting-State fMRI Data Analysis Toolbox
11. Output structure of DPARSF
12. Output structure of DPABISurf
13. Verification of Reproducibility of R-fMRI Metrics and Reproducible Network Underpinnings of Rumination
14. DPABI: Utilities
15. DPABISurf Advanced: More about FreeSurfer
The DPABINet Brain Network Open Course
(Mostly recorded at the DPABINet Brain Network Online Training Camp, April 18th ~ 22nd, 2022)
The human brain is one of the most complex network systems in the universe, consisting of about 1011 neurons and about 1015 connections. Since the human brain is a highly integrated and cooperating complex system, studying the functional integration between different brain regions is crucial for understanding the information interaction between brain regions and the coordination between brain networks. In order to facilitate brain network research, Dr. Chao-Gan Yan’s team (The R-fMRI Lab) developed a free and open source software DPABINet. DPABINet V1.1 has been released with DPABI V6.1, which can construct brain network, calculate graph theoretical analysis indices, perform statistic analyses and present brain network images.
In addition, to better construct the brain network, we recommend using DPABISurf for surface based preprocessing. DPABISurf is a surface-based resting-state fMRI data analysis toolbox evolved from DPABI/DPARSF, as easy-to-use as DPABI/DPARSF. DPABISurf is based on fMRIPrep, FreeSurfer, ANTs, FSL, AFNI, SPM, dcm2niix, PALM, GNU Parallel, Docker and DPABI. DPABISurf provides user-friendly graphical user interface (GUI) for pipeline surface-based preprocessing, statistical analyses and results viewing, while requires no programming/scripting skills from the users.
The use of DPABINet and DPABISurf is as simple as DPABI software, here we are pleased to release a free online course to make users proficient in DPABINet brain network analysis in the most correct and effective way. Please visit http://rfmri.org/Course#DPABINet for The DPABINet Brain Network Open Course!
1. Brain Network: Current Research and Methodological Issues
2. Brain Network: the REST-meta-MDD Project
3. Brain Network: Applications
4. Brain Network: Innovation Points
5. DPABI Data Organization
6. Surface-Based Brain Imaging Analysis and DPABISurf
7. Data Processing with DPABISurf
8. DPABINet: Network Construction
9. DPABINet: Statistical Analysis and Results Viewing
10. Graph Theoretical Analysis: Current Research and Methodological Issues
11. Graph Theoretical Analysis: Applications
12. DPABINet for Graph Theoretical Analysis
13. Graph Theoretical Analysis: Statistical Analysis and Results Viewing
14. Brain Network Analysis: Example of Rumination
15. DPABINet: Utilities
Practice 1. DPABISurf Preprocessing
Practice 2. DPABISurf Results
Practice 3. DPABINet: Network Analysis
Practice 4. Display Nodes with Significant Edges
Practice 5. DPABINet: Graph Theoretical Analysis
Practice 6. DPABINet: Display with text configuration
Practice 7. DPABINet: Correction Within Significant Edges Related Code
静息态功能磁共振成像数据处理教程自2009年7月15日发布以来，在过去的12年间，课程持续更新，被学习40万余人次。最新内容已迁移至严老师慕课网站，欢迎访问！
The R-fMRI Course V3.1 中文版
(录制于2020年8月29~31日"第七届DPABI/DPABISurf/DPARSF云端特训营")
一、静息态功能磁共振成像原理与数据分析
二、REST-meta-MDD抑郁症脑成像大数据项目介绍
三、静息态功能磁共振成像在心理学和脑疾病的应用
四、静息态功能磁共振成像数据处理原理与DPARSF使用
五、基于皮层的脑成像数据分析与DPABISurf
六、R-fMRI质量控制与The R-fMRI Maps Project
七、DPABI动物数据处理
八、R-fMRI动态性分析
九、统计分析、多重比较校正与结果显示
十、DPABISurf数据处理
十一、DPARSF输出文件夹与计算结果概述
十二、DPABISurf输出文件夹与计算结果概述
十三、R-fMRI可重复性及多重比较校正选择
十四、DPABI Utilities介绍
十五、DPABISurf进阶
十六、基于DPABISurf的The R-fMRI Maps Project
十七、DPABISurf Pipeline演示
十八、DPABI统计演示
十九、DPABI_VIEW演示
二十、DPABISurf_VIEW演示和基于皮层的多重比较校正
二十一、BrainImageNet脑影像深度学习平台
二十二、BrainImageNet脑影像深度学习平台Demo
二十三、YCPABI脑影像编程实战
二十四、代码讲解：个体化SCA计算
二十五、DPABISurf安装指南（Windows）
DPABINet 脑网络进阶课程
(录制于2021年4月24~26日"第一届DPABISurf/DPABINet脑网络进阶特训营")
一、脑网络原理与数据分析
二、REST-meta-MDD抑郁症脑成像大数据项目介绍
三、脑网络分析在心理学和脑疾病的应用
四、脑网络分析创新点
五、基于皮层的脑成像数据分析与DPABISurf
六、DPABISurf数据处理
七、DPABISurf输出文件夹与计算结果概述
八、DPABINet脑网络数据处理
九、脑网络统计分析、多重比较校正与结果显示
十、图论分析原理
十一、图论分析应用
十二、DPABINet图论分析数据处理
十三、图论分析统计、多重比较校正与结果显示
十四、脑网络分析发表实例——反刍思维
十五、BrainImageNet脑影像深度学习平台
十六、DPABINet Utilities
十七、DPABINet操作视频集锦