[Moved to Europe Friendly Time Zone]DPABINet Brain Network Online Training Camp

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 theory 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, but hands-on practical training can make users proficient in DPABINet brain network analysis in the most correct and effective way. As there was no online English video course for DPABINet yet, here we are launching the DPABINet Brain Network online Training Camp (April 18^th ~ 22^nd, 2022, the teaching language is English).

This DPABINet Brain Network online Training Camp is lectured by Dr. Chao-Gan Yan, and is aimed at researchers and students in the fields of psychology, cognitive neuroscience, biomedical engineering, clinical medicine, etc. Because this training camp requires basic knowledge of brain imaging, we encourage the trainees first learn The R-fMRI Course (freely available at http://rfmri.org/Course). In the training camp, trainees will first study DPABISurf for cortical reconstruction and data preprocessing, and then systematically learn brain network research, from the basic concepts of brain network to operations of DAPBINet software. More importantly, trainees are encouraged to come to study with their own projects and data, conduct in-depth research project discussions with the lecturer, process data with research questions, receive hands-on systematic scientific research training, get results and scientific interpretation for publication.

We sincerely welcome your participation in this training camp and look forward to seeing you online!

1. Time: April 18^th ~ 22^nd, 2022

2. Language: English

3. Place: Online Camp

This training camp is held in the form of cloud-based online small classes. Lectures and discussions will be held in the cloud conference Zoom room. One-on-one error-checking counseling by teaching assistants will also be held in the cloud small conference room. For trainees with insufficient local computing power, the cloud server will be opened for data processing exercises.

4. Lecturer

Chao-Gan YAN, Ph.D.

Dr. Chao-Gan Yan is a professor at the Institute of Psychology, Chinese Academy of Sciences (IPCAS). He is the Director of the Magnetic Resonance Imaging Research Center, the Director of International Big-Data Center for Depression Research, and the Principal Investigator of The R-fMRI Lab located at IPCAS. Before he joined the IPCAS in 2015, he worked as a Research Scientist at the Nathan Kline Institute for Psychiatric Research and a Research Assistant Professor at the Department of Child and Adolescent Psychiatry, New York University School of Medicine. Dr. Yan received his Ph.D. degree in Cognitive Neuroscience from State Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University in 2011. His research mainly focuses on the resting-state fMRI (R-fMRI) computational methodology, mechanisms of spontaneous brain activity, and their applications in depression. He has addressed fundamental methodological issues (e.g., impact of head motion (ESI Top 1‰ of highly cited papers), standardization (ESI Top 1% of highly cited papers) and multiple comparison correction (ESI Top 1% of highly cited papers)) on the study of resting-state functional connectomics. He has also developed data processing and analysis toolbox for R-fMRI, DPABI (ESI Top 1‰ of highly cited papers) and DPARSF, with the latter has been cited over 2000 times. He initiated a Chinese consortium for big data of brain imaging of depression (REST-meta-MDD), performed big data study of depression neuroimaging (PNAS 2019, ESI Top 1% of highly cited papers), and studied the brain mechanisms of depression through a longitudinal study of animal model. He has published 70+ peer-reviewed articles (30+ as first or corresponding author) in prestigious journals including PNAS, Molecular Psychiatry, NeuroImage, Human Brain Mapping. His work has been widely cited in the scientific community (total citations > 14000, http://scholar.google.com/citations?user=lJQ9B58AAAAJ), achieving an h-index of 37. Six of his first-author/corresponding author papers ranked as ESI Top 1% Highly Cited Papers, 2 of which were ESI Top 1‰. He has been ranked as 2019 and 2020 most cited Chinese Researchers by Elsevier. He was awarded the 2021 Early Career Investigator Award by The Organization for Human Brain Mapping (OHBM). Additionally, he currently serves as associate editor for Neuroimage: Reports and servers in the editorial boards of NeuroImage and Journal of Neuroscience Methods.

5. Schedule

(Of note: the schedule is not optimized for American time zone, we may launch another training camp optimized for American time zone depends on the needs.)

6. Enroll

Please visit http://camp.deepbrain.com to pay and enroll. (Tuition: $499)

If you have any questions, please contact Dr. Yan’s team (The R-fMRI Lab) at rfmrilab#gmail.com.