Dear colleagues,

We are pleased to invite you to the DPABI Brain Imaging Basics Online Training Camp (English), taking place online on December 14–18, 2026.

Led by Dr. Chao-Gan Yan, Professor at Tsinghua University and developer of DPABI, DPABISurf and DPARSF, this five-day course combines methodological foundations with guided hands-on practice in resting-state fMRI analysis, covering both volume-based and cortical surface-based workflows.

The camp welcomes researchers, clinicians and students who are new to brain imaging or would like structured guidance in using these tools. No prior programming experience is required for the core graphical-interface exercises. Participants are encouraged to bring their research questions and may use their own appropriately de-identified datasets for project discussions.

Programme at a glance

• Dec 14: Resting-state fMRI foundations, research applications and project introductions.
• Dec 15: Data organization, preprocessing with DPARSF, and an introduction to surface-based analysis with DPABISurf.
• Dec 16: Quality control, animal data processing, group-level statistics, multiple-comparison correction and results visualization.
• Dec 17: Hands-on practice with DPABISurf and DPABI, including analysis of participants’ own datasets.
• Dec 18: Multisite harmonization, gradient analysis, advanced DPABISurf and participant project presentations.

Course details

• Format: Live online lectures, software demonstrations, hands-on practice and project discussions, all in English.
• Dates: December 14–18, 2026.
• Time: 09:00–12:00 Beijing time (UTC+8) each day; 15 scheduled hours in total.
• North American times: December 13–17, 20:00–23:00 EST / 17:00–20:00 PST.

Registration, Full programme and International Session Times:
https://rfmri.org/DPABIBasicCamp

For enquiries, please contact rfmrilab@gmail.com.

We look forward to welcoming you and discussing your research projects. Please feel free to share this announcement with colleagues and students who may be interested.

Best,

Chao-Gan