Mental health problems and the incidence of mental illness are becoming increasingly prominent around the world. As a result, scientists and clinicians are more focused than every on how to effectively bridge the gap between basic research, clinical diagnosis and treatment. Computational psychiatry is an emerging discipline which employs quantitative approaches such as computational modeling and machine learning to explore the mechanisms behind the occurrence and development of mental illness and it is showing great prospects. So how can computational psychiatry help us better understand mental illness and further provide theoretical guidance and methodological tools for the diagnosis and treatment of mental illness? During the Tianqiao and Chrissy Chen Institute (TCCI®) ZNext seminar held on October 16, four young scientists discussed the advantages of computational psychiatry and challenges in clinical diagnosis and treatment. They also answered questions from online audiences.

The conference was hosted by Geng Haiyang, a postdoctoral fellow in the Department of Psychology, University of Hong Kong. Also engaged in the discussions were Chen Ji, a distinguished researcher in the Department of Psychology and Behavioral Sciences at Zhejiang University, Jin Jingwen, an assistant professor in the Department of Psychology at the University of Hong Kong and a TCCI® Investigator, and Zhang Ruyuan, an associate researcher of the Institute of Psychological and Behavioral Sciences at Shanghai Jiaotong University, also an associate researcher of the Shanghai Mental Health Center.