Peng SHI

City University of Hong Kong, Hong Kong, China

Title: High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology

---

Biography

Peng Shi is a professor in the Department of Biomedical Engineering at City University of Hong Kong. He received his bachelor’s degree in electrical engineering from Wuhan University and a Ph.D. degree in Biomedical Engineering from Columbia University. After his postdoctoral training at MIT in Electrical Engineering and Biological Engineering, he joined CityU Hong Kong and has been a faculty member in the BME department since 2011. Dr. Shi works at the convergence between neuroscience and engineering by taking advantage of an interdisciplinary approach that involves nano-/micro-fabrication, microfluidics, ultra-fast optics, high-resolution microscopy and imaging processing. He focuses on solving important emerging problems in translational neuro-engineering, especially in the development of high-throughput neuro-technology and screening platform for discovery of novel therapeutic targets. His work has led to more than 60 publications in top-tier research journals, including Science Advances, Nature communications and Advanced Materials etc., and 7 international patents and disclosures, one of which has been the foundation technology of a spin-off biotech company. Dr. Shi received the Simon’s research award in 2010, and was elected to the 1000 China Young Talent program. He also received the President Award for research excellence in 2017, outstanding supervisor award in 2018 at CityU, and a special recognition as Young Scholars by World Cultural Council in 2018. He is an associate editor for the journal Brain Research.

Abstract

Technologies for mapping the spatial and temporal patterns of neural activity have advanced our understanding of brain function in both health and disease. An important application of these technologies is the discovery of next-generation neurotherapeutics for neurological and psychiatric disorders. Here, we describe an in vivo drug screening strategy that combines high-throughput technology to generate large-scale brain activity maps (BAMs) with machine learning for predictive analysis. This platform enables evaluation of compounds’ mechanisms of action and potential therapeutic uses based on information-rich BAMs derived from drug-treated zebrafish larvae. From a screen of clinically used drugs, we found intrinsically coherent drug clusters that are associated with known therapeutic categories. Using BAM-based clusters as a functional classifier, we identify anti-seizure-like drug leads from non-clinical compounds and validate their therapeutic effects in the pentylenetetrazole zebrafish seizure model. Collectively, this study provides a framework to advance the field of systems neuropharmacology.