Johns Hopkins School of Medicine

A Roundtable with the Kavli Neuroscience Discovery Institute

By integrating neuroscience, engineering and data science, the new Kavli Institute at Johns Hopkins University aims to fuel new discoveries about how the brain functions.

NEUROSCIENTISTS TODAY ARE WIELDING ever-more-powerful tools for understanding the mysteries of the  brain. A suite of new approaches is allowing researchers to listen in on brain activity and to measure the molecular, cellular, and structural changes that underlie complex behaviors as well as neurological disorders such as Alzheimer’s and Parkinson’s disease. But this technological burst – spurred especially by the BRAIN (Brain Research for Advancing Innovative Neurotechnologies) Initiative – has also raised a major challenge in brain research: wrangling the deluge of data that these novel approaches produce.

The Kavli Neuroscience Discovery Institute (Kavli NDI) at Johns Hopkins University, launched on October 1, 2015, will promote collaboration between neuroscientists, engineers and data scientists in their efforts to organize, analyze and manipulate the wealth of data emerging at different scales of inquiry – from single cells to behavior – in order to build a unified understanding of brain function. Recently its director, Richard Huganir, joined with co-director Michael Miller to discuss the new institute’s vision, the changing face of brain research and the value of sharing data in this new era of neuroscience.

Richard Huganir, PhD – Director of the Kavli NDI. Huganir is a professor at Johns Hopkins University and has been director of the department of neuroscience since 2006. He is also the co-director of the Johns Hopkins Brain Science Institute. His work has helped lay the foundation for understanding the molecular basis of brain plasticity.

Michael Miller, PhD – Co-director of the Kavli NDI. Miller is a professor of biomedical engineering at Johns Hopkins University and director of the school’s Center for Imaging Science. He has been a pioneer in the field of computational anatomy, which uses algorithms constructed through structural and functional imaging to characterize the trajectory of neurodegenerative diseases and their correlation with psychiatric disorders.