Panel 1: The data that is collected and stored by neurotechnologies and the inferences that can be made from these data

Speakers will discuss the inferences that can be drawn from shared data that are collected through neurotechnologies regularly used in BRAIN-funded studies with human participants. Speakers will cover functional magnetic resonance imaging (fMRI), electroencephalography (EEG), deep brain stimulation (DBS), structural brain imaging, and brain-computer interfaces (BCI).

· Susie Huang, MD, PhD, Associate Professor of Radiology, Harvard Medical School

· Lorna Quandt, PhD, Associate Professor of Educational Neuroscience, Gallaudet University

· Douglas Greve, PhD, Assistant Professor of Radiology, Harvard Medical School

· Jennifer Collinger, PhD, Associate Professor of Physical Medicine and Rehabilitation, University of Pittsburgh

· Doris Wang, MD, PhD, Associate Professor of Neurosurgery, University of California at San Francisco

Panel 2: Inferences to be drawn from data and their implications for data sharing

Speakers will discuss the inferences that can be drawn from shared data across technologies, including combining data types and collecting data through from implanted neurotechnologies. Furthermore, implications of the ability to decode semantic representations and effects of racially exclusionary practices in collecting and managing brain data on data sharing will be discussed.

· Conor Russomanno, Founder and CEO, OpenBCI

· Alexander Huth, PhD, Assistant Professor of Neuroscience, University of Texas at Austin

· Michael Young, MD, Associate Director, NeuroRecovery Clinic, Massachusetts General Hospital

Panel 3: The potential risks to communities

Speakers will discuss potential risks that can arise from sharing human brain data, including potential risks to communities.

· Jocelyn Ricard, Graduate Student, Stanford University

· Krystal Tsosie, PhD, MPH, MA, Assistant Professor in the School of Life Sciences, Arizona State University