Three-year project will develop a software tool to help scientists and doctors understand how recorded brainwaves emerge from underlying neural activity.

PROVIDENCE, R.I. [Brown University] — In her research at Brown University, Stephanie Jones, research associate professor of neuroscience, has led the development of a unique computational model that explains how individual neurons and circuits of them produce the signals detected by external brainwave measurements, such as EEG or MEG sensors. Now, with a three-year, $1.6 million grant from the federal government's BRAIN Initiative, she hopes to share her innovation with other scientists.

"The aim of the grant is to turn the model into a user-friendly software tool that researchers and clinicians can use to test hypotheses about the neural origin of their MEG/EEG or electrocorticography data," said Jones, a member of the Brown Institute for Brain Science. "We are calling this tool the 'Human Neocortical Neurosolver.'"

Jones leads the research, which officially starts Sept. 30 in collaboration with Dr. Matti Hamalainen at Massachusetts General Hospital and Dr. Michael Hines at Yale University.

The team will also “integrate the neural model into existing source localization software so that researchers can study the location, time course and neural mechanisms of their human brain imaging data all in one software package,” Jones added.

She said the software will not only aid neuroscience, but also future patient care.

“While there are numerous studies connecting human MEG/EEG data to healthy and abnormal functions, the circuit level interpretation of the underlying neural dynamics is lacking,” she said. “This tool will foster the translational relevance of these technologies by allowing researchers to generate testable hypotheses that can guide further studies and ultimately novel therapeutics.”