Maryam Shanechi, the Andrew and Erna Viterbi Early Career Chair and assistant professor of electrical and computer engineering, will receive a highly coveted grant from the National Institutes of Health’s High Risk, High Reward Research Program.
She joins a select group of only 53 people in the country to receive the NIH Director’s New Innovator Award.
The award supports exceptionally creative researchers whose work has been deemed high-impact and innovative within the fields of biomedical, behavioral, or social sciences and includes over $2.4 million in support over a five-year period.
Shanechi’s work sits at the interface of machine learning and neuroscience. At her Neural Systems Engineering and Information Processing Lab (NSEIP LAB), she and her students develop groundbreaking brain-machine interface technology that can model, decode, and even control neural activity patterns. In just the past few years Shanechi has produced research showing her ability to understand how mood is represented in the human brain and to decode it in depression. She has also developed methods that can enhance brain-machine interfaces for restoring movement in paralyzed people. She further models how mental dysfunctions such as depression or anxiety can possibly be better treated through targeted personalized deep brain stimulation.
The work for which she received this grant focuses on a standing challenge within the neuroscience community: how to control the activity of large networks of interconnected neurons. Achieving a high level of control over these neurons can have profound implications for human health and well-being, as these networks play a major role in a whole host of brain functions and their abnormal activity patterns could be the cause of brain dysfunctions.
The challenge thus far has been producing effective models of the complex and non-linear activity patterns in these networks. This incomplete understanding of how our brain networks function is one of the challenges preventing targeted deep brain stimulation from one day possibly replacing expensive or addictive medications, which are not effective in all patients. To address this challenge, Shanechi will develop nonlinear models that are interpretable enough to study and to build technology around by leveraging mathematical concepts from geometry and machine learning. Her innovative methods will discover the geometry over which neural activity evolves and model its nonlinear dynamics.
If precise nonlinear dynamic modeling and control was possible, this would both elucidate the neural basis of behavior and treat the most prevalent and disabling mental disorders such as depression or addiction, which are a leading cause of disability worldwide,” said Shanechi. “The goal of this proposal is to move toward making this vision a reality. I am very excited to pursue this work and grateful to the NIH for this support.”
Simply put, Shanechi’s NIH-funded work could bring us immensely closer to understanding how complex neural activity patterns give rise to our functions and dysfunctions and to developing novel therapies that interface with the brain. The implications for such an understanding are vast.
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