Maurice A. Smith

Assistant Professor of Bioengineering

M.D. and Ph.D. from the Johns Hopkins University School of Medicine in the Department of Biomedical Engineering

B.E. (Triple Major: Biomedical Engineering, Electrical Engineering, and Mathematics) from Vanderbilt University



The human brain is perhaps the most remarkable motor control device in existence. More than 10 billion neurons comprise the motor system in the human brain. In the face of long sensory delays, high levels of noise from multiple sources, and more than 600 highly interdependent muscles to control, our motor systems can deftly and robustly command motor activation patterns that allow us to talk and sing and sit and stand and run and jump and throw and catch - often without even paying attention. Furthermore, with practice we can learn a myriad of seemingly "unnatural" motor skills from surfing and skiing to dribbling a basketball and driving a stick-shift car.

How do we accomplish these things? How do our brains maintain robust control over the physical movements of our bodies? How do we learn new motor skills? And how do these control mechanisms go bad in neurologic disease? Unfortunately, we, as neuroscientists and neuroengineers, do not yet have good answers to these questions. These are the questions that my research lab addresses.

We are broadly interested in how the human brain controls movement, in particular how the brain learns and perfects new motor skills. Our work focuses on understanding motor learning in general. However, as a model we study how the brain optimizes the execution of voluntary reaching arm movements. We study both how this process works in healthy individuals and how it goes awry in neurologic disease.

To explore these questions, our lab combines approaches from robotics, computational modeling, functional brain imaging, and behavioral neuroscience. We use robot arms to produce novel perturbations and force environments that alter the dynamics of movement. Subjects are asked to make arm movements while holding onto the handle of a robot manipulandum that produces these novel force environments, and with practice, their brains learn to adapt to these environments by gradually altering patterns of muscle activation.

Using such systems, we have shown that in-flight and trial-to-trial compensatory mechanisms have distinct neural bases and are differentially disturbed in certain neurologic diseases. We have shown that the brain’s ability to perform real-time error feedback control can be dramatically disturbed in Huntington’s disease. Furthermore, our work has revealed that such dysfunction begins to surface years before the disease’s symptoms become apparent, making it an attractive marker for future clinical tests of drugs to stave off the disease.

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See Also: http://www.deas.harvard.edu/motorlab