Precision Neural Dynamics Lab
Welcome to the Precision Neural Dynamics Lab! Directed by Dr. Adam Rouse, we explore how the brain puts our thoughts into action. Healthy people use their arms and hands every day to make complex and precise movements to perform the many activities of daily living. However, people with neurologic disease or spinal cord injury are severely limited in their abilities if it is difficult for their brain to signal their hand to move. Neural interfacing technology, modern computer processing and robotics are all leading to new ways that we can help patients with paralysis and movement disorders.
The Precision Neural Dynamics Lab is located at the University of Kansas Medical Center in the Department of Neurosurgery. In the lab, we collect and analyze large datasets looking at brain and spinal cord activity and behavior in animals, healthy humans and patients with neurologic disease. The number of nerve cells (neurons) in the brain and spinal cord that we can monitor simultaneously is growing rapidly and big data tools are allowing us to analyze and visualize the data in brand new ways. While in the past we may have only been able to say a neuron was connected to flex the elbow or extend the thumb, we now want to understand how a whole group of neurons work together to make coordinated movements like reaching and grasping, do it precisely, and make a corrective movement when needed. We are especially interested in how we can better use monitored neural signals to control external devices directly with brain-computer interfaces to restore lost function.
We design our experiments to generate data that tests current computational models of the nervous system and we then create new computational models based on the experimental data. Additionally, by being a research lab within a clinical department, we interact with neurosurgeons and other clinicians regularly to address current, real-world health care challenges.
Neural encoding of precision movements
The speed-accuracy tradeoff has been observed in both sensory and motor systems across a wide-range of animals. Fitts's law is well known to describe the trade-off between speed and accuracy of natural human movement across many tasks. Yet it remains difficult to explain what in our brain causes these fundamental limits. While certain limitations in robots and brain-computer interfaces are similar, they often are slower and noisier with different trade-offs. We want to develop better descriptions of why the neurons in our brain limit our ability to move both fast and precisely. We then are working to develop new decoding algorithms for brain-computer interfaces that can more closely mimic the speed and precision of natural human movement.
One area of motor precision we are particularly interested in is the relationship between target-independent and target-dependent neural signals. When we want to move our arm and hand, many neurons in the motor areas of the brain and spinal cord become active with any movement -- a target-independent signal. Additionally, different neurons become more or less active to signal which direction you want to move -- the target-dependent signal. Currently, it is often difficult to tell what part of any individual neuron's change in activity is linked to signaling a particular movement versus changes that happen for any movement just because the brain area became more active. Recent results in our lab have shown that the target-independent activity appears as a repeated pattern across a network of neurons with consistent timing that divides a movement into a set of submovements. We are further examining whether this is always the case or if there may be exceptions and how the timing of submovements influences one's ability to correct to incoming sensory information.
Neural mapping of hand function
We use our hand to perform a diverse set of movements. Many of these are grasping movements where the thumb and fingers must coordinate to contact an object in a precise and balanced way. When designing robots and brain-computer interfaces, it's easiest to think of each digit of the hand as a separate degree-of-freedom each with a separate control signal. Yet, when we move through our everyday lives, we rarely are consciously planning and aware of which digits we should use and how much to move each one. In the lab, we are developing new mathematical descriptions of how the brain organizes and maps a concept of a particular grasp to then signaled down the spinal cord to activate the individual muscles that move each joint. We're interested in identifying how neurons in the brain might organize and represent grasps as both (i) different categories of grasps - a pinch versus a whole-hand power grasp and (ii) different magnitudes - a hard versus soft grip. This mixture of being able to think in both categorical as well as continuous variables is a key aspect of not just hand movements but many forms of cognition and a common modeling challenge in computational neuroscience.
The Department of Neurosurgery at the University of Kansas Medical Center is grateful for the generous support of the research of Adam Rouse, M.D., Ph.D., by the Frank L. and Evangeline A. Thompson Opportunity Fund at KU Endowment.