Neuroscience Graduate Program at UCSF
Cortical mechanisms of sensorimotor integration and learning: measuring and manipulating large-scale neural circuits
The ability to flexibly and adaptively integrate information from a variety of sources is a fundamental feature of brain function, from higher cognition to sensory and motor processing. Even a simple behavior such as reaching to a target relies on the integration of multimodal sensory signals and, moreover, exhibits rapid adaptation in response to changes in these signals. Our research uses reaching and similar goal-directed movements as a model system for understanding these abilities and their underlying neural mechanisms and, ultimately, for harnessing these abilities to repair brain dysfunction.
Our lab employs a combination of complementary approaches:
Cortical Physiology. Cutting-edge physiological techniques allow us to study and manipulate large-scale activity in sensorimotor cortex during behavior. We record from multiple 96-channel electrode cortical arrays, allowing us to study neural activity at the level of the population responses and across cortical circuits. We are also developing techniques to control the activity of cortical populations, both with patterned electrical stimulation across many electrodes with and patterned light stimulation in tissue expressing light-sensitive ion channels ("optogenetics").
Computational and Theoretical Modeling. We use computational and theoretical models to link our understanding of brain and behavior. Two levels of modeling are used. We develop predictive models of behavior, typically cast in statistical or control-theoretical terms, in order to gain intuition about why the behavior is the way it is. We develop network models that approximation our behavioral models in order to gain intuition and about candidate neural mechanisms. These models generate testable hypotheses about the dynamics of cortical networks, and we use these models to design of our physiological experiments.
Human Psychophysics and Physiology. With human psychophysics (or quantitative behavioral studies), we identify behavioral phenomena that illustrate important features of sensorimotor processing. The goal of this component our work is to find phenomena that are experimentally tractable for human and animals and are amenable to theoretical/computational modeling. We also have access to a variety of human neurophysiological tools, including functional magnetic resonance imaging (fMRI) and electro-corticography (ECoG).
Examples of three currently funded projects in the lab:
1. Brain Reorganization and Plasticity to Accelerate Injury Recovery: Multi-scale and Multi-modal Models Enabled by Next Generation Neurotechnology (DARPA):
A key feature of sensorimotor cortical circuits is their ability to flexibly and adaptively integrate information from a variety of sources in order to perform the complex computations required for movement planning and control. The goal of this project develop techniques to write information into the brain, using either electrical or “optogenetic” stimulation, and then to train an animal to interpret the information as a novel sensory pathway. This work is aimed at clinical applications such as neuroprosthetics and recovery after brain injury.
2. Learning in neural circuits: applied optogenetics in nongenetic models (NINDS):
“Optogenetics” refers to the use of genetically encoded, light-sensitive ion channels to control neural activity. These tools have been used in coarse fashion in non-genetic organisms, but a great deal of work is required to refine the tools to make them readily useable for the study of neural circuits and how these circuits change during learning. We are working with a team at UCSF and Stanford to building the tools and technology needed to apply optogenetics to non-genetic species. This project includes a targeted program of viral development and testing and the technology development for combined optical, physiological and behavioral experiments.
3. The cortical basis of visuomotor adaptation (NEI)
Even simple behaviors such as goal-directed reaching exhibit rapid and robust adaptation in response to changes in sensory feedback. These forms of learning have been extensively characterized at the behavioral level, and a variety of models have been developed (by our lab and others) to provide an intuitive understanding of these phenomena. Yet despite this progress, very little is known about how the underlying neural circuits change with learning or how sensory feedback drives these changes. The activity of large neuronal populations are recorded from multiple cortical areas as animals performs sensorimotor tasks that include adaptation. Our experiments are designed and analyzed in the context of the computational and theoretical models mentioned above.
Maria Dadarlat, Graduate Student
Matthew Fellows, Postdoctoral Fellow
Jiwei He, Postdoctoral Fellow
Blakely Magliaro, Research Staff
Joseph Makin, Postdoctoral Fellow
Juliana Milano, Research Staff
Nan Tian, Research Staff
Sensory transformations and the use of multiple reference frames for reach planning. McGuire LMM and Sabes PN. Nature Neuroscience., 12(8):1056-61 (2009)
Visual-Shift Adaptation Is Composed of Separable Sensory and Task-Dependent Effects. Simani MC, McGuire LMM, and Sabes PN. J Neurophysiol.,98(5):2827-2841 (2007)
The trial-to-trial dynamics of visually guided reaching reveal continual changes driven by error corrective feedback and internal drift. Cheng S and Sabes PN. J Neurophysiol., 97(4):3057-69 (2007)
Flexible Strategies for Sensory Integration During Motor Planning. Sober SJ and Sabes PN. Nature Neuroscience. 8(4): 490 - 497 (2005)
See the Sabes Lab Webpage for a more complete and updated publication list.