The brain’s ability to organize and integrate different experiences so it can efficiently file and cross reference information is critical for daily life. Our goal is to understand how memories are stably stored and flexibly updated across time and experience. How do memories accumulated across a lifetime enable us to learn from past experiences to predict future outcomes? How do fear or trauma alter past memories or future experiences in adaptive or maladaptive ways? How can memories become inaccessible or lost as we age? We use a multilevel approach to investigate the dynamic neural mechanisms governing these complex processes in health and disease, including in vivo calcium imaging, in vivo optogenetics and chemogenetics, electrophysiology, immediate-early gene tagging, and various behavioral assays.
The Rich Lab studies how the collective activity of neural populations produces complex cognition. Their main goal is to reveal basic principles that organize brain function, and use this knowledge to gain insights into the causes of psychiatric symptoms. Ongoing studies focus on learning, memory, and decision making as critical processes frequently disrupted in psychiatric disease. Recent results have demonstrated heterogeneous dynamics of expected reward coding in the orbitofrontal cortex and dynamic rearrangement of memory codes with the use of mnemonic strategies. This work emphasizes the flexibility of neural systems, which likely contributes to our ability to accomplish a wide variety of cognitive tasks.
How do different pleasurable or aversive situations affect our emotional state? How do we weigh the costs and benefits of different courses of action to obtain things we want and avoid those that we dislike? What happens in the brain when these processes become dysfunctional in psychiatric disorders? In the Rudebeck Lab, we focus on answering these questions with the aim of determining the neural circuits engaged during emotional processing and decision-making. Our bigger aim is to establish the circuits and patterns of activity within those circuits that are engaged in healthy brain and characterize what happens in disease-like states to help develop interventions that will correct dysfunction in psychiatric disorders. To do this, we use a combination of behavioral, autonomic, electrophysiology, functional magnetic resonance imaging, and chemogenetic approaches in animal models.
The Schiller Lab is investigating the neural basis of emotional learning and memory, and social cognition. We particularly focus on the behavioral and neural determinants of memory modification and the conditions that allow memory updating. The lab also examines the use of imagery and imagination to modify the neural representation and behavioral manifestation of emotional memories. Another line of research demonstrates how experiencing ongoing social interactions is akin to an act of navigation, and shows parallels between the neural mechanisms of spatial and social navigation.
The Shuman Lab uses state-of-the-art recording and manipulation techniques to examine how circuits control behavior and how abnormal circuit processing can lead to neurological diseases such as epilepsy and Alzheimer’s disease (AD). Our primary goal is to find causal circuit mechanisms that lead to seizures and cognitive deficits in epilepsy and AD and to determine how they can be suppressed with novel interventions. We specialize in recording neural activity during active behavior using in vivo calcium imaging with miniature microscopes and in vivo electrophysiology with silicon probes. In recent studies, we have discovered profound deficits in the synchronization of neural activity in mice with chronic epilepsy and models of AD pathology. We are developing new tools to study and manipulate these circuits, including closed-loop optogenetics to manipulate the synchronization of individual cell populations. We hope these new tools will provide key insight into the neural circuit processes that drive these disorders so we can develop new treatment options.
Our work in computational psychiatry examines the neural and computational mechanisms underlying decision making and social behaviors in humans, and how they might go awry in neuropsychiatric conditions such as addiction, autism, depression, and personality disorders. In the Gu Lab, we use a combination of computational modeling, and both invasive (e.g., lesion, intracranial recording) and non-invasive (e.g., brain imaging) methods in humans.