Major depressive disorder affects an estimated 20% of the United States population and is one of the nation's leading causes of lost productivity, resulting in an estimated 200 million lost workdays each year. In order to design targeted and effective treatments for depression it is essential to understand the neural mechanisms underlying the entry into, maintenance of, and exit from the depressed state. The goals of this proposal are to discover the functional cellular-level neural signatures of depression by monitoring and decoding the network activity of large populations of identified single neurons. Leveraging the power of the most recently developed technologies for probing brain function, we will examine baseline neural activity patterns in a population of neurons during normal behavior, follow the evolution of these patterns in this same population of neurons during the induction and maintenance of a depression-like state, determine whether antidepressant therapies bring the network back to a baseline state or an alternate state, and attempt to normalize these pathological neural dynamics with fast, circuit-based, optogenetic intervention. Specifically, we will record the neural activity of genetically or topologically identified network of neurons using the genetically encoded calcium indicator GCaMP6f and a fluorescence microendoscope designed for use during free behavior. Neural activity will be monitored before, during, and after the induction of a depression-like state (with chronic mild stress) during behaviors that probe processes relevant to depression and during the resting state. Neuronal population data will be analyzed to identify network states associated with depression. We will then use targeted optogenetic stimulation to move the network dynamics back towards the baseline patterns seen before the induction of depression, a novel application of simultaneous optical control and readout. The research proposed here is extremely well suited to the goals of the New Innovator program. New technological approaches will be developed and used to ask questions about disease-related changes in neural circuit function that are not possible to address with existing methods, and new approaches to the circuit-specific treatment of depression will be tested with an eye towards translation to human patients. My scientific background in systems neurophysiology, computational neuroscience, and optogenetics combined with my demonstrated productivity in the field of depression research is the precise combination of skills and interests needed to execute this paradigm-shifting work.
Clinical depression is widespread in the United States and represents one of the nation's greatest health challenges. In order to design new treatments that are more specific, work more quickly, and have fewer side effects it is essential to understand the neural mechanisms underlying the onset, maintenance, and remission of depression. This research has the potential to identify and correct specific neural activity patterns that are associated with depression.
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