Jesse H. Goldberg, MD/PhD - US grants

This award is jointly made by two programs the Instrument Development for Biological Research program (IDBR) and Emerging Frontiers (EF) in the Directorate of Biological Sciences (BIO).

A key roadblock to expanding our knowledge of the brain is that existing tools to probe its function are simply not up to the enormity of the task. Techniques for recording neural signals allow at most signals from tens or hundreds of neurons to be recorded, when thousands or even millions are needed. To address this challenge, improved types of sensors are required that record neural activity with greater signal quality and are more suited to parallel recording than current approaches. This collaborative project will develop a new type of sensor based on flexible graphene transistors. Graphene is an atomically thin sheet of carbon atoms that exhibits desirable physical, chemical, and electrical properties for interfacing with biological systems. However, the use of graphene transistors for single neuron sensing is largely unexplored. Open questions include the nature of the contact between the neuron and the graphene, the ultimate strength of electrical signals, and the long-term biocompatibility of graphene-based electrodes in the brain. Our proposed work will address these fundamental questions. If successful, this project will ultimately lead to societal benefits such as better neural prosthetics and new treatments for neurological disorders. Training opportunities at the interface of nano- and neuroscience, a key area of need for America?s technological future, will be available for graduate students.

Graphene can be nanofabricated into arrays of conformable, stretchable transistors using techniques borrowed from the Japanese paper art of kirigami. Prior work of this research team has shown that unlike traditional electronic devices, such graphene transistors are both extremely flexible and can stretch by > 100% without degrading their electrical properties. The current project will investigate the electrical and mechanical interactions between kirigami graphene and individual neurons. Graphene will be cut into different patterns to optimize the mechanical contact between graphene and single neurons. Wrapping of graphene on the neuron will be maximized and the graphene will be used to measure voltage spikes produced by individual neurons, both in vitro and in vivo. With optimized conformal contact between graphene and neurons it will be possible to measure a large fraction of the intracellular potential (~ 70 mV). The semiconducting properties of graphene will also be used to amplify the bioelectronic signals to robust levels to facilitate multiplexed detection of thousands of neuron signals.

? DESCRIPTION (provided by applicant): Deficits in movement initiation, control and variability constitute the core dysfunctions of neurological disease, but we still don't know how these processes are implemented in the brain. The main obstacle is the sheer complexity of brain pathways for movement. The mammalian motor system is a distributed group of neural circuits, which are in turn comprised of complex microcircuits and specific cell types. Because we don't know how these small circuit elements influence behavior, current treatments lack effectiveness and specificity. To address this problem, we developed a panel of new technologies that will allow us to define how previously inaccessible microcircuits control motor behavior. First, we invented a touch-sensing joystick that quantifies mouse forelimb trajectories with unprecedented (micron-millisecond) spatiotemporal resolution. Second, we incorporate this joystick into automated, computer-controlled homecages that perform real-time behavioral analysis and high-throughput behavioral training. Third, we devise a new way of doing high-throughput optogenetics in untethered mice using newly available red-shifted opsins. Finally, we demonstrate for the first time that mice can learn complex center-out forelimb tasks similar to ones long used in primates. By establishing a new, sophisticated motor learning paradigm in mice - a tractable model system with powerful genetic tools - we are now poised to selectively manipulate neural activity in large batches of behaving animals. First, we will perform projection-specific optogenetic silencing to determine how each of fourteen pathways converging on mouse forelimb motor cortex controls movement initiation and variability in the joystick trajectories. Next, we will use Cre-transgenic mouse lines to test how distinct layers inside forelimb cortex differentially control these processes. For both of these experiments, real-time behavioral analysis will enable optogenetic manipulations to be time-locked to specific task events and animal postures, as well as at distinct stages of skill learning. In summary, the proposed work combines unprecedented readout of motor output with unprecedented tools for manipulating previously inaccessible parts of the mammalian motor system. Our new behavioral and experimental paradigm will identify yet-to-be discovered circuits controlling movement initiation, variability and learning. If successful, it will no longer be so mysterious where tremos, dystonias, akinesias and choreas come from. We will be able to point to specific pathways and cell types positioned to cause specific deficits, which in turn will provide a roadmap towards the next generation of more targeted therapies.

? DESCRIPTION (provided by applicant): A principle aim of the NINDS is to determine how motor sequences are constructed by the nervous system. Dopamine (DA)-basal ganglia (BG) circuits are required for motor sequence learning, but it remains unclear how these circuits guide the trial-and-error learning process. Remarkably, our current understanding of these pathways comes largely from studies of animals learning simple actions for external rewards such as food or juice. Yet symptoms of BG diseases such as Parkinson's, Huntington's and dystonia include degradation of motor behaviors unrelated to reward seeking. And most human behaviors, such as learning a sport or an instrument, are not simple actions in pursuit of external rewards but are instead complex motor sequences learned by matching performance to internal goals. The songbird model system offers a unique opportunity to study how internally guided motor sequences are constructed. Zebra finches learn their song by matching a complex vocal sequence to an auditory memory of a tutor song. This sensorimotor learning requires a DA-BG circuit that is part of a tractable 'song system.' We will apply our core strengths in awake- behaving electrophysiology to the tractable songbird model system to decipher how motor performance is evaluated during practice. First, to test if DA neurons evaluate motor performance (the 'error' part of learning) we will conduct the first-ever recordings of BG-projecting DA neurons while controlling song 'error' with distorted auditory feedback (Aim 1). Preliminary recordings support the hypothesis that DA neurons encode 'performance prediction error' signals during singing. To determine how upstream sensorimotor signals compute 'error,' we will record from auditory cortical and BG projections to DA neurons in singing birds during the error-feedback task (Aim 2). Finally, zebra finches sing in two DA-dependent motor states: a variable practice mode when alone and a female-directed, stereotyped performance mode. To test if DA can both evaluate performance and also control its variability, we will record DA neurons during the error feedback task during undirected-to-directed song state transitions (Aim 3). Altogether, these studies will identify the neural correlates of the internal evaluation system that construct motor sequences. A major impediment to understanding pathological activity patterns observed in BG-related diseases is a limited understanding of signal propagation through the healthy circuit. The proposed work aims to understand the functions of DA-BG signals and how they are processed at successive stages of the circuit. At stake in this issue is the potential to tailor therapies, such as neural circuit re-programming and deep brain stimulation for movement disorders, based on detailed knowledge of normal brain physiology.

Summary Our goal in this project is to develop a new class of electrical recording device that complements and piggy- backs on cutting edge imaging technologies. Whereas multi-electrical recording has provided detailed measurements of neural activity with high temporal precision, it is also invasive, provides relatively low spatial resolution, and provides little information about the identity of measured neurons. Optical imaging techniques, conversely, provide very fine spatial resolution, easing neural identification, but at the cost of significantly worse temporal resolution, and with the requirement of either chemical (through fluorescent dyes) or genetic modification of the tissue. In order to better bridge these two modalities, we envision developing untethered Microscale Optoelectronically Transduced Electrodes (MOTEs) which combine optoelectronic elements for power and communication with custom CMOS circuits for low-noise amplification and encoding of electrical signals. Each MOTE will be powered by optically stimulated micro-photovoltaic cells and will use the resulting 1-2µW of electrical power to measure, amplify, and encode electrophysiological signals, up-linking this information optically by driving an LED. MOTEs will avoid many of the problems associated with standard wire- and shank-based electrodes, where most of the volume of the implanted electrode, and so most of the tissue damage it does, stems from the long rigid shank that connects electrode sites to external electronics. To be most useful, MOTEs' photovoltaics will be designed to harvest power from optical stimuli of the same wavelengths and intensities as are used in stimulating fluorescence when imaging neural activity. Similarly, the LED used for uplink will be designed to emit light at wavelengths and intensities consistent with those detectable by a fluorescent imaging system. These choices will allow the both down- and up-link of optical signals to be handled by existing imaging systems with minimal modification. By employing a pulsed stimulation (as is used in multi-photon systems) and appropriately encoding and timing up-linked LED pulses, fluorescent and MOTE emissions can be segregated into adjacent sub-microsecond time bins. This combination of optical compatibility and temporal multiplexing will allow simultaneous imaging and electrical recording of neural activity from the same volume of neural tissue, using the same optical imaging and recording systems. This simultaneous, heterogeneous measurement capability will enable a much wider range of experiments and studies of neural activity than are presently possible.

PROJECT SUMMARY When Confucius said, ?Tell me who are your friends, and I?ll tell you who you are,? he was noticing that how we behave and communicate is shaped by who we choose to hang out with every day. We constantly mimic the mannerisms and behaviors of friends and loved ones. Yet the neural basis of how we imitate, and more importantly who we choose to emulate and why, is largely unknown. Parrots provide a powerful yet untapped model system for social learning. Parrots, like humans and non-human primates, live in a specific type of ?fission- fusion? social network in which making and maintaining friendships is the key to fitness. Like humans, they selectively imitate and learn the names of their carefully selected companions. Here we aim to launch parrots as a new animal model in systems neuroscience. In aims 1, we will record neural activity in the vocal motor cortical output of the song system (nucleus AAC) in pairs of budgerigars engaged in courtship interactions. In these first- ever neural recordings form awake, behaving parrots, we are finding that AAC neurons exhibit premotor signals for vocalizations (as expected) and for expressive gestures such as silent kissing, head-bobbing and allogrooming. This joint vocal and gestural neural control, observed in human Broca?s area but not in songbirds ? means that what was thought to be a songbird-like ?song system? is actually a more general system for social interaction. We next test the causal relationship between song system activity and social behavior. Inactivating AAC during courtship interactions will test if/how vocalizations and gestures degrade or lose their coordination (Aim 2.1). Inactivating frontal or posterior cortical inputs to AAC in bonded pairs will test the songbird-inspired idea that variability and order depend on distinct cortical pathways (Aim 2.2). For each inactivation experiment, a pair of interacting birds is conceptualized as a single dynamical system ? and we will use machine learning guided behavioral analysis to quantify how vocalizations and gestures change (or do not) in both the inactivated and non-inactivated partner. Finally, in Aim 3 we will image dopamine (DA) release using fiber photometry and genetically encoded DA sensors. Pilot data demonstrate feasibility of DA imaging in singing birds. These experiments will test for the first time if DA signals, known to evaluate the quality of reward outcomes, similarly evaluate social outcomes. Courtship dynamics are perfect because gestural ?requests? to allogroom or ?kiss? are rejected or accepted with visually and acoustically obvious ?consent? or ?deny? signals. Males make hundreds of advances per day and use female feedback to learn ? providing natural trial structure, within-session learning, and ?events? to which we can align simultaneously recorded male and female DA signals ? which may or may not come into alignment as a pair ?decides? to bond or not. Budgerigar interactions resemble human conversations ? a back-and-forth of vocalizations and gestures that both communicate agonistic or affiliative signals and control vocal learning and partner selection. Together, these experiments will help establish parrots as a new model system in social neuroscience and will ready us for a follow-up R01 submission in two years.