Long Ding - US grants

[unreadable] DESCRIPTION (provided by applicant): The objectives of this Pathway to Independence Award for Long Ding, Ph.D., are to expand the candidate's expertise in the field of visual perceptual decision-making and to establish a novel experimental model to fill the knowledge gap on how visual perception is affected by reward-induced internal preferences. Achieving the first objective will complement the candidate's previous training in basal ganglia physiology related to learning and neural representation of reward. Achieving the second objective will provide a launching point for the candidate to develop an independent research career, toward a long-term goal of gaining a better understanding of the basal ganglia functions in reward modulation of perception. These objectives will be accomplished in two phases. In the 2-year mentored phase, the candidate will conduct supervised research in Dr. Joshua Gold's lab at the University of Pennsylvania to identify neural correlates of external evidence-based perceptual decisions in the basal ganglia and frontal cortex, using single-unit recordings in monkeys performing a visual motion discrimination task (Aim 1). This training environment is uniquely suitable because of Dr. Gold's expertise in visual perceptual decision-making, the immediate availability of well-trained monkeys and the intellectual resources available at Penn, which is renowned for its research on vision and perception. In the next 3-year phase, the candidate's independent research will identify neural correlates of decision-making that integrates reward-induced internal preference and external evidence in the basal ganglia and frontal cortex (Aim 2). This research will be based on single unit [unreadable] recordings in monkeys performing a novel visual motion discrimination task that also incorporates reward bias. The proposed research is designed to test the central hypothesis that the neural representations of external evidence and reward-induced internal preference are co-localized and incorporated in individual neurons in the basal ganglia and frontal cortex. It represents an innovative merging of established lines of research and will advance our understanding of the neural mechanisms underlying normal visual perception. Relevance to public health: The proposed research represents an important research area of visual perception and will facilitate future identification of neural targets for better treatment of patients with perceptual impairment. [unreadable] [unreadable] [unreadable]

DESCRIPTION (provided by applicant): Everyday behaviors are often driven by a complex interplay between external visual cues and internal desires, aversions, and idiosyncratic biases. For example, catching a baseball requires following its trajectory in flight but also suppressing a fear of getting hit by it. Ordering a meal at a restaurant depends on personal food preference but can also be biased by the sights (and smells) of other meals being served nearby. Despite recent progress in our understanding of how the brain interprets visual inputs to form perceptual decisions and how the brain uses outcome expectations to form value-based decisions, it remains unclear how the brain coordinates these processes. Our goal is to establish and characterize the central role played by the basal ganglia, an interconnected network of subcortical brain regions implicated in learning, valuation, and action selection, in coordinating visual and non-sensory factors to guide oculomotor behavior. Our four specific Aims address this topic by combining quantitative measures of behavior with electrophysiological techniques. Aim 1 establishes an experimental and theoretical framework to quantitatively measure decision behavior based on both uncertain sensory information and reward-induced internal preferences. Aim 2 uses single-neuron recordings to identify the neural computations in caudate, a primate input structure in the basal ganglia oculomotor pathway, in animals performing the novel task developed in Aim 1. Aim 3 uses electrical microstimulation applied to sites in the caudate at different times within a trial of animals performing the task to test for a causal role in the decision process. Aim 4 uses anti- and orthodromic stimulation techniques to examine the nature of the signals sent from prefrontal cortex to caudate and to determine the extent to which decision- and reward-related processing emerges in caudate or is sent there directly. The proposed project represents the first systematic examination of the caudate's role in complex decision-making involving uncertain visual stimuli and varying reward expectations. Upon completion, the new results will substantially advance our understanding of neural basis of complex visual decision-making, by providing much-needed insights into how and where in the brain visual cues are combined with non-sensory factors to guide our decisions. In the long-term, these insights will help build a more complete understanding of clinical impairments in goal-directed behavior, especially those involving the basal ganglia.

PROJECT SUMMARY Complex decisions require appropriate interactions within and across regions of a large brain network. It remains unclear how decision-related computations are implemented by such a network, even for the most extensively examined oculomotor decisions. We propose to explore the contributions of zona incerta (ZI) to the oculomotor decision process. The ZI has diverse connections to most areas of the cortex, thalamus, substantia nigra pars compacta and pars reticulata, and superior colliculus. Because these latter areas have all been shown or implicated to be involved in oculomotor decision process, the ZI is anatomically well-positioned to exert control over the decision process. There is, however, a knowledge gap in the computational roles of ZI for decision making and cognition in general, largely due to lack of neurophysiological data from awake, behaving animals. We propose to explore the roles of ZI in non-human primates performing oculomotor decision tasks, using a combination of behavioral, neurophysiological and computational techniques. Specifically, in Aim 1, we will perform single-unit recordings of ZI neurons in monkeys performing on a demanding visual perceptual decision task with reward manipulations and characterize the task-related modulation patterns of ZI activity using descriptive statistics. In Aim 2, we will relate decision-related ZI activity to the drift-diffusion framework to infer ZI's specific computational roles. These results are expected to advance our understanding of neuronal mechanisms underlying decision-making, particularly ZI's contributions to cognition, and, in the longer term, facilitate the development and refinement of clinical interventions that target the ZI.

PROJECT SUMMARY Rodent models are highly valuable for elucidating the molecular and cellular mechanisms of chronic pain. Because rodents cannot articulate their sensation, ?pain-like? behaviors have been used as the proxy. However, sensitivity and specificity of many existing methods for measuring rodent ?pain? sensation, especially ?chronic pain?, are uncertain. Here we propose to explore the feasibility of a largely automated and data-driven behavioral assay for identifying spontaneous pain in freely behaving mice. Specifically, we will take advantage of recent advances in 3D motion analysis, which enable precise and robust measurements of movements without human intervention, to extract movement features from freely moving mice in various pain states (baseline, induced acute pain, chronic pain, and with painkiller treatment). We will generate a database of movement features of control mice and mice with induced acute cheek/leg pain or chronic neuropathic cheek/leg pain, using both sexes of two mouse strains. We will then use machine-learning algorithms to identify the best combination of movement features for predicting the pain state (a ?mouse chronic pain scale?). These efforts are expected to produce a novel and objective method to assess spontaneous pain, a characteristic feature of chronic pain, in mice. This method can supplement our recent method in measurements of evoked responses (a ?mouse acute pain scale?) to provide efficient, robust, and comprehensive assessments of pain-related rodent behaviors and facilitate mechanistic investigations of brain circuits in mediating and modulating pain. Our interdisciplinary team is well suited to complete these Aims, utilizing combined expertise in mouse somatosensory/pain system (PI Luo), behavioral, systems and computational neuroscience (PI Ding), and 3D imaging and computer vision (PI Park).

When decisions are made, which can be as simple as where to have lunch or as complicated as what career to pursue, time is often spent deliberating over uncertain evidence and possible outcomes. This deliberation process is central to behavioral and cognitive flexibility but requires overcoming a major challenge: determining when to stop deliberating and commit to a course of action. Many models that have been used to study human decision-making and to implement machine decision-making assume that decision commitment occurs when the accumulated evidence reaches a fixed value or bound. These “accumulate-to-bound” models, whose development last century helped formalize and improve decisions related to codebreaking, manufacturing, and other real-world applications, have also provided a useful starting point for understanding decision commitment in the brain. However, fixed bounds are most appropriate under fixed conditions, which are often used in laboratory experiments but rarely encountered in the real world. The goal of this study is to move beyond the “fixed bound” form of decision commitment and instead consider more flexible ways the brain uses to end deliberating and arrive at a decision. The research starts with a new, mathematically grounded theory that describes the advantages of using flexible forms of commitment under changing conditions. The goal of the project is to use this theory to design and interpret experiments that will provide a comprehensive new view of how human brains commit to decisions even when they are based on deliberations that occur during uncertain and changing conditions. This work will provide interdisciplinary training at the interface of mathematics, cognitive science, psychology, and neuroscience for undergraduate and graduate students from diverse backgrounds. The team will use research-related activities to encourage the participation of underrepresented groups in science. Also, the team will develop and disseminate resources, including novel datasets and analytic tools, that will benefit research and education in how the brain makes decisions. Finally, the team will increase public awareness of computational neuroscience and address the urgent need to increase scientific literacy and understanding of how science can benefit society. This will be accomplished via public lectures, contributions to the program “Engines of Our Ingenuity” broadcast by National Public Radio stations nationwide, Brain Awareness Week activities, and contributions to a website that explains brain research to non-specialists.<br/> <br/>Deliberative decisions free the brain from the immediacy of reflexive processing but pose a critical challenge: how does the brain decide when to stop deliberating and commit to a course of action? Our understanding of this commitment process has been dominated by a computational framework that assumes decisions are terminated once accumulated evidence reaches a predefined level or bound. These “accumulate-to-bound” models have close ties to normative theory and can explain a range of behavioral and neural findings. However, they are optimal only under the highly restrictive conditions used in many decision studies, in which the informativeness, rate of acquisition, and other features of the evidence are stable and known in advance. It is not known how the brain balances decision deliberation and commitment more generally, when temporally extended decisions must contend with our dynamic and uncertain world. The goal is to advance our understanding of the decision rules used by the brain under these conditions. The PIs start with a novel theoretical foundation that includes flexible decision bounds that are not predefined but instead can be updated while the decision is being formed to optimize performance even under changing conditions. They use this framework to guide the design and analysis of behavioral studies in humans and combine behavioral and neurophysiological studies in non-human primates, which they use to test their primary hypothesis that the primate brain uses dynamic, adaptive rules to support rational decision-making in changing and uncertain environments.<br/><br/>This project is co-funded by the Division of Mathematical Sciences (DMS) within the Mathematical and Physical Sciences Directorate (MPS), Division of Information and Intelligent Systems (IIS) in the Directorate of Computer and Information Science and Engineering (CISE), and Division of Behavioral and Cognitive Sciences (BCS) within the Directorate for Social, Behavioral, and Economic Sciences (SBE).<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.