2007 — 2009 |
Ding, Long |
K99Activity Code Description: To support the initial phase of a Career/Research Transition award program that provides 1-2 years of mentored support for highly motivated, advanced postdoctoral research scientists. |
Neuronal Basis of Reward-Biased Visual Perception @ University of Pennsylvania
Project summary: 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 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.
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2013 — 2021 |
Ding, Long Gold, Joshua I (co-PI) [⬀] |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Role of Basal Ganglia in Reward-Biased Visual Decisions @ University of Pennsylvania
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.
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2018 — 2019 |
Ding, Long Gold, Joshua I (co-PI) [⬀] |
R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
The Roles of Zona Incerta in Oculomotor Decision Making in Monkeys @ University of Pennsylvania
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.
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2020 |
Ding, Long Luo, Wenqin [⬀] Park, Hyun Soo (co-PI) [⬀] |
R34Activity Code Description: To provide support for the initial development of a clinical trial or research project, including the establishment of the research team; the development of tools for data management and oversight of the research; the development of a trial design or experimental research designs and other essential elements of the study or project, such as the protocol, recruitment strategies, procedure manuals and collection of feasibility data. |
Developing a Mouse Chronic Pain Scale by 3d Imaging and Measurement of Mouse Spontaneous Behaviors @ University of Pennsylvania
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).
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