Taosheng Liu, PhD - US grants

DESCRIPTION (provided by applicant): The environment contains far more information than the brain can process at once. To cope with such information overload, humans need to selectively attend to relevant information and prioritize its processing. In many situations, humans need to select arbitrary features and objects in the scene and maintain attention on the selected information. It is often assumed that an attentional priority signal encodes the current focus of attention and its deployment. However, how the brain computes and maintains attentional priority for features and objects is not known. The long-term goal is to understand how the brain selects different types of information and how selection shapes perception to serve goal-oriented behavior. The objective of this proposal is to delineate the cortical circuitry representing attentional priority for features and objects using functional magnetic resonance imaging (fMRI). Based on recent data obtained in our laboratory, we hypothesize that the dorsal frontoparietal network represents different types of selected information with distinct neural populations, forming a multiplexed representation of attentional priority. In this proposal, we wil test this hypothesis by pursuing four specific aims. First, we will determine the neural representation of attentional priority for visual objects. Second, we will seek to establish a quantitative link between priority signals and task performance. Third, we will determine the relationship between attentional priority signals for features and objects and those for spatial locations. Fourth, we will evaluate the degree of categorical representation of attentional priorit, which is essential for flexible deployment of attention. The proposed research is expected to significantly advance our understanding of how the brain selects and maintains non- spatial information, thus filling in a critical gap in the current scientific knowledge. A deeper understanding of how the brain selects features and objects will provide important constraints for models of attention and can potentially transform our understanding of visual information processing and cognitive control. The proposed research is innovative both in terms of conceptual and methodological advances. Conceptually, the research will test the novel hypothesis that the dorsal frontoparietal network represents attention priority for non-spatial dimensions, challenging the prevailing view that these cortical areas mainly represent spatial information. Methodologically, the application of cutting-edge machine learning and data mining techniques (pattern classification, similarity and clustering analysis) represents a novel approach that more fully exploits the complexity and richness of fMRI data than conventional methods. Finally, the proposed research can make connections to other fields such as category learning and decision making, and suggest interesting future directions to examine common neural processes underlying these cognitive functions.

Cognitive neuroscience research seeks to understand the basic brain mechanisms underlying cognitive and behavioral functions. While there are a variety of available research tools, the majority of these techniques are correlational, in that neural activity (or a proxy of neural activity) is measured while human subjects perform a task. Data from these techniques do not allow causal inference, which requires perturbation of the neural system. Transcranial Magnetic Stimulation (TMS) is currently the leading choice for neural perturbation in humans. With Major Research Instrumentation support from the National Science Foundation, Dr. McAuley and colleagues will purchase a TMS system to enhance research and training in cognitive neuroscience at Michigan State University. TMS can be used to produce temporary disruptions in neural activity or to stimulate the cortex in targeted brain regions. Recent developments in this technology allow image-guided TMS delivery, commonly referred to as neuronavigation. This method allows the TMS coil to be precisely positioned over a specified brain structure based on a person's neuroanatomical data obtained using magnetic resonance imaging (MRI) techniques. This capability is important because, although the structure of the brain is roughly similar across people, the exact anatomical location of neural structures can vary considerably. Targets for disruption/stimulation can be identified by selecting and highlighting the desired structure/locations with the brain. Image-guided (neuronavigated) TMS is quickly becoming a widely-used and standard technology in cognitive neuroscience research. The general value of this technology for cognitive neuroscience is that it is a non-invasive tool that can be coupled with functional and structural MRI data to make causal inferences about normal and disordered brain function that are not possible through fMRI/MRI studies alone.

The participating investigators are all active researchers in the cognitive neurosciences. The new instrumentation will transform Michigan State University's capability to conduct cutting-edge cognitive neuroscience studies that help unravel the neural bases of cognitive function in a diverse set of domains, including perception, attention, memory, cognitive control, and decision making. The acquisition will also more broadly contribute to university-wide neuroscience education and training initiatives at the graduate and undergraduate levels. As part of a new and popular undergraduate major in neuroscience, neuronavigated TMS will be included as one of the methods taught in a required core laboratory course that exposes students to a variety of different neuroscience methods. At the graduate level, the new instrumentation will strengthen introductory and advanced neuroimaging courses by providing new education and novel hands-on training and research opportunities. Michigan State University is a also leader in undergraduate research and training opportunities in STEM areas for women and underrepresented minorities, and the training opportunities created by the new instrumentation will interact synergistically with departmental, interdepartmental and university-wide initiatives. Opportunities for summer research experiences for high school students will be created through established contacts with science teachers in area high schools.

DESCRIPTION (provided by applicant): Impaired judgment and decision making is a key factor contributing to drug abuse. Research on the source of these impairments has focused largely on cognitive and neural deficits during two distinct periods. Before making a choice, drug abusers are overly sensitive to potential rewards and insensitive to long-term losses. After making a choice, drug abusers are hyper-reactive to rewards and show poor ability to learn from experience. Much less is known about the intervening process of deliberation when beliefs and desires are integrated over time to form a preference leading to a choice. This deliberation process can determine whether a person appears risk seeking or risk averse, impulsive or cautious, and slow or fast in responding. While these are characteristics directly relevant to drug abuse, the basic mechanisms of the process are not well understood. We conceptualize deliberation as a sequential sampling process where decision makers evaluate possible payoffs forming a subjective valence. These valences are accumulated over time forming a preference over the alternatives until a threshold is reached initiating a choice. In this application, we develop a theoretical and experimental framework that integrates computational modeling and cognitive neuroscience to characterize this deliberation process. Experimentally, we create a novel gambling task called the flash gambling task (FGT) in which participants choose between a sure payoff and a lottery that offers a draw from a distribution of payoffs. Instead of receiving verbal descriptions of the lottery, subjects watch simulated draws from this lottery that flash by every 50 ms (like watching a stock ticker run by). Thus, the FGT requires active integration of payoffs allowing more precise control over the deliberation process. Theoretically, we develop a framework that integrates computational models of decision making, neural studies of reward processing and perceptual decision making, and analytic models of hemodynamic response. Our model makes specific predictions regarding the fMRI BOLD signatures of different aspects of deliberation process during risky decision making. In this application, we use this model-based imaging approach to delineate the neural circuitry underlying the valuation and preference formation process in an fMRI experiment on a normal college sample. In a second study, we investigate the link between behavior and deliberation in the FGT and measures of risky drug use and impulsivity using a larger community sample. Results from these studies will offer new insights on the basic cognitive and neural mechanisms of risky decision making and establish potentially important links between process-level measures of choice behavior and drug use, thereby setting the stage for a greater understanding of the neural and computational basis of drug abuse.