Joseph W. Kable, Ph.D. - US grants

DESCRIPTION (provided by applicant): Previous studies of the neural mechanisms of decision-making have neglected an essential factor- that real world decisions almost always involve outcomes occurring at different times. Generally, delayed outcomes are valued less than immediate outcomes, a process called temporal discounting. Decision-making deficits seen in drug addiction, frontal lobe damage and psychiatric disease may centrally result from altered temporal discounting. This project investigates the neural mechanisms of temporal discounting in humans, by measuring neural activity with fMRI while subjects decide between monetary gains (Aim 1) or losses (Aim 2) that occur at different times. We will test whether any regions represent the subjective value of outcomes, discounted for the delay to their occurrence, and further how any such representations (neurometric discount functions) are related to psychophysical discount functions calculated from subjects' choices. Ultimately, a mature model of how the brain makes decisions must explain how different outcomes are valued given that they rarely occur at the same time. By aiming to establish a quantitative understanding of the neural processes involved in temporal discounting, this project hopes to build towards that goal.

DESCRIPTION (provided by applicant): When someone chooses to start a diet, to quit smoking, or to enter an alcohol treatment program, they are showing that their preferences have changed. This project investigates the neural mechanisms underlying changes in preference. The focus is specifically on preferences for immediate versus delayed rewards, since exaggerated discounting of delayed rewards is a central process in many addictions. Previous research has shown that neural activity in two specific regions-medial prefrontal cortex and striatum-encodes the subjective value of immediate and delayed rewards during decisions. The proposed research will test whether shifts in preference are due to shifts in these neural value signals. This hypothesis will be tested for three kinds of preference changes: momentary changes due to contextual biases in decision-making (Aim 1); long-term changes due to explicit education and the use of self-control (Aim 2); and changes due to social learning (Aim 3). In each domain, manipulations will be designed to reduce myopic choices of smaller, immediate rewards and to enhance preferences for larger, delayed rewards. Functional neuroimaging will be used to identify what brain regions are engaged by these manipulations, and individuals with focal brain damage will be studied to test what brain regions are necessary for these manipulations to succeed. Since many addictions are characterized by frontal dysfunction, the studies of frontal lesions in particular will inform how these manipulations could work in addiction, and set the groundwork for future studies in addicted populations.

DESCRIPTION (provided by applicant): We learn from experience to make more effective decisions, often adjusting our expectations to match outcomes that we have experienced in the past. In a dynamic world, this adjustment process must itself be adaptive, because sometimes changes occur that render past outcomes irrelevant to future expectations. For example, a forager must recognize that a dying tree will no longer yield as much fruit as when it was healthy. The goal of the proposed research is to test the novel hypothesis that certain individual differences in decision- making, including some that depend systematically on age, result from differences in how observed changes in the world affect the use of past experiences to inform future decisions. We propose that individuals differ substantially in how they recognize and respond to two different forms of uncertainty. One form of uncertainty, called noise, represents random fluctuations in an otherwise stable process. The other form of uncertainty, called volatility, represents fundamental changes in the process itself. Individual, adaptive decision-making ranges from a tendency to always adjust expectations in the face of either noisy or volatile new data, to a tendency to form stable expectations that are relatively unaffected by new data. We have identified computational principles that govern this kind of adaptive belief updating, which include the importance of prior expectations about the rate of occurrence of volatile changes in the current environment. We also propose that the important underlying computations are encoded by the activity of neurons in the anterior cingulate cortex (ACC). To test these hypotheses, we use two complementary Aims that provide multiple measures of ACC activity in individual subjects of different ages performing tasks designed to measure directly the amount of influence each new piece of information has on existing beliefs about a dynamic environment. The first Aim uses combined behavior and neurophysiological recordings in younger and older monkeys, providing measurements of the relevant neural computations with high spatial and temporal resolution. The second Aim uses combined behavior and fMRI- and EEG-based measurements of brain activity in human subjects, which can be compared directly to the higher-resolution monkey data. The human studies also include several versions of the task, including one designed to manipulate and measure prior expectations about the rate of environmental changes directly, that provide a broader view of the individual and age-related differences that occur with these kinds of decisions. The proposed research represents a novel field of study that is likely to provide far-reaching insights into how individuals make effective decisions in a dynamic world. PUBLIC HEALTH RELEVANCE: A critical task for decision makers is to determine the relevance of past experiences to the current environment. The proposed work tests a novel hypothesis about specific brain mechanisms responsible for this process, which we propose are responsible for certain individual, age-related differences in how we make decisions. This work will establish foundational, basic knowledge that, in the long term, will help to guide the development of new tools to diagnose and counteract conditions associated with abnormal decision making, including ADHD and schizophrenia, along with cognitive deficits that occur with aging.

DESCRIPTION (provided by applicant): This study addresses the provocative question: Why don't more people alter behaviors known to increase cancer risk? (PQ4). Emerging work in behavioral economics has shed light on the critical role of reward-based decision-making processes in health risk behavior. In parallel, research in cognitive neuroscience has clarified the central role of the dorsolateral prefrontal cortices (DLPFC) in cognitive control during decision-making. Thus, we propose to integrate these lines of research and advance the science of behavior change by testing whether enhancement of DLPFC function via neurocognitive training improves decision-making processes that contribute to risk behavior. Young adults (ages 18-30; n=150) will participate in a five-week web-based neurocognitive training program or a cognitive stimulation (control) condition, based on random assignment. The evidence-based neurocognitive training focuses on enhancement of targeted cognitive processes to facilitate self-control and goal-directed behavior: sustained attention, working memory, and response inhibition. This intervention, shown to be highly effective for cognitive remediation in neuropsychiatric illness, has been adapted as a web-based tool for the proposed study to enhance cognitive function in healthy subjects. Importantly, our pilot data support the feasibility, high levels of compliance, and beneficial effects on neurocognitive performance. Our primary aim is to evaluate effects of neurocognitive training on neural activity and decision-making behavior. Our secondary aim is to examine the neurobehavioral mechanisms that mediate effects of neurocognitive training, including changes in executive cognitive function. Changes in decision-making processes and neural activity associated with neurocognitive training will be assessed at baseline and post-training by acquiring functional magnetic resonance imaging (fMRI) while participants perform reward-based decision-making tasks, specifically delay discounting and risk sensitivity. Cognitive performance will be assessed at these time points using a validated battery of tasks, in order to examine mediation effects. A three-month follow-up assessment will test the durability of the effects of neurocognitive training beyond the training period. Thus, this application breaks new scientific ground by applying novel concepts and tools from the field of cognitive neuroscience to accelerate the study of basic mechanisms of behavior change. These data will inform the development of novel and more comprehensive interventions for behavior change (e.g., combining neurocognitive training with existing behavioral interventions). As a basic mechanism study, the knowledge generated will be relevant to multiple health risk behaviors, enabling a potentially broad impact on cancer prevention.

What makes people behave so differently from one another? Consider how we make decisions. Some people are quick and decisive but overly rigid, unable to adapt effectively to new opportunities or threats. In contrast, others may be more deliberative and less confident, making their decisions less predictable but more adaptable to changing circumstances. With funding from the National Science Foundation, Drs. Joshua Gold and Joseph Kable of the University of Pennsylvania are investigating a new theory that in the real world, there is a fundamental tradeoff between these two extremes. The theory includes a novel proposal that what has previously been dismissed by researchers as random variability in human behavior might instead reflect uncertain, adaptable decision-making linked with norepinephrine, a neurochemical implicated in learning and arousal. Does this characteristic explain other aspects of human personality and behavior? Can norepinephrine levels in the brain be manipulated to affect complex learning and decision-making behaviors? In answering these questions, this work will establish foundational, basic knowledge that, in the long term, will help to guide the development of new tools to diagnose and counteract conditions associated with abnormal learning and decision-making, including attention deficit hyperactivity disorder (ADHD), anxiety, depression, and schizophrenia. This knowledge about individual differences in learning will also inform how to best tailor educational and learning practices, as well as how to design computer programs that learn adaptively from experience. Other benefits of this work are resources that will assist research and education in cognitive and neural systems, including publically available datasets, computer code and machine learning algorithms; increased participation of underrepresented groups in this kind of integrative research, via summer research experiences for high school and undergraduate students; and an increased public awareness of neuroscience via public lectures, Brain Awareness Week activities, and contributions to a website that explains brain research in laymen's terms.

The work is based on a novel hypothesis about brain mechanisms that are responsible for certain idiosyncratic learning and decision processes. Specifically, in our unpredictable world, decision-makers face an inherent trade-off: higher certainty leads to more precise and accurate choices when the world is stable but an inability to adjust to change, whereas less certainty can lead to greater adaptability but also more variable and imprecise decisions. The investigators propose that this trade-off is regulated by interactions between arousal and cortical systems. To test this hypothesis, they use an interdisciplinary and integrative set of approaches with three primary objectives: 1) develop a theoretical framework describing inherent trade-offs between output stability and learning in hierarchical, probabilistic inference processes in unpredictable environments; 2) identify behavioral, physiological, and neural correlates of variability in how individuals navigate these trade-offs while making choices in unpredictable environments; and 3) identify causal influences of the brainstem nucleus locus coeruleus, a key component of the arousal system, on the variability in adaptive inference. The work forges meaningful connections across theory and experiment, spanning multiple spatial and temporal scales and levels of abstraction, to identify computational and physiological underpinnings of individual differences in learning.

Cognitive control is the ability to guide our thoughts and actions in accord with our internal intentions. It enables us to make good decisions, balance options, choose appropriate behaviors and inhibit inappropriate behaviors. Yet our understanding of how cognitive control works in the brain is critically lacking. The research outlined in this proposal will address this outstanding problem by developing and validating a mechanistic model to explain the fundamental principles enabling cognitive control. This problem is of urgent national interest and clinical relevance: greater understanding of how brain structure gives rise to cognitive control may be critical for the development of earlier and more effective treatments of the many neuropsychiatric disorders where cognitive control deficits are present. In addition, this project will create new research opportunities for undergraduate and graduate students in neuroscience, network theory, data sciences, and mathematics. The investigators will integrate the research into undergraduate and graduate teaching activities, providing a powerful bridge between theoretical and experimental applications for students at the University of Pennsylvania and the University of California at Riverside, one of America's most ethnically diverse research-intensive institutions. The investigators will also incorporate this material in extensive community and educational outreach efforts, in addition to translating this knowledge to mental health clinics.

In this research project, the investigators seek to develop, validate, and test a mechanistic theory of cognitive control. They postulate that the regulation of cognitive function is driven by a network-level control process akin to those utilized in technological, cyberphysical, and social systems. Their approach is grounded in network control theory, a relatively new subdiscipline of control and dynamical systems. In contrast to the descriptive statistics of graph theory, network control theory offers a principled mathematical modeling framework to inject energy into a networked system leading to a predictable alteration in the system's dynamics. Traditionally applied to mechanical and technological systems, this field builds on notions of structural controllability to ask specific questions about the difficulty of the control task and how to design realistic control strategies in finite time, with limited energy resources. The work will (i) develop a network-based theory of cognitive control informed by neuroimaging data, (ii) validate a network-based theory of cognitive control using data-informed computational models, (iii) define how network structure impacts individual differences in cognitive control performance in adults undergoing cognitive training, and (iv) release a publicly available toolbox for network controllability analysis. These theories and tools are the result of a truly integrated and cross-disciplinary approach to cognitive control, which blends the engineering and data sciences with empirical methodologies in neuroscience.

PROJECT SUMMARY/ABSTRACT Older adults are often victims of fraud, raising the question of whether age affects decision-making in such a way that makes people more susceptible to financial exploitation. One potential susceptibility is that older adults can have problems learning and acquiring information for later decision-making. However, significant gaps remain in our understanding of these potential deficits. Previous studies (1) have not examined learning in the social domain, which is most relevant for avoiding fraud; (2) have not examined multiple forms of learning, which may be differentially sensitive to age; and (3) have not examined whether deficits are a general function of age, or rather symptomatic of early pathological changes associated with preclinical Alzheimer's Disease or cerebrovascular disease, which can go undetected in otherwise cognitively normal older adults. This project will test older and middle-aged adults on two tasks using social stimuli that separately isolate hippocampal-mediated episodic learning, which encodes detail-rich associations between items and contexts from single events, and striatal-mediated stimulus-response learning, which encodes less flexible representations of a stimulus's value accrued over many events. Older adults will undergo a comprehensive clinical assessment and be determined to be cognitively normal as part of the Clinical Core longitudinal cohort of Penn's Alzheimer's Disease Core Center (ADCC). Age is expected to be associated with deficits in decisions based on both episodic memory and stimulus-response learning, though these deficits are expected to track with functional changes in different neural regions ? hippocampus and striatum, respectively ? as assessed with functional magnetic resonance imaging (MRI). As part of the ADCC, older adults will also receive positron emission tomography scans to assess amyloid pathology as well as MRI scans to assess white matter hyperintensity burden. Approximately 25% of cognitively normal adults show signs of either preclinical Alzheimer's Disease (amyloid pathology) or cerebrovascular disease (white matter hyperintensities). These data will be used to determine if social decision-making deficits are due to these previously undetected pathological changes, or to other brain structural changes that occur with healthy aging. Social decisions based on episodic memory may be particularly sensitive to preclinical Alzheimer's Disease, given that this targets the hippocampus, whereas social decisions based on stimulus-response learning may be particularly sensitive to cerebrovascular disease, given its impact on frontostriatal circuitry.

DESCRIPTION (provided by applicant): This application is a request for continued support of a predoctoral training program in Behavioral and Cognitive Neuroscience at the University of Pennsylvania. The program prepares exceptional students for productive research careers in the neuroscience of healthy and disordered behavior. With the recent shift in mental health research toward dimensional, systems-based frameworks such as the Research Domain Criteria there is unprecedented need for training of the kind offered through this program, specifically training in the molecular, large-scale neural systems and in behavioral approaches to learning, motivation, affect, perception, cognition, language, stress, sleep, circadian rhythms and synaptic plasticity. Penn is an ideal place to provide this training, with a large and diverse training faculty (representing 14 different departments in the five Schools at Penn) and a tradition of interdisciplinary graduate training that draws from independent interdepartmental Graduate Groups, which foster multi-disciplinary training courses and laboratory rotations from a rich and varied menu. The separate, University-mandated Office of Biomedical Graduate Studies (BGS) ensures curricular development, quality control, and uniform admissions standards. As in previous years, management of the training program will be by an interdepartmental executive committee that sets and reviews policy and selects trainees and evaluates their progress. The training program serves as the major source of support for students interested in the neuroscience of normal and disordered behavior and cognition from three Graduate Groups (Neuroscience, Psychology, and Cellular and Molecular Biology). Predoctoral trainees participate in interdisciplinary and collaborative research in the areas of motivated behaviors, affect and stress, animal models of endophenotypes of psychiatric disorders, neural plasticity, learning and memory and cognitive neuroscience. This program encourages broad training by offering cooperatively taught courses, a biweekly Behavioral and Cognitive Neuroscience Seminar Series and associated journal club, monthly translational neuroscience lunches and an annual Behavioral and Cognitive Neuroscience Retreat.