Kevin LaBar - US grants

DESCRIPTION: (provided by applicant) Memories for emotional episodes are intricately associated with our autobiographical experience, yet little is known about the brain mechanisms involved in encoding and retaining emotional events in memory. The proposed research uses modern cognitive neuroscience techniques to reveal insights into the organization of large-scale brain networks that link emotional stimuli to long-term memory functions. To achieve this aim, psychophysiological, event-related potential (ERP), and functional magnetic resonance imaging (fMRI) methods will be used to investigate the neurophysiological impact of emotional cues during memory encoding and retrieval tasks in normal human subjects. New techniques will be applied to improve the fMRI signal in frontolimbic brain areas and to infer their time course of activation. The spatial and temporal information obtained across parallel ERP and fMRI studies will be combined to specify how limbic forebrain areas sensitive to emotional salience manipulations dynamically interact with cortical networks specialized for mnemonic and perceptual processes. It is hypothesized that the ventromedial prefrontal cortex and amygdala will modulate activity in sensory association cortex in response to arousing stimuli. As a result, emotional events will receive enhanced stimulus binding and facilitated retrieval from memory-related areas in the medial temporal lobe and prefrontal cortex. The modality specificity of the emotional network components will be interrogated, and the brain activation patterns will be correlated with peripheral changes in autonomic physiology and memory scores in individual subjects. In drug addiction populations, episodic memories for prior drug intake and mnemonic associations triggered by drug-related environmental cues play an important role in motivational aspects of the addictive process and contribute to physiological adaptations underlying drug tolerance. The proposed studies may lead to new insights into understanding these effects by providing a more accurate and detailed account of the dynamic interplay between emotion and memory systems in the human brain.

With National Science Foundation support, Dr. Labar and colleagues will conduct a five-year CAREER award that combines event-related potential (ERP), functional magnetic resonance imaging (fMRI) and patient-based studies to investigate the impact of emotional cues during memory encoding and retrieval tasks in human subjects. The long term scientific goal of the project is to reveal insights into the organization of large-scale brain networks that link emotional stimuli to long-term memory. Memories for emotional episodes are intricately associated with our autobiographical experience, yet little is known about the brain mechanisms involved in encoding and retaining emotional events in memory. Arousal and valence dimensions of emotion are hypothesized to differentially impact memory functions. Arousal effects on memory are predicted to be mediated by an amygdala-centered frontolimbic network, whereas valence effects on memory are predicted to be mediated by a semantic network centered on lateral frontotemporal cortex. Specific experiments are planned to delineate the scope of the amygdala's contribution to arousal-mediated memory processes, and to identify the frontotemporal semantic networks that regulate emotional valence effects on memory organization. The known role of the amygdala and associated frontolimbic regions will be extended along three lines of inquiry: (1) performance on new implicit emotional memory tasks, (2) arousalmediated attentional effects at encoding, and (3) retrieval of autobiographical and non-autobiographical emotional memories. The contribution of semantic networks to emotional memory will be evaluated for affectively valenced and categorized neutral stimuli in three domains: (1) explicit memory organization, (2) subsequent memory effects, and (3) retrieval biases. Collectively, these studies will provide experimental support for the applicant's overarching goal of developing a comprehensive 2-factor neuropsychological model of emotional memory. In parallel with the research plan, an educational curriculum will be executed to advance the study of affective neuroscience. Because cognitive neuroscience applications to studying human emotion are relatively new, appropriate educational tools are not available and must be created. Recent developments in a university-wide initiative in cognitive neuroscience at Duke University provide the applicant with a means to incorporate topics on emotion through a two-tiered approach. (1) Development of undergraduate and graduate courses that disseminate the fundamental principles of affective neuroscience, emphasizing human-based approaches, and (2) training in research methodologies through laboratory-based independent study and research practical opportunities. A final goal of the educational initiative is to write an affective neuroscience textbook to educate students and interested professionals more broadly. The integration of these educational and research plans should help poise the applicant to make unique career contributions to the modern study of cognitive-emotional interactions in the brain

Emotions often exert a powerful influence over memory abilities. The impact of emotion can be either detrimental or beneficial to the organism, depending on the intensity and kind of emotion being experienced. For instance, students who experience mild stress while studying for an exam tend to have better long-term retention of the material compared to those who experience high stress or none at all. Current theories posit that such effects are mediated by the interaction between brain systems specialized to process emotions with others that perform specific memory operations. Understanding how these systems interact can yield new insights into the mechanisms underlying memory benefits and losses as a function of ongoing emotional states. With support from the National Science Foundation, Dr. Kevin LaBar's research will use modern cognitive neuroscience techniques to characterize the interaction of emotion and memory systems in the human brain. The research focuses in particular on situations where emotions affect memory without conscious awareness on the part of the individual. Until recently, unconscious processes were difficult to investigate in the human brain. Dr. LaBar and his students at Duke University will combine functional brain imaging techniques with physiological recordings of emotional arousal to tackle this complex problem. Human subjects will perform memory tasks, such as maze navigation, that require gradual learning over many trials while they experience different levels of emotional arousal. The researchers aim to show how emotion impacts the learning rates and strategies people use, even when they lack insight into the correct answer or solution to the task. The brain activity patterns will be analyzed and compared to measures of emotional physiology and memory performance to characterize the mental processes underlying unconscious emotional memories.

Prior studies in this area have focused on conscious forms of emotional memory, as when individuals try to recall an emotionally evocative event from their past. However, many scholars believe that much of our mental life is governed by processes and influences that are not available to consciousness. The outcome of Dr. LaBar's experiments will lead to a broader theoretical appreciation for how emotion affects cognition in real-world contexts, since memory processes always operate across fluctuations of an individual's emotional and motivational states. Scientific advances fostered by the research may have practical implications for developing emotional rehabilitation strategies to counteract memory loss as well as for identifying ways to use emotion to enhance learning in educational settings. The funding will help support the training of students at Duke, who will gain valuable hands-on experience in the methods of cognitive neuroscience and the study of emotion. The results from the experiments will be broadly disseminated through publications, databases, and websites accessible to the public and to scientists and journalists at specialized conferences, where the research will gain greater national and international exposure.

DESCRIPTION (provided by applicant): (03) Biomarker Discovery and Validation general challenge Area and the 03-MH-101 Biomarkers in mental disorders specific challenge topic. Anorexia nervosa (AN) is a terrifying and perplexing disorder. Eating disorders, in general, rank among the top ten causes for disability among women, while AN has the highest mortality rate of any psychiatric disorder with a 57 fold increased risk of death due to suicide relative to an age- matched cohort. Despite these sobering statistics, one of the greatest mysteries of AN is that the ill state is prized by the individuals afflicted with this disorder. They report 'feeling better'while starved. In contrast, prior to the ill state, the majority with AN are diagnosed with an anxiety disorder and experience elevated levels of gastrointestinal symptoms, - conditions associated with increased visceral sensitivity. Notably, the severe starvation of AN principally onsets during adolescence, a vulnerable period of neural maturation and modification. Indeed, the course of AN is notable for a 'critical period'of intervention. The likelihood of improvement in symptoms of AN diminishes markedly if aggressive treatment is not undertaken during the vulnerable developmental window of early to middle adolescence. This body of evidence suggests that sensitivity to change in the internal state of the body (i.e. interoceptive sensitivity) exists premorbidly in those with AN, that biological alterations during adolescence may potentiate somatic sensitivity, that starvation during this period may dampen somatic experience, and that critical alterations in brain neural circuitry during this period may be crucial in shaping disorder course. In a sample of 75 adolescents (25 in the acute state of malnourishment of anorexia nervosa, 25 weight-restored from anorexia nervosa, and 25 typically developing controls), we will complete the following aims. We will: 1) characterize differences in interoceptive signaling from the upper GI tract and the interoceptive cortex, 2) characterize the differences in prefrontal cortical modulation of interoceptive cortex, and 3) characterize differences in the role of orbitofrontal cortex on modulation of cortical activity and connectivity with interoceptive cortex. By studying patterns of neural activation and psychophysical response that alter as a function of starvation and are associated with interoceptive sensitivity, we can formulate novel hypotheses on biological changes associated with starvation that are reinforcing for this group and derive novel treatment targets. PUBLIC HEALTH RELEVANCE STATEMENT: Anorexia nervosa (AN) is a terrifying and perplexing disorder. Eating disorders, in general, rank among the top ten causes for disability among women, while AN has the highest mortality rate of any psychiatric disorder with a 57 fold increased risk of death due to suicide relative to an age-matched cohort. Despite these sobering statistics, one of the greatest mysteries of AN is that the ill state is prized by the individuals afflicted with this disorder. They report 'feeling better'while starved. In contrast, prior to the ill state, the majority with AN are diagnosed with an anxiety disorder and experience elevated levels of gastrointestinal symptoms, - conditions associated with increased sensitivity to body changes (e.g. sensing the pit in your gut when you have done something wrong or the butterflies in your gut when you are worried about something). In fact, sensitivity to these internal sensations (called interoceptive sensitivity) has profound implications well beyond anorexia nervosa. Interoceptive sensitivity is associated with the strength of emotional memories, the depth with which we can understand others, and may be associated with the strength of emotional learning. As adolescence is a time of profound brain and body change, this developmental period may be a key window to study how individuals differ in this sensitivity and the boundaries that define pathological development. In fact, the severe starvation of AN principally begins during adolescence. Thus, malnourishment during this period may have particularly profound negative effects. In fact, the course of AN is notable for a 'critical period'of intervention. The likelihood of improvement in symptoms of AN diminishes markedly if aggressive treatment is not undertaken during the vulnerable developmental window of early to middle adolescence. Using functional neuroimaging we will examine neural circuits in the brain that may help us to identify brain regions that may contribute to difficulties with sensitivity to internal bodily states in those with anorexia nervosa and how these differ from typically developing adolescents. By studying patterns of neural activation and psychophysical response that alter as a function of starvation and are associated with interoceptive sensitivity, we can formulate novel hypotheses on biological changes associated with starvation that are reinforcing for this group and derive novel treatment targets for those with anorexia nervosa and better understand the transitioning mind-body connection of adolescence.

A major challenge to treating drug addiction is understanding how learned associations to aversive reinforcers promote avoidant behavior and trigger the onset and relapse of drug use. Studies in non-human animals have begun to elucidate the neurobehavioral mechanisms of avoidance learning, but there have been few efforts to translate these findings to human populations. Neuroplastic brain mechanisms that support long-term memory formation are hypothesized to alter the representations of stimuli and strengthen contextual associations as a function of their incentive properties. The proposed research adopts a cognitive neuroscience perspective to characterize how motivational brain systems modulate declarative memory formation in humans and lead to behavioral avoidance. Although human declarative memory is traditionally probed using list- learning paradigms, recent advances in computer graphics interfaces and immersive virtual reality (VR) technology permit the development of novel navigational avoidance tasks that provide a tighter link with the animal literature and more closely model real-world avoidant behaviors exhibited by drug addicts in response to environmental stressors. Healthy participants will undergo a series of functional magnetic resonance imaging studies that present reinforcing stimuli within the context of both traditional list-learning and novel VR-based navigational learning and memory tasks. The first series of experiments compares the influence of appetitive versus aversive instrumental reinforcers on declarative memory systems. The second series of experiments determines how negative mood states amplify the mnemonic effects of the incentive properties of instrumental reinforcers and motivate reward-seeking (relief) as a form of mood repair. The third series of experiments develops a multisensory, immersive VR paradigm that simulates stress-induced avoidance and escape on a naturalistic memory task that combines navigational and list-learning approaches. Functional connectivity modeling, in combination with multiple regression and independent components analyses, will characterize the interactions of motivational and memory systems and their relationship to individual differences in behavioral performance indices and trait markers of avoidance. The proposed studies thus represent a systematic and innovative approach to human avoidance learning that combines cutting-edge VR technology and functional neuroimaging methods. The research findings will bridge a translational gap in understanding how positive reinforcers, mood states, and stressors modify the impact of aversive behavioral consequences on learning and memory systems that help establish internal maps of salient features of the environment.

Brain imaging is an emerging neuroscientific tool that has become central to characterizing the pathophysiology of drug addiction. Structural and functional brain imaging studies have contributed to an understanding of the component neural systems and cognitive processes that mediate maladaptive drugseeking, risky choice, and avoidant behaviors, including those related to learning and memory. Due to the rapid pace of imaging research, the cost of the studies, the need to increase statistical power, and the complexity of the data collected, it is vital to share existing neuroimaging data sets with other researchers to facilitate scientific discovery into the causes and consequences of drug-related behaviors. This proposal is a one-year administrative supplement to a NIDA-funded R01 grant to establish a public data repository on brain imaging studies related to motivated learning and memory (LAM). Structural and functional imaging data collected as part of the parent grant will be shared and linked to another public repository of resting state data in addictive disorders (RAD). Some unique features of the data to be shared include the focus on LAM processes, high-resolution functional imaging of medial temporal, striatal, and frontal regions involved in drug addiction, and a core set of reward and punishment-related tasks and manipulations suitable for meta-analysis. Intellectual property rights and other regulations for data access and publication will be developed, and database linking structures will be created. While early attempts to establish large, monolithic brain imaging data repositories failed due to problems of scale and maintenance, we envision an alternate approach of linked ¿niche¿ databases whose organization is inspired by brain architecture and social networks.

DESCRIPTION (provided by applicant): A central goal of affective neuroscience is to understand the brain systems and mechanisms underlying the evaluation, experience, and expression of emotion. For example, a widely studied and hotly debated issue in the field is the manner in which biophysical responses to emotional stimuli can be characterized, whether by distinct categories or alternatively along dimensions of valence and arousal. Advances in the fields of psychology, neuroscience, and computer science have fostered significant progress in identifying brain regions involved in processing emotion generally; however, consistent and specific neural markers for distinct affective states have yet to be found. This proposal uses a cutting-edge approach to this core, unresolved question in the field by harnessing emerging pattern classification techniques that are capable of detecting subtle yet coordinated signals from an array of sources. The overarching goal is to identify multivariate patterns of behavioral and biological responding to specific affective states and determine whether these states are organized according to categorical or dimensional architectures. By combining psychophysiology (Aim 1) and functional magnetic resonance imaging (fMRI) (Aim 2), these studies will examine how humans respond to emotional stimuli that vary in duration, modality, and categorical nature. Study 1 focuses on distinct emotions elicited by instrumental music and movie clips whereas Study 2 focuses on those elicited by facial and vocal affect. Together, the aims will provide an integrative, computationally-rigorous method to identify biomarkers of specific emotions that are typically overlooked by conventional univariate statistical approaches. Applying machine learning algorithms in this innovative way could be fruitful for identifying how emotional representations are altered in affective disorders, with the potential for developing novel therapeutic targets. Moreover, identifying patterns of overlap between specific emotion categories may further aid efforts to understand comorbidity issues in anxiety and mood disorders.

High-resolution immersive interactive visualization systems allow users to explore and understand data from a first person perspective using natural techniques. Immersive visualization improves spatial understanding, enables training in safe yet realistic environments and allows the understanding of and interaction with data in an intuitive way. Particularly, researchers and students in engineering, science and the humanities can benefit from high-definition immersive visualization systems because many times the nature of the data in those disciplines is spatial and highly detailed. Within this context, this Major Research Instrumentation award allows Duke University to build the High-fidelity Duke immersive Virtual Environment (HiDiVE). Built on the success of the Duke immersive Virtual Environment (DiVE) over the past decade, the HiDiVE will consist of a six-sided room in the form of a cube, with two projectors for each surface, in a total resolution of 22.1 million pixels. This level of detail is roughly four times what the current DiVE system has and will allow for analysis of high-resolution data. Motion trackers will enable users to interact with data in the HiDiVE using natural and intuitive motions. This next generation system opens the possibility for research and education projects using high-definition data and computer graphics.

Dr. Regis Kopper, along with co-investigators Dr. Kevin LaBar and Dr. Silvia Ferrari, six senior personnel and five users will initially use the HiDiVE in projects spanning several areas of knowledge, including cognitive neuroscience, intelligent systems and control, digital archeology and human-factors engineering. Specifically, the HiDiVE will support studies to understand human fear recovery and response to stress triggers. The HiDiVE will also be used for human-robot interaction experiments, particularly in the understanding of human-decision making to design robots that mimic human behavior and to validate robotic path planning. Another use of the HiDiVE will be in digital archeology, where users will be able to experience virtual dig sites, explore artifacts, stratigraphy, and conceptual reconstructions of pre-historic sites. In human-factors engineering, the HiDiVE will be a platform for studying the motions of humans doing repetitive work and how visual complexity may impact human mental workload. Apart from being a research facility, the HiDiVE will support educational and outreach activities. Students will use the system as a development platform for computer science and human-computer interaction courses. The HiDiVE will partner with the NSF IGERT on Wireless Intelligent Sensor Networks (WISeNet)to recruitand support minority students to engage in research activities and will be an inclusive environment with reach beyond the boundaries of the university, allowing visitors from schools and underrepresented groups to experience an advanced immersive virtual reality system.

This research program investigates why memories for fearful events are hard to forget and what can be done to help individuals who are susceptible to developing debilitating fear memories. When people experience threatening situations, the memories for the events can haunt them long after the event has passed, causing prolonged and sometimes traumatic stress. Returning to the context in which the trauma occurred, or being exposed to cues that remind people of their trauma, can cause a potent return of fear, even several years later. It is not well known how the brain lays down long-term memory traces of fearful events, or how trauma reminders activate brain pathways that lead to the return of fear. It is also not known why some people are particularly prone to developing long-term distress after a threatening experience, whereas others are more resilient. Finally, it is not known how best to help individuals overcome their fears in a way that makes them less likely to being re-experienced over time.

The proposed research will tackle these important and challenging problems by conducting experiments in healthy adults who are exposed to fearful events and are trained to extinguish their fear memories. People will be placed into a magnetic resonance imaging (MRI) scanner while they encounter fearful or threatening stimuli viewed through virtual reality simulators. Bodily and brain responses that are activated by fear will be recorded. The use of virtual reality is an innovative approach that allows researchers an ethical way to better model how fears are learned, suppressed, and recovered in complex environments. The researchers will manipulate several factors to determine how they promote or hinder the recovery of fear. These include varying the distance between the research participant and the fearful stimulus in the virtual world, and determining the effectiveness of having people extinguish their fears in multiple virtual environments. Differences in people's cognitive abilities and emotional reactions will be linked to the brain data to determine why some individuals are more likely to exhibit the recovery of fear memories.

The research has important societal implications for helping people who suffer from traumatic memories. The research can also assist in efforts to identify individuals for certain professions who may be more resilient to developing debilitating fear memories, including emergency health care workers, police forces, and military personnel, who are routinely exposed to potentially traumatic experiences. The research will advance scientific knowledge in identifying the brain mechanisms that contribute to fear memory and will highlight how virtual reality technology can be integrated into neuroscience-based applications.

ABSTRACT The ability to regulate one?s emotional responses is critical for maintaining emotional health in the face of adverse events that cumulate over the lifespan. Although some emotion regulation abilities are thought to be maintained or even improve in healthy older adults, such beneficial maturation effects are moderated by individual differences in depression and neurocognition that contribute to disability, morbidity, and loss of quality of life into old age. Frontolimbic circuit dysfunction is a hallmark of both younger and older adults with major depressive disorder (MDD), while both activation in and connectivity among components of these circuits predicts treatment response. Such disruptions impact core cognitive processes, including cognitive control and attentional biasing, that influence emotion regulation ability, although it is unknown how their susceptibility to depressive influences varies across the adult lifespan. Moreover, MDD patients are less able to utilize compensatory resources that help older adults cope with adversity, such as social support, in the face of age- associated neurocognitive decline. Given the projected growth of the elderly population in the U.S. and the associated burden on the public health system, it is imperative to develop effective interventions to target regulatory deficits associated with depression in late life and to begin to identify neurocognitive predictors of increasing depressive symptoms. Preliminary evidence from the study team demonstrates that the effectiveness of regulatory strategies such as reappraisal and distraction vary with age and depressive status. However, there is a pressing need for a comprehensive, integrative approach to study emotion regulation strategy use that links brain circuitry integrity, cognitive function, social support, and clinical symptoms, and investigates how these relationships change with age. The central hypothesis of the proposed study is that age, diagnostic status, neurocognitive functioning, and social support will differentially impact reappraisal and distraction efficacy, and that their combined effect on strategy use will predict depressive symptoms at 1 year post-scan. The proposed study is expected to yield new insights in how maturational changes contribute to the conscious ability to reduce negative affect in depressed adults. A total of 200 adults in stratified age groups from 35 to 75 years with and without MDD will undergo structural and task-based functional neuroimaging. We will test age- and diagnosis-specific differences in the success of two different emotion regulation strategies in reducing experimentally induced negative affect, identify brain regions associated with successful use of reappraisal and distraction using structural and functional magnetic resonance imaging, and test emotion regulation as a predictor of future depression symptom severity. Results will be used to better target emotion regulation interventions based on a patient?s age and diagnostic status.

Maintaining an adaptive balance of emotions is central to well-being, and dysregulated emotions contribute broadly to clinical disorders that impart high personal and societal burdens. Recognizing the transdiagnostic importance of emotion to mental health, the National Institute of Health's Research Domain Criteria (RDoC) matrix contains overarching domains of Negative Valence, Positive Valence, and Arousal. However, the matrix underspecifies how specific affective states like sadness, anxiety, or craving are organized within and across these domains, in part because it is unknown whether representations of discrete emotions are reliably differentiated. Other RDoC constructs, such as rumination and worry, modify the temporal parameters of emotions that confer psychopathology risk and exacerbate symptom maintenance. Nonetheless, it is unknown how these processes interface with emotional brain circuits to impact affect dynamics, particularly as they often occur spontaneously during mind wandering. The proposed research promises to improve the RDoC depiction of these emotion-related constructs by taking an affective computing approach. During combined recording of psychophysiology and functional magnetic resonance imaging (fMRI), adult participants will experience emotions to vignettes and movie clips spanning the arousal and valence dimensions, and will report on their spontaneous emotions during resting-state fMRI scans. Machine learning algorithms will decode emotion- specific signals across the levels of analysis, which will be integrated using Bayesian state-space modeling. An analysis of classifier errors will test competing predictions from emotion theories regarding the optimal structure of affective space. Using graph theoretic tools, we will characterize the neural network architecture of the discrete emotion representations to identify provincial and connector hubs that can be used as novel targets for future symptom-specific or co-morbid neuromodulation interventions, respectively. We will apply the emotion-specific maps to resting-state data from the same participants to create neurophysiological indices of spontaneous emotions and to relate their frequencies to measures of trait and state affect as a validation step. Using stochastic modeling of the resting-state data, we will derive temporal dynamics metrics to test the hypothesis that rumination and worry promote emotional inertia during mind wandering. Finally, we will use existing data repositories to demonstrate that our novel indices of affect dynamics transdiagnostically differentiate resting-state fMRI activity patterns in mental health disorders from healthy controls. The proposed research will improve upon current RDoC formulations of Negative Affect, Positive Affect, and Arousal domains by informing how discrete emotions are organized within and across these domains, by integrating emotion representations across multiple RDoC units of analysis, by informing how rumination and worry impact neurophysiological signatures of spontaneous emotions, and by establishing the clinical utility of computationally-derived metrics of emotion dynamics.