Xiaosi Gu, Ph.D. - US grants

SUMMARY Dopamine and serotonin systems in the human brain represent two key neuromodulatory signalling systems that impact mood, value-based decision-making, learning, and a host of other cognitive functions. Despite their importance, there is no consensus view from a psychological or computational perspective concerning their exact information-processing functions. Consequently, there is a scarceness of strategies to treat disorders that afflict these systems. We believe that this gap exists primarily because of methodological limitations. While there are many fast methods for recording action potential activity and local field potentials, similar progress for tracking `the other end of the problem' - neurochemical dynamics - has lagged far behind. This is unfortunate since just considering the catecholamines dopamine, serotonin, and norepinephrine, the worldwide health burden of dysfunction in these neuromodulatory systems is immense approaching 400 million people worldwide if one includes just Major Depression and Attention-deficit hyperactivity disorders (WHO, 2017). The overall goal of this project is to investigate the computational and neuromodulatory substrates of interactive social processes hypothesized to be trans-diagnostic RDoC constructs. We will utilize two levels of analysis (molecules/circuits and behavioral/computational) across two RDoC constructs (systems for social processes: perception and understanding of others, subconstruct understanding mental states, and positive valence systems: reward learning, subconstruct reward prediction error) to make inroads into understanding the computational and neural underpinnings of social interaction in humans. Crucially, the interactive social tasks used in this proposal generate an important class of learning signal ? a reward prediction error signal ? but expressed in the context of an inter-personal interaction. Our two levels of analysis employ (1) computational models of human (two-agent) social exchange estimated from detailed, observed behavior and (2) unique sub- second measurements of dopamine and serotonin from human striatum (three separate sites: caudate, putamen, ventral striatum) during the window-of-opportunity afforded by deep brain stimulating (DBS) electrode implantation surgery for Parkinson's Disease, Essential Tremors, Obsessive Compulsive Disorder.

Project Summary Severe impairments in interpersonal functioning are hallmarks of personality disorders. Borderline personality disorder (BPD), for example, is characterized by inability to maintain relationships, inflexibility in dealing with changes in relationships, and heightened needs to control and manipulate others. Avoidant personality disorder (AvPD), in contrast, is primarily marked by social withdrawal and avoidance, as well as reduced sense of control in social relationships. While social neuroscience has been growing rapidly in recent years, the complexity of human social interactions has not been well quantified with computational models, particularly as applied to personality disorders. The overarching aim of this project is to utilize novel computational models and paradigms, combined with 7-Tesla imaging and brain connectivity measures, to capture the neural computations underlying proactive and dynamic social behaviors in BPD, AvPD, and healthy controls (HC; n=60 per group). Specifically, we will focus on two novel and complex social behaviors that mimic real-life social interaction: 1) social controllability, the ability to exert control over one?s social environment and, 2) social navigation, the process of navigating dynamically changing social relationships. In Aim 1, we will examine the computational and neural mechanisms of social controllability in BPD and AvPD using a social exchange paradigm in which participants either could or could not influence their partners? monetary offers in a novel computational framework. We will capture key parameters such as estimated controllability (?), sensitivity to norm violation (?), and beliefs about control. In Aim 2, we will identify neurocomputational indices of dynamic social relationships in BPD and AvPD, using a novel social interaction game in which participants interact and develop relationships with virtual characters. We will devise novel measures that track the trajectories of social relationships and geometrically quantify the overall structure of individuals? two-dimensional social space framed by power and affiliation. In Aim 3, we will use state-of-the-art machine learning approaches and the neurocomputational parameters derived from Aims 1 & 2 to predict each participant?s diagnosis/group label (BPD, AvPD, or HC) and patients? symptom severity. Upon successful completion of these aims, this project will provide important neurocomputational characterization for proactive social behaviors and how they might break down in BPD and AvPD, potentially breaking new grounds and filling critical knowledge gaps for social neuroscience and computational psychiatry research. The resulting paradigms, models, and findings will be critical for a wide range of personality and other psychiatric disorders. Thus, the proposed neurocomputational framework could parameterize social interactions, providing novel quantitative measures of social pathology, treatment change, and the nature of patient- psychotherapist interactions.

Project Summary Social interaction deficits are at the crux of autism spectrum disorder (ASD) and contribute to significant functional impairment, including poorer relationship quality and low employment rates in individuals with ASD. Despite an enormous amount of research dollars invested and thousands of research papers published on the topic, we remain far from understanding the basic neural computations underlying social processes in ASD. In the current proposal, we posit that this information gap is due in part to the rarity with which computational model- based analyses are used in ASD neuroimaging research. Additionally, most studies use passive paradigms (e.g. face perception) rather than examining brain functioning while participants engage in ecologically-relevant, interactive social tasks more akin to the type of interactions with which people with ASD struggle in their daily lives. This proposal takes an innovative computational psychiatry approach to understanding aberrant neural computations of social interactions in ASD, using high-resolution (7T) functional magnetic resonance imaging (fMRI) and virtual reality-like tasks that test individuals? abilities to proactively and dynamically engage in simulated social interactions. In particular, we focus on the ability of individuals with ASD to: 1) discriminate and track levels of closeness and power when navigating social interactions in a choose-your-own-adventure style interactive paradigm, and 2) understand and adapt to social norms and exert control over social others in the context of a proactive social exchange paradigm. We use novel computational models to examine the neural computations and connectivity underlying proactive social behavior, focusing on brain regions (e.g., hippocampus) that have been understudied in the context of social deficits in ASD. Finally, we use machine learning approaches to explore ASD heterogeneity along dimensions of dynamic and proactive social interactions and apply these indices to make clinically-meaningful predictions. We hypothesize that: 1) hippocampal tracking of social space will be less robust in ASD as compared to neurotypical controls and will correlate with social symptoms; 2) ASD individuals will show slower norm adaptation rate, greater aversion to norm violation, and reduced social controllability, accompanied by reduced neural encoding of social values in anterior insula and ventral striatum; and 3) these parameters will help identify subtypes of ASD and predict ASD- relevant outcomes (e.g. social skills, adaptive social functioning, quality of life). We expect that findings from this project will break new ground and fill critical knowledge gaps regarding the neurobiology of ASD. In particular, we expect our findings will greatly enhance understanding of the neural and computational mechanisms underlying deficits in proactive social behavior in ASD and will allow us to identify distinct, neurobiologically- driven clusters. In so doing, the results of this project could offer new tools by which to subtype the ASD phenotype and provide novel insights into treatment targets.