Daniela Schiller - US grants

DESCRIPTION (provided by applicant): The emotional sequelae of combat and other trauma exposure primarily give rise to five core symptom clusters: re-experiencing, avoidance, emotional numbing/dysphoria, dysphoric arousal, and anxious arousal. This approach to characterizing trauma-related symptomatology is inherently transdiagnostic and dimensional in nature and accords with the NIMH Research Domain Criteria (RDoC) project, which seeks to classify individuals with respect to unique behavioral and neural patterns irrespective of DSM diagnosis. Although these factors constitute a sound phenotypic model of trauma-induced psychopathology, little is known about the neural circuitry that underlies these dimensions or how characteristics of trauma-related psychopathology, such as fear learning, relate to component aspects of this heterogeneous phenotype. Elucidation of links between phenotypic characteristics of trauma-induced psychopathology, based on dimensions of observable behavior and neurobiological measures, could uncover individual differences and promote personally- tailored clinical interventions. We propose to assess these associations in trauma-exposed individuals using two highly validated protocols: reversal learning and retrieval-extinction. In reversal learning, participants first undergo fear acquisition where they encounter two stimuli and learn that one of them terminates with threat, whereas the other one does not. In the subsequent reversal phase, participants learn that the formerly safe stimulus now predicts threat, and the fearful one is now safe. In the retrieval-extinction paradigm, extinction training occurs after memory reactivation, i.e., during reconsolidation, allowing the updating of the fear memory with the safety information learned in extinction, which was shown to successfully prevent old fear memories from resurfacing. These paradigms provides a unique platform to investigate the neurocircuitry and psychophysiology of fear and safety learning and memory modification, and their relation to phenotypic measures of negative valence systems implicated in threat, arousal and regulatory systems, and loss. In the proposed study, we aim to evaluate the neural, psychophysiological, and computational mechanisms that govern fear and safety learning and memory, and their association to stress related psychopathology as expressed in combat veterans presenting with a transdiagnostic and full dimensional range of associated psychiatric symptoms.

DESCRIPTION (provided by applicant): Environmental cues previously paired with drug-related behaviors are potent triggers of drug seeking and relapse. The Pavlovian-to-instrumental transfer (PIT) effect, referring to the invigoration of ongoing goal-seeking behavior by previously reinforced cues, is suggested to model important aspects of drug seeking and relapse. While extensively studied in animals, the neural mechanisms of PIT in addictive humans remain largely unknown. A key component in maintaining goal-directed behaviors are covert desires, urges, and wishes pertinent to obtaining rewards. Using a unique real-time fMRI experimental design, we recently published a study that explored the neural mechanisms underlying the influence Pavlovian cues exert on a reward- associated imagery task in healthy individuals. In this study, participants first learned an instrumental association between motor imagery and monetary reward by receiving monetary rewards for successful activation in motor cortex during a motor imagery task, using a real-time fMRI set-up. Next, conditioned stimuli were associated with either monetary gain or loss using a partial reinforcement Pavlovian learning protocol. Finally, in the transfer test, brain activity was assessed during motor imagery in the presence of the gain and loss related cues, allowing for the assessment of a transfer PIT effect. We reported that cues with motivational salience had an invigorating effect on brain activation in regions involved in value representation and motor imagery. Moreover, we found that during motor imagery, these networks co-activated to higher degree upon the presentation of appetitive vs. aversive cues. The current proposal aims at extending these findings to addictive population by employing two versions of the described experimental design in individuals with different degrees of nicotine dependence. First, we propose to examine the exact same protocol on nicotine-dependent individuals to test the relation between substance dependency and motivational effects on the neural correlates of mental imagery. Second, we wish to study how Pavlovian cues associated with either smoking- related or neutral images affect smoking-related imagery processes in nicotine-dependent individuals. The suggested proposal is thus designed to unravel the neural mechanisms of covert processes preceding actual behavior in human addictive populations, potentially promoting treatment development for addictive behavior and relapse prevention.

Project Summary The human brain tracks multiplexed signals during social interactions. The breakdown of any of these computations could lead to social deficits observed in many psychiatric disorders. While social neuroscience has been growing rapidly in recent years, the complexity of human social interactions has not been well quantified with computational models. Importantly, previous social neuroscience research generally assumes that the structure of social environments are stochastic and social agents act in a reactive way, leaving at least two knowledge gaps in the literature: 1) the proactive nature of social agents and 2) the dynamic and multidimensional feature of social space. The overarching aim of this project is to develop novel computational models and paradigms to capture social controllability and social navigation in ?unselected? human participants (laboratory study n=100, mobile app n=10,000), which can ultimately be used to capture social failures across disorders. In Aim 1, we will develop a novel generative model and paradigm for social controllability, based on a rich literature on model-based decision-making and our previous work on social learning. Key subject-level parameters include: simulated controllability (delta), future thinking weight (i.e. weight put on planning future interactions), and learning rate (epsilon). In Aim 2, we will delineate navigational computations of dynamic social relationships 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 machine learning to 1) deep phenotype participants along the dimensions of social controllability and navigation and 2) predict clinical and subclinical symptoms among a large sample of ?unfiltered? volunteers. Upon successful completion of these aims, this proof-of-concept project will provide important validation for new computational frameworks for social controllability and social navigation, potentially breaking new grounds for computational psychiatry research of social dysfunction. The resulting paradigms, models, and findings will be critical for a wide range of clinical disorders including psychotic, mood, and personality disorders. Furthermore, the proposed paradigms can be back-translatable to animal models, in relation to the social defeat model of depression and other animal models of social behaviors. Thus, the proposed computational framework could have far-reaching influences that would exceed the specific focus on social control and social space navigation, advancing the possibility to advance mechanistic understanding of and develop individualized diagnosis and treatments across multiple psychiatric disorders.

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.