Author: Johanne

Miriam Klein-Flügge
Assistant Professor, Wellcome Henry Dale and ERC-UKRI Fellow
Oxford Centre for Integrative Neuroimaging (OxCIN)
University of Oxford

In this talk, I will present recent work examining deep subcortical circuits and their interactions with prefrontal cortex during affective decision-making. I will focus on three complementary strands of research that together inform our understanding of these networks. First, I will describe progress in resolving small subcortical structures that are critical for emotional and motivational processes—such as the amygdala and hypothalamus—at the level of individual subnuclei. Using high-resolution neuroimaging, we show nucleus-specific patterns of brain connectivity that explain variance in mental well-being, including individual differences in negative affect and stress. Second, I will discuss advances in causal approaches that allow us to move beyond correlational descriptions of function and directly manipulate activity non-invasively in deep brain circuits. Using transcranial ultrasound stimulation, we have characterised the causal contribution of the basolateral amygdala to affective approach–avoidance decisions, and ongoing work extends this work to examine causal roles of prefrontal and striatal regions in affective behaviour. Third, across multiple domains, we are beginning to move beyond the timescale of individual trials to study intermediate, more naturalistic timescales. This work aims to characterise how background contextual features shape motivation, social behaviour, reward learning, and emotion processing. Taken together, our work in human cognitive neuroscience has moved from correlational studies with coarser anatomical and functional resolution toward causal investigations of deep subcortical-cortical brain circuits at the functional scale of individual nuclei and using timescales of increasing relevance for flexible human behaviour. We believe this is an important step towards understanding the neural mechanisms underlying affective decision-making across health and disease.

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Payam Piray
Assistant Professor
Department of Psychology and Neuroscience
University of Southern California

Adaptive learning requires distinguishing two causes of uncertainty, moment-to-moment stochasticity in observations and environmental volatility, that demand opposite adjustments to learning rate. Yet both increase experienced noise, making their dissociation computationally difficult and prone to systematic errors. I will present a computational framework, behavioral paradigm, and large-scale data investigating how humans dissociate these two sources of noise, and how this process gives rise to two kinds of learning failure.

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Silvia Lopez-Guzman
Chief
Unit on Computational Decision Neuroscience
National Institute of Mental Health (NIMH) | National Institute on Drug Abuse (NIDA)

An individual’s decisions reflect their goals, but decisions can sometimes be maladaptive and short-sighted. Powerful affective and motivational states like craving or stress can bias choices towards higher immediate gratification at the expense of future wellbeing. What are ways in which these effects can be minimized or managed? Awareness of one’s own physical, emotional, and cognitive state may be key to regulation, but these abilities have traditionally only been measured through self-report questionnaires. I will introduce a novel computational framework for measuring metacognition for value-based decision-making such as impulsive or risky decisions. With this computational approach, I will present work that shows that these metacognitive computations are both trait-like and domain general, as well as susceptible to changes induced by stress and other affective states.

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Hannah Snyder
Associate Professor
Department of Psychology and Neuroscience Program
Brandeis University

Emerging adulthood is a key risk period for depression and anxiety, potentially in part because still-developing executive function is not adequate to fully cope with new roles and demands, increasing stress. I will discuss two lines of research which seek to better understand these risk factors. The first tests a risk pathway linking poor executive function to internalizing psychopathology via stress generation and repetitive negative thinking. The second further probes links between stress and internalizing psychopathology risk, focusing on the role of stressor appraisals and stress coping.

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Angela Radulescu
Assistant Professor
Department of Psychiatry
Icahn School of Medicine at Mt. Sinai

Bipolar disorder (BD) is marked by striking fluctuations in motivation and goal-directed behavior, yet the cognitive mechanisms underlying these changes remain unclear. In this talk, I will present a reinforcement learning (RL) perspective on BD that links learning and motivation within a unified computational framework. This approach aims to identify cognitive markers of BD, highlighting positive overgeneralization — the excessive spread of learned value across contexts — as a potential marker present outside of acute episodes. Modeling results show that heightened sensitivity in self-efficacy belief updating can produce overgeneralized value representations and goal-directed behaviors resembling (hypo)manic symptoms. Finally, this perspective helps reconcile puzzling findings from ongoing work on value-based attention in BD, offering a cohesive account of how learning mechanisms contribute to cognitive vulnerability across the bipolar spectrum.

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Nicole Rust
Professor
Director of Visual Memory Lab
Department of Psychology
University of Pennsylvania

In 1998, Eric Kandel published the brilliant essay A New Intellectual Framework for Psychiatry. In it, he described a revolution to move psychiatry away from the psychoanalytic tradition (focused exclusively on the mind) to one that incorporated biology, with an emphasis on genetic inheritance and genetic expression. He predicted that this revolution would have a profound impact on psychiatry. While notable discoveries have been made in the 27 years since, those profound impacts have yet to be realized. For instance, one goal has long been to create biological tests (like blood tests and brain scans) to diagnose psychiatric conditions, and those tests still do not exist. In this talk, I will argue that a second revolution is happening in this space — one required to fulfill Kandel’s original vision. To illustrate these changes, I will update Kandel’s framework to incorporate emerging ideas. I will also caution (as Kandel did) that success will require interaction between practitioners focused on broader psychiatric considerations and those focused on more detailed biological insights.

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Nicole Karcher
Assistant Professor
Department of Psychiatry
Washington University School of Medicine

Psychotic-like experiences (PLEs), including unusual thoughts and perceptual experiences, represent transdiagnostic markers of later diagnosable mental health concerns. Dr. Karcher’s research program focuses on identifying the genetic, cognitive, neural, and environmental mechanisms underlying PLEs, with the goal of clarifying pathways of vulnerability and resilience. Leveraging large-scale longitudinal datasets, including the Adolescent Brain Cognitive Development (ABCD) Study, this research applies advanced analytic approaches, such as machine learning and structural equation modeling, to characterize patterns of symptoms and risk factors over time. Findings to date demonstrate that PLEs are associated with widespread impairments across domains, including elevated polygenic liability for schizophrenia, cognitive deficits, and structural and functional neural alterations. Additionally, PLEs are strongly linked to environmental exposures, such as trauma life events, underscoring the multifactorial etiology of these experiences. Importantly, this research highlights that PLEs that are both persistent and distressing are associated with greater impairments across risk domains, pointing to a subset of youth at heightened risk for adverse outcomes. Taken together, this research advances understanding of the developmental origins of psychosis spectrum symptoms and provides a foundation for identifying targets for early detection and intervention efforts in youth.

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Tim Clare
Author, Poet, and Creative Writing Podcaster

Author Tim Clare shares his experience of spending a year researching and trying every intervention for anxiety and panic he could, in an effort to manage his severe anxiety and daily panic attacks. The book he wrote about it, Coward, is an exploration of where we are with anxiety research across a wide range of disciplines, the gulf between public perceptions of what we know and where the science actually is, and how every generation believes it lives in the Age of Anxiety.

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Gaurav Patel
Department of Psychiatry
Columbia University
Associate Professor
Co-director, C3N Center

Making inferences about people’s thoughts and feelings requires the real-time integration of complex dynamic sensory information with ongoing theory-of-mind (ToM) operations.  The temporoparietal junction (TPJ) and superior temporal sulcus (STS) contain areas relevant to face-emotion recognition, processing of speech, visual attention, and ToM. Using naturalistic movie and story fMRI paradigms, I will detail how these areas form the basis of three TPJ-STS social inference pathways, relate them to distinct functions, and show how they are disrupted in schizophrenia (Sz), which is associated with poor social functioning.

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Peter Frank Hitchcock
Department of Psychology
Emory University

This talk applies computational models to address two questions core to depression and internalizing disorders broadly: First, how do we judge ourselves negatively? Second, why do we sometimes engage in repetitive negative thinking (RNT), such as rumination and worry? Addressing the first question, in part one I will describe a new task and computational approach to investigate self-judgment. Self-judgment tasks are designed to infer individual differences in latent self-schemas, and elucidate how such schemas are sampled from to form self-judgments. Yet, these tasks tend to confound explicit self-beliefs with the judgment process itself. We drew on methods from value-based decision-making to de-confound these variables. We found that more depressed individuals judge themselves more negatively, beyond what is explained by their explicit self-beliefs. Addressing the second question, in part two I will offer a normative account of RNT from a computational perspective. I will show that a model that learns to gate emotional content into working memory via trial and error exhibits the hallmark features of RNT. This suggests that some degree of RNT may arise as a byproduct of adaptive—but blind and incremental—learning about what emotional content is useful to hold in mind. Together, these projects show how computational theories and tools can offer insight into complex and vexing clinical phenomena. 

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