Author: Johanne

Alexander Shackman
Professor
Department of Psychology
University of Maryland

Anxiety—widely conceptualized as a sustained state of heightened distress and arousal triggered by uncertain threat—is a fundamental aspect of mammalian experience and a key component of the National Institute of Mental Health’s influential RDoC framework. When extreme or pervasive, anxiety imposes a staggering burden on patients, public health, and the global economy. Existing treatments are far from curative for many, underscoring the urgency of developing a deeper understanding of the underlying mechanisms. Here, I will highlight data gleaned from studies of humans and monkeys. Using a combination of approaches—from genetic assays and loss-of-function experiments to ecological momentary assessment and computational neuroimaging—this work provides fresh insights into the nature and the neurobiology of anxiety related states, traits, and psychiatric disorders.

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Christopher Rozell
Professor, Executive Director of Institute for Neuroscience, Neurotechnology & Society (INNS)
Department of of Electrical & Computer Engineering
Georgia Institute of Technology

Simultaneous advances in neurotechnology and data science have created new opportunities to measure, modulate and understand the neural activity underlying complex psychiatric disorders such as treatment resistant depression (TRD). Despite promising explorations of deep brain stimulation (DBS) as a therapy for TRD, one of the barriers to scaling this treatment is the lack of objective information supporting clinical decision making during recovery. I will describe our recent efforts collecting and analyzing longitudinal intracranial electrophysiology data from TRD patients undergoing subcallosal cingulate cortex (SCC) DBS, as well as our development of novel AI techniques to identify electrophysiology biomarkers of disease recovery state. Finally, I will show how this data-driven biomarker can potentially be used in clinical decision support through patient case studies, as well as shed light on the microcircuit structures and complex behaviors that underly TRD pathology and recovery.

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Vanessa Brown
Assistant Professor
Department of Psychology
Emory University

Avoidance is a central impairment in anxiety disorders, but what causes maladaptive avoidance in this population is unclear. To date, empirical data suggest that the uncertainty associated with negative outcomes, rather than the presence of negative outcomes alone, drives avoidance in anxiety. Here, we used the explore-exploit dilemma – whether to explore options with uncertain options versus stay with known choices with certain options – to understand how exploration goes awry in anxiety and leads to avoidance. Across two tasks, we did not find an overall avoidance of uncertainty, manifesting as increased exploitation, in anxiety. Instead, people with anxiety were unable to adaptively adjust exploration, under-exploring when exploration was adaptive and over-exploring when exploration was harmful. This impairment was specific to anxiety and not to depression or general internalizing symptoms. In a follow-up study, we tested whether this maladaptive exploration of uncertainty was distinct from previously established alterations in model-based decision-making in compulsivity. Anxiety was associated with increased model-based planning while compulsivity showed patterns of exploratory behavior distinct from anxiety. These results show a computational dissociation of exploratory behavior among internalizing symptom clusters and suggest that maladaptive exploration of uncertainty is a unique signature of anxiety-related disorders.

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Vinny Costa
Associate Professor
Department of Psychiatry and Behavioral Sciences
Emory University

Curiosity and exploration support learning and adaptive decision-making in uncertain environments. While these processes are sensitive to motivational context, it remains unclear how outcome valence shapes exploration across species. Human studies suggest that aversive contexts increase exploration, but these effects often rely on verbal framing and explicit instructions. To gain deeper insight into how exploration strategies emerge from experience alone, this study investigated the influence of hedonic valence on novelty seeking, exploration, and reinforcement learning in rhesus macaques. Using visual tokens as secondary reinforcers, we found that monkeys explored novel, uncertain options more frequently when exploitation would lead to losses rather than gains. However, our analyses clarified that this heightened novelty seeking was primarily a consequence of the monkeys employing an optimistic prior belief about the value of novelty, rather than a categorical, valence-dependent shift in their underlying curiosity or the information bonus associated with exploration. Approach and avoidance motivation did influence other aspects of reinforcement learning. Monkeys demonstrated faster learning from losses than from gains, indicating that they were averse to losing tokens. They also frequently chose an option and then quickly cancelled their choices. These choice cancellations were strategic responses to approach-avoidance conflicts and uncertainty, and represented self-generated bouts of exploratory behavior that led to valence-dependent use of directed and random exploration. These findings suggest that different strategies are used to manage explore-exploit tradeoffs induced by novelty or internal motivational conflicts, revealing dissociable effects of curiosity and hedonic valence on reinforcement learning.

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Robb Rutledge
Assistant Professor of Psychology and Psychiatry
Yale University

The happiness of individuals is an important metric for societies, but we know relatively little about how daily life events are aggregated into subjective feelings. We show that happiness depends on the history of expectations and prediction errors resulting from those expectations, a result we have now replicated in thousands of individuals using smartphone-based data collection (https://happinessquest.app). Using fMRI, we show how happiness relates to neural activity and to the neuromodulator dopamine. We apply this computational framework to a range of problems including how happiness depends on intrinsic reward and how it changes during conversations with artificial intelligence. Using computational models for decision making and reinforcement learning, we show how feelings vary across individuals during multiple tasks and in relation to major depression.

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Claire Gillan
Professor in Psychology
Trinity College Dublin

Theories about the role of the brain’s habit system in compulsive disorders have been around for decades. Although progress has been made, most research has focused on goal-directed processes, i.e., the brain systems that help rein in habits. This is because habits themselves, although perhaps the most ubiquitous mode of action selection, remain among the most difficult phenomena to study empirically. This has led to a lack of mechanistic clarity in leading theories of compulsion and a prevailing ‘deficit’ model centred on decreased functioning of prefrontal brain regions that support cognitive control. This overlooks the potential adaptive value of a potentiated habit system in compulsivity: the rapid acquisition of stimulus–response representations that promote early automaticity, efficiency, and functional advantage. In this talk, I discuss new research that aims to remedy this and reveals ‘hidden habits’: patterns detectable through behavioural and electrophysiological methods, both inside and outside the lab, in healthy and transdiagnostic populations. Across various temporal horizons, I convey a mechanistic account of the role of habit in compulsive disorders that separates state from trait, risk from resilience, and benefits versus costs. Given the clear benefits of habits in everyday life, I conclude by considering what a neurodiversity-informed framework for compulsivity might entail.

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Golia Shafiei
CIHR Postdoctoral Fellow | PennLINC
Department of Psychiatry (OxCIN)
University of Pennsylvania

Major mental illnesses are increasingly understood as disorders of brain development. Neuroimaging studies of brain development can help track healthy brain maturation and have the potential to identify deviations from normal development linked to psychopathology. However, large and diverse samples are required to capture reliable neurodevelopmental patterns on the population level. While it is possible to aggregate data across multiple resources, data aggregation is not a straightforward process given the differences in neuroimaging and psychiatric phenotyping protocols used by independent studies. To this end, we introduce Reproducible Brain Charts (RBC), an open data resource that integrates data from five large and prominent studies of brain development in youth from three continents. We used bifactor models to generate harmonized psychiatric phenotypes that capture major dimensions of psychopathology. Additionally, we applied rigorous quality assurance protocols and used uniform and consistent pipelines to carefully curate and process neuroimaging data in a reproducible manner. All RBC data–including harmonized psychiatric phenotypes, unprocessed neuroimaging data, and fully processed imaging derivatives–are publicly shared without a data use agreement via the International Neuroimaging Data-sharing Initiative. Taken together, RBC facilitates large-scale, robust, and reproducible research in developmental and psychiatric neuroscience.

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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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