Associations between aversive learning processes and transdiagnostic psychiatric symptoms revealed by large-scale phenotyping

Background: Aversive learning processes are a candidate source of dysfunction in psychiatric disorders. Here symptom expression in a range of conditions is linked to altered threat perception, manifesting particularly in uncertain environments. How precise computational mechanisms that support aversive learning, and uncertainty estimation, relate to the presence of specific psychiatric symptoms remains undetermined. Methods: 400 subjects completed a novel online game-based aversive learning task, requiring avoidance of negative outcomes, in conjunction with completing measures of common psychiatric symptoms. We used a probabilistic computational model to measure distinct processes involved in learning, in addition to inferred estimates of safety likelihood and uncertainty. We tested for associations between learning processes and traditional psychiatric constructs alongside transdiagnostic factors using linear models. We used partial least squares regression to identify components of psychopathology grounded in both aversive learning behaviour and symptom self-report. Results: State anxiety and a transdiagnostic compulsivity-related factor were associated with enhanced learning from safety. However, data-driven analysis using partial least squares regression indicated the presence of two separable components across our behavioural and questionnaire data: one linked enhanced safety learning and lower estimated uncertainty to physiological anxiety, compulsivity, and impulsivity; the other linked enhanced threat learning and heightened uncertainty estimation to symptoms of depression and social anxiety. Conclusions: Our findings implicate aversive learning processes under uncertainty to the expression of psychiatric symptoms that cut across traditional diagnostic boundaries. These relationships are more complex than previously conceptualised. Future research should focus on understanding the neural mechanisms underlying alterations in aversive learning and how these lead to the development of symptoms and disorder

Recommendations for Bayesian hierarchical model specifications for case-control studies in mental health

Hierarchical model fitting has become commonplace for case-control studies of cognition and behaviour in mental health. However, these techniques require us to formalise assumptions about the data-generating process at the group level, which may not be known. Specifically, researchers typically must choose whether to assume all subjects are drawn from a common population, or to model them as deriving from separate populations. These assumptions have profound implications for computational psychiatry, as they affect the resulting inference (latent parameter recovery) and may conflate or mask true group-level differences. To test these assumptions we ran systematic simulations on synthetic multi-group behavioural data from a commonly used multi-armed bandit task (reinforcement learning task). We then examined recovery of group differences in latent parameter space under the two commonly used generative modelling assumptions: (1) modelling groups under a common shared group-level prior (assuming all participants are generated from a common distribution, and are likely to share common characteristics); (2) modelling separate groups based on symptomatology or diagnostic labels, resulting in separate group-level priors. We evaluated the robustness of these approaches to variations in data quality and prior specifications on a variety of metrics. We found that fitting groups separately (assumptions 2), provided the most accurate and robust inference across all conditions. Our results suggest that when dealing with data from multiple clinical groups, researchers should analyse patient and control groups separately as it provides the most accurate and robust recovery of the parameters of interest.Comment: Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstrac

Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling

Most psychiatric disorders do not occur in isolation, and most psychiatric symptom dimensions are not uniquely expressed within a single diagnostic category. Current treatments fail to work for around 25% to 40% of individuals, perhaps due at least in part to an overreliance on diagnostic categories in treatment development and allocation. In this review, we describe ongoing efforts in the field to surmount these challenges and precisely characterize psychiatric symptom dimensions using large-scale studies of unselected samples via remote, online, and "citizen science" efforts that take a dimensional, mechanistic approach. We discuss the importance that efforts to identify meaningful psychiatric dimensions be coupled with careful computational modeling to formally specify, test, and potentially falsify candidate mechanisms that underlie transdiagnostic symptom dimensions. We refer to this approach, i.e., where symptom dimensions are identified and validated against computationally well-defined neurocognitive processes, as computational factor modeling. We describe in detail some recent applications of this method to understand transdiagnostic cognitive processes that include model-based planning, metacognition, appetitive processing, and uncertainty estimation. In this context, we highlight how computational factor modeling has been used to identify specific associations between cognition and symptom dimensions and reveal previously obscured relationships, how findings generalize to smaller in-person clinical and nonclinical samples, and how the method is being adapted and optimized beyond its original instantiation. Crucially, we discuss next steps for this area of research, highlighting the value of more direct investigations of treatment response that bridge the gap between basic research and the clinic

Associations between aversive learning processes and transdiagnostic psychiatric symptoms in a general population sample

Symptom expression in psychiatric conditions is often linked to altered threat perception, however how computational mechanisms that support aversive learning relate to specific psychiatric symptoms remains undetermined. We answer this question using an online game-based aversive learning task together with measures of common psychiatric symptoms in 400 subjects. We show that physiological symptoms of anxiety and a transdiagnostic compulsivity-related factor are associated with enhanced safety learning, as measured using a probabilistic computational model, while trait cognitive anxiety symptoms are associated with enhanced learning from danger. We use data-driven partial least squares regression to identify two separable components across behavioural and questionnaire data: one linking enhanced safety learning and lower estimated uncertainty to physiological anxiety, compulsivity, and impulsivity; the other linking enhanced threat learning and heightened uncertainty estimation to symptoms of depression and social anxiety. Our findings implicate aversive learning processes in the expression of psychiatric symptoms that transcend diagnostic boundaries

Ambiguity Drives Higher-Order Pavlovian Learning

In the natural world, stimulus-outcome associations are often noisy and ambiguous. Learning to disambiguate these associations to identify which specific outcomes will occur is critical for survival. Pavlovian occasion setters are stimuli that determine whether other stimuli that are ambiguous will result in a specific outcome. Occasion setting is a well-established field, but very little investigation has been conducted on how occasion setters are disambiguated when they themselves are ambiguous. We investigated the role of higher-order Pavlovian occasion setting in humans. We also developed and tested the first computational model predicting direct associations, traditional occasion setting, and 2nd-order occasion setting. Results showed that occasion setters affected ambiguous but not unambiguous lower-order stimuli and that 2nd-order occasion setting was indeed learned. Our computational model demonstrated excellent fit with the data, advancing our theoretical understanding of learning with ambiguity. These results may ultimately improve treatment of Pavlovian-based mental health disorders (e.g., anxiety)

Neural encoding of socially adjusted value during competitive and hazardous foraging

In group foraging organisms, optimizing the conflicting demands of competitive food loss and safety is critical. We demonstrate that humans select competition avoidant and risk diluting strategies during foraging depending on socially adjusted value. We formulate a mathematically grounded quantification of socially adjusted value in foraging environments and show using multivariate fMRI analyses that socially adjusted value is encoded by mid-cingulate and ventromedial prefrontal cortices, regions that integrate value and action signals

The Role of the Medial Prefrontal Cortex in Spatial Margin of Safety Calculations

Humans, like many other animals, pre-empt danger by moving to locations that maximize their success at escaping future threats. We test the idea that spatial margin of safety (MOS) decisions, a form of pre-emptive avoidance, results in participants placing themselves closer to safer locations when facing more unpredictable threats. Using multivariate pattern analysis on fMRI data collected while subjects engaged in MOS decisions with varying attack location predictability, we show that while the hippocampus encodes MOS decisions across all types of threat, a vmPFC anterior-posterior gradient tracked threat predictability. The posterior vmPFC encoded for more unpredictable threat and showed functional coupling with the amygdala and hippocampus. Conversely, the anterior vmPFC was more active for the more predictable attacks and showed coupling with the striatum. Our findings suggest that when pre-empting danger, the anterior vmPFC may provide a safety signal, possibly via predictable positive outcomes, while the posterior vmPFC drives prospective danger signals