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

Naturalistic observations show that animals pre-empt danger by moving to locations that increase their success in avoiding future threats. To test this in humans, we created a spatial margin of nsafety (MOS) decision task that quantifies pre-emptive avoidance by measuring the distance subjects place themselves to safety when facing different threats whose attack locations vary in predictability. Behavioral results show that human participants place themselves closer to safe locations when facing threats that attack in spatial locations with more outliers. Using both univariate and multivariate pattern analysis (MVPA) on fMRI data collected during a 2-hour session on participants of both sexes, we demonstrate a dissociable role for the vmPFC in MOS-related decision-making. MVPA results revealed that the posterior vmPFC encoded for more unpredictable threats with univariate analyses showing a 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 converge in showing that during pre-emptive danger, the anterior vmPFC may provide a safety signal, possibly via foreseeable outcomes, while the posterior vmPFC drives unpredictable danger signals.Significance Statement A common observation in nature is that under conditions of uncertain danger, animals will stay close to safety - a behavioral metric known as spatial margin of safety (MOS). We adapt this metric to examine risky and safety decisions to unpredictable attack distances. Using multivariate and univariate fMRI, we demonstrate a novel dissociation of vmPFC in decision-making: the posterior vmPFC encoded for the more unpredictable threat and showed functional coupling with the amygdala and hippocampus, while the anterior vmPFC was more active for more predictable attacks. Our findings suggest that when pre-empting danger, the anterior vmPFC may provide a safety signal associated with predictable outcomes, while the posterior vmPFC may drive uncertain danger signals.

Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo.

Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (ß = -18.3, 95% CI (-34.3 to -2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.

Incorporating uncertainty within dynamic interoceptive learning.

Introduction: Interoception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. One method of measuring respiratory interoceptive learning that has shown promising results is the Breathing Learning Task (BLT). While the original BLT required binary predictions regarding the presence or absence of an upcoming inspiratory resistance, here we extended this paradigm to capture continuous measures of prediction (un)certainty. Methods: Sixteen healthy participants completed the continuous version of the BLT, where they were asked to predict the likelihood of breathing resistances on a continuous scale from 0.0 to 10.0. In order to explain participants' responses, a Rescorla-Wagner model of associative learning was combined with suitable observation models for continuous or binary predictions, respectively. For validation, we compared both models against corresponding null models and examined the correlation between observed and modeled predictions. The model was additionally extended to test whether learning rates differed according to stimuli valence. Finally, summary measures of prediction certainty as well as model estimates for learning rates were considered against interoceptive and mental health questionnaire measures. Results: Our results demonstrated that the continuous model fits closely captured participant behavior using empirical data, and the binarised predictions showed excellent replicability compared to previously collected data. However, the model extension indicated that there were no significant differences between learning rates for negative (i.e. breathing resistance) and positive (i.e. no breathing resistance) stimuli. Finally, significant correlations were found between fatigue severity and both prediction certainty and learning rate, as well as between anxiety sensitivity and prediction certainty. Discussion: These results demonstrate the utility of gathering enriched continuous prediction data in interoceptive learning tasks, and suggest that the updated BLT is a promising paradigm for future investigations into interoceptive learning and potential links to mental health.

Naturalistic reinforcement learning.

Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans' ability to navigate complex, multidimensional real-world environments so successfully.

Interactive cognitive maps support flexible behavior under threat.

In social environments, survival can depend upon inferring and adapting to other agents' goal-directed behavior. However, it remains unclear how humans achieve this, despite the fact that many decisions must account for complex, dynamic agents acting according to their own goals. Here, we use a predator-prey task (total n = 510) to demonstrate that humans exploit an interactive cognitive map of the social environment to infer other agents' preferences and simulate their future behavior, providing for flexible, generalizable responses. A model-based inverse reinforcement learning model explained participants' inferences about threatening agents' preferences, with participants using this inferred knowledge to enact generalizable, model-based behavioral responses. Using tree-search planning models, we then found that behavior was best explained by a planning algorithm that incorporated simulations of the threat's goal-directed behavior. Our results indicate that humans use a cognitive map to determine other agents' preferences, facilitating generalized predictions of their behavior and effective responses.

The Fake IQ Test: a novel measure of self-reflection in major depressive disorder.

BACKGROUND: Excessive negative self-referential processing plays an important role in the development and maintenance of major depressive disorder (MDD). Current measures of self-reflection are limited to self-report questionnaires and invoking imagined states, which may not be suitable for all populations. AIMS: The current study aimed to pilot a new measure of self-reflection, the Fake IQ Test (FIQT). METHOD: Participants with MDD and unaffected controls completed a behavioural (experiment 1, n = 50) and functional magnetic resonance imaging version (experiment 2, n = 35) of the FIQT. RESULTS: Behaviourally, those with MDD showed elevated negative self-comparison with others, higher self-dissatisfaction and lower perceived success on the task, compared with controls; however, FIQT scores were not related to existing self-report measures of self-reflection. In the functional magnetic resonance imaging version, greater activation in self-reflection versus control conditions was found bilaterally in the inferior frontal cortex, insula, dorsolateral prefrontal cortex, motor cortex and dorsal anterior cingulate cortex. No differences in neural activation were found between participants with MDD and controls, nor were there any associations between neural activity, FIQT scores or self-report measures of self-reflection. CONCLUSIONS: Our results suggest the FIQT is sensitive to affective psychopathology, but a lack of association with other measures of self-reflection may indicate that the task is measuring a different construct. Alternatively, the FIQT may measure aspects of self-reflection inaccessible to current questionnaires. Future work should explore relationships with alternative measures of self-reflection likely to be involved in perception of task performance, such as perfectionism.

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.

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Computationally-defined markers of uncertainty aversion predict emotional responses during a global pandemic.

Exposure to stressful life events involving threat and uncertainty often results in the development of anxiety. However, the factors that confer risk and resilience for anxiety following real world stress at a computational level remain unclear. We identified core components of uncertainty aversion moderating response to stress posed by the COVID-19 pandemic derived from computational modeling of decision making. Using both cross-sectional and longitudinal analyses, we investigated both immediate effects at the onset of the stressor, as well as medium-term changes in response to persistent stress. 479 subjects based in the United States completed a decision-making task measuring risk aversion, loss aversion, and ambiguity aversion in the early stages of the pandemic (March 2020). Self-report measures targeting threat perception, anxiety, and avoidant behavior in response to the pandemic were collected at the same time point and 8 weeks later (May 2020). Cross-sectional analyses indicated that higher risk aversion predicted higher perceived threat from the pandemic, and ambiguity aversion for guaranteed gains predicted perceived threat and pandemic-related anxiety. In longitudinal analyses, ambiguity aversion for guaranteed gains predicted greater increases in perceived infection likelihood. Together, these results suggest that individuals who have a low-level aversion toward uncertainty show stronger negative emotional reactions to both the onset and persistence of real-life stress. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Model-based prioritization for acquiring protection.

Protection often involves the capacity to prospectively plan the actions needed to mitigate harm. The computational architecture of decisions involving protection remains unclear, as well as whether these decisions differ from other beneficial prospective actions such as reward acquisition. Here we compare protection acquisition to reward acquisition and punishment avoidance to examine overlapping and distinct features across the three action types. Protection acquisition is positively valenced similar to reward. For both protection and reward, the more the actor gains, the more benefit. However, reward and protection occur in different contexts, with protection existing in aversive contexts. Punishment avoidance also occurs in aversive contexts, but differs from protection because punishment is negatively valenced and motivates avoidance. Across three independent studies (Total N = 600) we applied computational modeling to examine model-based reinforcement learning for protection, reward, and punishment in humans. Decisions motivated by acquiring protection evoked a higher degree of model-based control than acquiring reward or avoiding punishment, with no significant differences in learning rate. The context-valence asymmetry characteristic of protection increased deployment of flexible decision strategies, suggesting model-based control depends on the context in which outcomes are encountered as well as the valence of the outcome.

Brain activity during pursuit and goal-conflict threat avoidance in major depressive disorder.

Threat avoidance is a prominent symptom of affective disorders, yet its biological basis remains poorly understood. Here, we used a validated task, the Joystick Operated Runway Task (JORT), combined with fMRI, to explore whether abnormal function in neural circuits responsible for avoidance underlies these symptoms. Eighteen individuals with major depressive disorder (MDD) and 17 unaffected controls underwent the task, which involved using physical effort to avoid threatening stimuli, paired with mild electric shocks on certain trials. Activity during anticipation and avoidance of threats was explored and compared between groups. Anticipation of aversive stimuli was associated with significant activation in the dorsal anterior cingulate cortex, superior frontal gyrus, and striatum, while active avoidance of aversive stimuli was associated with activity in dorsal anterior cingulate cortex, insula, and prefrontal cortex. There were no significant group differences in neural activity or behavioral performance on the JORT; however, participants with depression reported more dread while being chased on the task. The JORT effectively identified neural systems involved in avoidance and anticipation of aversive stimuli. However, the absence of significant differences in behavioral performance and activation between depressed and non-depressed groups suggests that MDD is not associated with abnormal function in these networks. Future research should investigate the basis of passive avoidance in major depression. Further, the JORT should be explored in patients with anxiety disorders, where threat avoidance may be a more prominent characteristic of the disorder.

Ambiguity drives higher-order Pavlovian learning.

In the natural world, stimulus-outcome associations are often ambiguous, and most associations are highly complex and situation-dependent. Learning to disambiguate these complex associations to identify which specific outcomes will occur in which situations is critical for survival. Pavlovian occasion setters are stimuli that determine whether other stimuli will result in a specific outcome. Occasion setting is a well-established phenomenon, but very little investigation has been conducted on how occasion setters are disambiguated when they themselves are ambiguous (i.e., when they do not consistently signal whether another stimulus will be reinforced). In two preregistered studies, we investigated the role of higher-order Pavlovian occasion setting in humans. We developed and tested the first computational model predicting direct associative learning, traditional occasion setting (i.e., 1st-order occasion setting), and 2nd-order occasion setting. This model operationalizes stimulus ambiguity as a mechanism to engage in higher-order Pavlovian learning. Both behavioral and computational modeling results suggest that 2nd-order occasion setting was learned, as evidenced by lack and presence of transfer of occasion setting properties when expected and the superior fit of our 2nd-order occasion setting model compared to the 1st-order occasion setting or direct associations models. These results provide a controlled investigation into highly complex associative learning and may ultimately lead to improvements in the treatment of Pavlovian-based mental health disorders (e.g., anxiety disorders, substance use).

Pavlovian occasion setting in human fear and appetitive conditioning: Effects of trait anxiety and trait depression.

Contexts and discrete stimuli often hierarchically influence the association between a stimulus and outcome. This phenomenon, called occasion setting, is central to modulation-based Pavlovian learning. We conducted two experiments with humans in fear and appetitive conditioning paradigms, training stimuli in differential conditioning, feature-positive discriminations, and feature-negative discriminations. We also investigated the effects of trait anxiety and trait depression on these forms of learning. Results from both experiments showed that participants were able to successfully learn which stimuli predicted the electric shock and monetary reward outcomes. Additionally, as hypothesized, the stimuli trained as occasion setters had little-to-no effect on simple reinforced or non-reinforced stimuli, suggesting the former were indeed occasion setters. Lastly, in fear conditioning, trait anxiety was associated with increases in fear of occasion setter/conditional stimulus compounds; in appetitive conditioning, trait depression was associated with lower expectations of monetary reward for the trained negative occasion setting compound and transfer of the negative occasion setter to the simple reinforced stimulus. These results suggest that clinically anxious individuals may have enhanced fear of occasion setting compounds, and clinically depressed individuals may expect less reward with compounds involving the negative occasion setter.

Neural encoding of perceived patch value during competitive and hazardous virtual foraging.

Natural observations suggest that in safe environments, organisms avoid competition to maximize gain, while in hazardous environments the most effective survival strategy is to congregate with competition to reduce the likelihood of predatory attack. We probed the extent to which survival decisions in humans follow these patterns, and examined the factors that determined individual-level decision-making. In a virtual foraging task containing changing levels of competition in safe and hazardous patches with virtual predators, we demonstrate that human participants inversely select competition avoidant and risk diluting strategies depending on perceived patch value (PPV), a computation dependent on reward, threat, and competition. We formulate a mathematically grounded quantification of PPV in social foraging environments and show using multivariate fMRI analyses that PPV is encoded by mid-cingulate cortex (MCC) and ventromedial prefrontal cortices (vMPFC), regions that integrate action and value signals. Together, these results suggest humans utilize and integrate multidimensional information to adaptively select patches highest in PPV, and that MCC and vMPFC play a role in adapting to both competitive and predatory threats in a virtual foraging setting.

Structural brain correlates in major depression, anxiety disorders and post-traumatic stress disorder: A voxel-based morphometry meta-analysis.

The high comorbidity of Major Depressive Disorder (MDD), Anxiety Disorders (ANX), and Posttraumatic Stress Disorder (PTSD) has hindered the study of their structural neural correlates. The authors analyzed specific and common grey matter volume (GMV) characteristics by comparing them with healthy controls (HC). The meta-analysis of voxel-based morphometry (VBM) studies showed unique GMV diminutions for each disorder (p < 0.05, corrected) and less robust smaller GMV across diagnostics (p < 0.01, uncorrected). Pairwise comparison between the disorders showed GMV differences in MDD versus ANX and in ANX versus PTSD. These results endorse the hypothesis that unique clinical features characterizing MDD, ANX, and PTSD are also reflected by disorder specific GMV correlates.

Model-based aversive learning in humans is supported by preferential task state reactivation.

Harm avoidance is critical for survival, yet little is known regarding the neural mechanisms supporting avoidance in the absence of trial-and-error experience. Flexible avoidance may be supported by a mental model (i.e., model-based), a process for which neural reactivation and sequential replay have emerged as candidate mechanisms. During an aversive learning task, combined with magnetoencephalography, we show prospective and retrospective reactivation during planning and learning, respectively, coupled to evidence for sequential replay. Specifically, when individuals plan in an aversive context, we find preferential reactivation of subsequently chosen goal states. Stronger reactivation is associated with greater hippocampal theta power. At outcome receipt, unchosen goal states are reactivated regardless of outcome valence. Replay of paths leading to goal states was modulated by outcome valence, with aversive outcomes associated with stronger reverse replay than safe outcomes. Our findings are suggestive of avoidance involving simulation of unexperienced states through hippocampally mediated reactivation and replay.

Promises and challenges of human computational ethology.

The movements an organism makes provide insights into its internal states and motives. This principle is the foundation of the new field of computational ethology, which links rich automatic measurements of natural behaviors to motivational states and neural activity. Computational ethology has proven transformative for animal behavioral neuroscience. This success raises the question of whether rich automatic measurements of behavior can similarly drive progress in human neuroscience and psychology. New technologies for capturing and analyzing complex behaviors in real and virtual environments enable us to probe the human brain during naturalistic dynamic interactions with the environment that so far were beyond experimental investigation. Inspired by nonhuman computational ethology, we explore how these new tools can be used to test important questions in human neuroscience. We argue that application of this methodology will help human neuroscience and psychology extend limited behavioral measurements such as reaction time and accuracy, permit novel insights into how the human brain produces behavior, and ultimately reduce the growing measurement gap between human and animal neuroscience.

Cholinergic modulation of disorder-relevant human defensive behaviour in generalised anxiety disorder.

Drugs that are clinically effective against anxiety disorders modulate the innate defensive behaviour of rodents, suggesting these illnesses reflect altered functioning in brain systems that process threat. This hypothesis is supported in humans by the discovery that the intensity of threat-avoidance behaviour is altered by the benzodiazepine anxiolytic lorazepam. However, these studies used healthy human participants, raising questions as to their validity in anxiety disorder patients, as well as their generalisability beyond GABAergic benzodiazepine drugs. BNC210 is a novel negative allosteric modulator of the alpha 7 nicotinic acetylcholine receptor and we recently used functional Magnetic Resonance Imaging to show it reduced amygdala responses to fearful faces in generalised anxiety disorder patients. Here we report the effect of BNC210 on the intensity of threat-avoidance behaviour in 21 female GAD patients from the same cohort. We used the Joystick Operated Runway Task as our behavioural measure, which is a computerised human translation of the Mouse Defense Test Battery, and the Spielberger state anxiety inventory as our measure of state affect. Using a repeated-measures, within-subjects design we assessed the effect of BNC210 at two dose levels versus placebo (300 mg and 2000 mg) upon two types of threat-avoidance behaviour (Flight Intensity and Risk Assessment Intensity). We also tested the effects of 1.5 mg of the benzodiazepine lorazepam as an active control. BNC210 significantly reduced Flight Intensity relative to placebo and the low dose of BNC210 also significantly reduced self-reported state anxiety. Risk Assessment Intensity was not significantly affected. Results show both human defensive behaviour and state anxiety are influenced by cholinergic neurotransmission and there provide converging evidence that this system has potential as a novel target for anxiolytic pharmacotherapy.

Focusing on Comorbidity-A Novel Meta-Analytic Approach and Protocol to Disentangle the Specific Neuroanatomy of Co-occurring Mental Disorders.

BACKGROUND: In mental health, comorbidities are the norm rather than the exception. However, current meta-analytic methods for summarizing the neural correlates of mental disorders do not consider comorbidities, reducing them to a source of noise and bias rather than benefitting from their valuable information. OBJECTIVES: We describe and validate a novel neuroimaging meta-analytic approach that focuses on comorbidities. In addition, we present the protocol for a meta-analysis of all major mental disorders and their comorbidities. METHODS: The novel approach consists of a modification of Seed-based d Mapping-with Permutation of Subject Images (SDM-PSI) in which the linear models have no intercept. As in previous SDM meta-analyses, the dependent variable is the brain anatomical difference between patients and controls in a voxel. However, there is no primary disorder, and the independent variables are the percentages of patients with each disorder and each pair of potentially comorbid disorders. We use simulations to validate and provide an example of this novel approach, which correctly disentangled the abnormalities associated with each disorder and comorbidity. We then describe a protocol for conducting the new meta-analysis of all major mental disorders and their comorbidities. Specifically, we will include all voxel-based morphometry (VBM) studies of mental disorders for which a meta-analysis has already been published, including at least 10 studies. We will use the novel approach to analyze all included studies in two separate single linear models, one for children/adolescents and one for adults. DISCUSSION: The novel approach is a valid method to focus on comorbidities. The meta-analysis will yield a comprehensive atlas of the neuroanatomy of all major mental disorders and their comorbidities, which we hope might help develop potential diagnostic and therapeutic tools.