A Bayesian computational model reveals a failure to adapt interoceptive precision estimates across depression, anxiety, eating, and substance use disorders.

Recent neurocomputational theories have hypothesized that abnormalities in prior beliefs and/or the precision-weighting of afferent interoceptive signals may facilitate the transdiagnostic emergence of psychopathology. Specifically, it has been suggested that, in certain psychiatric disorders, interoceptive processing mechanisms either over-weight prior beliefs or under-weight signals from the viscera (or both), leading to a failure to accurately update beliefs about the body. However, this has not been directly tested empirically. To evaluate the potential roles of prior beliefs and interoceptive precision in this context, we fit a Bayesian computational model to behavior in a transdiagnostic patient sample during an interoceptive awareness (heartbeat tapping) task. Modelling revealed that, during an interoceptive perturbation condition (inspiratory breath-holding during heartbeat tapping), healthy individuals (N = 52) assigned greater precision to ascending cardiac signals than individuals with symptoms of anxiety (N = 15), depression (N = 69), co-morbid depression/anxiety (N = 153), substance use disorders (N = 131), and eating disorders (N = 14)-who failed to increase their precision estimates from resting levels. In contrast, we did not find strong evidence for differences in prior beliefs. These results provide the first empirical computational modeling evidence of a selective dysfunction in adaptive interoceptive processing in psychiatric conditions, and lay the groundwork for future studies examining how reduced interoceptive precision influences visceral regulation and interoceptively-guided decision-making.

Genetic Correlates of Treatment-Resistant Depression: Insights from Polygenic Scores Across Cognitive, Temperamental, and Sleep Traits in the All of US cohort.

Background: Treatment-resistant depression (TRD) is a major challenge in mental health, affecting a significant number of patients and leading to considerable economic and social burdens. The etiological factors contributing to TRD are complex and not fully understood. Objective: To investigate the genetic factors associated with TRD using polygenic scores (PGS) across various traits, and to explore their potential role in the etiology of TRD using large-scale genomic data from the All of Us Research Program (AoU). Methods: Data from 292,663 participants in the AoU were analyzed using a case-cohort design. Treatment resistant depression (TRD), treatment responsive Major Depressive Disorder (trMDD), and all others who have no formal diagnosis of Major Depressive Disorder (non-MDD) were identified through diagnostic codes and prescription patterns. Polygenic scores (PGS) for 61 unique traits from seven domains were used and logistic regressions were conducted to assess associations between PGS and TRD. Finally, Cox proportional hazard models were used to explore the predictive value of PGS for progression rate from the diagnostic event of Major Depressive Disorder (MDD) to TRD. Results: In the discovery set (104128 non-MDD, 16640 trMDD, and 4177 TRD), 44 of 61 selected PGS were found to be significantly associated with MDD, regardless of treatment responsiveness. Eleven of them were found to have stronger associations with TRD than with trMDD, encompassing PGS from domains in education, cognition, personality, sleep, and temperament. Genetic predisposition for insomnia and specific neuroticism traits were associated with increased TRD risk (OR range from 1.05 to 1.15), while higher education and intelligence scores were protective (ORs 0.88 and 0.91, respectively). These associations are consistent across two other independent sets within AoU (n = 104,388 and 63,330). Among 28,964 individuals tracked over time, 3,854 developed TRD within an average of 944 days (95% CI: 883 ~ 992 days) after MDD diagnosis. All eleven previously identified and replicated PGS were found to be modulating the conversion rate from MDD to TRD. Thus, those having higher education PGS would experiencing slower conversion rates than those who have lower education PGS with hazard ratios in 0.79 (80th versus 20th percentile, 95% CI: 0.74 ~ 0.85). Those who had higher insomnia PGS experience faster conversion rates than those who had lower insomnia PGS, with hazard ratios in 1.21 (80th versus 20th percentile, 95% CI: 1.13 ~ 1.30). Conclusions: Our results indicate that genetic predisposition related to neuroticism, cognitive function, and sleep patterns play a significant role in the development of TRD. These findings underscore the importance of considering genetic and psychosocial factors in managing and treating TRD. Future research should focus on integrating genetic data with clinical outcomes to enhance our understanding of pathways leading to treatment resistance.

Transdiagnostic failure to adapt interoceptive precision estimates across affective, substance use, and eating disorders: A replication and extension of previous results.

Recent Bayesian theories of interoception suggest that perception of bodily states rests upon a precision-weighted integration of afferent signals and prior beliefs. In a previous study, we fit a computational model of perception to behavior on a heartbeat tapping task to test whether aberrant precision-weighting could explain misestimation of cardiac states in psychopathology. We found that, during an interoceptive perturbation designed to amplify afferent signal precision (inspiratory breath-holding), healthy individuals increased the precision-weighting assigned to ascending cardiac signals (relative to resting conditions), while individuals with anxiety, depression, substance use disorders, and/or eating disorders did not. In this pre-registered study, we aimed to replicate and extend our prior findings in a new transdiagnostic patient sample (N = 285) similar to the one in the original study. As expected, patients in this new sample were also unable to adjust beliefs about the precision of cardiac signals - preventing the ability to accurately perceive changes in their cardiac state. Follow-up analyses combining samples from the previous and current study (N = 719) also afforded power to identify group differences between narrower diagnostic categories, and to examine predictive accuracy when logistic regression models were trained on one sample and tested on the other. With this confirmatory evidence in place, future studies should examine the utility of interceptive precision measures in predicting treatment outcomes and test whether these computational mechanisms might represent novel therapeutic targets.

Polygenic risk for neuroticism is associated with less efficient control in more difficult situations.

Neuroticism is a heritable trait and a risk factor for mental health due to its relevance to poor control of negative events. To examine the relationship between genetic propensity for neuroticism and control processing, we used the polygenic risk score (PRS) approach and a stop signal task during fMRI. We hypothesized that genetic propensity for neuroticism may moderate control processing as a function of control difficulty. PRSs for neuroticism were computed from a transdiagnostic group of individuals (n=406) who completed the stop signal task. The level of control difficulty was a function of the stop signal asynchrony: shorter asynchrony allows easier stopping whereas longer asynchrony makes stopping difficult. The relationship between PRS for neuroticism and neural activity for controlling responses was examined by the stop signal asynchrony. Although PRS for neuroticism did not relate to the overall inhibitory control, individuals with high PRS for neuroticism showed greater activity in left dorsal prefrontal cortex, middle temporal gyrus, and dorsal posterior cingulate cortex for difficult control. Thus, the genetic propensity for neuroticism affects neural processing in a difficult control context, which may help to explain why individuals with high levels of neuroticism exert poor control of negative events in difficult situations.

Transdiagnostic behavioral and genetic contributors to repetitive negative thinking: A machine learning approach.

BACKGROUND: Repetitive negative thinking (RNT) is a symptom that can negatively impact the treatment and course of common psychiatric disorders such as depression and anxiety. We aimed to characterize behavioral and genetic correlates of RNT to infer potential contributors to its genesis and maintenance. METHODS: We applied a machine learning (ML) ensemble method to define the contribution of fear, interoceptive, reward, and cognitive variables to RNT, along with polygenic risk scores (PRS) for neuroticism, obsessive compulsive disorder (OCD), worry, insomnia, and headaches. We used the PRS and 20 principal components of the behavioral and cognitive variables to predict intensity of RNT. We employed the Tulsa-1000 study, a large database of deeply phenotyped individuals recruited between 2015 and 2018. RESULTS: PRS for neuroticism was the main predictor of RNT intensity (R2=0.027,p<0.001). Behavioral variables indicative of faulty fear learning and processing, as well as aberrant interoceptive aversiveness, were significant contributors to RNT severity. Unexpectedly, we observed no contribution of reward behavior and diverse cognitive function variables. LIMITATIONS: This study is an exploratory approach that must be validated with a second, independent cohort. Furthermore, this is an association study, limiting causal inference. CONCLUSIONS: RNT is highly determined by genetic risk for neuroticism, a behavioral construct that confers risk to a variety of internalizing disorders, and by emotional processing and learning features, including interoceptive aversiveness. These results suggest that targeting emotional and interoceptive processing areas, which involve central autonomic network structures, could be useful in the modulation of RNT intensity.

Transdiagnostic failure to adapt interoceptive precision estimates across affective, substance use, and eating disorders: A replication study.

Recent computational theories of interoception suggest that perception of bodily states rests upon an expected reliability- or precision-weighted integration of afferent signals and prior beliefs. The computational psychiatry framework further suggests that aberrant precision-weighting may lead to misestimation of bodily states, potentially hindering effective visceral regulation and promoting psychopathology. In a previous study, we fit a Bayesian computational model of perception to behavior on a heartbeat tapping task to test whether aberrant precision-weighting was associated with misestimation of bodily states. We found that, during an interoceptive perturbation designed to amplify afferent signal precision (inspiratory breath-holding), healthy individuals increased the precision-weighting assigned to ascending cardiac signals (relative to resting conditions), while individuals with symptoms of anxiety, depression, substance use disorders, and/or eating disorders did not. A second study also replicated the pattern observed in healthy participants. In this pre-registered study, we aimed to replicate our prior findings in a new transdiagnostic patient sample (N=285) similar to the one in the original study. These new results successfully replicated those found in our previous study, indicating that, transdiagnostically, patients were unable to adjust beliefs about the reliability of interoceptive signals - preventing the ability to accurately perceive changes in their bodily state. Follow-up analyses combining samples from the previous and current study (N=719) also afforded the power to identify group differences within narrower diagnostic groups and to examine predictive accuracy when logistic regression models were trained on one sample and tested on the other. Given the increased confidence in the generalizability of these effects, future studies should examine the utility of interceptive precision measures in predicting treatment outcomes or identify whether these computational mechanisms might represent novel therapeutic targets for improving visceral regulation.

Functional magnetic resonance imaging data for the association between polygenic risk scores for neuroticism and reward-punishment processing.

Neuroticism as a personality trait represents a heritable risk for psychiatric disorders. The polygenic risk score for neuroticism (N-PRS) is used to study genetic vulnerability to neuroticism. The current data present the association of the genetic risk for neuroticism to neural reward-punishment processing using functional magnetic resonance imaging. N-PRS was computed based on the individual's genotype information and a genome-wide association study on the UK Biobank data. While individuals performed a monetary incentive delay task, their neural activations for upcoming incentives (reward: gain, punishment: loss) were measured in blood oxygen level dependent (BOLD) signals during the delay phase. Multivariate ANCOVAs were used to analyze BOLD signals for finding the association between N-PRS and reward-punishment processing by the incentive valence (Related research article: H. Park, K.L. Forthman, R. Kuplicki, T.A. Victor, Tulsa 1000 Investigators, H.W. Yeh, W.K. Thompson, M.P. Paulus, Polygenic risk for neuroticism modulates response to gains and losses in the amygdala and caudate: evidence from a clinical cohort. J. Affect. Disord. 293 (2021) 124-132. https://doi.org/10.1016/j.jad.2021.06.016). These data can be used as reference data for future studies examining the role of the genetic propensity for personality traits in the context of psychiatric disorders.

Latent Variables Quantifying Neighborhood Characteristics and Their Associations with Poor Mental Health.

Neighborhood characteristics can have profound impacts on resident mental health, but the wide variability in methodologies used across studies makes it difficult to reach a consensus as to the implications of these impacts. The aim of this study was to simplify the assessment of neighborhood influence on mental health. We used a factor analysis approach to reduce the multi-dimensional assessment of a neighborhood using census tracts and demographic data available from the American Community Survey (ACS). Multivariate quantitative characterization of the neighborhood was derived by performing a factor analysis on the 2011-2015 ACS data. The utility of the latent variables was examined by determining the association of these factors with poor mental health measures from the 500 Cities Project 2014-2015 data (2017 release). A five-factor model provided the best fit for the data. Each factor represents a complex multi-dimensional construct. However, based on heuristics and for simplicity we refer to them as (1) Affluence, (2) Singletons in Tract, (3) African Americans in Tract, (4) Seniors in Tract, and (5) Hispanics or Latinos in Tract. African Americans in Tract (with loadings showing larger numbers of people who are black, single moms, and unemployed along with fewer people who are white) and Affluence (with loadings showing higher income, education, and home value) were strongly associated with poor mental health (R2=0.67, R2=0.83). These findings demonstrate the utility of this factor model for future research focused on the relationship between neighborhood characteristics and resident mental health.

Perceptual insensitivity to the modulation of interoceptive signals in depression, anxiety, and substance use disorders.

This study employed a series of heartbeat perception tasks to assess the hypothesis that cardiac interoceptive processing in individuals with depression/anxiety (N = 221), and substance use disorders (N = 136) is less flexible than that of healthy individuals (N = 53) in the context of physiological perturbation. Cardiac interoception was assessed via heartbeat tapping when: (1) guessing was allowed; (2) guessing was not allowed; and (3) experiencing an interoceptive perturbation (inspiratory breath hold) expected to amplify cardiac sensation. Healthy participants showed performance improvements across the three conditions, whereas those with depression/anxiety and/or substance use disorder showed minimal improvement. Machine learning analyses suggested that individual differences in these improvements were negatively related to anxiety sensitivity, but explained relatively little variance in performance. These results reveal a perceptual insensitivity to the modulation of interoceptive signals that was evident across several common psychiatric disorders, suggesting that interoceptive deficits in the realm of psychopathology manifest most prominently during states of homeostatic perturbation.

Polygenic risk for neuroticism moderates response to gains and losses in amygdala and caudate: Evidence from a clinical cohort.

BACKGROUND: Neuroticism is a heritable trait that contributes to the vulnerability to depression. We used polygenic risk scores (PRS) to examine genetic vulnerability to neuroticism and its associations with reward/punishment processing in a clinical sample with mood, anxiety, and substance use disorders. It was hypothesized that higher PRS for neuroticism is associated with attenuated neural responses to reward/punishment. METHOD: Four hundred sixty-nine participants were genotyped and their PRSs for neuroticism were computed. Associations between PRS for neuroticism and anticipatory processing of monetary incentives were examined using functional magnetic resonance imaging. RESULTS: Individuals with higher PRS for neuroticism showed less anticipatory activation in the left amygdala and caudate region to incentives regardless of incentive valence. Further, these individuals exhibited altered sensitivity to gain/loss processing in the right anterior insula. Higher PRSs for neuroticism were also associated with reduced processing of gains in the precuneus. LIMITATIONS: The study population consisted of a transdiagnostic sample with dysfunctions in positive and negative valence processing. PRS for neuroticism may be correlated with current clinical symptoms due to the vulnerability to psychiatric disorders. CONCLUSIONS: Greater genetic loading for neuroticism was associated with attenuated anticipatory responsiveness in reward/punishment processing with altered sensitivity to valences. Thus, a higher genetic risk for neuroticism may limit the degree to which positive and/or negative outcomes influence the current mood state, which may contribute to the development of positive and negative affective dysfunctions in individuals with mood, anxiety, and addictive disorders.

Neighborhood affluence is not associated with positive and negative valence processing in adults with mood and anxiety disorders: A Bayesian inference approach.

Survey-based studies show that neighborhood disadvantage is associated with community reported mental health problems. However, fewer studies have examined whether neighborhood characteristics have measurable impact on mental health status of individuals in general and whether neighborhood characteristics impact positive/negative valence processing at both behavioral and brain levels. This study addressed these questions by investigating effects of census-based neighborhood affluence on self-reported symptoms, brain functions, and structures associated with positive/negative valence processing in a sample of individuals with mood and anxiety disorders (n = 262). Employing a Bayesian inference approach, our investigation demonstrates that neighborhood affluence fails to be associated with positive/negative valence processing measured across multiple modalities, with the only effects of neighborhood affluence identified in trait anxiety scores. These findings highlight that while community-based relationships between neighborhood characteristics and mental health problems are strong, it is much less clear that these characteristics have a measurable impact on the individual.