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

Elevated decision uncertainty and reduced avoidance drives in depression, anxiety and substance use disorders during approach-avoidance conflict: a replication study

BACKGROUND

Decision-making under approach-avoidance conflict (AAC; e.g., sacrificing quality of life to avoid feared outcomes) may be affected in multiple psychiatric disorders. Recently, we used a computational (active inference) model to characterize information processing differences during AAC in individuals with depression, anxiety and/or substance use disorders. Individuals with psychiatric disorders exhibited increased decision uncertainty (DU) and reduced sensitivity to unpleasant stimuli. This preregistered study aimed to determine the replicability of this processing dysfunction.

METHODS

A new sample of participants completed the AAC task. Individual-level computational parameter estimates, reflecting decision uncertainty and sensitivity to unpleasant stimuli ("emotion conflict"; EC), were obtained and compared between groups. Subsequent analyses combining the prior and current samples allowed assessment of narrower disorder categories.

RESULTS

The sample in the present study included 480 participants: 97 healthy controls, 175 individuals with substance use disorders and 208 individuals with depression and/or anxiety disorders. Individuals with substance use disorders showed higher DU and lower EC values than healthy controls. The EC values were lower in females, but not males, with depression and/or anxiety disorders than in healthy controls. However, the previously observed difference in DU between participants with depression and/or anxiety disorders and healthy controls did not replicate. Analyses of specific disorders in the combined samples indicated that effects were common across different substance use disorders and affective disorders.

LIMITATIONS

There were differences, although with small effect size, in age and baseline intellectual functioning between the previous and current sample, which may have affected replication of DU differences in participants with depression and/or anxiety disorders.

CONCLUSION

The now robust evidence base for these clinical group differences motivates specific questions that should be addressed in future research: can DU and EC become behavioural treatment targets, and can we identify neural substrates of DU and EC that could be used to measure severity of dysfunction or as neuromodulatory treatment targets?

Providing Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference

Classical null hypothesis significance testing is limited to the rejection of the point-null hypothesis; it does not allow the interpretation of non-significant results. This leads to a bias against the null hypothesis. Herein, we discuss statistical approaches to ‘null effect’ assessment focusing on the Bayesian parameter inference (BPI). Although Bayesian methods have been theoretically elaborated and implemented in common neuroimaging software packages, they are not widely used for ‘null effect’ assessment. BPI considers the posterior probability of finding the effect within or outside the region of practical equivalence to the null value. It can be used to find both ‘activated/deactivated’ and ‘not activated’ voxels or to indicate that the obtained data are not sufficient using a single decision rule. It also allows to evaluate the data as the sample size increases and decide to stop the experiment if the obtained data are sufficient to make a confident inference. To demonstrate the advantages of using BPI for fMRI data group analysis, we compare it with classical null hypothesis significance testing on empirical data. We also use simulated data to show how BPI performs under different effect sizes, noise levels, noise distributions and sample sizes. Finally, we consider the problem of defining the region of practical equivalence for BPI and discuss possible applications of BPI in fMRI studies. To facilitate ‘null effect’ assessment for fMRI practitioners, we provide Statistical Parametric Mapping 12 based toolbox for Bayesian inference.

Common Data Elements, Scalable Data Management Infrastructure, and Analytics Workflows for Large-Scale Neuroimaging Studies

Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.

Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample

Maladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes versus reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. We analyzed 1-year follow-up data from a subset of the same participants (N = 325) to assess parameter stability and relationships to other clinical and task measures. We assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N = 48), SUDs (N = 29), and DEP/ANX (N = 121). We also assessed 2-3 week reliability in a separate sample of 30 HCs. Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46, respectively) and moderate to excellent correlations over the shorter period (.84 and .54, respectively). Similar to previous baseline findings, parameters correlated with multiple response time measures (ps < .001) and self-reported anxiety (r = .30, p < .001) and decision difficulty (r = .44, p < .001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p = .009) and lower in emotional conflict (SUDs, p = .004, DEP/ANX, p = .02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.

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.

Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach

BACKGROUND

Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict).

METHODS

A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence.

RESULTS

The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002).

LIMITATIONS

This study was limited by heterogeneity of the clinical sample and an inability to examine learning.

CONCLUSION

These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.

Relationship and Effects of Community Problems on Anxiety and Suicide: A Case-Control Psychological Autopsy Study

This study aimed to explore the relationship and the effects of community problems on anxiety and suicide. Data were from a case-control psychological autopsy study (392 suicides and 416 community living controls) carried out in 16 rural counties from three provinces in China. The World Health Organization Suicide Prevention-Multisite Intervention Study on Suicidal behaviors scale of community stress and problems and Spielberger State-Trait Anxiety Inventory were used to measure the community problems and trait anxiety. Spearman's correlation and multivariable regression methods were used to probe the effect of community problems on anxiety and suicide. There were 12 and 6 community problems correlated with anxiety (rs = 0.169, p < 0.001) and suicide (rs = 0.081, p = 0.022), respectively. Medical care, alcohol abuse, superstition, and job security were important community problems for anxiety and suicide. Community problems had stronger correlation with anxiety than suicide. It might be an effective method to reduce anxiety and suicide risk by solving different community problems.

Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach

BACKGROUND

Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict).

METHODS

A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence.

RESULTS

The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002).

LIMITATIONS

This study was limited by heterogeneity of the clinical sample and an inability to examine learning.

CONCLUSION

These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.