Socioeconomic inequalities in molecular risk for chronic diseases observed in young adulthood.

Many common chronic diseases of aging are negatively associated with socioeconomic status (SES). This study examines whether inequalities can already be observed in the molecular underpinnings of such diseases in the 30s, before many of them become prevalent. Data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a large, nationally representative sample of US subjects who were followed for over two decades beginning in adolescence. We now have transcriptomic data (mRNA-seq) from a random subset of 4,543 of these young adults. SES in the household-of-origin and in young adulthood were examined as covariates of a priori-defined mRNA-based disease signatures and of specific gene transcripts identified de novo. An SES composite from young adulthood predicted many disease signatures, as did income and subjective status. Analyses highlighted SES-based inequalities in immune, inflammatory, ribosomal, and metabolic pathways, several of which play central roles in senescence. Many genes are also involved in transcription, translation, and diverse signaling mechanisms. Average causal-mediated effect models suggest that body mass index plays a key role in accounting for these relationships. Overall, the results reveal inequalities in molecular risk factors for chronic diseases often decades before diagnoses and suggest future directions for social signal transduction models that trace how social circumstances regulate the human genome.

Socioeconomic Inequalities and Molecular Risk for Aging in Young Adulthood.

Diverse manifestations of biological aging often reflect disparities in socioeconomic status (SES). In this paper, we examine associations between indicators of SES and an mRNA-based aging signature during young adulthood, before clinical indications of aging are common. We use data from wave V (2016-2018) of the National Longitudinal Study of Adolescent to Adult Health, a nationally representative study of adults aged 33-43 years, with transcriptomic data from a subset of 2,491 participants. Biological aging is measured using 1) a composite transcriptomic aging signature previously identified by Peters et al.'s out-of-sample meta-analysis (Nat Commun. 2015;6:8570) and 2) 9 subsets that represent functional pathways of coexpressed genes. SES refers to income, education, occupation, subjective social status, and a composite measure combining these 4 dimensions. We examine hypothesized mechanisms through which SES could affect aging: body mass index, smoking, health insurance status, difficulty paying bills, and psychosocial stress. We find that SES-especially the composite measure and income-is associated with transcriptomic aging and immune, mitochondrial, ribosomal, lysosomal, and proteomal pathways. Counterfactual mediational models suggest that the mediators partially account for these associations. The results thus reveal that numerous biological pathways associated with aging are already linked to SES in young adulthood.

The Early Life Course of Body Weight and Gene Expression Signatures for Disease.

We examined the way body-weight patterns through the first 4 decades of life relate to gene expression signatures of common forms of morbidity, including cardiovascular disease (CVD), type 2 diabetes (T2D), and inflammation. As part of wave V of the nationally representative National Longitudinal Study of Adolescent to Adult Health (1997-2018) in the United States, mRNA abundance data were collected from peripheral blood (n = 1,132). We used a Bayesian modeling strategy to examine the relative associations between body size at 5 life stages-birth, adolescence, early adulthood, young adulthood, and adulthood-and gene expression-based disease signatures. We compared life-course models that consider critical or sensitive periods, as well as accumulation over the entire period. Our results are consistent with a sensitive-period model when examining CVD and T2D gene expression signatures: Birth weight has a prominent role for the CVD and T2D signatures (explaining 33.1% and 22.1%, respectively, of the total association accounted for by body size), while the most recent adult obesity status (ages 33-39) is important for both of these gene expression signatures (24.3% and 35.1%, respectively). Body size in all life stages was associated with inflammation, consistent with the accumulation model.

Valence and agency influence striatal response to feedback in patients with major depressive disorder.

BACKGROUND: Reduced sensitivity to positive feedback is common in patients with major depressive disorder (MDD). However, findings regarding negative feedback are ambiguous, with both exaggerated and blunted responses being reported. The ventral striatum (VS) plays a major role in processing valenced feedback, and previous imaging studies have shown that the locus of controls (self agency v. external agency) over the outcome influences VS response to feedback. We investigated whether attributing the outcome to one's own action or to an external agent influences feedback processing in patients with MDD. We hypothesized that depressed participants would be less sensitive to the feedback attribution reflected by an altered VS response to self-attributed gains and losses. METHODS: Using functional MRI and a motion prediction task, we investigated the neural responses to self-attributed (SA) and externally attributed (EA) monetary gains and losses in unmedicated patients with MDD and healthy controls. RESULTS: We included 21 patients and 25 controls in our study. Consistent with our prediction, healthy controls showed a VS response influenced by feedback valence and attribution, whereas in depressed patients striatal activity was modulated by valence but was insensitive to attribution. This attribution insensitivity led to an altered ventral putamen response for SA - EA losses in patients with MDD compared with healthy controls. LIMITATIONS: Depressed patients with comorbid anxiety disorder were included. CONCLUSION: These results suggest an altered assignment of motivational salience to SA losses in patients with MDD. Altered striatal response to SA negative events may reinforce the belief of not being in control of negative outcomes contributing to a cycle of learned helplessness.

Functional lateralization of the anterior insula during feedback processing.

Effective adaptive behavior rests on an appropriate understanding of how much responsibility we have over outcomes in the environment. This attribution of agency to ourselves or to an external event influences our behavioral and affective response to the outcomes. Despite its special importance to understanding human motivation and affect, the neural mechanisms involved in self-attributed rewards and punishments remain unclear. Previous evidence implicates the anterior insula (AI) in evaluating the consequences of our own actions. However, it is unclear if the AI has a general role in feedback evaluation (positive and negative) or plays a specific role during error processing. Using functional magnetic resonance imaging and a motion prediction task, we investigate neural responses to self- and externally attributed monetary gains and losses. We found that attribution effects vary according to the valence of feedback: significant valence × attribution interactions in the right AI, the anterior cingulate cortex (ACC), the midbrain, and the right ventral putamen. Self-attributed losses were associated with increased activity in the midbrain, the ACC and the right AI, and negative BOLD response in the ventral putamen. However, higher BOLD activity to self-attributed feedback (losses and gains) was observed in the left AI, the thalamus, and the cerebellar vermis. These results suggest a functional lateralization of the AI. The right AI, together with the midbrain and the ACC, is mainly involved in processing the salience of the outcome, whereas the left is part of a cerebello-thalamic-cortical pathway involved in cognitive control processes important for subsequent behavioral adaptations.

Surprise beyond prediction error.

Surprise drives learning. Various neural "prediction error" signals are believed to underpin surprise-based reinforcement learning. Here, we report a surprise signal that reflects reinforcement learning but is neither un/signed reward prediction error (RPE) nor un/signed state prediction error (SPE). To exclude these alternatives, we measured surprise responses in the absence of RPE and accounted for a host of potential SPE confounds. This new surprise signal was evident in ventral striatum, primary sensory cortex, frontal poles, and amygdala. We interpret these findings via a normative model of surprise.

A Bayesian functional approach to test models of life course epidemiology over continuous time.

BACKGROUND: Life course epidemiology examines associations between repeated measures of risk and health outcomes across different phases of life. Empirical research, however, is often based on discrete-time models that assume that sporadic measurement occasions fully capture underlying long-term continuous processes of risk. METHODS: We propose (i) the functional relevant life course model (fRLM), which treats repeated, discrete measures of risk as unobserved continuous processes, and (ii) a testing procedure to assign probabilities that the data correspond to conceptual models of life course epidemiology (critical period, sensitive period and accumulation models). The performance of the fRLM is evaluated with simulations, and the approach is illustrated with empirical applications relating body mass index (BMI) to mRNA-seq signatures of chronic kidney disease, inflammation and breast cancer. RESULTS: Simulations reveal that fRLM identifies the correct life course model with three to five repeated assessments of risk and 400 subjects. The empirical examples reveal that chronic kidney disease reflects a critical period process and inflammation and breast cancer likely reflect sensitive period mechanisms. CONCLUSIONS: The proposed fRLM treats repeated measures of risk as continuous processes and, under realistic data scenarios, the method provides accurate probabilities that the data correspond to commonly studied models of life course epidemiology. fRLM is implemented with publicly-available software.

Variational Bayesian mixed-effects inference for classification studies.

Multivariate classification algorithms are powerful tools for predicting cognitive or pathophysiological states from neuroimaging data. Assessing the utility of a classifier in application domains such as cognitive neuroscience, brain-computer interfaces, or clinical diagnostics necessitates inference on classification performance at more than one level, i.e., both in individual subjects and in the population from which these subjects were sampled. Such inference requires models that explicitly account for both fixed-effects (within-subjects) and random-effects (between-subjects) variance components. While models of this sort are standard in mass-univariate analyses of fMRI data, they have not yet received much attention in multivariate classification studies of neuroimaging data, presumably because of the high computational costs they entail. This paper extends a recently developed hierarchical model for mixed-effects inference in multivariate classification studies and introduces an efficient variational Bayes approach to inference. Using both synthetic and empirical fMRI data, we show that this approach is equally simple to use as, yet more powerful than, a conventional t-test on subject-specific sample accuracies, and computationally much more efficient than previous sampling algorithms and permutation tests. Our approach is independent of the type of underlying classifier and thus widely applicable. The present framework may help establish mixed-effects inference as a future standard for classification group analyses.

An indirect treatment comparison (ITC) and matching-adjusted indirect comparison (MAIC) between a 15-valent (V114) and a 20-valent (PCV20) pneumococcal conjugate vaccine among healthy infants.

OBJECTIVES: Immunogenicity between 15-valent V114 (PCV15) and 20-valent PCV20 pneumococcal conjugate vaccines in healthy infants is compared in an indirect treatment comparison and matching-adjusted indirect comparison. Hypotheses: immunogenicity of V114 is non-inferior to PCV20 for all PCV13 serotypes, and superior to PCV20 for serotype 3 based on lower bound margins. METHODS: Two phase 3 pivotal studies on 3 + 1 pediatric vaccination schedule at age 2, 4, 6, and 12-15 months compared V114 (N = 858) to PCV13 (N = 856) and PCV20 (N = 1001) to PCV13 (N = 987). Infant's age and race in V114 study were matched to those in PCV20 study. Primary endpoints were serotype-specific Immunoglobulin G (IgG) response rate difference (RRD) 30 days post-dose (PD)3; IgG geometric mean concentration (GMC) ratios 30 days PD3 and PD4. RESULTS: V114 was non-inferior (marginRRD>-10%-point; marginGMCratio >0.5) to PCV20 (p-value <0.001) for all endpoints. V114 was superior (marginRRD >0%-point; marginGMCratio >1.2) to PCV20 (p-value <0.001) for serotype 3: RRD was 34.5% (95%CI 27.9%-41.1%) PD3, and IgG GMC ratios were 2.39 (95%CI 2.12-2.68) PD3 and 2.15 (95%CI 1.90-2.41) PD4. CONCLUSION: Immune response to V114 administered in a 3 + 1 schedule in healthy infants was considered non-inferior to PCV20 for all 13 PCV13 serotypes and superior for serotype 3 PD3 and PD4. CLINICAL TRIAL REGISTRATION: www.clinicaltrials.gov identifiers NCT03893448, NCT04382326.

A Bayesian approach to comparing common models of life-course epidemiology.

BACKGROUND: Life-course epidemiology studies people's health over long periods, treating repeated measures of their experiences (usually risk factors) as predictors or causes of subsequent morbidity and mortality. Three hypotheses or models often guide the analyst in assessing these sequential risks: the accumulation model (all measurement occasions are equally important for predicting the outcome), the critical period model (only one occasion is important) and the sensitive periods model (a catch-all model for any other pattern of temporal dependence). METHODS: We propose a Bayesian omnibus test of these three composite models, as well as post hoc decompositions that identify their best respective sub-models. We test the approach via simulations, before presenting an empirical example that relates five sequential measurements of body weight to an RNAseq measure of colorectal-cancer disposition. RESULTS: The approach correctly identifies the life-course model under which the data were simulated. Our empirical cohort study indicated with >90% probability that colorectal-cancer disposition reflected a sensitive process, with current weight being most important but prior body weight also playing a role. CONCLUSIONS: The Bayesian methods we present allow precise inferences about the probability of life-course models given the data and are applicable in realistic scenarios involving causal analysis and missing data.

Loss Aversion and Risk Aversion in Non-Clinical Negative Symptoms and Hypomania.

In the field of behavioral decision-making, "loss aversion" is a behavioral phenomenon in which individuals show a higher sensitivity to potential losses than to gains. Conversely, "risk averse" individuals have an enhanced sensitivity/aversion to options with uncertain consequences. Here we examine whether hypomania or negative symptoms predict the degree of these choice biases. We chose to study these two symptom dimensions because they present a common theme across many syndromes with compromised decision-making. In our exploratory study, we employed a non-clinical sample to dissociate the hypomanic from negative symptom dimension regarding choice behavior. We randomly selected a sample of 45 subjects from a student population (18-37 years) without self-reported psychiatric diagnoses (n = 835). We stratified them based on percentiles into a low hypomania/low negative symptoms (n = 15), a hypomania (n = 15), and a negative symptoms group (n = 15) using the hypomanic personality scale (HPS-30) and community assessment of psychic experiences (CAPE). Participants completed a loss aversion task consisting of forced binary choices between a monetary gamble and a riskless choice without gain or loss. We found a reduced loss aversion in participants with higher negative symptoms. In addition, risk aversion was reduced in participants with higher hypomania and negative symptoms compared to low hypomania/negative symptoms. This study adds to the understanding of underlying psychological mechanisms of loss and risk aversion. Given the partially opposing nature of hypomania and negative symptoms, further work is needed to examine whether they affect loss and risk aversion via dissociable mechanisms.

False discovery rate revisited: FDR and topological inference using Gaussian random fields.

In this note, we revisit earlier work on false discovery rate (FDR) and evaluate it in relation to topological inference in statistical parametric mapping. We note that controlling the false discovery rate of voxels is not equivalent to controlling the false discovery rate of activations. This is a problem that is unique to inference on images, in which the underlying signal is continuous (i.e., signal which does not have a compact support). In brief, inference based on conventional voxel-wise FDR procedures is not appropriate for inferences on the topological features of a statistical parametric map (SPM), such as peaks or regions of activation. We describe the nature of the problem, illustrate it with some examples and suggest a simple solution based on controlling the false discovery rate of connected excursion sets within an SPM, characterised by their volume.

A computational perspective on social attachment.

Humans depend on social relationships for survival and wellbeing throughout life. Yet, individuals differ markedly in their ability to form and maintain healthy social relationships. Here we use a simple mathematical model to formalize the contention that a person's attachment style is determined by what they learn from relationships early in life. For the sake of argument, we therefore discount individual differences in the innate personality or attachment style of a child, assuming instead that all children are simply born with an equivalent, generic, hardwired desire and instinct for social proximity, and a capacity to learn. In line with the evidence, this innate endowment incorporates both simple bonding instincts and a capacity for cognitively sophisticated beliefs and generalizations. Under this assumption, we then explore how distinct attachment styles might emerge through interaction with the child's early caregivers. Our central question is, how an apparently adaptive capacity to learn can yield enduring maladaptive attachment styles that generalize to new relationships. We believe extensions of our model will ultimately help clarify the complex interacting mechanisms - both acquired and innate - that underpin individual differences in attachment styles. While our model is relatively abstract, we also attempt some connection to known biological mechanisms of attachment.

Attractor models of working memory and their modulation by reward.

This work reports an empirical examination of two key issues in theoretical neuroscience: distractibility in the context of working memory (WM) and its reward dependence. While these issues have been examined fruitfully in isolation (e.g. Macoveanu et al. in Biol Cybern 96(4): 407-19, 2007), we address them here in tandem, with a focus on how distractibility and reward interact. In particular, we parameterise an observation model that embodies the nonlinear form of such interactions, as described in a recent neuronal network model (Gruber et al. in J Comput Neurosci 20:153-166, 2006). We observe that memory for a target stimulus can be corrupted by distracters in the delay period. Interestingly, in contrast to our theoretical predictions, this corruption was only partial. Distracters do not simply overwrite target; rather, a compromise is reached between target and distracter. Finally, we observed a trend towards a reduced distractibility under conditions of high reward. We discuss the implications of these findings for theoretical formulations of basal and dopamine (DA)-modulated neural bump- attractor networks of working memory.

Affect and the computer game player: the effect of gender, personality, and game reinforcement structure on affective responses to computer game-play.

Previous research on computer games has tended to concentrate on their more negative effects (e.g., addiction, increased aggression). This study departs from the traditional clinical and social learning explanations for these behavioral phenomena and examines the effect of personality, in-game reinforcement characteristics, gender, and skill on the emotional state of the game-player. Results demonstrated that in-game reinforcement characteristics and skill significantly effect a number of affective measures (most notably excitement and frustration). The implications of the impact of game-play on affect are discussed with reference to the concepts of "addiction" and "aggression."

Inhibition of the human two-pore domain potassium channel, TREK-1, by fluoxetine and its metabolite norfluoxetine.

1. Block of the human two-pore domain potassium (2-PK) channel TREK-1 by fluoxetine (Prozac) and its active metabolite, norfluoxetine, was investigated using whole-cell patch-clamp recording of currents through recombinant channels in tsA 201 cells. 2. Fluoxetine produced a concentration-dependent inhibition of TREK-1 current that was reversible on wash. The IC50 for block was 19 microM. Block by fluoxetine was voltage-independent. Fluoxetine (100 microM) produced an 84% inhibition of TREK-1 currents, but only a 31% block of currents through a related 2-PK channel, TASK-3. 3. Norfluoxetine was a more potent inhibitor of TREK-1 currents with an IC50 of 9 microM. Block by norfluoxetine was also voltage-independent. 4. Truncation of the C-terminus of TREK-1 (delta89) resulted in a loss of channel function, which could be restored by intracellular acidification or the mutation E306A. The mutation E306A alone increased basal TREK-1 current and resulted in a loss of the slow phase of TREK-1 activation. 5. Progressive deletion of the C-terminus of TREK-1 had no effect on the inhibition of the channel by fluoxetine. The E306A mutation, on the other hand, reduced the magnitude of fluoxetine inhibition, with 100 microM producing only a 40% inhibition. 6. It is concluded that fluoxetine and norfluoxetine are potent inhibitors of TREK-1. Block of TREK-1 by fluoxetine may have important consequences when the drug is used clinically in the treatment of depression.

Apathy but not diminished expression in schizophrenia is associated with discounting of monetary rewards by physical effort.

Negative symptoms in schizophrenia have been grouped into the 2 factors of apathy and diminished expression, which might be caused by separable pathophysiological mechanisms. Recently, it has been proposed that apathy could be due to dysfunctional integration of reward and effort during decision making. We asked whether apathy in particular is associated with stronger devaluation ("discounting") of monetary rewards that require physical effort. Thirty-one patients with schizophrenia and 20 healthy control participants performed a computerized effort discounting task in which they could choose to exert physical effort on a handgrip to obtain monetary rewards. This procedure yields an individual measure for the strength of effort discounting. The degree of effort discounting was strongly correlated with apathy, but not with diminished expression. Importantly, the association between apathy and effort discounting was not driven by cognitive ability, antipsychotic medication, or other clinical and demographic variables. This study provides the first evidence for a highly specific association of apathy with effort-based decision making in patients with schizophrenia. Within a translational framework, the present effort discounting task could provide a bridge between apathy as a psychopathological phenomenon and established behavioral tasks to address similar states in animals.

Does general motivation energize financial reward-seeking behavior? Evidence from an effort task.

We aimed to predict how hard subjects work for financial rewards from their general trait and state reward-motivation. We specifically asked 1) whether individuals high in general trait "reward responsiveness" work harder 2) whether task-irrelevant cues can make people work harder, by increasing general motivation. Each trial of our task contained a 1 second earning interval in which male subjects earned money for each button press. This was preceded by one of three predictive cues: an erotic picture of a woman, a man, or a geometric figure. We found that individuals high in trait "reward responsiveness" worked harder and earned more, irrespective of the predictive cue. Because female predictive cues are more rewarding, we expected them to increase general motivation in our male subjects and invigorate work, but found a more complex pattern.

A Metropolis-Hastings algorithm for dynamic causal models.

Dynamic causal modelling (DCM) is a modelling framework used to describe causal interactions in dynamical systems. It was developed to infer the causal architecture of networks of neuronal populations in the brain [Friston, K.J., Harrison, L, Penny, W., 2003. Dynamic causal modelling. NeuroImage. Aug; 19 (4): 1273-302]. In current formulations of DCM, the mean structure of the likelihood is a nonlinear and numerical function of the parameters, which precludes exact or analytic Bayesian inversion. To date, approximations to the posterior depend on the assumption of normality (i.e., the Laplace assumption). In particular, two arguments have been used to motivate normality of the prior and posterior distributions. First, Gaussian priors on the parameters are specified carefully to ensure that activity in the dynamic system of neuronal populations converges to a steady state (i.e., the dynamic system is dissipative). Secondly, normality of the posterior is an approximation based on general asymptotic results, regarding the form of the posterior under infinite data [Friston, K.J., Harrison, L, Penny, W., 2003. Dynamic causal modelling. NeuroImage. Aug; 19 (4): 1273-302]. Here, we provide a critique of these assumptions and evaluate them numerically. We use a Bayesian inversion scheme (the Metropolis-Hastings algorithm) that eschews both assumptions. This affords an independent route to the posterior and an external means to assess the performance of conventional schemes for DCM. It also allows us to assess the sensitivity of the posterior to different priors. First, we retain the conventional priors and compare the ensuing approximate posterior (Laplace) to the exact posterior (MCMC). Our analyses show that the Laplace approximation is appropriate for practical purposes. In a second, independent set of analyses, we compare the exact posterior under conventional priors with an exact posterior under newly defined uninformative priors. Reassuringly, we observe that the posterior is, for all practical purposes, insensitive of the choice of prior.