Development of a multivariate prediction model for antidepressant resistant depression using reward-related predictors.

Introduction: Individuals with depression who do not respond to two or more courses of serotonergic antidepressants tend to have greater deficits in reward processing and greater internalizing symptoms, yet there is no validated self-report method to determine the likelihood of treatment resistance based on these symptom dimensions. Methods: This online case-control study leverages machine learning techniques to identify differences in self-reported anhedonia and internalizing symptom profiles of antidepressant non-responders compared to responders and healthy controls, as an initial proof-of-concept for relating these indicators to medication responsiveness. Random forest classifiers were used to identify a subset from a set of 24 reward predictors that distinguished among serotonergic medication resistant, non-resistant, and non-depressed individuals recruited online (N = 393). Feature selection was implemented to refine model prediction and improve interpretability. Results: Accuracies for full predictor models ranged from .54 to .71, while feature selected models retained 3-5 predictors and generated accuracies of .42 to .70. Several models performed significantly above chance. Sensitivity for non-responders was greatest after feature selection when compared to only responders, reaching .82 with 3 predictors. The predictors retained from feature selection were then explored using factor analysis at the item level and cluster analysis of the full data to determine empirically driven data structures. Discussion: Non-responders displayed 3 distinct symptom profiles along internalizing dimensions of anxiety, anhedonia, motivation, and cognitive function. Results should be replicated in a prospective cohort sample for predictive validity; however, this study demonstrates validity for using a limited anhedonia and internalizing self-report instrument for distinguishing between antidepressant resistant and responsive depression profiles.

Toward a General Framework of Biased Reasoning: Coherence-Based Reasoning.

A considerable amount of experimental research has been devoted to uncovering biased forms of reasoning. Notwithstanding the richness and overall empirical soundness of the bias research, the field can be described as disjointed, incomplete, and undertheorized. In this article, we seek to address this disconnect by offering "coherence-based reasoning" as a parsimonious theoretical framework that explains a sizable number of important deviations from normative forms of reasoning. Represented in connectionist networks and processed through constraint-satisfaction processing, coherence-based reasoning serves as a ubiquitous, essential, and overwhelmingly adaptive apparatus in people's mental toolbox. This adaptive process, however, can readily be overrun by bias when the network is dominated by nodes or links that are incorrect, overweighted, or otherwise nonnormative. We apply this framework to explain a variety of well-established biased forms of reasoning, including confirmation bias, the halo effect, stereotype spillovers, hindsight bias, motivated reasoning, emotion-driven reasoning, ideological reasoning, and more.

Integrating kelp genomic analyses and geological data to reveal ancient earthquake impacts.

Detached buoyant kelp can disperse thousands of kilometres at sea and can colonize newly available shores in the wake of disturbances that wipe out competitors. Localized earthquake uplift can cause extirpation of intertidal kelp populations followed by recolonization. Sources of recolonizing kelp can be detectable in genomic structure of contemporary populations. Our field observations combined with LiDAR mapping identified a previously unrecognized zone of uplifted rocky coastline in a region that is slowly subsiding. Intertidal kelp (Durvillaea antarctica) on the uplifted section of coast is genetically distinctive from nearby populations, with genomic signatures most similar to that of kelp 300 km to the south. Genetic divergence between these locations suggests reproductive isolation for thousands of years. Combined geological and genetic data suggest that this uplift event occurred during one of four major earthquakes between 6000 and 2000 years ago, with one of the younger events most likely. Extirpation of the pre-existing kelp required sudden uplift of approximately 2 metres, precluding several small incremental uplift events. Our results show the power of integrating biological (genomic) analyses with geological data to understand ancient geological processes and their ecological impacts.

Yersinia pestis genomes reveal plague in Britain 4000 years ago.

Extinct lineages of Yersinia pestis, the causative agent of the plague, have been identified in several individuals from Eurasia between 5000 and 2500 years before present (BP). One of these, termed the 'LNBA lineage' (Late Neolithic and Bronze Age), has been suggested to have spread into Europe with human groups expanding from the Eurasian steppe. Here, we show that the LNBA plague was spread to Europe's northwestern periphery by sequencing three Yersinia pestis genomes from Britain, all dating to ~4000 cal BP. Two individuals were from an unusual mass burial context in Charterhouse Warren, Somerset, and one individual was from a single burial under a ring cairn monument in Levens, Cumbria. To our knowledge, this represents the earliest evidence of LNBA plague in Britain documented to date. All three British Yersinia pestis genomes belong to a sublineage previously observed in Bronze Age individuals from Central Europe that had lost the putative virulence factor yapC. This sublineage is later found in Eastern Asia ~3200 cal BP. While the severity of the disease is currently unclear, the wide geographic distribution within a few centuries suggests substantial transmissibility.

Behavioral regulation relies on interacting forces and predictive models.

OBJECTIVE: We discuss how our recent neural network model of personality and motivation can explain many aspects of the regulation of behavior. METHOD: Contrary to approaches that focus on a goal-corrected, set-point, and discrepancy-reducing mechanism, we argue that many aspects of regulation can be understood in terms of two other mechanisms. First, many aspects of the stability and coherence of personality, as well as the dynamics of personality, can be understood in terms of the interaction of forces within organized motivational systems, and their interaction with features of the environment and interoceptive states, that identify an individual's current needs. This has been described as a settling point or equilibrium of forces model, rather than a set-point architecture. Second, regulation has been shown to depend also on the use of predictive models of the world, either learned or innate. Such models can be thought of as feedforward models, in contrast to the feedback models characteristic of set-point, goal-corrected systems. RESULTS AND CONCLUSIONS: We describe a neural network model of these processes that simulates the behavior over time and situations of an individual and shows how important regulatory processes can operate through a process of interactive forces and predictive models of the world.

Psychological Intimate Partner Violence, Insecure Attachment, and Parental Psychological Control from Adolescence to Emerging Adulthood.

We examine if psychological intimate partner violence (pIPV) is predicted by parental psychological control (PPC) via insecure attachment. Our results analyzing longitudinal data from the Child Development Project show that PPC perceived at age 16 predicts insecure attachment at age 18, which then predicts pIPV at age 24. Moreover, the paths with attachment anxiety are consistently significant while ones with attachment avoidance are not. Further, all the paths are significant regardless of the gender of the adolescents and parents, which indicates that PPC is detrimental regardless of the gender of the adolescents or parents. Lastly, PPC perceived at age 16 does not directly predict pIPV at age 24, which suggests that social learning theory of aggression (Bandura, 1978) may not explain the association from PPC to pIPV. Our results suggest that research and practice would benefit by considering PPC as an antecedent of pIPV via insecure attachment from adolescence to emerging adulthood.

Modeling incentive salience in Pavlovian learning more parsimoniously using a multiple attribute model.

We present a multi-attribute incentive salience (MAIS) model as a computational account of incentive salience in model-based Pavlovian learning. A model of incentive salience as a joint function of reward value and physiological state has been previously proposed by Zhang et al. (2009). In that model, the function takes additive or multiplicative forms depending on whether a preference shifts from positive to negative or vice versa. We demonstrate that arbitrarily varying this function is unnecessary to explain observed data. A multiplicative function is sufficient if one takes into account empirical data suggesting the incentive salience function for an incentive is comprised of multiple physiological signals. We compare our model to the previously proposed model on two datasets. We find the MAIS model predicts the outcomes equally well, fits empirical data describing multiple sensory representations of a single stimulus, better approximates the dual-structure appetitive-aversive nature of the reward system, is compatible with existing knowledge about incentive salience in Pavlovian learning, and better describes revaluation in Pavlovian learning. This model addresses a call (Dayan & Berridge, 2014) for algorithmic and computational models of model-based Pavlovian learning that consistently and fully explain empirical observations. Because a multi-attribute model is relevant even for simple Pavlovian associations, it should be useful in a wide variety of decision-making contexts, including agent modeling and addiction research.

User-Agent Bond in Generalizable Environments: Long-Term Risk-Reduction via Nudged Virtual Choices.

Avatars or agents are digitized self-representations of a player in mediated environments. While using agents to navigate through mediated environments, players form bonds with their self-agents or characters, a process referred to as identification. Identification can involve automatic, but temporary, self-concept "shifts in implicit self-perceptions" (Klimmt et al., 2010, p. 323) of the media user by adopting or emphasizing the action choices on behalf of the social expectation of the avatar in the mediated environment. In the current study, we test the possibility that users' identification with video game avatars-a bond built between avatars and players- would account for subsequent behavior changes. We did so by using 3-month longitudinal data involving a narratively-based serious game: Socially Optimized Learning in Virtual Environments (SOLVE), a 3D-interactive game designed to reduce risky sexual behaviors among young men who have sex with men (n = 444). Results show that video game identification predicts both the reduction of risky sexual behaviors over time, and reduction in the number of non-primary partners with whom risky sex occurs. And when players identify with the game character, they tend to make healthier choices, which significantly mediates the link between video game identification and reduction of risky behaviors.

Fatal and non-fatal events within 14 days after early, intensive mobilization post stroke.

OBJECTIVE: This tertiary analysis from AVERT examined fatal and non-fatal Serious Adverse Events (SAEs) at 14 days. METHOD: AVERT was a prospective, parallel group, assessor blinded, randomized international clinical trial comparing mobility training commenced <24 hours post stroke, termed very early mobilization (VEM) to usual care (UC). Primary outcome was assessed at 3 months. Included: Patients with ischaemic and haemorrhagic stroke within 24 hours of onset. Treatment with thrombolytics allowed. Excluded: Patients with severe premorbid disability and/or comorbidities. Interventions continued for 14 days or hospital discharge if less. The primary early safety outcome was fatal SAEs within 14 days. Secondary outcomes were non-fatal SAEs classified as neurologic, immobility-related, and other. Mortality influences were assessed using binary logistic regression adjusted for baseline stroke severity (NIHSS) and age. RESULTS: 2,104 participants were randomized to VEM (n = 1,054) or UC (n = 1,050) with a median age of 72 years (IQR 63-80) and NIHSS 7 (IQR 4-12). By 14 days, 48 had died in VEM, 32 in UC, age and stroke severity adjusted Odds Ratio of 1.76 (95% CI 1.06-2.92, p = 0.029). Stroke progression was more common in VEM. Exploratory subgroup analyses showed higher odds of death in intracerebral haemorrhage and >80 years subgroups, but there was no significant treatment by subgroup interaction. No difference in non-fatal SAEs found. CONCLUSION: While the overall case fatality at 14 days post-stroke was only 3.8%, mortality adjusted for age and stroke severity was increased with high dose, intensive training compared to usual care. Stroke progression was more common in VEM. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that very early mobilization increases mortality at 14 days post stroke. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry, ACTRN12606000185561.

The relationship between social norms and sexual risk-reduction intentions and behavior among men who have sex with men: Findings from an eHealth intervention.

Normative-based research has found that norms are significant predictors of safe sex behavioral intentions and behavior. Research shows that group identity moderates the relationship between norms and intentions/behavior. The present study used the theory of normative social behavior to evaluate whether identification with characters in an HIV-prevention interactive video moderated the relationship between sexual risk-taking norms and risk reduction intentions/behavior. Participants included 540 men between the ages 18 and 30 enrolled in a randomized controlled trial with a 3-month follow-up. We found support for the hypothesized interaction. At low levels of character identification, the negative relationship between sexual risk-taking norms and risk reduction intentions was strong. However, as character identification increased, the link between norms and intentions became weaker. The mean intentions score of high-risk norm participants was elevated to the level reported by low risk norm participants, demonstrating the favorable effect of character identification on narrative persuasion in entertainment-education. The utility of a social norms approach to health behavior change is discussed in the context of eHealth interventions. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Using Neural Networks as Models of Personality Process: A Tutorial.

This paper presents a tutorial for creating neural network models of personality processes. Such models enable researchers to create explicit models of both personality structure and personality dynamics, and to address issues of recent concern in personality, such as, "If personality is stable, then how is it possible that within subject variability in personality states can be as large as or larger than between subject variability in personality?" or "Is it possible to understand personality dynamics and personality structure within a common framework?" We discuss why one should want to use neural networks, review what a neural network model is, review a previous model we have constructed, discuss how to conceptualize issues in such a way that they can be computationally modeled, show how that conceptualization can be translated into a model, and discuss the utility of such models for understanding personality structure and personality dynamics. To build our model we use a neural network modeling package called emergent that is freely available, and a specific architecture called Leabra to build a runnable model that addresses one of the questions posed above: How can within subject variability in personality related states be as large as between subject variability in personality?

Neurocognitive decision-making processes of casual methamphetamine users.

Neuroadaptations caused by chronic methamphetamine (MA) use are likely major contributors to high relapse rate following treatment. Thus, focusing intervention efforts at pre-empting addiction in vulnerable populations, thereby preventing MA-use-induced neurological changes that make recovery so challenging, may prove more effective than targeting chronic users. One approach is studying casual/recreational users, not diagnosed with substance use disorder. This group may be at high risk for addiction due to their experience with MA. On the other hand, they may be resilient against addiction since they were able to maintain casual use over the years and not become addicted. Understanding their neuro-cognitive characteristics during decision-making and risk-taking would help solve this dilemma and, may help identify intervention strategies. Unfortunately, research on neuro-cognitive characteristics of casual MA users is currently lacking. In this work we begin to address this deficit. This study was part of a larger investigation of neural correlates of risky sexual decision-making in men who have sex with men. While undergoing functional magnetic resonance imaging, 31 casual MA users and 66 non-users performed the CUPS task, in which they decided to accept or refuse a series of mixed gambles. Convergent results from whole brain, region of interest and psychophysiological interaction (PPI) analyses are presented. Whole brain analysis identified an amygdala-striatal cluster with weaker activation in casual MA users compared to non-users during decision-making. Activity in that cluster inversely correlated with decisions to gamble: lower activation corresponded to higher risk taking. Using this cluster as a seed in PPI analyses, we identified a wide range of neural network differences between casual MA users and non-users. Parametric whole brain analyses identified clusters in the ventral striatum, posterior insula and precuneus where activations modulated by risk and reward were significantly weaker in casual MA users than in non-users. The striatal cluster identified in these analyses overlapped with the amygdala-striatal cluster. This work identified neural differences in casual MA users' reward processing and outcome learning systems which may underlie their increased real-world risk-taking. It suggests that while making decisions casual MA users focus primarily on potential gain unlike non-users who also take the riskiness of the choice into consideration.

Causal Inference in Generalizable Environments: Systematic Representative Design.

Causal inference and generalizability both matter. Historically, systematic designs emphasize causal inference, while representative designs focus on generalizability. Here, we suggest a transformative synthesis - Systematic Representative Design (SRD) - concurrently enhancing both causal inference and "built-in" generalizability by leveraging today's intelligent agent, virtual environments, and other technologies. In SRD, a "default control group" (DCG) can be created in a virtual environment by representatively sampling from real-world situations. Experimental groups can be built with systematic manipulations onto the DCG base. Applying systematic design features (e.g., random assignment to DCG versus experimental groups) in SRD affords valid causal inferences. After explicating the proposed SRD synthesis, we delineate how the approach concurrently advances generalizability and robustness, cause-effect inference and precision science, a computationally-enabled cumulative psychological science supporting both "bigger theory" and concrete implementations grappling with tough questions (e.g., what is context?) and affording rapidly-scalable interventions for real-world problems.

The Virtual Personalities Neural Network Model: Neurobiological Underpinnings.

The Virtual Personalities Model is a motive-based neural network model that provides both a psychological model and a computational implementation that explicates the dynamics and often large within-person variability in behavior that arises over time. At the same time the same model can produce -- across many virtual personalities - between subject variability in behavior that when factor analyzed yields familiar personality structure (e.g., the Big-5). First, we describe our personality model and its implementation as a neural network model. Second, we focus on detailing the neurobiological underpinnings of this model. Third, we examine the learning mechanisms, and their biological substrates, as ways that the model gets "wired up", discussing Pavlovian and instrumental conditioning, Pavlovian to instrumental transfer (PIT), and habits. Finally, we describe the dynamics of how initial differences in propensities (e.g., dopamine functioning), wiring differences due to experience, and other factors could operate together to develop and change personality over time, and how this might be empirically examined. Thus, our goal is to contribute to the rising chorus of voices seeking a more precise neurobiologically-based science of the complex dynamics underlying personality.

Prefrontal Cortical Activity During the Stroop Task: New Insights into the Why and the Who of Real-World Risky Sexual Behavior.

Background: Research suggests that deficits in both executive functioning and trait impulsivity may play a role in risky sexual behavior. At the neural level, differences in regulation of the prefrontal cortex have been linked to impulsivity, measured neurocognitively and through self-report. The relationship between neurocognitive measures of executive control and trait impulsivity in predicting risky sexual behavior has not been investigated. Purpose: To investigate the relationship between neural functioning during the Stroop task and risky sexual behavior, as well as the effect of individual differences in urgent (positive and negative) impulsivity on this relationship. Methods: A total of 105 sexually active men who have sex with men completed the Stroop task during functional magnetic resonance imaging scanning. They also completed impulsivity inventories and self-reported their risky sexual behavior (events of condomless anal sex in the last 90 days). Results: Risky participants had greater activation than safe participants during the color congruent condition of the Stroop task in anterior cingulate cortex/dorsomedial prefrontal cortex, dorsolateral prefrontal cortex, left frontal pole, and right insula. Across these regions, this neural activation mediated the link between (positive and/or negative) urgent impulsivity and risky sexual behavior. Conclusions: Findings suggest that the brains of men who engage in risky sexual behavior may employ a different distribution of cognitive resources during tasks of executive functioning than men who practice safe sex, and that this may relate to differences in the prefrontal cortical/fronto-insular system responsible for impulse control.

The role of the dorsal anterior insula in sexual risk: Evidence from an erotic Go/NoGo task and real-world risk-taking.

The insula plays an important role in response inhibition. Most relevant here, it has been proposed that the dorsal anterior insular cortex (dAIC) plays a central role in a salience network that is responsible for switching between the default mode network and the executive control network. However, the insula's role in sexually motivated response inhibition has not yet been studied. In this study, eighty-five 18- to 30-year-old sexually active men who have sex with men (MSM) performed an erotic Go/NoGo task while in an MRI scanner. Participants' real-world sexual risk-taking (frequency of condomless anal intercourse over the past 90 days) was then correlated with their neural activity during the task. We found greater activity in bilateral anterior insular cortex (both dorsal and ventral) on contrasts with stronger motivational information (attractive naked male pictures versus pictures of clothed, middle-aged females) and on contrasts requiring greater response inhibition (NoGo versus Go). We also found that activity in the right dAIC was negatively correlated with participants' real-world sexual risk-taking. Our results confirmed the involvement of the insular cortex in motivated response inhibition. Especially, the decreased right dAIC activity may reduce the likelihood that the executive control network will come online when individuals are faced with situations requiring inhibitory control and thus lead them to make more risky choices.

A meta-analytical review of brain activity associated with intertemporal decisions: Evidence for an anterior-posterior tangibility axis.

In temporal discounting experiments, subjects are repeatedly presented with option sets in which they must choose between receiving a small amount of money sooner (SmallerSooner) or a larger amount of money at a more distant point in time (LargerLater). Although over 50 temporal discounting experiments using fMRI are described in literature, there has not been a meta-analysis identifying regions activated when subjects choose SmallerSooner versus LargerLater alternatives. Evidence suggests a prefrontal cortex 'abstraction hierarchy', from abstract planning in more anterior regions to concrete processing in posterior regions. Because abstraction has been linked with making LargerLater choices, we hypothesized an association between LargerLater choices and more anterior prefrontal cortex activity, and an association between SmallerSooner choices and more posterior activity. Across thirteen fMRI temporal discounting studies including 436 subjects, we observed LargerLater activity anterior of SmallerSooner activity, both in the left inferior frontal gyrus pars triangularis, consistent with our pre-registered hypothesis. We call for further work linking temporal discounting and hierarchical processing of abstract and concrete information in the prefrontal cortex.

Virtually 'in the heat of the moment': insula activation in safe sex negotiation among risky men.

HIV is most prevalent among men who have sex with men (MSM), and although most MSM use condoms consistently during casual sex, some take risks. To better understand the psychology of those risky decisions, we examined neural correlates of playing a virtual sexual 'hook up' game in an functional magnetic resonance imaging scanner in MSM who had, in the past 90 days, been sexually risky (N = 76) or safe (N = 31). We found that during potentially risky sexual choices, previously risky MSM had more right insula activity than previously safe MSM. Real-life sexual risk was related to trait positive and negative urgency. Insula activity that differentiated risky and safe MSM was related to trait positive and negative urgency. Future work should further examine if, and to what extent, insula activation during safe sex negotiation drives MSM's rash risky sexual decision-making.