Neurobiological Foundations Of Stability And Flexibility

In order to adapt to changing and uncertain environments, humans and other organisms must balance stability and flexibility in learning and behavior. Stability is necessary to learn environmental regularities and support ongoing behavior, while flexibility is necessary when beliefs need to be revised or behavioral strategies need to be changed. Adjusting the balance between stability and flexibility must often be based on endogenously generated decisions that are informed by information from the environment but not dictated explicitly. This dissertation examines the neurobiological bases of such endogenous flexibility, focusing in particular on the role of prefrontally-mediated cognitive control processes and the neuromodulatory actions of dopaminergic and noradrenergic systems. In the first study (Chapter 2), we examined the role of frontostriatal circuits in instructed reinforcement learning. In this paradigm, inaccurate instructions are given prior to trial-and-error learning, leading to bias in learning and choice. Abandoning the instructions thus necessitates flexibility. We utilized transcranial direct current stimulation over dorsolateral prefrontal cortex to try to establish a causal role for this area in this bias. We also assayed two genes, the COMT Val158Met genetic polymorphism and the DAT1/SLC6A3 variable number tandem repeat, which affect prefrontal and striatal dopamine, respectively. The results support the role of prefrontal cortex in biasing learning, and provide further evidence that individual differences in the balance between prefrontal and striatal dopamine may be particularly important in the tradeoff between stability and flexibility. In the second study (Chapter 3), we assess the neurobiological mechanisms of stability and flexibility in the context of exploration, utilizing fMRI to examine dynamic changes in functional brain networks associated with exploratory choices. We then relate those changes to changes in norepinephrine activity, as measured indirectly via pupil diameter. We find tentative support for the hypothesis that increased norepinephrine activity around exploration facilitates the reorganization of functional brain networks, potentially providing a substrate for flexible exploratory states. Together, this work provides further support for the framework that stability and flexibility entail both costs and benefits, and that optimizing the balance between the two involves interactions of learning and cognitive control systems under the influence of catecholamines

Blocking D2/D3 dopamine receptors in male participants increases volatility of beliefs when learning to trust others

The ability to learn about other people is crucial for human social functioning. Dopamine has been proposed to regulate the precision of beliefs, but direct behavioural evidence of this is lacking. In this study, we investigate how a high dose of the D2/D3 dopamine receptor antagonist sulpiride impacts learning about other people’s prosocial attitudes in a repeated Trust game. Using a Bayesian model of belief updating, we show that in a sample of 76 male participants sulpiride increases the volatility of beliefs, which leads to higher precision weights on prediction errors. This effect is driven by participants with genetically conferred higher dopamine availability (Taq1a polymorphism) and remains even after controlling for working memory performance. Higher precision weights are reflected in higher reciprocal behaviour in the repeated Trust game but not in single-round Trust games. Our data provide evidence that the D2 receptors are pivotal in regulating prediction error-driven belief updating in a social context

Stress, genotype and norepinephrine in the prediction of mouse behavior using reinforcement learning

Individual behavioral performance during learning is known to be affected by modulatory factors, such as stress and motivation, and by genetic predispositions that influence sensitivity to these factors. Despite numerous studies, no integrative framework is available that could predict how a given animal would perform a certain learning task in a realistic situation. We found that a simple reinforcement learning model can predict mouse behavior in a hole-box conditioning task if model metaparameters are dynamically controlled on the basis of the mouse's genotype and phenotype, stress conditions, recent performance feedback and pharmacological manipulations of adrenergic alpha-2 receptors. We find that stress and motivation affect behavioral performance by altering the exploration-exploitation balance in a genotype-dependent manner. Our results also provide computational insights into how an inverted U-shape relation between stress/arousal/norepinephrine levels and behavioral performance could be explained through changes in task performance accuracy and future reward discounting

Modelling the effect of learning and evolving rules on the use of common-pool resources

The extend to which common-pool resources are used and managed sustainably depends highly on incentives. Incentives influence the behaviour of individuals with respect to natural resource management and are determined by institutional arrangements comprising of formal and informal rules and markets. Changes in institutional arrangements will affect individual incentives and will therefore have an impact on resource use. In order to model the connections between institutional arrangements and the sustainable use of common-pool resources we must take into consideration the behaviour of individuals. Game-theoretical models appear to be an adequate modelling technique with which to assess the behaviour of individuals as well as the development of institutions with regards to common-pool resource regimes. The implementation of a game-theoretical framework in the form of an agent-based model appears to be a particularly appropriate tool with which to assess common-pool resource use regimes as such models enable the behaviour of different agents to be modelled as strategies. Traditionally with agent-based models, the strategies that agents pursue are given, with their expression endogenously determined by the set of rules which govern their behaviour. In this paper I focus on the implementation of mechanisms that also allow for rules to adapt endogenously. Such an approach will be applied to common-pool resource use in order to analyse the effect of rule changes.Institutional arrangements, agent-based modelling, learning, evolving rules

The cultural epigenetics of psychopathology: The missing heritability of complex diseases found?

We extend a cognitive paradigm for gene expression based on the asymptotic limit theorems of information theory to the epigenetic epidemiology of mental disorders. In particular, we recognize the fundamental role culture plays in human biology, another heritage mechanism parallel to, and interacting with, the more familiar genetic and epigenetic systems. We do this via a model through which culture acts as another tunable epigenetic catalyst that both directs developmental trajectories, and becomes convoluted with individual ontology, via a mutually-interacting crosstalk mediated by a social interaction that is itself culturally driven. We call for the incorporation of embedding culture as an essential component of the epigenetic regulation of human mental development and its dysfunctions, bringing what is perhaps the central reality of human biology into the center of biological psychiatry. Current US work on gene-environment interactions in psychiatry must be extended to a model of gene-environment-culture interaction to avoid becoming victim of an extreme American individualism that threatens to create paradigms particular to that culture and that are, indeed, peculiar in the context of the world's cultures. The cultural and epigenetic systems of heritage may well provide the 'missing' heritability of complex diseases now under so much intense discussion

The Multi-Dimensional Contributions of Prefrontal Circuits to Emotion Regulation during Adulthood and Critical Stages of Development

The prefrontal cortex (PFC) plays a pivotal role in regulating our emotions. The importance of ventromedial regions in emotion regulation, including the ventral sector of the medial PFC, the medial sector of the orbital cortex and subgenual cingulate cortex, have been recognized for a long time. However, it is increasingly apparent that lateral and dorsal regions of the PFC, as well as neighbouring dorsal anterior cingulate cortex, also play a role. Defining the underlying psychological mechanisms by which these functionally distinct regions modulate emotions and the nature and extent of their interactions is a critical step towards better stratification of the symptoms of mood and anxiety disorders. It is also important to extend our understanding of these prefrontal circuits in development. Specifically, it is important to determine whether they exhibit differential sensitivity to perturbations by known risk factors such as stress and inflammation at distinct developmental epochs. This Special Issue brings together the most recent research in humans and other animals that addresses these important issues, and in doing so, highlights the value of the translational approach

Network-based identification of driver pathways in clonal systems

Highly ethanol-tolerant bacteria for the production of biofuels, bacterial pathogenes which are resistant to antibiotics and cancer cells are examples of phenotypes that are of importance to society and are currently being studied. In order to better understand these phenotypes and their underlying genotype-phenotype relationships it is now commonplace to investigate DNA and expression profiles using next generation sequencing (NGS) and microarray techniques. These techniques generate large amounts of omics data which result in lists of genes that have mutations or expression profiles which potentially contribute to the phenotype. These lists often include a multitude of genes and are troublesome to verify manually as performing literature studies and wet-lab experiments for a large number of genes is very time and resources consuming. Therefore, (computational) methods are required which can narrow these gene lists down by removing generally abundant false positives from these lists and can ideally provide additional information on the relationships between the selected genes. Other high-throughput techniques such as yeast two-hybrid (Y2H), ChIP-Seq and Chip-Chip but also a myriad of small-scale experiments and predictive computational methods have generated a treasure of interactomics data over the last decade, most of which is now publicly available. By combining this data into a biological interaction network, which contains all molecular pathways that an organisms can utilize and thus is the equivalent of the blueprint of an organisms, it is possible to integrate the omics data obtained from experiments with these biological interaction networks. Biological interaction networks are key to the computational methods presented in this thesis as they enables methods to account for important relations between genes (and gene products). Doing so it is possible to not only identify interesting genes but also to uncover molecular processes important to the phenotype. As the best way to analyze omics data from an interesting phenotype varies widely based on the experimental setup and the available data, multiple methods were developed and applied in the context of this thesis: In a first approach, an existing method (PheNetic) was applied to a consortium of three bacterial species that together are able to efficiently degrade a herbicide but none of the species are able to efficiently degrade the herbicide on their own. For each of the species expression data (RNA-seq) was generated for the consortium and the species in isolation. PheNetic identified molecular pathways which were differentially expressed and likely contribute to a cross-feeding mechanism between the species in the consortium. Having obtained proof-of-concept, PheNetic was adapted to cope with experimental evolution datasets in which, in addition to expression data, genomics data was also available. Two publicly available datasets were analyzed: Amikacin resistance in E. coli and coexisting ecotypes in E.coli. The results allowed to elicit well-known and newly found molecular pathways involved in these phenotypes. Experimental evolution sometimes generates datasets consisting of mutator phenotypes which have high mutation rates. These datasets are hard to analyze due to the large amount of noise (most mutations have no effect on the phenotype). To this end IAMBEE was developed. IAMBEE is able to analyze genomic datasets from evolution experiments even if they contain mutator phenotypes. IAMBEE was tested using an E. coli evolution experiment in which cells were exposed to increasing concentrations of ethanol. The results were validated in the wet-lab. In addition to methods for analysis of causal mutations and mechanisms in bacteria, a method for the identification of causal molecular pathways in cancer was developed. As bacteria and cancerous cells are both clonal, they can be treated similar in this context. The big differences are the amount of data available (many more samples are available in cancer) and the fact that cancer is a complex and heterogenic phenotype. Therefore we developed SSA-ME, which makes use of the concept that a causal molecular pathway has at most one mutation in a cancerous cell (mutual exclusivity). However, enforcing this criterion is computationally hard. SSA-ME is designed to cope with this problem and search for mutual exclusive patterns in relatively large datasets. SSA-ME was tested on cancer data from the TCGA PAN-cancer dataset. From the results we could, in addition to already known molecular pathways and mutated genes, predict the involvement of few rarely mutated genes.nrpages: 246status: publishe

What does the amygdala contribute to social cognition?

The amygdala has received intense recent attention from neuroscientists investigating its function at the molecular, cellular, systems, cognitive, and clinical level. It clearly contributes to processing emotionally and socially relevant information, yet a unifying description and computational account have been lacking. The difficulty of tying together the various studies stems in part from the sheer diversity of approaches and species studied, in part from the amygdala's inherent heterogeneity in terms of its component nuclei, and in part because different investigators have simply been interested in different topics. Yet, a synthesis now seems close at hand in combining new results from social neuroscience with data from neuroeconomics and reward learning. The amygdala processes a psychological stimulus dimension related to saliency or relevance; mechanisms have been identified to link it to processing unpredictability; and insights from reward learning have situated it within a network of structures that include the prefrontal cortex and the ventral striatum in processing the current value of stimuli. These aspects help to clarify the amygdala's contributions to recognizing emotion from faces, to social behavior toward conspecifics, and to reward learning and instrumental behavior