Effects of PPP1R1B (DARPP-32) Polymorphism on Feedback-Related Brain Potentials Across the Life Span

Maximizing gains during probabilistic reinforcement learning requires the updating of choice – outcome expectations at the time when the feedback about a specific choice or action is given. Extant theories and evidence suggest that dopaminergic modulation plays a crucial role in reinforcement learning and the updating of choice – outcome expectations. Furthermore, recently a positive component of the event-related potential about 200 ms (P2) after feedback has been suggested to reflect such updating. The efficacy of dopaminergic modulation changes across the life span. However, to date investigations of age-related differences in feedback-related P2 during reinforcement learning are still scarce. The present study thus aims to investigate whether individual differences in the feedback-related P2 would be associated with polymorphic variations in a dopamine relevant gene PPP1R1B (also known as DARPP-32) and whether the genetic effect may differ between age groups. We observed larger P2 amplitudes in individuals carrying the genotype associated with higher dopamine receptor efficacy, i.e., a allele homozygotes of a single nucleotide polymorphism (rs907094) of the PPP1R1B gene. Moreover, this effect was more pronounced in children and older adults in comparison to adolescents and younger adults. Together, our findings indicate that polymorphic variations in a dopamine relevant gene are associated with individual differences in brain-evoked potentials of outcome updating and hint at the possibility that genotype effects on neurocognitive phenotypes may vary as a function of brain maturation and aging

Deep Brain Stimulation Programming 2.0: Future Perspectives for Target Identification and Adaptive Closed Loop Stimulation

Deep brain stimulation has developed into an established treatment for movement disorders and is being actively investigated for numerous other neurological as well as psychiatric disorders. An accurate electrode placement in the target area and the effective programming of DBS devices are considered the most important factors for the individual outcome. Recent research in humans highlights the relevance of widespread networks connected to specific DBS targets. Improving the targeting of anatomical and functional networks involved in the generation of pathological neural activity will improve the clinical DBS effect and limit side-effects. Here, we offer a comprehensive overview over the latest research on target structures and targeting strategies in DBS. In addition, we provide a detailed synopsis of novel technologies that will support DBS programming and parameter selection in the future, with a particular focus on closed-loop stimulation and associated biofeedback signals

Toward a multiscale modeling framework for understanding serotonergic function

Despite its importance in regulating emotion and mental wellbeing, the complex structure and function of the serotonergic system present formidable challenges toward understanding its mechanisms. In this paper, we review studies investigating the interactions between serotonergic and related brain systems and their behavior at multiple scales, with a focus on biologically-based computational modeling. We first discuss serotonergic intracellular signaling and neuronal excitability, followed by neuronal circuit and systems levels. At each level of organization, we will discuss the experimental work accompanied by related computational modeling work. We then suggest that a multiscale modeling approach that integrates the various levels of neurobiological organization could potentially transform the way we understand the complex functions associated with serotonin

Deep Brain Stimulation Programming 2.0: Future Perspectives for Target Identification and Adaptive Closed Loop Stimulation

Deep brain stimulation has developed into an established treatment for movement disorders and is being actively investigated for numerous other neurological as well as psychiatric disorders. An accurate electrode placement in the target area and the effective programming of DBS devices are considered the most important factors for the individual outcome. Recent research in humans highlights the relevance of widespread networks connected to specific DBS targets. Improving the targeting of anatomical and functional networks involved in the generation of pathological neural activity will improve the clinical DBS effect and limit side-effects. Here, we offer a comprehensive overview over the latest research on target structures and targeting strategies in DBS. In addition, we provide a detailed synopsis of novel technologies that will support DBS programming and parameter selection in the future, with a particular focus on closed-loop stimulation and associated biofeedback signals

Biomarkers and neuromodulation techniques in substance use disorders

Addictive disorders are a severe health concern. Conventional therapies have just moderate success and the probability of relapse after treatment remains high. Brain stimulation techniques, such as transcranial Direct Current Stimulation (tDCS) and Deep Brain Stimulation (DBS), have been shown to be effective in reducing subjectively rated substance craving. However, there are few objective and measurable parameters that reflect neural mechanisms of addictive disorders and relapse. Key electrophysiological features that characterize substance related changes in neural processing are Event-Related Potentials (ERP). These high temporal resolution measurements of brain activity are able to identify neurocognitive correlates of addictive behaviours. Moreover, ERP have shown utility as biomarkers to predict treatment outcome and relapse probability. A future direction for the treatment of addiction might include neural interfaces able to detect addiction-related neurophysiological parameters and deploy neuromodulation adapted to the identified pathological features in a closed-loop fashion. Such systems may go beyond electrical recording and stimulation to employ sensing and neuromodulation in the pharmacological domain as well as advanced signal analysis and machine learning algorithms. In this review, we describe the state-of-the-art in the treatment of addictive disorders with electrical brain stimulation and its effect on addiction-related neurophysiological markers. We discuss advanced signal processing approaches and multi-modal neural interfaces as building blocks in future bioelectronics systems for treatment of addictive disorders

Different varieties of uncertainty in human decision-making

The study of uncertainty in decision-making is receiving greater attention in the fields of cognitive and computational neuroscience. Several lines of evidence are beginning to elucidate different variants of uncertainty. Particularly, risk, ambiguity, and expected and unexpected forms of uncertainty are well articulated in the literature. In this article we review both empirical and theoretical evidence arguing for the potential distinction between three forms of uncertainty; expected uncertainty, unexpected uncertainty, and volatility. Particular attention will be devoted to exploring the distinction between unexpected uncertainty and volatility which has been less appreciated in the literature. This includes evidence mainly from neuroimaging, neuromodulation, and electrophysiological studies. We further address the possible differentiation of cognitive control mechanisms used to deal with these forms of uncertainty. Finally, we explore whether the dual modes of control theory provides a theoretical framework for understanding the distinction between unexpected uncertainty and volatility