Raymond J. Dolan

Bio: Raymond J. Dolan is an academic researcher from University College London. The author has contributed to research in topics: Prefrontal cortex & Functional magnetic resonance imaging. The author has an hindex of 196, co-authored 919 publications receiving 138540 citations. Previous affiliations of Raymond J. Dolan include VU University Amsterdam & McGovern Institute for Brain Research.

Reward Expectation and Affective Responses across Psychiatric Disorders - A Dimensional Approach

Abstract: Studying psychiatric disorders across nosological boundaries aims at a better understanding of mental disorders by identifying comprehensive signatures of core symptoms. Here, we studied neurobiological correlates of emotion processing in several major psychiatric disorders. We assessed differences between diagnostic groups, and investigated whether there is a psychopathological correlate of emotion processing that transcends disorder categories. 135 patient with psychiatric disorders (alcohol dependence, n=29; schizophrenia, n=37; major depressive disorder (MDD), n=25; acute manic episode of bipolar disorder, n=12; panic disorder, n=12, attention deficit/hyperactivity disorder (ADHD), n=20) and healthy controls (n=40) underwent an functional magnetic resonance imaging (fMRI) experiment with affectively positive, aversive and neutral pictures from the International Affective Picture System (IAPS). Between-group differences were assessed with full-factorial ANOVAs, with age, gender and smoking habits as covariates. Self-ratings of depressed mood and anxiety were correlated with activation clusters showing significant stimulus-evoked fMRI activation. Furthermore, we examined functional connectivity with the amygdala as seed region during the processing of aversive pictures. During the presentation of pleasant stimuli, we observed across all subjects significant activation of the ventromedial prefrontal cortex (vmPFC), bilateral middle temporal gyrus and right precuneus, while a significant activation of the left amygdala and the bilateral middle temporal gyrus was found during the presentation of aversive stimuli. We did neither find any significant interaction with diagnostic group, nor any correlation with depression and anxiety scores at the activated clusters or with amygdala connectivity. Positive and aversive IAPS-stimuli were consistently processed in limbic and prefrontal brain areas, irrespective of diagnostic category. A dimensional correlate of these neural activation patterns was not found.

Noradrenaline modulates tabula-rasa exploration

Abstract: An exploration-exploitation trade-off, the arbitration between sampling lesser-known alternatives against harvesting a known rich option, is thought to be solved by humans using computationally demanding exploration algorithms. Given known limitations in human cognitive resources, we hypothesised that additional energy efficient strategies are deployed. We examine such heuristics in choice behaviour and using computational modelling show this involves a tabula-rasa exploration that ignores all prior knowledge and a novelty exploration that only targets novel options. In a double-blind, placebo-controlled drug study, assessing contributions of dopamine (400mg amisulpride) and noradrenaline (40mg propranolol), we show that tabula-rasa exploration is attenuated when noradrenaline, but not dopamine, is blocked. Our findings demonstrate that humans deploy distinct computationally efficient exploration strategies and that one of these is under noradrenergic control.

Associations between aversive learning processes and transdiagnostic psychiatric symptoms revealed by large-scale phenotyping

Abstract: Background: Aversive learning processes are a candidate source of dysfunction in psychiatric disorders. Here symptom expression in a range of conditions is linked to altered threat perception, manifesting particularly in uncertain environments. How precise computational mechanisms that support aversive learning, and uncertainty estimation, relate to the presence of specific psychiatric symptoms remains undetermined. Methods: 400 subjects completed a novel online game-based aversive learning task, requiring avoidance of negative outcomes, in conjunction with completing measures of common psychiatric symptoms. We used a probabilistic computational model to measure distinct processes involved in learning, in addition to inferred estimates of safety likelihood and uncertainty. We tested for associations between learning processes and traditional psychiatric constructs alongside transdiagnostic factors using linear models. We used partial least squares regression to identify components of psychopathology grounded in both aversive learning behaviour and symptom self-report. Results: State anxiety and a transdiagnostic compulsivity-related factor were associated with enhanced learning from safety. However, data-driven analysis using partial least squares regression indicated the presence of two separable components across our behavioural and questionnaire data: one linked enhanced safety learning and lower estimated uncertainty to physiological anxiety, compulsivity, and impulsivity; the other linked enhanced threat learning and heightened uncertainty estimation to symptoms of depression and social anxiety. Conclusions: Our findings implicate aversive learning processes under uncertainty to the expression of psychiatric symptoms that cut across traditional diagnostic boundaries. These relationships are more complex than previously conceptualised. Future research should focus on understanding the neural mechanisms underlying alterations in aversive learning and how these lead to the development of symptoms and disorder.

The impact of the initial COVID-19 outbreak on young adults’ mental health: a longitudinal study of risk and resilience factors

Abstract: Few studies assessing the effects of COVID-19 on mental health include prospective markers of risk and resilience necessary to understand and mitigate the combined impacts of the pandemic, lockdowns, and other societal responses. This population-based study of young adults includes individuals from the Neuroscience in Psychiatry Network (n = 2403) recruited from English primary care services and schools in 2012-2013 when aged 14-24. Participants were followed up three times thereafter, most recently during the initial outbreak of the COVID-19 outbreak when they were aged between 19 and 34. Repeated measures of psychological distress (K6) and mental wellbeing (SWEMWBS) were supplemented at the latest assessment by clinical measures of depression (PHQ-9) and anxiety (GAD-7). A total of 1000 participants, 42% of the original cohort, returned to take part in the COVID-19 follow-up; 737 completed all four assessments [mean age (SD), 25.6 (3.2) years; 65.4% female; 79.1% White]. Our findings show that the pandemic led to pronounced deviations from existing mental health-related trajectories compared to expected levels over approximately seven years. About three-in-ten young adults reported clinically significant depression (28.8%) or anxiety (27.6%) under current NHS guidelines; two-in-ten met clinical cut-offs for both. About 9% reported levels of psychological distress likely to be associated with serious functional impairments that substantially interfere with major life activities; an increase by 3% compared to pre-pandemic levels. Deviations from personal trajectories were not necessarily restricted to conventional risk factors; however, individuals with pre-existing health conditions suffered disproportionately during the initial outbreak of the COVID-19 pandemic. Resilience factors known to support mental health, particularly in response to adverse events, were at best mildly protective of individual psychological responses to the pandemic. Our findings underline the importance of monitoring the long-term effects of the ongoing pandemic on young adults' mental health, an age group at particular risk for the emergence of psychopathologies. Our findings further suggest that maintaining access to mental health care services during future waves, or potential new pandemics, is particularly crucial for those with pre-existing health conditions. Even though resilience factors known to support mental health were only mildly protective during the initial outbreak of the COVID-19 pandemic, it remains to be seen whether these factors facilitate mental health in the long term.

Reinforcement Learning: An Introduction

Abstract: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

An integrative theory of prefrontal cortex function

Abstract: ▪ Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.