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.

Adaptive anterior hippocampal responses to oddball stimuli

Abstract: An efficient memory system requires the ability to detect and preferentially encode novel stimuli. Human electrophysiological recordings demonstrate differential hippocampal responses to novel vs. familiar stimuli, as well as to oddball stimuli. Although functional imaging experiments of novelty detection have demonstrated hippocampal activation, oddball-evoked hippocampal activation has not been demonstrated. Here we use event-related functional magnetic resonance imaging (fMRI) to measure hippocampal responses to three types of oddball words: perceptual, semantic, and emotional. We demonstrate left anterior hippocampal sensitivity to all three oddball types, with adaptation of responses across multiple oddball presentations. This adaptive hippocampal oddball response was not modulated by depth of processing, suggesting a high degree of automaticity in the underlying process. However, an interaction with depth of encoding for semantic oddballs was evident in a more lateral left anterior hippocampal region. We conclude that the hippocampal response to oddballs demonstrates a second-order novelty effect, being sensitive to the "novelty of novelty" of oddball stimuli. The data provide support for a more general theory that a function of the anterior hippocampus is to register mismatches between expectation and experience.

Mental stress and sudden cardiac death: asymmetric midbrain activity as a linking mechanism

Abstract: Patients with specific neurological, psychiatric or cardiovascular conditions are at enhanced risk of cardiac arrhythmia and sudden death. The neurogenic mechanisms are poorly understood. However, in many cases, stress may precipitate cardiac arrhythmia and sudden death in vulnerable patients, presumably via centrally driven autonomic nervous system responses. From a cardiological perspective, the likelihood of arrhythmia is strongly associated with abnormalities in electrical repolarization (recovery) of the heart muscle after each contraction. Inhomogeneous and asymmetric repolarization, reflected in ECG T-wave abnormalities, is associated with a greatly increased risk of arrhythmia, i.e. a proarrhythmic state. We therefore undertook a study to identify the brain mechanisms by which stress can induce cardiac arrhythmia through efferent autonomic drive. We recruited a typical group of 10 out-patients attending a cardiological clinic. We simultaneously measured brain activity, using H2(15)O PET, and the proarrhythmic state of the heart, using ECG, during mental and physical stress challenges and corresponding control conditions. Proarrhythmic changes in the heart were quantified from two ECG-derived measures of repolarization inhomogeneity and were related to changes in magnitude and lateralization of regional brain activity reflected in regional cerebral blood flow. Across the patient group, we observed a robust positive relationship between right-lateralized asymmetry in midbrain activity and proarrhythmic abnormalities of cardiac repolarization (apparent in two independent ECG measures) during stress. This association between stress-induced lateralization of midbrain activity and enhanced arrhythmic vulnerability provides empirical support for a putative mechanism for stress-induced sudden death, wherein lateralization of central autonomic drive during stress results in imbalanced activity in right and left cardiac sympathetic nerves. A right-left asymmetry in sympathetic drive across the surface of the heart disrupts the electrophysiological homogeneity of ventricular repolarization, predisposing to arrhythmia. Our findings highlight a proximal brain basis for stress-induced cardiac arrhythmic vulnerability.

Neural Coding of Tactile Decisions in the Human Prefrontal Cortex

Abstract: The neural processes underlying tactile decisions in the human brain remain elusive. We addressed this question in a functional magnetic resonance imaging study using a somatosensory discrimination task, requiring participants to compare the frequency of two successive tactile stimuli. Tactile stimuli per se engaged somatosensory, parietal, and frontal cortical regions. Using a statistical model that accounted for the relative difference in frequencies (i.e., Weber fraction) and discrimination accuracy (i.e., correct or incorrect), we show that trial-by-trial relative frequency difference is represented linearly by activity changes in the left dorsolateral prefrontal cortex (DLPFC), the dorsal anterior cingulate cortex, and bilateral anterior insular cortices. However, a circumscribed region within the left DLPFC showed a different response pattern expressed as activity changes that were monotonically related to relative stimulation difference only for correct but not for incorrect trials. Our findings suggest that activity in the left DLPFC encodes stimulus representations that underlie veridical tactile decisions in humans.

The neural correlates of the noradrenergic modulation of human attention, arousal and learning

Abstract: The prefrontal cortex has been suggested as a site of action for the noradrenergic modulation of cognition. In healthy volunteers attentional deficits can be induced by the alpha 2 adrenoceptor agonist clonidine, without impairment of more explicit tests of frontal lobe function. It is therefore possible that the effects of noradrenaline cannot be localized to a specific brain area such as the prefrontal cortex, but instead involve structures in a more widespread attentional network. A 1.5 micrograms/kg dose of clonidine or placebo was administered to 13 healthy male volunteers performing the rapid visual information processing task, which places demands on both sustained attention and working memory. Twelve positron emission tomography measurements of regional cerebral blood flow (rCBF) were collected during performance of this task and also during a rest state. A second experiment in 12 healthy volunteers examined the effects of a 1.3 micrograms/kg dose of clonidine on the rCBF changes associated with performance of a paired associates learning task compared with passive listening to word pairs. Comparison of each of the experimental tasks with its respective control replicated previous findings. A significant drug x task interaction, common to the two studies, was found in the right thalamus. Inspection of the adjusted rCBF values showed that the effect was due to attenuation of thalamic rCBF during the control states rather than to any effects of clonidine during performance of the cognitive tasks, although the effect was stronger in the rapid visual information processing study than in the paired associates learning study. The significant effect of clonidine during the control as opposed to the "cognitive' activation state is consistent with previous findings in animals and humans demonstrating greater effects of clonidine during states of relatively low arousal. The results suggest neuroanatomical dissociation of the noradrenergic modulation of arousal (via the thalamus) and attention.

Mirror movements in X-linked Kallmann's syndrome. II. A PET study

Abstract: To investigate the mechanism of mirror movements seen in X-linked Kallmann's syndrome, we measured changes of regional cerebral blood flow with H2 15O-PET. We studied six right-handed Kallmann male subjects and six matched, right-handed control subjects during an externally paced finger opposition task. The analyses were done both on a single subject and a group basis. The Kallmann group showed a strong primary motor cortex (M1) activation contralateral to the voluntarily moved hand, but there was also a significant degree of M1 activation ipsilateral to the voluntarily moved hand, i.e. contralateral to the mirroring hand. However, when comparing contralateral to ipsilateral M1 activation, the M1 activation contralateral to the voluntarily moved hand was significantly stronger. In the controls, significant increases in rCBF were seen in the contralateral M1 during voluntary movement of either hand; a small ipsilateral M1 activation was found in two out of six normal subjects when they moved their left hand. In a second experiment it was shown that, in two out of two Kallmann subjects, passive movements of the right hand resulted in left M1 activation that was similar to the activation in the left M1 when subjects made mirror movements with their right hand. This suggests, but does not prove, that the small but significant activation of the ipsilateral M1 in Kallmann's subjects may be due to sensory feedback from the involuntarily mirroring hand.

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.