Robin M. Murray

Bio: Robin M. Murray is an academic researcher from King's College London. The author has contributed to research in topics: Psychosis & Schizophrenia. The author has an hindex of 171, co-authored 1539 publications receiving 116362 citations. Previous affiliations of Robin M. Murray include University of Cambridge & National Institutes of Health.

Neurological Signs at the First Psychotic Episode as Correlates of Long-Term Outcome: Results From the AESOP-10 Study.

Abstract: Minor neurological signs are subtle deficits in sensory integration, motor coordination, and sequencing of complex motor acts present in excess in the early stages of psychosis. Still, it remains unclear whether at least some of these signs represent trait or state markers for psychosis and whether they are markers of long-term disease outcome of clinical utility. We examined the relationship between neurological function at illness onset assessed with the Neurological Evaluation Scale and subsequent illness course in 233 patients from AESOP-10 (Aetiology and Ethnicity in Schizophrenia and Other Psychoses), a 10-year follow-up study of a population-based cohort of individuals recruited at the time of their first episode of psychosis in the United Kingdom. In 56 of these patients, we also explored changes in neurological function over time. We included a group of 172 individuals without psychosis as controls. After 10 years, 147 (63%) patients had developed a non-remitting course of illness, and 86 (37%) a remitting course. Already at first presentation, patients who developed a non-remitting course had significantly more primary, motor coordination, and total signs than both remitting patients and healthy controls. While Motor Coordination signs did not change over time, rates of Primary, Sensory Integration, and Total signs increased, independently of illness course type. These findings suggest that motor coordination problems could be a useful early, quick, and easily detectable marker of subsequent clinical outcome. With other motor abnormalities, a measure of motor incoordination could contribute to the identification of the most vulnerable individuals, who could benefit from targeted and more assertive treatment approaches.

Liberal Acceptance Bias, Momentary Aberrant Salience, and Psychosis: An Experimental Experience Sampling Study.

Abstract: Cognitive models of psychosis posit that reasoning biases are an important mechanism contributing to the formation of psychotic symptoms, in part through transforming anomalous experiences of aberrant salience into frank psychotic symptoms. This study aimed to investigate the interplay of liberal acceptance (LA) bias, which is a specific type of reasoning bias, and momentary aberrant salience in the development of paranoid and psychotic experiences in daily life in first-episode psychosis patients (FEP), at-risk mental state participants (ARMS), and controls. We used a novel experimental Experience Sampling Methodology (eESM) task for measuring LA bias (ie, decisions based on low probability estimates) and ESM measures of momentary aberrant salience and paranoid and psychotic experiences in 51 FEP, 46 ARMS, and 53 controls. We found evidence that LA bias was more likely to occur in FEP than in controls. Further, LA bias was associated with psychotic and paranoid experiences (all P < .007) and modified the association between momentary aberrant salience and psychotic experiences (χ2(df) = 7.4(2), P = .025) in ARMS, such that momentary salience was associated with more intense psychotic experiences in the presence of LA bias in ARMS, but not in FEP and controls. Our findings suggest that LA bias may be central for anomalous experiences such as momentary aberrant salience to increase intensity of psychotic experiences in at-risk individuals. Further, LA bias appears to be more likely to be present, but not directly linked to current intensity of psychotic experiences, in treated FEP. Novel eESM tasks open new avenues for targeting psychological processes under real-world conditions.

The impact of molecular genetics on our understanding of the psychoses.

Abstract: Studies demonstrating the linkage to separate chromosomal locations of Alzheimer's disease, manic depression, and schizophrenia require re-evaluation of our ideas of their genetic aetiology. This article reviews the findings, and explores the increasing contribution of the 'new genetics' to our understanding of the organic and functional psychoses.

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.).

Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption-II

Abstract: The Alcohol Use Disorders Identification Test (AUDIT) has been developed from a six-country WHO collaborative project as a screening instrument for hazardous and harmful alcohol consumption. It is a 10-item questionnaire which covers the domains of alcohol consumption, drinking behaviour, and alcohol-related problems. Questions were selected from a 150-item assessment schedule (which was administered to 1888 persons attending representative primary health care facilities) on the basis of their representativeness for these conceptual domains and their perceived usefulness for intervention. Responses to each question are scored from 0 to 4, giving a maximum possible score of 40. Among those diagnosed as having hazardous or harmful alcohol use, 92% had an AUDIT score of 8 or more, and 94% of those with non-hazardous consumption had a score of less than 8. AUDIT provides a simple method of early detection of hazardous and harmful alcohol use in primary health care settings and is the first instrument of its type to be derived on the basis of a cross-national study.

Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Complex brain networks: graph theoretical analysis of structural and functional systems

Abstract: Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.

Standards of Medical Care in Diabetes

Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.