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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.


Papers
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Journal ArticleDOI
TL;DR: Investigation of the prevalence and social correlates of psychotic‐like experiences in a general population sample of Black and White British subjects found that black subjects had more psychotic-like experiences than white subjects.
Abstract: We sought to investigate the prevalence and social correlates of psychotic-like experiences in a general population sample of Black and White British subjects. Data were collected from randomly selected community control subjects, recruited as part of the SOP study, a three-centre population based study of first-episode psychosis. The proportion of subjects reporting one or more psychotic-like experience was 19% (n = 72/372). These were more common in Black Caribbean (OR 2.08) and Black African subjects (OR 4.59), compared with White British. In addition, a number of indicators of childhood and adult disadvantage were associated with psychotic-like experiences. When these variables were simultaneously entered into a regression model, Black African ethnicity, concentrated adult disadvantage, and separation from parents retained a significant effect. The higher prevalence of psychotic-like experiences in the Black Caribbean, but not Black African, group was explained by high levels of social disadvantage over the life course.

133 citations

Journal ArticleDOI
10 Nov 2001-BMJ
TL;DR: In this paper, the authors found that intensive case management does not reduce the prevalence of violence in psychotic patients in comparison with standard care, while increasing the intensity of contact between patients and case managers does not significantly reduce the risk of violence.
Abstract: Objectives: To establish whether intensive case management reduces violence in patients with psychosis in comparison with standard case management. Design: Randomised controlled trial with two year follow up. Setting: Four inner city community mental health services. Participants: 708 patients with established psychotic illness allocated at random to intervention (353) or control (355) group. Intervention: Intensive case management (caseload 10–15 per case manager) for two years compared with standard case management (30-35 per case manager). Main outcome measure: Physical assault over two years measured by interviews with patients and case managers and examination of case notes. Results: No significant reduction in violence was found in the intensive case management group compared with the control group (22.7% v 21.9%, P=0.86). Conclusions: Intensive case management does not reduce the prevalence of violence in psychotic patients in comparison with standard care. What is already known on this topic Psychosis and violence are known to be associated Community psychiatric interventions aimed at reducing the risk of violence have not been evaluated What this study adds Increasing the intensity of contact between patients and case managers does not reduce the prevalence of violent behaviour in patients with psychosis Younger age, learning difficulties, and a history of violence, drug misuse, and victimisation predict violent behaviour in psychotic patients

132 citations

Journal ArticleDOI
12 Oct 2011-PLOS ONE
TL;DR: Alterations of white matter microstructure persist into adulthood in VPT individuals and are associated with cognitive function.
Abstract: BACKGROUND AND PURPOSE: Individuals born very preterm (before 33 weeks of gestation, VPT) are at risk of damage to developing white matter, which may affect later cognition and behaviour. METHODS: We used diffusion tensor MRI (DT-MRI) to assess white matter microstructure (fractional anisotropy; FA) in 80 VPT and 41 term-born individuals (mean age 19.1 years, range 17-22, and 18.5 years, range 17-22 years, respectively). VPT individuals were part of a 1982-1984 birth cohort which had been followed up since birth; term individuals were recruited by local press advertisement. General intellectual function, executive function and memory were assessed. RESULTS: The VPT group had reduced FA in four clusters, and increased FA in four clusters relative to the Term group, involving several association tracts of both hemispheres. Clusters of increased FA were associated with more severe neonatal brain injury in the VPT group. Clusters of reduced FA were associated with lower birth weight and perinatal hypoxia, and with reduced adult cognitive performance in the VPT group only. CONCLUSIONS: Alterations of white matter microstructure persist into adulthood in VPT individuals and are associated with cognitive function.

132 citations

Journal ArticleDOI
TL;DR: Environmental risk factors together with the identification of a polygenic risk score for schizophrenia, research on gene-environment interaction and environment–environment interaction have hugely increased knowledge of the disorder.
Abstract: We review recent developments on risk factors in schizophrenia. The way we think about schizophrenia today is profoundly different from the way this illness was seen in the twentieth century. We now know that the etiology of schizophrenia is multifactorial and reflects an interaction between genetic vulnerability and environmental contributors. Environmental risk factors such as pregnancy and birth complications, childhood trauma, migration, social isolation, urbanicity, and substance abuse, alone and in combination, acting at a number of levels over time, influence the individual’s likelihood to develop the disorder. Environmental risk factors together with the identification of a polygenic risk score for schizophrenia, research on gene–environment interaction and environment–environment interaction have hugely increased our knowledge of the disorder.

132 citations

Journal ArticleDOI
TL;DR: Reduction of the left hippocampal volume was associated with the diagnosis of schizophrenia but was present only in patients with a history of severe PBCs, the earlier the onset of psychosis.

131 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).

13,246 citations

Journal ArticleDOI
TL;DR: The 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.
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.

11,042 citations

Journal Article
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
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

9,847 citations

Journal ArticleDOI
TL;DR: This article reviews studies investigating complex brain networks in diverse experimental modalities and provides an accessible introduction to the basic principles of graph theory and highlights the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
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.

9,700 citations

01 Jan 2014
TL;DR: 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.
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.

9,618 citations