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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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TL;DR: Those with psychosis are at considerable risk of violent victimisation in the community, and victims of violence were significantly more likely to report severe psychopathological symptoms, homelessness, substance misuse and previous violent behaviour and to have a comorbid personality disorder.
Abstract: Background Since de-institutionalisation, much has been written about the risk posed to the community by those with severe mental illness. However, violent victimisation of people with mental illnesses has received little attention. Aims To establish the 1-year prevalence of violent victimisation in community-dwelling patients with psychosis and to identify the socio-demographic and clinical correlates of violent victimisation. Method A total of 691 subjects with established psychotic disorders were interviewed. The past-year prevalence of violent victimisation was estimated and compared with general population figures. Those who reported being violently victimised were compared with those who did not on a range of social and clinical characteristics. Results Sixteen per cent of patients reported being violently victimised. Victims of violence were significantly more likely to report severe psychopathological symptoms, homelessness, substance misuse and previous violent behaviour and were more likely to have a comorbid personality disorder. Conclusions Those with psychosis are at considerable risk of violent victimisation in the community. Victimisation experience should be recorded in the standard psychiatric interview.

207 citations

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TL;DR: It was found that dimensional representations of psychopathological features were considerably more useful than categorical representations (DSM-III-R and ICD-10) as predictors of illness course and treatment decisions.
Abstract: The aim of this study was to identify underlying dimensions of psychopathology in a cohort of patients with functional psychosis of recent onset, and to examine their prognostic value. Factor analysis of the psychopathological features of 166 consecutively admitted patients with functional psychosis of recent onset revealed seven psychopathological dimensions, which explained 63% of the variance. Five of these seven syndromes bore differential associations with subsequent treatment and illness course, independent of: (i) associations with DSM-III-R diagnosis; (ii) associations with other prognostic factors; and (iii) associations with the baseline values of outcome variables. The most striking associations were shown for an early and insidious onset syndrome with affective flattening, which predicted a more disabled course of illness on three of four outcome dimensions, and which was more common in males and unmarried individuals. A second syndrome, characterized by bizarre behaviour, inappropriate affect, catatonia, and poor rapport showed similar, slightly less striking, associations with illness course, as well as with poor pre-morbid social functioning. A third syndrome, characterized by positive psychotic symptoms was to a lesser degree associated with poorer outcome, whereas a fourth syndrome distinguished by manic symptomatology predicted a more benign illness course. A fifth syndrome identified by lack of insight predicted more time in hospital and admission under a section of the Mental Health Act during the follow-up period.A further finding was that dimensional representations of psychopathological features were considerably more useful than categorical representations (DSM-III-R and ICD-10) as predictors of illness course and treatment decisions.

206 citations

Journal ArticleDOI
TL;DR: The severity of positive FTD was inversely correlated with the level of activity in the Wernicke area, a region implicated in the production of coherent speech, and reduced activity in this area might contribute to the articulation of incoherent speech.
Abstract: Background Formal thought disorder (FTD) is a core symptom of schizophrenia, but its pathophysiology is little understood. We examined the neural correlates of FTD using functional magnetic resonance imaging. Methods Blood oxygenation level–dependent contrast was measured using functional magnetic resonance imaging while 6 patients with schizophrenia and 6 control subjects spoke about 7 Rorschach inkblots for 3 minutes each. In patients, varying degrees of thought-disordered speech were elicited during each "run." In a within-subject design, the severity of positive FTD was correlated with the level of blood oxygenation level–dependent contrast in the 2 runs that showed the highest variance of FTD in each patient. Results The severity of positive FTD in patients was negatively correlated ( P Conclusions The severity of positive FTD was inversely correlated with the level of activity in the Wernicke area, a region implicated in the production of coherent speech. Reduced activity in this area might contribute to the articulation of incoherent speech. Because of the small sample size, these findings should be considered preliminary.

205 citations

Journal ArticleDOI
TL;DR: Analysis of factors associated with pre-morbid deficits showed a highly significant interaction of diagnosis with sex, such that schizophrenic men showed much greater pre-Morbid impairment than either schizophrenic women or men with affective disorder.
Abstract: Pre-morbid schizoid and schizotypal traits and social adjustment were assessed blind to diagnosis by interviewing the mothers of 73 consecutively admitted patients with DSM-III schizophrenia or affective psychosis. Analysis of factors associated with pre-morbid deficits showed a highly significant interaction of diagnosis with sex, such that schizophrenic men showed much greater pre-morbid impairment than either schizophrenic women or men with affective disorder. Poor pre-morbid adjustment predicted an early age at first admission. The results can be explained by a neurodevelopmental disorder in some schizophrenic males.

205 citations

Journal ArticleDOI
TL;DR: It is shown that rare, damaging variants contribute to the risk of schizophrenia both with and without intellectual disability and support an overlap of genetic risk between schizophrenia and other neurodevelopmental disorders.
Abstract: By performing a meta-analysis of rare coding variants in whole-exome sequences from 4,133 schizophrenia cases and 9,274 controls, de novo mutations in 1,077 family trios, and copy number variants from 6,882 cases and 11,255 controls, we show that individuals with schizophrenia carry a significant burden of rare, damaging variants in 3,488 genes previously identified as having a near-complete depletion of loss-of-function variants. In patients with schizophrenia who also have intellectual disability, this burden is concentrated in risk genes associated with neurodevelopmental disorders. After excluding known risk genes for neurodevelopmental disorders, a significant rare variant burden persists in other genes intolerant of loss-of-function variants; although this effect is notably stronger in patients with both schizophrenia and intellectual disability, it is also seen in patients with schizophrenia who do not have intellectual disability. Together, our results show that rare, damaging variants contribute to the risk of schizophrenia both with and without intellectual disability and support an overlap of genetic risk between schizophrenia and other neurodevelopmental disorders.

202 citations


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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

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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