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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.


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Journal ArticleDOI
TL;DR: In people with schizophrenia whose medication is changed for clinical reasons, there is no disadvantage across 1 year in terms of quality of life, symptoms, or associated costs of care in using FGAs rather than nonclozapine SGAs.
Abstract: Context Second-generation (atypical) antipsychotics (SGAs) are more expensive than first-generation (typical) antipsychotics (FGAs) but are perceived to be more effective, with fewer adverse effects, and preferable to patients. Most evidence comes from short-term efficacy trials of symptoms. Objective To test the hypothesis that in people with schizophrenia requiring a change in treatment, SGAs other than clozapine are associated with improved quality of life across 1 year compared with FGAs. Design A noncommercially funded, pragmatic, multisite, randomized controlled trial of antipsychotic drug classes, with blind assessments at 12, 26, and 56 weeks using intention-to-treat analysis. Setting Fourteen community psychiatric services in the English National Health Service. Participants Two hundred twenty-seven people aged 18 to 65 years withDSM-IVschizophrenia and related disorders assessed for medication review because of inadequate response or adverse effects. Interventions Randomized prescription of either FGAs or SGAs (other than clozapine), with the choice of individual drug made by the managing psychiatrist. Main Outcome Measures Quality of Life Scale scores, symptoms, adverse effects, participant satisfaction, and costs of care. Results The primary hypothesis of significant improvement in Quality of Life Scale scores during the year after commencement of SGAs vs FGAs was excluded. Participants in the FGA arm showed a trend toward greater improvements in Quality of Life Scale and symptom scores. Participants reported no clear preference for either drug group; costs were similar. Conclusions In people with schizophrenia whose medication is changed for clinical reasons, there is no disadvantage across 1 year in terms of quality of life, symptoms, or associated costs of care in using FGAs rather than nonclozapine SGAs. Neither inadequate power nor patterns of drug discontinuation accounted for the result.

1,035 citations

Journal ArticleDOI
19 Sep 1987-BMJ
TL;DR: Much research implicates the left rather than the right cerebral hemisphere in schizophrenia, and there is evidence that schizophrenics are more likely to be left handed than controls, and the normal development of lateralised cerebral dominance can be disrupted by premature birth with a resultant increase in left handedness.
Abstract: A well established fact about schizophrenia is that first degree relatives have an increased risk of the disorder. Few now doubt that schizophrenia has a genetic basis, yet its mode of inheritance has to be explained. Even the identical twin of a schizophrenic stands a better than 50% chance of escaping the illness.' Genetic factors are not the whole story. Kraepelin, who derived the concept of schizophrenia, considered that both heredity and organic brain disease were implicated, but somehow the organic aspects were neglected until the publication of a study using computed tomography by Johnstone et al in 1976.2 A decade of such research has confirmed that the cerebral ventricles or cortical sulci are enlarged in many schizophrenics. Such changes are nonspecific and can follow head injury, intracranial infections, and alcoholism and other cerebral insults.3 As they are present in the earliest stage of schizophrenia and are not progressive they may be the sequelae of earlier events of aetiological importance. But what events-and how early? The epidemiology ofschizophrenia probably still holds the key. The disorder generally begins in early adult life, but the peak incidence in men is nearly a decade earlier than that in women.4 The reason for this is unclear. An equally puzzling but equally consistent finding is the small excess of births of schizophrenics in the cold winter months.5 This excess is not shared by the siblings of schizophrenics and is greater in those without a family history and in men with paranoid illness.67 The most likely explanation is that some environmental factor associated with winter birth causes neural damage in the fetus or neonate. The cause could be a viral infection or a seasonal difference in other complications that occur during pregnancy or delivery.8 9 Schizophrenics seem more likely than controls to have a history of obstetric complications,'0 11 and, despite the various events encompassed by the term, obstetric complications may increase the risk ofschizophrenia. Furthermore, increased ventricular size and other abnormalities seen on computed tomography are more common in those schizophrenics with a history of obstetric complications.\"2 Much research implicates the left rather than the right cerebral hemisphere in schizophrenia.'3 There is evidence that schizophrenics are more likely to be left handed than controls,'4 and possibly the normal development of lateralised cerebral dominance can be disrupted by premature birth with a resultant increase in left handedness.'5 What mechanism could explain the relation between obstetric complications, abnormalities on computed tomo-

986 citations

Journal ArticleDOI
TL;DR: Cases of psychotic disorder could be prevented by discouraging cannabis use among vulnerable youths and research is needed to understand the mechanisms by which cannabis causes psychosis.
Abstract: Background Controversy remains as to whether cannabis acts as a causal risk factor for schizophrenia or other functional psychotic illnesses. Aims To examine critically the evidence that cannabis causes psychosis using established criteria of causality. Method We identified five studies that included a well-defined sample drawn from population-based registers or cohorts and used prospective measures of cannabis use and adult psychosis. Results On an individual level, cannabis use confers an overall twofold increase in the relative risk for later schizophrenia. At the population level, elimination of cannabis use would reduce the incidence of schizophrenia by approximately 8%, assuming a causal relationship. Cannabis use appears to be neither a sufficient nor a necessary cause for psychosis. It is a component cause, part of a complex constellation of factors leading to psychosis. Conclusions Cases of psychotic disorder could be prevented by discouraging cannabis use among vulnerable youths. Research is needed to understand the mechanisms by which cannabis causes psychosis.

860 citations

Journal ArticleDOI
Phil Lee, Verneri Anttila, Hyejung Won1, Yen-Chen Anne Feng1  +603 moreInstitutions (10)
12 Dec 2019-Cell
TL;DR: Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes.

781 citations

Journal ArticleDOI
TL;DR: A substantial genetic contribution to variance in liability was confirmed for the major diagnostic categories except Research Diagnostic Criteria depressive psychosis and unspecified functional psychosis, where familial transmission was confirmed, but the relative contribution of genetic and common environmental factors was unclear.
Abstract: Background Previous twin studies have supported a genetic contribution to the major categories of psychotic disorders, but few of these have employed operational diagnostic criteria, and no such study has been based on a sample that included the full range of functional psychotic disorders. Methods A total of 224 twin probands (106 monozygotic, 118 dizygotic) with a same-sex co-twin and a lifetime history of psychosis was ascertained from the service-based Maudsley Twin Register in London, England. Research Diagnostic Criteria psychotic diagnoses were made on a lifetime-ever basis. Main-lifetime diagnoses of DSM-III-R and International Statistical Classification of Diseases, 10th Revision schizophrenia were also made. Probandwise concordance rates and correlations in liability were calculated, and biometrical model fitting applied. Results A substantial genetic contribution to variance in liability was confirmed for the major diagnostic categories except Research Diagnostic Criteria depressive psychosis and unspecified functional psychosis, where familial transmission was confirmed, but the relative contribution of genetic and common environmental factors was unclear. Heritability estimates for Research Diagnostic Criteria schizophrenia, schizoaffective disorder, mania, DSM-III-R schizophrenia, and International Statistical Classification of Diseases, 10th Revision schizophrenia were all between 82% and 85%. None of the estimates differed significantly from any other. Conclusions Heritability estimates for schizophrenia, schizoaffective disorder, and mania were substantial and similar. Population morbid risk estimates were inferred rather than directly measured, but the results were very similar to those from studies where morbid risks were directly estimated.

777 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