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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: Assessment of whether intensive case management (small case load) reduces hospitalisation and costs compared with standard case management in mild and severe disability and in people of African–Caribbean origin and White Caucasians.
Abstract: Background Case management, particularly in intensive form, has been widely introduced for the treatment of severe mental illness. However, the optimal intensity of case management has not been determined. Aims We aimed to assess whether intensive case management (small case load) reduces hospitalisation and costs compared with standard case management. Method Development and rationale of a large randomised controlled trial comparing intensive case management (case load per worker? 15 patients) with standard case management (case load 30–35 patients) Results Two-year outcome data will be obtained on patients representative of the seriously mentally ill in inner-city mental health services. Conclusions The study planned with 700 patients should be sufficient to detect small differences in the readmission of patients to hospital (10%), the number of days spent in hospital over a two-year period (10 days) and the average weekly cost of care per patient. The sample is large enough to compare the cost-effectiveness of intensive and standard case management in mild and severe disability and in people of African–Caribbean origin and White Caucasians.

86 citations

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TL;DR: Evidence is provided for a role for the GABAA receptor subunit cluster on chromosome 5q33 in susceptibility to the alcohol dependence syndrome and Korsakoff's psychosis.
Abstract: A role for the GABA/benzodiazepine receptor complex in alcohol dependence syndrome has been suggested by several lines of evidence To elucidate the role of GABAA subunits in human alcohol dependence syndrome, we identified polymorphisms in the GABAAbeta2 and GABAAalpha6 receptor subunit genes on 5q33 and assessed their potential contribution in an association study, together with a NciI RFLP at the GABAAgamma2 receptor subunit gene One hundred and eight alcohol-dependent subjects and 54 unrelated controls were recruited from Scotland Two novel genetic markers were identified at the GABAAbeta2 and GABAAalpha6 receptor subunit genes and examined for association with the alcohol dependence syndrome and subgroups of subjects with Korsakoff's psychosis and without Korsakoff's psychosis, together with a NciI RFLP at the GABAAgamma2 receptor subunit gene The chi2 tests demonstrated associations between all alcohol-dependent subjects (not stratified) and the BanI RFLP at the GABAAbeta2 receptor subunit gene (P = 0015), and the AlwNI RFLP at the GABAAalpha6 receptor gene (P = 0013) Significant associations were also found between the alcohol-dependent subjects with Korsakoff's psychosis and the BanI RFLP (P = 0039) and the AlwNI RFLP (P = 0003) Haplotype analysis also provided evidence of association when all alcohol-dependent subjects (P = 0013) and the subjects with Korsakoff's psychosis (P = 0007) were compared with controls Our findings provide evidence for a role for the GABAA receptor subunit cluster on chromosome 5q33 in susceptibility to the alcohol dependence syndrome and Korsakoff's psychosis

85 citations

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TL;DR: In this article, the reproducibility of VBM and cortical thickness measurement was investigated to detect group differences and estimate heritability when MRI scans from different scanners running different acquisition protocols in a multicenter setup are included.
Abstract: Multicenter structural MRI studies can have greater statistical power than single-center studies. However, across-center differences in contrast sensitivity, spatial uniformity, etc., may lead to tissue classification or image registration differences that could reduce or wholly offset the enhanced statistical power of multicenter data. Prior work has validated volumetric multicenter MRI, but robust methods for assessing reliability and power of multisite analyses with voxel-based morphometry (VBM) and cortical thickness measurement (CORT) are not yet available. We developed quantitative methods to investigate the reproducibility of VBM and CORT to detect group differences and estimate heritability when MRI scans from different scanners running different acquisition protocols in a multicenter setup are included. The method produces brain maps displaying information such as lowest detectable effect size (or heritability) and effective number of subjects in the multicenter study. We applied the method to a five-site multicenter calibration study using scanners from four different manufacturers, running different acquisition protocols. The reliability maps showed an overall good comparability between the sites, providing a reasonable gain in sensitivity in most parts of the brain. In large parts of the cerebrum and cortex scan pooling improved heritability estimates, with "effective-N" values upto the theoretical maximum. For some areas, "optimal-pool" maps indicated that leaving out a site would give better results. The reliability maps also reveal which brain regions are in any case difficult to measure reliably (e.g., around the thalamus). These tools will facilitate the design and analysis of multisite VBM and CORT studies for detecting group differences and estimating heritability.

85 citations

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TL;DR: The familiality of the psychomotor poverty, disorganisation, and manic dimensions supports their use in the delineation of homogeneous subsets for genetic studies.

85 citations

Journal ArticleDOI
TL;DR: The variation in hippocampal and ventricular volumes within discordant monozygotic pairs indicates a role for environmental factors in determining these volume abnormalities in schizophrenia.

85 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