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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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TL;DR: SZ is characterized by significant deficits in Premorbid intellectual function but the evidence regarding premorbid function in BD is equivocal.
Abstract: Background Schizophrenia (SZ) is characterized by a broad global cognitive impairment that precedes the onset of the disease. By contrast, some studies suggest that premorbid deficits are absent, or even reversed, in bipolar disorder (BD). However, studies have shown impairments in cognitive functioning after the illness onset in both disorders. The aim of this study was to systematically review and meta-analyze those studies that compared premorbid and/or post-onset global cognitive function between SZ and BD. Method We searched Medline (PubMed), EMBASE and PsycINFO for studies where information on cognitive functioning was collected in both SZ and BD within the same study or using the same methods. Results Compared to healthy comparison groups, SZ patients showed a significant premorbid cognitive impairment [standardized mean difference (SMD) −0.597, 95% confidence interval (CI) −0.707 to −0.487, p < 0.0001] and a large post-onset impairment (SMD −1.369, 95% CI −1.578 to −1.160, p < 0.0001). We found small significant deficits in premorbid intellectual function in the BD group when this was assessed retrospectively (−0.147, 95% CI −0.238 to −0.056, p = 0.001) but not prospectively (−0.029, 95% CI −0.199 to + 0.142, p = 0.744), and moderate cognitive impairment after onset (SMD −0.623, 95% CI −0.717 to −0.529, p < 0.0001). Conclusions SZ is characterized by significant deficits in premorbid intellectual function but the evidence regarding premorbid function in BD is equivocal. After illness onset, patients with both disorders seem to suffer a further decline in cognitive function but the magnitude of the impairment remains greater in SZ than in BD.

113 citations

Journal ArticleDOI
TL;DR: The results suggest that pathological change in schizophrenia may be expressed at two mutually independent levels of anatomical organization: global change in a grey matter/ventricular system and supra-regionalchange in a frontal-temporal system.
Abstract: At what levels of brain organization might pathological change in schizophrenia be anatomically expressed: global, regional or supraregional? We hypothesised that brain structure reflects a set of supra-regional anatomical systems with common developmental influences. We conducted an exploratory analysis to identify supraregional brain systems and to investigate whether abnormal brain architecture in schizophrenia is manifested within one or more of these systems. Magnetic resonance (MR) images were acquired from 27 patients with schizophrenia and 37 control subjects. After segmentation and registration of each individual MRI dataset in the standard space of Talairach and Tournoux, grey matter and ventricular-cerebrospinal fluid (CSF) maps were automatically parcellated into 104 regions. We used principal components analysis of the multiple regional grey matter and ventricular-CSF measurements, on all 64 subjects, to extract the five main normative supra-regional systems. The first two of these components represented global variation in grey matter and ventricular-CSF regional measures. We interpreted the other three components as representing supra-regional systems comprising: a frontal-parietal system, a frontal-temporal system and a frontal-basal ganglia system. Schizophrenic group mean scores on the first component (global grey matter-ventricular contrast) and fourth component (frontal-temporal system) were significantly reduced compared to controls. These results suggest that pathological change in schizophrenia may be expressed at two mutually independent levels of anatomical organization: global change in a grey matter/ventricular system and supra-regional change in a frontal-temporal system.

112 citations

Journal ArticleDOI
TL;DR: The incidence of schizophrenia has doubled in south-east London over the past three decades and was greatest in people under 35 years of age and was not gender-specific.
Abstract: Background There has been much debate about changes in the incidence of schizophrenia. Aims To identify any changes in incidence of schizophrenia in Camberwell, south-east London, between 1965 and 1997. Method Research Diagnostic Criteria and DSM—III—R diagnoses were generated for all first contacts by the OPCRIT computer program, and incidence rates of schizophrenia in seven time periods were measured. Indirect standardisation and Poisson models were used to measure the effect of time period and to examine interactions with age and gender. Results There was a continuous and statistically significant increase in the incidence of schizophrenia, which was greatest in people under 35 years of age and was not gender-specific. Conclusions The incidence of schizophrenia has doubled in south-east London over the past three decades.

111 citations

Journal ArticleDOI
TL;DR: Data indicate that bipolar patients show a strikingly different pattern of frontal responses compared to those with schizophrenia and provide further evidence that abnormal frontal activation in psychotic disorders is more apparent during verbal fluency than semantic decision.

111 citations

Journal ArticleDOI
TL;DR: It is found that increased national deaths from bronchopneumonia preceded, by three and five months respectively, increased numbers of schizophrenic births some months later, which may be a reflection of the fact that bronchipneumonia deaths increase markedly during influenza epidemics.
Abstract: BACKGROUND Recently, several investigators have reported an association between influenza epidemics and increased birth rates of 'preschizophrenic' individuals some four to six months later Here we examine whether maternal exposure to other infectious diseases can also predispose the foetus to later schizophrenia METHOD Two independent sets of dates of birth of first admission schizophrenic patients, born between 1938 and 1965 in England and Wales, were obtained from the Mental Health Enquiry in England and Wales Data on the number of deaths per month from 16 infectious diseases between 1937 and 1965 in England and Wales were also collected We used a Poisson regression model to examine the relationship between deaths from infectious diseases and schizophrenic births RESULTS In the two separate data sets, increased national deaths from bronchopneumonia preceded, by three and five months respectively, increased numbers of schizophrenic births We did not find any other significant associations between schizophrenic births and any of the other 15 infectious diseases CONCLUSIONS The association between deaths from bronchopneumonia and increased schizophrenic births some months later may be a reflection of the fact that bronchopneumonia deaths increase markedly during influenza epidemics

111 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

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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