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.

First episodes of psychosis in Afro-Caribbean and White people. An 18-year follow-up population-based study.

Abstract: BACKGROUND There have been few prospective studies of the long-term outcome of psychosis in people of Afro-Caribbean origin in the UK. METHOD We followed-up a population-based, consecutive series of 34 Afro-Caribbean and 54 White people with psychosis who had been extensively investigated during their first admission in 1973/74. Diagnoses were made by direct interview using the Present State Examination at both first admission and follow-up. RESULTS Ninety-seven percent of the original sample were traced. A slightly greater proportion of the Afro-Caribbean people were assigned to the S+ Catego class (schizophrenia), both on first assessment and at follow-up. No difference was found between the two groups in the consistency of diagnosis over the 18 years or in the proportion of patients considered psychotic but Afro-Caribbean people tended to have fewer negative symptoms at follow-up. There were striking differences between the two groups in their experience of psychiatric care; Afro-Caribbean people were more likely to have been readmitted, to have experienced longer hospitalisations, and to have undergone more involuntary admissions than their White counterparts. CONCLUSIONS Afro-Caribbean people who met clinical and research criteria for schizophrenia had a less satisfactory experience of, and response to, psychiatric care over 18 years than their White counterparts.

Comparison of the outcome and treatment of psychosis in people of Caribbean origin living in the UK and British Whites. Report from the UK700 trial.

Abstract: Background The comparative outcome of psychosis in British Whites and UK African—Caribbeans is unclear. Some report that African—Caribbeans have worse outcome, whereas others claim better symptomatic outcome and a more benign course. Aims To compare the course, outcome and treatment of psychosis in African—Caribbeans and British Whites in a large multi-centre sample. Method A secondary analysis of 708 patients with research diagnostic criteria-defined psychosis from a 2-year, randomised controlled trial of case management. Outcome measures (hospitalisation, illness course, self-harm, social disability and treatment received) were adjusted for socio-economic and clinical differences between groups at baseline using regression analysis. Results African—Caribbeans were less likely to have a continuous illness and to receive treatment with antidepressant or psychotherapy. Conclusions The outcome of psychosis is complex but differs between UK African—Caribbeans and British Whites. This may reflect risk factors that increase the rate of psychosis in UK African—Caribbeans. Treatment differences require further investigation.

Auditory P300 in patients with bipolar disorder and their unaffected relatives.

Abstract: Objectives: There is evidence that genetic susceptibility may be shared between bipolar disorder (BD) and schizophrenia, but electrophysiological phenotypes which have been extensively used in studies of genetic susceptibility for schizophrenia remain far less explored in bipolar illness. This study assesses whether auditory P300 latency delays and amplitude reductions, which have been demonstrated in patients with schizophrenia and their unaffected first-degree relatives, are associated with familial liability to psychotic bipolar illness. Methods: The P300 auditory evoked potential was obtained using an oddball task from 37 participants with BD who had a history of psychotic symptoms, 38 of their unaffected first-degree relatives and 42 healthy unrelated comparison subjects. Patients and relatives came from families multiply affected with BD or another functional psychotic disorder. P300 amplitude and latency at midline sites were compared between the groups, using linear regression analyses and robust variance estimators for clustered data, including age and gender as covariates. Results: Bipolar disorder patients with a history of psychosis and their unaffected relatives showed significantly delayed P300 latency at Pz compared to controls. The groups did not differ in P300 amplitude. Conclusions: P300 latency delays are associated with both psychotic BD and familial liability for this illness. Sample size limited our ability to test for multimodal distribution of P300 measures among relatives, which might be expected if only a subgroup inherits any deficits. In future it will be of interest to directly compare groups of families with psychotic and non-psychotic forms of BD to explore further the role of psychotic symptoms with regard to P300 measures in the disorder. Our results indicate that delayed P300 latency is a promising candidate endophenotype for psychotic BD, as well as schizophrenia, and may reflect the impact of shared susceptibility genes for both types of psychosis.

Machine learning

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.).

Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption-II

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.

Human biochemical genetics

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

Complex brain networks: graph theoretical analysis of structural and functional systems

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.

Standards of Medical Care in Diabetes

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.