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.

A quantitative association study between schizotypal traits and COMT, PRODH and BDNF genes in a healthy Chinese population

Abstract: Previous studies have suggested that catechol- O -methyltransferase (COMT), proline dehydrogenase (PRODH), and brain-derived neurotrophic factor (BDNF) genes are possible susceptibility genes for schizophrenia. We hypothesized that these genes are also associated with schizotypal traits, which are heritable and related to schizophrenia. We genotyped five single nucleotide polymorphism (SNPs) from the COMT, PRODH and BDNF genes, and performed a series of association analyses between alleles, genotypes or haplotypes, and quantitative schizotypal trait scores derived from the Schizotypal Personality Questionnaire (SPQ), in 465 Chinese healthy subjects. We found that ‘years of education' was a major influence on seven out of nine schizotypal components, three schizotypal factors and the total SPQ scores. Molecular genetic analysis of COMT, PRODH and BDNF genes showed no significant effects of any variants on schizotypal components or factors of SPQ after correction for multiple testing, although there were weak association between COMT Val158Met (rs4680G/A) and the odd speech subscale (allele-wise, P =0.04; genotype-wise, P =0.049), between COMT Val158Met (rs4680G/A) and the suspiciousness subscale (genotype-wise, P =0.024), and between BDNF Val66Met and the Factor 2 interpersonal measure (genotype-wise, P =0.027) before correction. Furthermore, we found SNP Val158Met (rs4680) of the COMT gene significantly influenced the scores of some of schizotypal traits including total SPQ score, the disorganization factor and the constricted affect subscale in male subjects only. However, the effect was in the opposite direction of an earlier association with the SPQ reported by Avramopoulos et al. [Avramopoulos, D., Stefanis, N.C., Hantoumi, I., Smyrnis, N., Evdokimidis, I., Stefanis, C.N., 2002. Higher scores of self reported schizotypy in healthy young males carrying the COMT high activity allele. Molecular Psychiatry 7, 706–711]. We conclude that SNP Val158Met (rs4680) in the COMT gene may be associated with some schizotypal traits in male subjects, but our results are not conclusive.

Mortality in first-contact psychosis patients in the UK: a cohort study

Abstract: BACKGROUND The excess mortality following first-contact psychosis is well recognized. However, the causes of death in a complete incidence cohort and mortality patterns over time compared with the general population are unknown. METHOD All 2723 patients who presented for the first time with psychosis in three defined catchment areas of the U.K. in London (1965-2004, n=2056), Nottingham (1997-1999, n=203) and Dumfries and Galloway (1979-1998, n=464) were traced after a mean of 11.5 years follow-up and death certificates were obtained. Data analysis was by indirect standardization. RESULTS The overall standardized mortality ratio (SMR) for first-contact psychosis was 184 [95% confidence interval (CI) 167-202]. Most deaths (84.2%, 374/444) were from natural causes, although suicide had the highest SMR (1165, 95% CI 873-1524). Diseases of the respiratory system and infectious diseases had the highest SMR of the natural causes of death (232, 95% CI 183-291). The risk of death from diseases of the circulatory system was also elevated compared with the general population (SMR 139, 95% CI 117-164) whereas there was no such difference for neoplasms (SMR 111, 95% CI 86-141). There was strong evidence that the mortality gap compared with the general population for all causes of death (p<0.001) and all natural causes (p=0.01) increased over the four decades of the study. There was weak evidence that cardiovascular deaths may be increasing relative to the general population (p=0.07). CONCLUSIONS People with first-contact psychosis have an overall mortality risk that is nearly double that of the general population. Most excess deaths are from natural causes. The widening of the mortality gap over the last four decades should be of concern to all clinicians involved in delivering healthcare.

Fetal Brain Development and Later Schizophrenia

Abstract: Computed tomography and magnetic resonance imaging studies have shown cerebral ventricular enlargement and a decreased volume of temporal lobe structures in a proportion of schizophrenic patients Neuropathological investigations confirm these findings and also show diminished volume of the hippocampus and abnormal pre-alpha cell clusters in the parahippocampal gyrus Compared with controls, schizophrenic patients are more likely to have minor physical anomalies, to have a history of obstetric complications, and to have been born in the late winter Together the evidence regarding structural brain abnormalities and epidemiology suggests that a significant proportion of cases of schizophrenia have their origins in fetal or neonatal life The mechanisms involved in the aberrant neurodevelopment remain obscure but some impairment of neuronal migration is an appealing hypothesis

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.