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.

Combining dimensional and categorical representation of psychosis: the way forward for DSM-V and ICD-11?

Abstract: Background - There is good evidence that psychotic symptoms segregate into symptom dimensions. However, it is still unclear how these dimensions are associated with risk indicators and other clinical variables, and whether they have advantages over categorical diagnosis in clinical practice. We investigated symptom dimensions in a first-onset psychosis sample and examined their associations with risk indicators and clinical variables. We then examined the relationship of categorical diagnoses to the same variables. Method - We recruited 536 patients as part of a population-based, incidence study of psychosis. Psychopathology was assessed using the Schedules for Clinical Assessment in Neuropsychiatry (SCAN). A principal axis factor analysis was performed on symptom scores. The relationship of dimension scores with risk indicators and with clinical variables was then examined employing regression analyses. Finally, regression models were compared to assess the contribution of dimensions versus diagnosis in explaining these variables. Results - Factor analysis gave rise to a five-factor solution of manic, reality distortion, negative, depressive and disorganization symptom dimensions. The scores of identified dimensions were differentially associated with specific variables. The manic dimension had the highest number of significant associations; strong correlations were observed with shorter duration of untreated psychosis, acute mode of onset and compulsory admission. Adding dimensional scores to diagnostic categories significantly increased the amount of variability explained in predicting these variables; the reverse was also true but to a lesser extent. Conclusions - Categorical and dimensional representations of psychosis are complementary. Using both appears to be a promising strategy in conceptualising psychotic illnesses.

Genetic association of the HLA DRB1 gene locus on chromosome 6p21.3 with schizophrenia.

Abstract: Objective : The authors investigated the human leukocyte antigens (HLA) DRB1*04 gene in schizophrenic patients because it is positively associated with rheumatoid arthritis, an autoimmune disease that exhibits a strong negative association with schizophrenia. The HLA DQB1*0602 allele was also studied because of previous reports of genetic association between it and schizophrenia. Maternal HLA was investigated because of the reported association between prenatal influenza and schizophrenia and the central role of HLA molecules in the immune response to viral infections. Method : Polymerase chain reactions and sequence-specific oligonucleotide probes were used to genotype 94 unrelated patients with DSM-III-R schizophrenia, 92 mothers of schizophrenic offspring who were not related either to each other or to the 94 patients, and 177 healthy comparison subjects. Results : The frequency of DRB1*04 alleles was significantly lower in both the schizophrenic patients and the unrelated mothers of schizophrenic offspring than in the healthy comparison subjects. No significant differences were found for DQB1*0602. Conclusions : DRB1*04 alleles may partially account for the genetic predisposition to schizophrenia. The association reported here may be explained by genetic linkage or by an autoimmune pathophysiology for a proportion of schizophrenia cases. Alternatively, it may be that maternal B lymphocytes that do not express the DR4 antigen encoded by DRB1*04 respond to influenza virus by producing antibodies that perturb neurodevelopment, thus underpinning a proportion of schizophrenia cases.

Violence in women with psychosis in the community: prospective study.

Abstract: Background Little is known about the determinants of violence in women with psychosis. Aims To identify predictors of violence in a community sample of women with chronic psychosis. Method The 2-year prevalence of physical assault was estimated for a sample of 304 women with psychosis. Baseline socio-demographic and clinical factors were used to identify predictors of assault. Results The 2-year prevalence of assault in the sample was 17%. Assaultive behaviour was associated with previous violence (OR=5.87,95% CI 2.42–14.25), non-violentconvictions (OR=2.63,95% CI 1.17–5.93), victimisation (OR=2.46, 95% CI1.02–5.93), African–Caribbean ethnicity (OR=2.24,95% CI1.02–4.77), cluster B personality disorder (OR=2.66, 95% CI1.11–6.38) and high levels of unmet need (OR=1.17,95% CI1.01–1.35). An interaction between African–Caribbean ethnicity and cluster B personality disorder was identified in relation to violent outcome. Violent women were found to be more costly to services. Conclusions Nearly a fifth of community-dwelling women with chronic psychosis committed assault over a period of 2 years. Six independent risk factors were found to predict violence.

Association analysis of the dopamine D4 gene exon III VNTR and heroin abuse in Chinese subjects.

Abstract: Although social and cultural influences are clearly important, family, twin and adoption studies indicate that genes contribute significantly to substance abuse. Substance abuse is associated with novelty seeking, a heritable human personality trait which may be influenced by alleles of the dopamine D4 (DRD4) gene exon III VNTR. Consequently Kotler et al analysed the DRD4 VNTR in opiate-dependent subjects from Israel, and found a significant excess of the 7-repeat allele. We have attempted to replicate this finding using a Han Chinese case-control sample of 121 heroin-dependent subjects and 154 normal controls. We found two 7-repeat alleles which occurred exclusively in the patient group, and overall there was an excess of longer alleles, which did not reach significance (chi 2 = 7.04; P = 0.07). When the D4 VNTR was divided into 'long' (5-7 repeats) and 'short' (2-4 repeats), a significant excess of long alleles was observed in the patient group (P = 0.023, one-tailed), with an odds ratio of 2.30 (95% CI 1.07-4.93). We conclude that our findings support the hypothesis that alleles of the DRD4 exon III VNTR are susceptibility factors for heroin abuse.

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.