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.

Full spectrum of psychiatric outcomes among offspring with parental history of mental disorder.

Abstract: Context While concordant parent/offspring risks for specific mental disorders are well established, knowledge of the broader range of psychiatric outcomes among offspring with parental history of mental disorder is lacking. Objective To examine the full range of mental health outcomes among offspring of parents with serious and other mental disorders compared with those whose parents had no such history. Design Population-based cohort study. Offspring were followed up from their 14th birthday for the development of mental disorders based on both outpatient and inpatient hospital data. Setting Danish population. Participants All offspring born in Denmark between 1980 and 1994 (N = 865 078) with follow-up to December 2008. Main Outcome Measures Incidence rates, incidence rate ratios, and cumulative incidences for offspring psychiatric outcomes. Results Parental serious mental disorder (SMD) (nonaffective or affective psychosis) was found to be positively associated with virtually all offspring psychiatric outcomes, including those not hitherto regarded as clinically related. Offspring of parents without SMD but with a history of “other mental disorder” were also found to be at increased risk of developing a range of mental disorders. The strongest associations were found where both parents had a history of mental disorder (eg, offspring of 2 parents with SMD were 13 times more likely to develop schizophrenia). Elevated risks were not confined to concordant parent/offspring disorders (eg, offspring of 2 parents with SMD were 8 times more likely to develop substance misuse disorders). Conclusions The impact of parental history of mental disorder was not confined to elevated offspring risk of concordant disorders but rather offspring are at increased risk of a wide range of mental disorders, particularly those with 2 affected parents. Our results imply an important role for etiological factors giving rise to broad, as well as specific, familial vulnerabilities. These findings also have potential implications for diagnostic classification.

Causal Association Between Cannabis and Psychosis: Examination of the Evidence

Abstract: Background:Controversy remains as to whether cannabis acts as a causal risk factor for schizophrenia or other functional psychotic illnesses. Aims: To examine critically the evidence that cannabis causes psychosis using established criteria of causality. Method: We identified five studies that included a well-defined sample drawn from population-based registers or cohorts and used prospective measures of cannabis use and adult psychosis. Results: On an individual level, cannabis use confers an overall twofold increase in the relative risk for later schizophrenia. At the population level, elimination of cannabis use would reduce the incidence of schizophrenia by approximately 8%, assuming a causal relationship. Cannabis use appears to be neither a sufficient nor a necessary cause for psychosis. It is a component cause, part of a complex constellation of factors leading to psychosis. Conclusions: Cases of psychotic disorder could be prevented by discouraging cannabis use among vulnerable youths. Research is...

Gene-Environment Interplay Between Cannabis and Psychosis

Abstract: Cannabis use is considered a contributory cause of schizophrenia and psychotic illness. However, only a small proportion of cannabis users develop psychosis. This can partly be explained by the amount and duration of the consumption of cannabis and by its strength but also by the age at which individuals are first exposed to cannabis. Genetic factors, in particular, are likely to play a role in the short- and the long-term effects cannabis may have on psychosis outcome. This review will therefore consider the interplay between genes and exposure to cannabis in the development of psychotic symptoms and schizophrenia. Studies using genetic, epidemiological, experimental, and observational techniques will be discussed to investigate gene-environment correlation gene-environment interaction, and higher order interactions within the cannabis-psychosis association. Evidence suggests that mechanisms of gene-environment interaction are likely to underlie the association between cannabis and psychosis. In this respect, multiple variations within multiple genes—rather than single genetic polymorphisms—together with other environmental factors (eg, stress) may interact with cannabis to increase the risk of psychosis. Further research on these higher order interactions is needed to better understand the biological pathway by which cannabis use, in some individuals, may cause psychosis in the short- and long term.

Parental separation, loss and psychosis in different ethnic groups: a case-control study

Abstract: Background. Numerous studies have reported high rates of psychosis in the Black Caribbean and Black African populations in the UK. However, few studies have investigated the role of specific risk factors in different ethnic groups. We sought to investigate the relationship between long-term separation from, and death of, a parent before the age of 16 and risk of adult psychosis in different ethnic groups. Method. All patients with a first episode of psychosis who made contact with psychiatric services in defined catchment areas in London and Nottingham, UK and a series of community controls were included in the AESOP (Aetiology and Ethnicity in Schizophrenia and Other Psychoses) study. Data relating to clinical and social variables, including parental separation and loss, were collected from patients and controls. Results. Separation from, and death of, a parent before the age of 16 were both strongly associated with a two- to threefold increased risk of psychosis. The strength of these associations were similar for White British and Black Caribbean (but not Black African) subjects. Separation from (but not death of) a parent was more common among Black Caribbean controls than White British controls. Conclusions. Early separation may have a greater impact in the Black Caribbean population, because it is more common, and may contribute to the excess of psychosis in this population.

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.