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.

'At risk mental state' clinics for psychosis - an idea whose time has come - and gone!

Abstract: At Risk Mental State (ARMS) clinics are specialised mental health services for young, help-seeking people, thought to be at ultra-high risk of developing psychosis. Their stated purpose is to reduce transitions from the ARMS state to clinical psychotic disorder. Reports of ARMS clinics provide 'evidence-based recommendations' or 'guidance' for the treatment of such individuals, and claim that such clinics prevent the development of psychosis. However, we note that in an area with a very well-developed ARMS clinic (South London), only a very small proportion (4%) of patients with first episode psychosis had previously been seen at this clinic with symptoms of the ARMS. We conclude that the task of reaching sufficient people to make a major contribution to the prevention of psychosis is beyond the power of ARMS clinics. Following the preventative approaches used for many medical disorders (e.g. lung cancer, coronary artery disease), we consider that a more effective way of preventing psychosis will be to adopt a public health approach; this should attempt to decrease exposure to environmental factors such as cannabis use which are known to increase risk of the disorder.

Minor physical anomalies in familial and sporadic schizophrenia: the Maudsley family study.

Abstract: OBJECTIVES—(1) To test the hypothesis that minor physical anomalies are increased in patients with schizophrenia and (2) to investigate differences in the prevalence of minor physical anomalies in patients with familial and sporadic schizophrenia and their first degree relatives. METHODS—A weighted Waldrop assessment was carried out on 214 subjects in five groups: schizophrenic patients from multiply affected families; first degree relatives of these familial schizophrenic patients; sporadic schizophrenic patients; first degree relatives of these sporadic schizophrenic patients, and normal controls. Broad and narrow criteria for abnormality were defined based on the distribution of minor physical anomalies in the control group. RESULTS—(1) The total schizophrenic group did not have a significant increase in minor physical anomalies using a narrow criterion of abnormality, but did when a broader criterion was used. (2) A significant increase in the proportion of subjects with an abnormally high number of minor physical abnormalities was shown in the group of sporadic schizophrenic patients (uncorrected p<0.01). Separate analyses for males and females showed a significant increase in the male sporadic group (uncorrected p<0.05), and a smaller non-significant increase in the female sporadic group. Neither the familial schizophrenic group nor either group of first degree relatives showed any significant increases in the proportion of patients with high abnormality scores. CONCLUSION—This work supports prenatal developmental abnormality as a mechanism for sporadic, but not familial, schizophrenia.

A linkage study of schizophrenia with DNA markers from the long arm of chromosome 11

Abstract: We report the results of a collaborative linkage study using 12 polymorphic markers (9 loci) from the long arm of chromosome 11, and 24 families multiply affected with schizophrenia and other closely related disorders. This region is of interest because several families have been reported in which balanced translocations involving 11q apparently co-segregate with psychotic illness. In addition, the dopamine D2 receptor, porphobilinogen deaminase, and tyrosinase genes map within the region studied and may be aetiologically involved in schizophrenia. We have primarily analysed genotypic data by the LOD score method using a range of single gene models. In order to minimize error due to mis-specification of genetic parameters we have analysed data from markers at candidate gene loci by the non-parametric extended sib-pair method in addition to the LOD score method. Our results suggest that most of the region can be excluded from containing a gene of major effect in the aetiology of this disease.

Genetics of platelet MAO activity in discordant schizophrenic and normal twins.

Abstract: Platelet monoamine oxidase (MAO) activity was compared in four age and sex-matched groups: monozygotic (MZ) twins discordant for schizophrenia, normal MZ twins, normal dizygotic (DZ) twins and unrelated individuals. Among the twin groups, schizophrenic and normal there was a remarkably consistent degree of genetic control amounting to 70-80 per cent of the variation in activity. The mean platelet MAO activity of the schizophrenic twins was significantly lower than that of controls, but not than that of their psychiatrically well, neuroleptic-free cotwins; indeed the correlation for the MZ twins discordant for schizophrenia was almost exactly the same as that for the normal MZs. Thus, lower platelet MAO activity in schizophrenia, where it is found, is genetically modulated and not the result of the illness or its treatment.

Does recurrent depression lead to a change in neuroticism

Abstract: The hypothesis that recurrent or chronic depressive illness produces a long-term change in neuroticism was examined in a sample (N = 34) from a consecutive series of 89 depressed patients admitted to the Maudsley Hospital in 1965/6. The Eysenck Personality Inventory (EPI) was administered at the time of the index illness both when the patients were depressed and on recovery, and then again at follow-up 18 years later. The change in the neuroticism (N) score over the 18-year-period was compared in good and poor outcome groups defined variously by a global rating of outcome, frequency of episodes, extent of subsequent hospitalization and the presence or absence of subsequent chronicity. The mean N score for the sample as a whole did not change significantly over the 18 years, and no differential change in the N score was observed between any of the good and poor outcome groups. Thus, the hypothesis was not supported.

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.