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.

Saccadic distractibility is elevated in schizophrenia patients, but not in their unaffected relatives.

Abstract: Background. Saccadic distractibility, as measured by the antisaccade task, has attracted attentionas a putative endophenotypic marker for schizophrenia. Some studies have suggested that thismeasure is elevated in the unaffected relatives of schizophrenia patients. However, recent studieshave called this into question and the topic remains controversial.Method. Saccadic distractibility was measured in 53 patients with DSM-IV schizophrenia, 80unaffected first-degree relatives and 41 unaffected controls.Results. Schizophrenia patients performed worse than relatives and controls combined (p<0.00001), but relatives did not differ significantly from controls. Performance in multiply affectedfamilies was no worse than that in singly affected families. Relatives with a high presumed geneticrisk for schizophrenia performed no worse than other relatives. The performance of the patientsdid not predict that of their relatives.Conclusions. These results demonstrate that saccadic distractibility is strongly associated withdisease status but not with genetic loading for schizophrenia. We conclude that saccadic distract-ibility is unlikely to be useful as an endophenotypic marker in schizophrenia.INTRODUCTIONFamily, twin and adoption studies have indi-cated that operationally defined schizophreniahas a heritability of over 0.8 (Cardno et al.1999). However, the non-Mendelian segregationwithin families and the disparate linkage find-ings suggest that schizophrenia is a complex,polygenicdisorder(Gottesman&Shields,1982).Genetic transmission in schizophrenia is likelyto be further complicated by epistasis (Wade,2001) (non-additive interactionsbetween genes),pleiotropy (Hodgkin, 1998) (a single gene deter-mining two or more characteristics), incompletepenetrance (Levinson et al. 1996), interactionswith environmental factors (Van Os & Sham,2002), and the probable aetiological hetero-geneity of the clinically defined disorder(Cardno & Gottesman, 2000).One approach to overcoming these problemsis the use of intermediate phenotypes, or bio-logical markers. These are anatomical, physio-logical or biochemical variables that segregatewith genetic risk for the disorder, and whichare assumed to have a simpler genetic architec-ture than the disorder itself (Weinberger, 2002),with a more proximal relationship to theunderlying genes. It is hoped that the use ofbiological markers will identify more homo-geneous groups of subjects within the broaderclinically defined phenotype, and thus lead toimproved success in the search for susceptibilitygenes.The necessary criteria for a biological markerwere set out by Wickham & Murray (1997).

Meta‐analytic approaches to determine gender differences in the age‐incidence characteristics of schizophrenia and related psychoses

Abstract: A recent systematic review and meta-analysis of the incidence and prevalence of schizophrenia and other psychoses in England investigated the variation in the rates of psychotic disorders. However, some of the questions of interest, and the data collected to answer these, could not be adequately addressed using established meta-analysis techniques. We developed a novel statistical method, which makes combined use of fractional polynomials and meta-regression. This was used to quantify the evidence of gender differences and a secondary peak onset in women, where the outcome of interest is the incidence of schizophrenia. Statistically significant and epidemiologically important effects were obtained using our methods. Our analysis is based on data from four studies that provide 50 incidence rates, stratified by age and gender. We describe several variations of our method, in particular those that might be used where more data is available, and provide guidance for assessing the model fit. Copyright © 2013 John Wiley & Sons, Ltd.

Observations on switching patients with schizophrenia to risperidone treatment. Risperidone Switching Study Group.

Abstract: This study examined one possible strategy for switching patients to treatment with risperidone involving immediate cessation of current neuroleptics and gradual withdrawal of anticholinergic treatments. All patients received risperidone monotherapy for at least 4 weeks. Side-effects and symptoms were rated and successful switching was defined as completion of the study with no consistent worsening in any rating scales. Of the 41 patients entered, five withdrew for reasons unconnected with the study. Of the remaining 36 patients, 64% (23 patients) were switched successfully. Overall, the rating scales showed significant improvements (mean score on Krawiecka scale, 11.0 to 6.6, P < 0.001), and side-effects decreased (mean score on Simpson & Angus scale, 5.1 to 2.9, P = 0.004). The strategy appeared to be successful for most patients, especially those who had previously received depot medication. However, more gradual withdrawal of previous treatments, including anticholinergics, may be advisable in some cases.

A single dose of cannabidiol modulates medial temporal and striatal function during fear processing in people at clinical high risk for psychosis.

Abstract: Emotional dysregulation and anxiety are common in people at clinical high risk for psychosis (CHR) and are associated with altered neural responses to emotional stimuli in the striatum and medial temporal lobe. Using a randomised, double-blind, parallel-group design, 33 CHR patients were randomised to a single oral dose of CBD (600 mg) or placebo. Healthy controls (n = 19) were studied under identical conditions but did not receive any drug. Participants were scanned with functional magnetic resonance imaging (fMRI) during a fearful face-processing paradigm. Activation related to the CHR state and to the effects of CBD was examined using a region-of-interest approach. During fear processing, CHR participants receiving placebo (n = 15) showed greater activation than controls (n = 19) in the parahippocampal gyrus but less activation in the striatum. Within these regions, activation in the CHR group that received CBD (n = 15) was intermediate between that of the CHR placebo and control groups. These findings suggest that in CHR patients, CBD modulates brain function in regions implicated in psychosis risk and emotion processing. These findings are similar to those previously evident using a memory paradigm, suggesting that the effects of CBD on medial temporal and striatal function may be task independent.

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.