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.

Randomised controlled trials of conventional antipsychotic versus new atypical drugs, and new atypical drugs versus clozapine, in people with schizophrenia responding poorly to, or intolerant of, current drug treatment.

Abstract: Objectives: To determine the clinical and costeffectiveness of different classes of antipsychotic drug treatment in people with schizophrenia responding inadequately to, or having unacceptable side-effects from, their current medication. Design: Two pragmatic, randomised controlled trials (RCTs) were undertaken. The first RCT (band 1) compared the class of older, inexpensive conventional drugs with the class of new atypical drugs in people with schizophrenic disorders, whose current antipsychotic drug treatment was being changed either because of inadequate clinical response or owing to side-effects. The second RCT (band 2) compared the new (non-clozapine) atypical drugs with clozapine in people whose medication was being changed because of poor clinical response to two or more antipsychotic drugs. Both RCTs were four-centre trials with concealed randomisation and three follow-up assessments over 1 year, blind to treatment. Setting: Adult mental health settings in England. Participants: In total, 227 participants aged 18?65 years (40% of the planned sample) were randomised to band 1 and 136 (98% of the planned sample) to band 2. Interventions: Participants were randomised to a class of drug. The managing clinician selected the individual drug within that class, except for the clozapine arm in band 2. The new atypical drugs included risperidone, olanzapine, quetiapine and amisulpride. The conventional drugs included older drugs, including depot preparations. As in routine practice, clinicians and participants were aware of the identity of the prescribed drug, but clinicians were asked to keep their participating patient on the randomised medication for at least the first 12 weeks. If the medication needed to be changed, the clinician was asked to prescribe another drug within the same class, if possible. Main outcome measures: The primary outcome was the Quality of Life Scale (QLS). Secondary clinical outcomes included symptoms [Positive and Negative Syndrome Scale (PANSS)], side-effects and participant satisfaction. Economic outcomes were costs of health and social care and a utility measure. Results: Recruitment to band 1 was less than anticipated (40%) and diminished over the trial. This appeared largely due to loss of perceived clinical equipoise (clinicians progressively becoming more convinced of the superiority of new atypicals). Good follow-up rates and a higher than expected correlation between QLS score at baseline and at follow-up meant that the sample as recruited had 75% power to detect a difference in QLS score of 5 points between the two treatment arms at 52 weeks. The recruitment to band 2 was approximately as planned. Follow-up assessments were completed at week 52 in 81% of band 1 and 87% of band 2 participants. Band 1 data showed that, on the QLS and symptom measures, those participants in the conventional arm tended towards greater improvements. This suggests that the failure to find the predicted advantage for new atypicals was not due to inadequate recruitment and statistical power in this sample. Participants reported no clear preference for either class of drug. There were no statistically significant differential outcomes for participants entering band 1 for reasons of treatment intolerance to those entering because of broadly defined treatment resistance. Net costs over the year varied widely, with a mean of �18,850 in the conventional drug group and �20,123 in the new atypical group, not a statistically significant difference. Of these costs, 2.1% and 3.8% were due to antipsychotic drug costs in the conventional and atypical group, respectively. There was a trend towards participants in the conventional drug group scoring more highly on the utility measure at 1 year. The results for band 2 showed an advantage for commencing clozapine in quality of life (QLS) at trend level (p = 0.08) and in symptoms (PANSS), which was statistically significant (p = 0.01), at 1 year. Clozapine showed approximately a 5-point advantage on PANSS total score and a trend towards having fewer total extrapyramidal side-effects. Participants reported at 12 weeks that their mental health was significantly better with clozapine than with new atypicals (p < 0.05). Net costs of care varied widely, but were higher than in band 1, with a mean of �33,800 in the clozapine group and �28,400 in the new atypical group. Of these costs, 4.0% and 3.3%, respectively, were due to antipsychotic drug costs. The increased costs in the clozapine group appeared to reflect the licensing requirement for inpatient admission for commencing the drug. There was a trend towards higher mean participant utility scores in the clozapine group. Conclusions: For band 1, there is no disadvantage in terms of quality of life and symptoms, or associated costs of care, over 1 year in commencing conventional antipsychotic drugs rather than new atypical drugs. Conventional drugs were associated with nonsignificantly better outcomes and lower costs. Drug costs represented a small proportion of the overall costs of care (<5%). For band 2, there is a statistically significant advantage in terms of symptoms but not quality of life over 1 year in commencing clozapine rather than new atypical drugs, but with increased associated costs of care. The results suggest that conventional antipsychotic drugs, which are substantially cheaper, still have a place in the treatment of patients unresponsive to, or intolerant of, current medication. Further analyses of this data set are planned and further research is recommended into areas such as current antipsychotic treatment guidance, valid measures of utility in serious mental illness, lowdose ?conventional? treatment in first episode schizophrenia, QLS validity and determinants of QLS score in schizophrenia, and into the possible financial and other mechanisms of rewarding clinician participation in trials.

Treated Incidence of Psychotic Disorders in the Multinational EU-GEI Study.

Abstract: The European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) Project is funded by grant agreement HEALTH-F2-2010-241909 (Project EU-GEI) from the European Community’s Seventh Framework Programme. The Brazilian study was funded by grant 2012/0417-0 from the Sao Paulo Research Foundation. Dr Kirkbride is funded by the Wellcome Trust and grant 101272/Z/13/Z from the Royal Society. Ms Jongsma and Dr Jones are funded by the National Institute of Health Research Collaboration of Leadership in Applied Health Research and Care East of England.

A combined analysis of D22S278 marker alleles in affected sib-pairs: Support for a susceptibility locus for schizophrenia at chromosome 22q12

Abstract: Several groups have reported weak evidence for linkage between schizophrenia and genetic markers located on chromosome 22q using the lod score method of analysis. However these findings involved different genetic markers and methods of analysis, and so were not directly comparable. To resolve this issue we have performed a combined analysis of genotypic data from the marker D22S278 in multiply affected schizophrenic families derived from 11 independent research groups worldwide. This marker was chosen because it showed maximum evidence for linkage in three independent datasets (Vallada et al., Am J Med Genet 60:139-146, 1995; Polymeropoulos et al., Neuropsychiatr Genet 54:93-99, 1994; Lasseter et al., Am J Med Genet, 60:172-173, 1995. Using the affected sib-pair method as implemented by the program ESPA, the combined dataset showed 252 alleles shared compared with 188 alleles not share (chi-square 9.31, 1df, P = 0.001) where parental genotype data was completely known. When sib-pairs for whom parental data was assigned according to probability were included the number of alleles shared was 514.1 compared with 437.8 not shared (chi-square 6.12, 1df, P = 0.006). Similar results were obtained when a likelihood ratio method for sib-pair analysis was used. These results indicate that may be a susceptibility locus for schizophrenia at 22q12.

The neural correlates of inner speech and auditory verbal imagery in schizophrenia: relationship to auditory verbal hallucinations.

Abstract: BACKGROUND Auditory verbal hallucinations are thought to arise from the disordered monitoring of inner speech (thinking in words). We tested the hypothesis that a predisposition to verbal auditory hallucinations would be associated with an abnormal pattern of brain activation during tasks which involved the generation and monitoring of inner speech. METHOD The neural correlates of tasks which engaged inner speech and auditory verbal imagery were examined using positron emission tomography in (a) schizophrenic patients with a strong predisposition to auditory verbal hallucinations (hallucinators), (b) schizophrenic patients with no history of hallucinations (nonhallucinators), and (c) normal controls. RESULTS There were few between-group differences in activation during the inner speech task. However, when imagining sentences spoken in another person's voice, which entails the monitoring of inner speech, hallucinators showed reduced activation in the left middle temporal gyrus and the rostral supplementary motor area, regions which were strongly activated by both normal subjects and nonhallucinators (P < 0.001). Conversely, when nonhallucinators imagined speech, they differed from both hallucinators and controls in showing reduced activation in the right parietal operculum. CONCLUSIONS A predisposition to verbal hallucinations in schizophrenia is associated with a failure to activate areas implicated in the normal monitoring of inner speech, whereas the absence of a history of hallucinations may be linked to reduced activation in an area concerned with verbal prosody.

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.