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.

Self-harm in first-episode psychosis.

Abstract: Background: Little is known about self-harm occurring during the period of untreated first-episode psychosis. Aims: To establish the prevalence, nature, motivation and risk factors for self-harm occurring during the untreated phase of first-episode psychosis. Method: As part of the AESOP (Aetiology and Ethnicity in Schizophrenia and Other Psychoses) study, episodes of self-harm were identified among all incident cases of psychosis presenting to services in south-east London and Nottingham over a 2-year period. Results: Of the 496 participants, 56 (11.3%) had engaged in self-harm between the onset of psychotic symptoms and first presentation to services. The independent correlates of self-harm were: male gender, belonging to social class I/II, depression and a prolonged period of untreated psychosis. increased insight was also associated with risk of self-harm. Conclusions: Self-harm is common during the pre-treatment phase of first-episode psychosis. A unique set of fixed and malleable risk factors appear to operate in those with first-episode psychosis. Reducing treatment delay and modifying disease attitudes may be key targets for suicide prevention.

Perceptions of disadvantage, ethnicity and psychosis

Abstract: Background: People from Black ethnic groups (African-Caribbean and Black African) are more prone to develop psychosis in Western countries. This excess might be explained by perceptions of disadvantage. Aims: To investigate whether the higher incidence of psychosis in Black people is mediated by perceptions of disadvantage. Method: A population-based incidence and case-control study of first-episode psychosis (Aetiology and Ethnicity in Schizophrenia and Other Psychoses (ASOP)). A total of 482 participants answered questions about perceived disadvantage. Results: Black ethnic groups had a higher incidence of psychosis (OR=4.7, 95% CI 3.1-7.2). After controlling for religious affiliation, social class and unemployment, the association of ethnicity with psychosis was attenuated (OR=3.0, 95% Cl 1.6-5.4) by perceptions of disadvantage. Participants in the Black non-psychosis group often attributed their disadvantage to racism, whereas Black people in the psychosis group attributed it to their own situation. Conclusions: Perceived disadvantage is partly associated with the excess of psychosis among Black people living in the UK. This may have implications for primary prevention.

Catechol-O-methyltransferase Val158Met polymorphism: frequency analysis in Han Chinese subjects and allelic association of the low activity allele with bipolar affective disorder.

Abstract: Catechol-O-methyltransferase catalyses the O-methylation of biologically active or toxic catechols and is a major component of the metabolism of drugs and neurotransmitters such as L-dopa, noradrenaline, adrenaline, and dopamine. Human catechol-O-methyltransferase activity is an autosomal partially dominant trait and is strongly associated with a valine to methionine substitution at codon 158 of the protein. About 25% of Caucasians have low activity, 50% intermediate activity and 25% high activity as determined by either phenotypic or genotypic measurement. In black populations, the low activity allele (Met158; COMTL) is less frequent with about 7% being homozygous. Using a PCR based genotyping assay, we report that the Met158 allele is also less frequent in normal Han Chinese subjects with about 3% of the population being homozygous. Because of its role in catecholamine metabolism and several lines of evidence pointing to a locus for psychosis near the COMT gene on chromosome 22q11, we have analysed the COMT Val158Met polymorphism as a candidate susceptibility factor for bipolar affective disorder. We report an association between bipolar affective disorder and the Met158 allele (p = 0.004) and genotype (p = 0.01) in 93 affected Chinese subjects and 98 controls. We hypothesize that either the low activity allele of catechol-O-methyltransferase is a risk factor for bipolar affective disorder in Chinese populations or is in linkage disequilibrium with a nearby susceptibility gene or polymorphism.

Reliability of brain volumes from multicenter MRI acquisition: A calibration study

Abstract: Multicenter studies can provide additional information over single center studies because of their increased statistical power. Because similar acquisition protocols are being used internationally for structural magnetic resonance imaging (MRI) studies of the human brain, volumetric MRI data studies seem suitable for this purpose. Possible systematic differences between sites should be avoided, however, particularly when subtle differences in tissue volume are being searched for, such as in neuropsychiatric diseases. In this calibration study, the brains of six healthy volunteers were (re)scanned with MR scanners from four different manufacturers at five different sites, using the local acquisition protocols. The images were segmented at a central reference site. The intraclass correlation coefficient (ICC) was determined for the whole brain, gray and white matter, cerebellum, and lateral and third ventricle volumes. When required, the processing algorithms were calibrated for each site. Calibration of the histogram analysis was needed for segmentation of total brain volume at one site and for gray and white matter volume at all sites. No (additional) calibration was needed for cerebellum and ventricle volumes. The ICCs were ≥0.96 for total brain, ≥0.92 for cerebellum, ≥0.96 for lateral ventricle, ≥0.21 for third ventricle, ≥0.84 for gray matter, and ≥0.78 for white matter volume. Calibration of segmentation procedures allows morphologic MRI data acquired at different research sites to be combined reliably in multicenter studies. Hum. Brain Mapping 22:312–320, 2004. © 2004 Wiley-Liss, Inc.

Obstetric complications and schizophrenia: a computed tomographic study

Abstract: All patients aged 16-50 years who had been discharged from the Maudsley Hospital over a 4-year period with a diagnosis of schizophrenia were ascertained retrospectively. Case records were rated blindly for a history of obstetric complications (OCs). Sixty-one patients who satisfied the RDC for schizophrenia had undergone CT scanning. Those with a definite history of OCs presented at an earlier age. In this group widening of cortical sulci and fissures was more strongly correlated with VBR than in subjects without OCs. Moreover, large VBRs when accompanied by widened cortical sulci and fissures occurred more commonly in subjects with OCs than in those without. These results support the hypothesis that OCs, or a factor associated with them, lead to brain damage of aetiological significance in some cases of schizophrenia.

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.