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.

Genome‐wide significant schizophrenia risk variation on chromosome 10q24 is associated with altered cis‐regulation of BORCS7, AS3MT, and NT5C2 in the human brain

Abstract: Chromosome 10q24.32-q24.33 is one of the most robustly supported risk loci to emerge from genome-wide association studies (GWAS) of schizophrenia. However, extensive linkage disequilibrium makes it difficult to distinguish the actual susceptibility gene(s) at the locus, limiting its value for improving biological understanding of the condition. In the absence of coding changes that can account for the association, risk is likely conferred by altered regulation of one or more genes in the region. We, therefore, used highly sensitive measures of allele-specific expression to assess cis-regulatory effects associated with the two best-supported schizophrenia risk variants (SNP rs11191419 and indel ch10_104957618_I/rs202213518) on the primary positional candidates BORCS7, AS3MT, CNNM2, and NT5C2 in the human brain. Heterozygosity at rs11191419 was associated with increased allelic expression of BORCS7 and AS3MT in the fetal and adult brain, and with reduced allelic expression of NT5C2 in the adult brain. Heterozygosity at ch10_104957618_I was associated with reduced allelic expression of NT5C2 in both the fetal and adult brain. Comparisons between cDNA ratios in heterozygotes and homozygotes for the risk alleles indicated that cis-effects on NT5C2 expression in the adult dorsolateral prefrontal cortex could be largely accounted for by genotype at these two risk variants. While not excluding effects on other genes in the region, this study implicates altered neural expression of BORCS7, AS3MT, and NT5C2 in susceptibility to schizophrenia arising from genetic variation at the chromosome 10q24 locus. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

Neonatal Ultrasound Results Following Very Preterm Birth Predict Adolescent Behavioral and Cognitive Outcome

Abstract: This study investigated the association between different neonatal ultrasonographic classifications and adolescent cognitive, educational, and behavioral outcomes following very preterm birth. Participants included a group of 120 adolescents who were born very preterm (<33 weeks of gestation), subdivided into three groups according to their neonatal cerebral ultrasound (US) classifications: (a) normal (N = 69), (b) periventricular hemorrhage (PVH, N = 37), and (c) PVH with ventricular dilatation (PVH + DIL, N = 14), and 50 controls. The cognitive functions assessed were full-scale IQ, phonological and semantic verbal fluency, and visual-motor integration. Educational outcomes included reading and spelling; behavioral outcomes were assessed with the Rutter Parents' Scale and the Premorbid Adjustment Scale (PAS). Adolescent outcome scores were compared among the four groups. A main effect for group was observed for full-scale IQ, Rutter Parents' Scale total scores, and PAS total scores, after controlling for gestational age, socioeconomic status and gender, with the PVH + DIL group showing the most impaired scores compared to the other groups. The current results demonstrate that routine neonatal ultrasound classifications are associated with later cognitive and behavioral outcome. Neonatal ultrasounds could aid in the identification of subgroups of children who are at increased risk of neurodevelopmental problems. These at risk subgroups could then be referred to appropriate early intervention services.

Characteristics and prognosis of alcoholic doctors.

Abstract: Five medically qualified women and 36 men who were being treated for alcoholism at a London postgraduate hospital were studied. Most were middle-aged and at an advanced stage of alcoholism. They had usually started drinking heavily in the wake of well-established drug dependence or other psychiatric disorder; as students or housemen; and in the armed forces. Thirty-six doctors were followed up for a mean of 63 months. Five doctors either killed themselves or died of cirrhosis, and nine persisted in almost continuous dependent drinking, while seven completely overcame their alcohol problem and 10 had only occasional relapses. Their prealcoholic careers had ranged from repeated failure to spectacular success, but of 29 doctors alive at follow-up only eight were practising satisfactorily.

A study of psychiatrists' concepts of mental illness.

Abstract: Background: There are multiple models of mental illness that inform professional and lay understanding. Few studies have formally investigated psychiatrists' attitudes. We aimed to measure how a group of trainee psychiatrists understand familiar mental illnesses in terms of propositions drawn from different models. Method: We used a questionnaire study of a sample of trainees from South London and Maudsley National Health Service (NHS) Foundation Trust designed to assess attitudes across eight models of mental illness (e.g. biological, psychodynamic) and four psychiatric disorders. Methods for analysing repeated measures and a principal components analysis (PCA) were used. Results: No one model was endorsed by all respondents. Model endorsement varied with disorder. Attitudes to schizophrenia were expressed with the greatest conviction across models. Overall, the ‘biological’ model was the most strongly endorsed. The first three components of the PCA (interpreted as dimensions around which psychiatrists, as a group, understand mental illness) accounted for 56% of the variance. Each main component was classified in terms of its distinctive combination of statements from different models: PC1 33% biological versus non-biological; PC2 12% ‘eclectic’ (combining biological, behavioural, cognitive and spiritual models); and PC3 10% psychodynamic versus sociological. Conclusions: Trainee psychiatrists are most committed to the biological model for schizophrenia, but in general are not exclusively committed to any one model. As a group, they organize their attitudes towards mental illness in terms of a biological/non-biological contrast, an ‘eclectic’ view and a psychodynamic/sociological contrast. Better understanding of how professional group membership influences attitudes may facilitate better multidisciplinary working.

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.