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.

The serotonin transporter is a potential susceptibility factor for bipolar affective disorder

Abstract: The serotonin transporter is a strong candidate for aetiological involvement in affective disorders and psychosis. We analysed a VNTR in intron 2 of the human serotonin transporter gene (hSERT) for allelic association with bipolar affective disorder, unipolar depression and schizophrenia. An increased frequency of allele 12 of the VNTR was observed in subjects with bipolar affective disorder (n = 191; chi 2 p = 0.00048 by allele) but not unipolar depression (n = 86; chi 2 p = 0.18, ns) or schizophrenia (n = 129; chi 2 p = 0.08, ns), although a trend towards an excess of allele 12 was observed for the latter. There was also a significant difference in the frequency of allele 12 between bipolar affective disorder and unipolar depression (p = 0.0087). The relative risk for bipolar affective disorder with respect to allele 12 was 1.84 (95% CI 0.97-3.56) for heterozygotes, and 3.10 (95% CI 1.60-6.07) for homozygotes, with evidence for a gene-dosage effect. Because allele 12 is common in the population, the attributable risk is 50.8% (95% CI 14.5%-73.3%). We hypothesize that either the VNTR affects regulation of expression of hSERT at the transcriptional level or it is in linkage disequilibrium with another functional polymorphism in the gene, and this results in an increased risk for the development of bipolar affective disorder.

Volumetric MRI measurements in bipolars compared with schizophrenics and healthy controls.

Abstract: Twenty-six patients with RDC bipolar disorder were compared with a previously reported group of 48 RDC schizophrenics and 34 healthy controls, using volumetric MRI measurements of cerebral, cortical and sulcal volumes. The bipolar group appeared no different from the controls, and both of these groups had significantly larger cerebral and cortical volumes than the schizophrenics. Our previous report of a significantly reduced cortical volume in the schizophrenic group, with a corresponding increase in the volume of sulcal fluid is, therefore, not a generalized feature of psychotic illness but may be more specific to schizophrenia.

Gender differences in the association between childhood abuse and psychosis.

Abstract: Background Studies demonstrating an association between childhood trauma and psychosis in adulthood have not systematically explored gender differences. Aims To investigate gender differences in the prevalence of childhood sexual and physical abuse among people with psychosis in comparison with healthy controls. Method The Childhood Experiences of Care and Abuse Questionnaire was completed to elicit experiences of sexual and physical abuse during childhood in first-episode psychosis cases and population-based controls. Results Among women, those in the cases group were twice as likely to report either physical or sexual abuse compared with controls following adjustment for all confounders. In particular, the effect of physical abuse in women was stronger and more robust than that for sexual abuse. A similar trend was found for psychotic-like experiences in the female control group. No association was found in men. Conclusions Reports of severe childhood physical or sexual abuse were associated with psychosis in women but not in men.

Pathways to schizophrenia: the impact of environmental factors.

Abstract: Schizophrenia is an aetiologically complex disorder arising from the interaction of a range of factors acting at various stages of life. Schizophrenic individuals inherit genes that cause structural brain deviations which may be compounded by early environmental insults. As a result some pre-schizophrenic children exhibit subtle developmental delays, cognitive problems, or poor interpersonal relationships. They are susceptible to dysregulation of dopamine, the final pathway leading to the onset of a psychotic illness. Dopamine dysregulation may arise through a process of sensitization, which, in animals, can be caused by repeated administration of dopamine-releasing drugs. It is clear that the same process occurs in humans, and that some individuals are particularly sensitive to the effects of such drugs for either genetic reasons or through early environmental damage. Stress has also been shown to induce dopamine release in animal studies, and epidemiological studies have demonstrated that social stresses can precipitate schizophrenia. Thus, stresses, such as drug use and social adversity, in adolescence or early adult life may propel the neurodevelopmentally impaired individual over a threshold into frank psychosis.

Cumulative social disadvantage, ethnicity and first-episode psychosis: a case-control study

Abstract: Background. Numerous studies have reported high rates of psychosis in the Black Caribbean population in the UK. Recent speculation about the reasons for these high rates has focused on social factors. However, there have been few empirical Studies. We sought to compare the prevalence of specific indicators of social disadvantage and isolation, and variations by ethnicity, in subjects with a first episode of psychosis and a series of healthy controls. Method. All cases with a first episode of psychosis who made contact with psychiatric services in defined catchment areas in London and Nottingham, UK and a series of community controls were recruited over a 3-year period. Data g to clinical and social variables were collected from cases and controls. Results. On all indicators, cases were more socially, disadvantaged and isolated than controls, after controlling for potential confounders. These associations held when the sample was restricted to those with in affective diagnosis and to those with a short prodrome and short duration Of untreated psychosis. There was a clear linear relationship between concentrated disadvantage and odds of psychosis. Similar patterns were evident in the two main ethnic groups, White British and Black Caribbean. However, indicators of social disadvantage and isolation were more common in Black Caribbean Subjects than White British subjects. Conclusions. We found strong associations between indicators of disadvantage and psychosis. If these variables index exposure to factors that increase risk of psychosis, their greater prevalence in the Black Caribbean Population may contribute to the reported high rates of psychosis in this population.

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.