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.

Genetic overlap between bipolar illness and event-related potentials.

Abstract: Background. Electrophysiological endophenotypes are far less explored in bipolar disorder as compared to schizophrenia. No previous twin study of event-related potentials (ERPs) in bipolar illness has been reported. This study uses a twin design and advanced genetic model fitting analyses aiming to (1) assess and quantify the relationship of a range of ERP components with bipolar disorder with psychotic features, and (2) examine the source of the relationship (due to genetic or environmental factors). Method. P300, P50 suppression and mismatch negativity (MMN) were recorded in 10 discordant monozygotic (MZ) bipolar twin pairs, six concordant MZ bipolar twin pairs and 78 control twin pairs. Statistical analyses were based on structural equation modelling. Results. Bipolar disorder was significantly associated with smaller P300 amplitude and decreased P50 suppression. Genetic correlations were the main source of the associations, estimated to be x0 . 33 for P300 amplitude and 0 . 46 for P50 ratio. Individual-specific environmental influences were not significant. MMN and P300 latency were not associated with the illness. Conclusions. The results provide supporting evidence that P300 amplitude and P50 suppression ratio are ERP endophenotypes for bipolar disorder.

Evidence for association between novel polymorphisms in the PRODH gene and schizophrenia in a Chinese population.

Abstract: Haploinsufficiency for or mutation in at least one gene from the velocardiofacial syndrome (VCFS) region at chromosome 22q11 is implicated in psychosis. Linkage disequilibrium mapping of the region in patients identified a segment containing two genes, proline dehydrogenase (PRODH) and DGCR6, as candidates [Liu et al., 2002a] and by analysis of additional polymorphisms the PRODH gene was associated with schizophrenia in adult and early onset patients. In the present study we provide additional evidence in support of genetic association between PRODH and schizophrenia in a Chinese population. We analyzed the PRODH gene in a samples of schizophrenic patients and their families from Sichuan, SW China consisting of 528 family trios and sibling pairs. We genotyped six SNPs, PRODH*1195C T, PRODH*1482C T, PRODH*1483A G, PRODH*1766A G, PRODH*1852G A PRODH*1945T C, two of which (PRODH*1483A G and PRODH*1852G A) have not been previously reported. We found association with schizophrenia for two haplotypes consisting of PRODH*1945T C and PRODH*1852G A (Global P = 0.006), and PRODH*1852G A and PRODH*1766A G (Global P = 0.01) which include one of the newly identified markers. After six-fold Bonferroni correction for multiple testing the PRODH*1945T-C/PRODH*1852G-A haplotypes remained significant. This is a sub-haplotype of the PRODH haplotype previously associated with schizophrenia and it also maps to the 3′ region of the gene, indicating that this is the region most likely to contain the underlying risk alleles. Overall this finding supports a role for the PRODH locus in schizophrenia. © 2004 Wiley-Liss, Inc.

Persecutory delusions and the determination of self-relevance: an fMRI investigation.

Abstract: Background People with persecutory delusions regard ambiguous data in the social domain as self-relevant and selectively attend to threatening information. This study aimed to characterize these social cognitive biases in functional neuroanatomical terms. Method Eight schizophrenic patients with active persecutory delusions and eight matched normal controls underwent functional magnetic resonance imaging while determining the self-relevance of ambiguous self-relevant or unambiguous other-relevant neutral and threatening statements. Results In determining self-relevance, the deluded subjects showed a marked absence of rostral-ventral anterior cingulate activation together with increased posterior cingulate gyrus activation in comparison to the normal subjects. The influence of threat on self-relevance determination did not yield statistically significant differences between deluded and normal subjects. Conclusions Abnormalities of cingulate gyrus activation while determining self-relevance suggest impaired self-reflection in the persecutory deluded state. This may contribute to persecutory belief formation and maintenance.

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.