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.

Attenuated Frontal Activation During a Verbal Fluency Task in Patients With Schizophrenia

Abstract: OBJECTIVE: Functional magnetic resonance imaging was used to study changes in cerebral blood oxygenation in schizophrenic patients during a verbal fluency task. METHOD: Five right-handed male schizophrenic patients and five volunteers matched on demographic variables and verbal fluency performance participated in the study. Echoplanar images were acquired over 5 minutes at 1.5 T while the subjects performed two tasks. The first involved paced silent generation of words beginning with an aurally presented cue letter. This task alternated with paced silent repetition of the aurally presented word “rest.” Generic brain activation maps were constructed from individual images by sinusoidal regression and nonparametric hypothesis testing. Between-group differences in the mean power of experimental response were identified on a voxelwise basis by an analysis of covariance that controlled for between-group differences in stimulus-correlated motion. RESULTS: The comparison group showed significant responses in the...

Twin concordance for operationally defined schizophrenia. Confirmation of familiality and heritability.

Abstract: Six sets of operational criteria for diagnosing schizophrenia were applied to a systematically ascertained twin series by raters who were blind to zygosity and to the psychiatric status of the co-twin. Assuming a multifactorial/threshold model of transmission, twin correlations in liability and, where possible, approximate broad heritabilities were calculated for each criterion. All definitions resulted in significant monozygotic twin correlations. The highest heritabilities (of approximately 0.8) were given by the Research Diagnostic Criteria and by the categories "probable" plus "definite" schizophrenia according to the criteria of Feighner et al. In contrast, Schneider's first-rank symptoms defined a form of schizophrenia with a heritability of 0 and, together with the criteria of Carpenter et al and Taylor et al, proved to be excessively restrictive, identifying fewer than half of the probands as schizophrenic.

Stress and inflammation reduce brain-derived neurotrophic factor expression in first-episode psychosis: a pathway to smaller hippocampal volume.

Abstract: Background Reduced brain-derived neurotrophic factor (BDNF) levels have been reported in the serum and plasma of patients with psychosis. The aim of this study was to investigate potential causes and consequences of reduced BDNF expression in these patients, by examining the association between BDNF levels and measures of stress, inflammation and hippocampal volume in first-episode psychosis.

School performance in Finnish children and later development of schizophrenia: a population-based longitudinal study.

Abstract: Background We examined whether children who are diagnosed as having schizophrenia in adulthood could be distinguished from their peers on performance in elementary school. Methods We used a case-control study design nested within a population-based birth cohort of all individuals born in Helsinki, Finland, between January 1, 1951, and December 31, 1960. Case ascertainment was from 3 national health care registers. Elementary school records were obtained for 400 children who were diagnosed as having schizophrenia in adulthood and for 408 controls. Results were analyzed for the 4 years of schooling (ages 7-11 years) that were common to all pupils. School subjects were entered into a principal components analysis and produced 3 factors: academic, nonacademic, and behavioral. These factors were compared between cases and controls after adjusting for sex and social group. Eligibility for high school and progression to high school were investigated among cases and controls. Results Cases performed significantly worse than controls only on the nonacademic factor (which loaded sports and handicrafts). There were no differences between the groups on the academic or behavioral factors, and there were no significant clinical correlates of factor scores. Cases were significantly less likely than controls to progress to high school, despite similar eligibility. Conclusions Poor performance in sports and handicrafts during elementary school, which may indicate a motor coordination deficit, appears to be a risk factor for later schizophrenia. Poor academic performance in elementary school was not a risk factor for schizophrenia in this study. Lack of expected progression to high school among cases, despite good academic grades, provides evidence for deteriorating premorbid functional adjustment in schizophrenia.

Progressive increase in striatal dopamine synthesis capacity as patients develop psychosis: a PET study.

Abstract: Progressive increase in striatal dopamine synthesis capacity as patients develop psychosis: a PET study

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.