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.

Daily Use, Especially of High-Potency Cannabis, Drives the Earlier Onset of Psychosis in Cannabis Users.

Abstract: UNLABELLED: Cannabis use is associated with an earlier age of onset of psychosis (AOP). However, the reasons for this remain debated. METHODS: We applied a Cox proportional hazards model to 410 first-episode psychosis patients to investigate the association between gender, patterns of cannabis use, and AOP. RESULTS: Patients with a history of cannabis use presented with their first episode of psychosis at a younger age (mean years = 28.2, SD = 8.0; median years = 27.1) than those who never used cannabis (mean years = 31.4, SD = 9.9; median years = 30.0; hazard ratio [HR] = 1.42; 95% CI: 1.16-1.74; P < .001). This association remained significant after controlling for gender (HR = 1.39; 95% CI: 1.11-1.68; P < .001). Those who had started cannabis at age 15 or younger had an earlier onset of psychosis (mean years = 27.0, SD = 6.2; median years = 26.9) than those who had started after 15 years (mean years = 29.1, SD = 8.5; median years = 27.8; HR = 1.40; 95% CI: 1.06-1.84; P = .050). Importantly, subjects who had been using high-potency cannabis (skunk-type) every day had the earliest onset (mean years = 25.2, SD = 6.3; median years = 24.6) compared to never users among all the groups tested (HR = 1.99; 95% CI: 1.50- 2.65; P < .0001); these daily users of high-potency cannabis had an onset an average of 6 years earlier than that of non-cannabis users. CONCLUSIONS: Daily use, especially of high-potency cannabis, drives the earlier onset of psychosis in cannabis users.

Grey and white matter distribution in very preterm adolescents mediates neurodevelopmental outcome.

Abstract: Very preterm (VPT) birth is associated with altered cortical development and long-term neurodevelopmental sequelae. We used voxel-based morphometry to investigate white (WM) and grey matter (GM) distribution in VPT adolescents and controls, and the association with gestational age and neonatal ultrasound findings in the VPT individuals. GM and WM volumes were additionally investigated in relation to adolescent neurodevelopmental outcome. Structural MRI data were acquired with a 1.5 Tesla machine in 218 VPT adolescents (<33 weeks, gestation) and 128 controls aged 14-15 years, and analysed using SPM2 software. VPT individuals compared to controls showed reduced GM in temporal, frontal, occipital cortices and cerebellum, including putamen, insula, cuneus, fusiform gyrus, thalamus and caudate nucleus, and increased GM predominantly in temporal and frontal lobes, including cingulate and fusiform gyri and cerebellum. WM loss was concentrated in the brainstem, internal capsule, temporal and frontal regions and the major fasciculi. WM excesses were observed in temporal, parietal and frontal regions. Investigation of the inter-relationships between brain regions and changes revealed that all selected areas where between-group increased and decreased WM and GM volumes differences were observed, were structurally associated, highlighting the influence that abnormalities in one brain area may exert over others. VPT individuals with evidence of periventricular haemorrhage and ventricular dilatation on neonatal ultrasound exhibited the greatest WM and GM alterations. VPT adolescents obtained lower scores than controls on measures of language and executive function and were more likely to show cognitive impairment compared to controls (27% versus 14%, respectively). Several areas where VPT individuals demonstrated decreased GM and WM volume were linearly associated with gestational age and mediated cognitive impairment. To summarize, our data demonstrates that VPT birth is associated with altered brain structure in adolescence. GM and WM alterations are associated with length of gestation and mediate adolescent neurodevelopmental impairment. Thus, anatomical brain changes may contribute to specific cognitive deficits associated with VPT birth and could be used in the identification of those individuals who may be at increased risk for cognitive impairment.

Association of genetic risks for schizophrenia and bipolar disorder with specific and generic brain structural endophenotypes

Abstract: Context For more than a century, it has been uncertain whether or not the major diagnostic categories of psychosis—schizophrenia and bipolar disorder—are distinct disease entities with specific genetic causes and neuroanatomical substrates. Objective To investigate the relationship between genetic risk and structural variation throughout the entire brain in patients and their unaffected relatives sampled from multiply affected families with schizophrenia or bipolar disorder. Design Analysis of the association between genetic risk and variation in tissue volume on magnetic resonance images. Setting Psychiatric research center. Participants Subjects comprised 25 patients with schizophrenia, 36 of their unaffected first-degree relatives, 37 patients with bipolar 1 disorder who experienced psychotic symptoms during illness exacerbation, and 50 of their unaffected first-degree relatives. Main Outcome Measures We used computational morphometric techniques to map significant associations between a continuous measure of genetic liability for each subject and variation in gray or white matter volume. Results Genetic risk for schizophrenia was specifically associated with distributed gray matter volume deficits in the bilateral fronto-striato-thalamic and left lateral temporal regions, whereas genetic risk for bipolar disorder was specifically associated with gray matter deficits only in the right anterior cingulate gyrus and ventral striatum. A generic association between genetic risk for both disorders and white matter volume reduction in the left frontal and temporoparietal regions was consistent with left frontotemporal disconnectivity as a genetically controlled brain structural abnormality common to both psychotic disorders. Conclusions Genetic risks for schizophrenia and bipolar disorder are associated with specific gray matter but generic white matter endophenotypes. Thus, Emil Kraepelin’s pivotal distinction was neither wholly right nor wholly wrong: the 2 major psychoses show both distinctive and similar patterns of brain structural abnormality related to variable genetic risk.

Meta-Analysis of the Association of Urbanicity With Schizophrenia

Abstract: The association between urbanicity and risk of schizophrenia is well established. The incidence of schizophrenia has been observed to increase in line with rising levels of urbanicity, as measured in terms of population size or density. This association is expressed as Incidence Rate Ratio (IRR), and the results are usually presented by comparing the most urban with the most rural environment. In this study, we undertook to express the effect of urbanicity on the risk of schizophrenia in a linear form and to perform a meta-analysis of all available evidence. We first employed a simple regression analysis of log (IRR) as given in each study on the urbanicity category, assuming a uniform distribution and a linear association. In order to obtain more accurate estimates, we developed a more sophisticated method that generates individual data points with simulation from the summary data presented in the original studies, and then fits a logistic regression model. The estimates from each study were combined with meta-analysis. Despite the challenges that arise from differences between studies as regards to the number and relative size of urbanicity levels, a linear association was observed between the logarithm of the odds of risk for schizophrenia and urbanicity. The risk for schizophrenia at the most urban environment was estimated to be 2.37 times higher than in the most rural environment. The same effect was found when studies measuring the risk for nonaffective psychosis were included.

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.