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.

Meta-analysis of Cognitive Deficits in Ultra-high Risk to Psychosis and First-Episode Psychosis: Do the Cognitive Deficits Progress Over, or After, the Onset of Psychosis?

Abstract: Cognitive dysfunction is a well-established feature of schizophrenia, and there is evidence suggesting that cognitive deficits are secondary to abnormal neurodevelopment leading to problems in acquiring such abilities. However, it is not clear whether there is also a decline in cognitive performance over, or after, the onset of psychosis. Our objective was to quantitatively examine the longitudinal changes in cognitive function in patients who presented with first-episode psychosis (FEP), ultra-high risk (UHR) for psychosis, and controls. Electronic databases were searched for the studies published between January 1987 and February 2013. All studies reporting longitudinal cognitive data in FEP and UHR subjects were retrieved. We conducted meta-analyses of 25 studies including 905 patients with FEP, 560 patients at UHR, and 405 healthy controls. The cognitive performances of FEP, UHR, and healthy controls all significantly improved over time. There was no publication bias, and distributions of effect sizes were very homogenous. In FEP, the degree of improvement in verbal working memory and executive functions was significantly associated with reduction in negative symptoms. There was no evidence of cognitive decline in patients with UHR and FEP. In contrast, the cognitive performances of both groups improved at follow-up. These findings suggest that cognitive deficits are already established before the prodromal phases of psychosis. These data support the neurodevelopmental model rather than neurodegenerative and related staging models of schizophrenia.

Preterm Birth and Psychiatric Disorders in Young Adult Life

Abstract: Context: Preterm birth, intrauterine growth restriction, and delivery-related hypoxia have been associated with schizophrenia. It is unclear whether these associations pertain to other adult-onset psychiatric disorders and whether these perinatal events are independent. Objective: To investigate the relationships among gestational age, nonoptimal fetal growth, Apgar score, and various psychiatric disorders in young adult life. Design: Historical population-based cohort study. Setting: Identification of adult-onset psychiatric admissions using data from the National Board of Health and Welfare, Stockholm, Sweden. Participants: All live-born individuals registered in the nationwide Swedish Medical Birth Register between 1973 and 1985 and living in Sweden at age 16 years by December 2002 (n=1 301 522). Main Outcome Measures: Psychiatric hospitalization with nonaffective psychosis, bipolar affective disorder, depressive disorder, eating disorder, drug dependency, or alcohol dependency, diagnosed according to the International Classification of Diseases codes for 8 through 10. Cox proportional hazards regression models were used to estimate hazard ratios and 95% CIs. Results: Preterm birth was significantly associated with increased risk of psychiatric hospitalization in adulthood (defined as16 years of age) in a monotonic manner across a range of psychiatric disorders. Compared with term births (37-41 weeks), those born at 32 to 36 weeks’ gestation were 1.6 (95% CI, 1.1-2.3) times more likely to have nonaffective psychosis, 1.3 (95% CI, 1.1-1.7) times more likely to have depressive disorder, and 2.7 (95% CI, 1.6-4.5) times more likely to have bipolar affective disorder. Those born at less than 32 weeks’ gestation were 2.5 (95% CI, 1.0-6.0) times more likely to have nonaffective psychosis, 2.9 (95% CI, 1.8-4.6) times more likely to have depressive disorder, and 7.4 (95% CI, 2.7-20.6) times more likely to have bipolar affective disorder. Conclusions: The vulnerability for hospitalization with a range of psychiatric diagnoses may increase with younger gestational age. Similar associations were not observed for nonoptimal fetal growth and low Apgar score.

Rare loss-of-function variants in SETD1A are associated with schizophrenia and developmental disorders

Abstract: By analyzing the whole-exome sequences of 4,264 schizophrenia cases, 9,343 controls and 1,077 trios, we identified a genome-wide significant association between rare loss-of-function (LoF) variants in SETD1A and risk for schizophrenia (P = 3.3 × 10−9). We found only two heterozygous LoF variants in 45,376 exomes from individuals without a neuropsychiatric diagnosis, indicating that SETD1A is substantially depleted of LoF variants in the general population. Seven of the ten individuals with schizophrenia carrying SETD1A LoF variants also had learning difficulties. We further identified four SETD1A LoF carriers among 4,281 children with severe developmental disorders and two more carriers in an independent sample of 5,720 Finnish exomes, both with notable neuropsychiatric phenotypes. Together, our observations indicate that LoF variants in SETD1A cause a range of neurodevelopmental disorders, including schizophrenia. Combining these data with previous common variant evidence, we suggest that epigenetic dysregulation, specifically in the histone H3K4 methylation pathway, is an important mechanism in the pathogenesis of schizophrenia.

Cannabidiol inhibits THC-elicited paranoid symptoms and hippocampal-dependent memory impairment

Abstract: Community-based studies suggest that cannabis products that are high in Δ⁹-tetrahydrocannabinol (THC) but low in cannabidiol (CBD) are particularly hazardous for mental health. Laboratory-based studies are ideal for clarifying this issue because THC and CBD can be administered in pure form, under controlled conditions. In a between-subjects design, we tested the hypothesis that pre-treatment with CBD inhibited THC-elicited psychosis and cognitive impairment. Healthy participants were randomised to receive oral CBD 600 mg (n=22) or placebo (n=26), 210 min ahead of intravenous (IV) THC (1.5 mg). Post-THC, there were lower PANSS positive scores in the CBD group, but this did not reach statistical significance. However, clinically significant positive psychotic symptoms (defined a priori as increases ≥ 3 points) were less likely in the CBD group compared with the placebo group, odds ratio (OR)=0.22 (χ²=4.74, p<0.05). In agreement, post-THC paranoia, as rated with the State Social Paranoia Scale (SSPS), was less in the CBD group compared with the placebo group (t=2.28, p<0.05). Episodic memory, indexed by scores on the Hopkins Verbal Learning Task-revised (HVLT-R), was poorer, relative to baseline, in the placebo pre-treated group (-10.6 ± 18.9%) compared with the CBD group (-0.4% ± 9.7 %) (t=2.39, p<0.05). These findings support the idea that high-THC/low-CBD cannabis products are associated with increased risks for mental health.

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.