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.

Sexual dysfunction in patients taking conventional antipsychotic medication

Abstract: Background Antipsychotic drugs are associated with sexual dysfunction but the mechanisms are poorly understood. Aims To ascertain the frequency of sexual dysfunction in patients taking conventional antipsychotics and to determine the possible underlying mechanisms. Method Sexual dysfunction was assessed in 101 patients receiving conventional antipsychotic medication, 57 normal controls and 55 controls attending a sexual dysfunction clinic. Results Sexual dysfunction occurred in 45% of patients taking antipsychotic medication, 17% of normal controls and 61% of controls attending a sexual dysfunction clinic. Sexual dysfunction was associated with autonomic side-effects in normoprolactinaemic males, but the presence of hyperprolactinaemia overrode other causes of sexual dysfunction. For women, hyperprolactinaemia was the main cause of sexual dysfunction. Conclusions Conventional anti-psychotic medications cause significant levels of sexual dysfunction. Clinicians should routinely enquire about sexual symptoms prior to the prescription of antipsychotics and on follow-up.

Corpus callosum size and very preterm birth: relationship to neuropsychological outcome.

Abstract: Thinning of the corpus callosum (CC) is often observed in individuals who were born very preterm. Damage to the CC during neurodevelopment may be associated with poor neuropsychological performance. This study aimed to explore any evidence of CC pathology in adolescents aged 14-15 years who were born very preterm, and to investigate the relationship between CC areas and verbal skills. Seventy-two individuals born before 33 weeks of gestation and 51 age- and sex-matched full-term controls received structural MRI and neuropsychological assessment. Total CC area in very preterm adolescents was 7.5% smaller than in controls, after adjusting for total white matter volume (P = 0.015). The absolute size of callosal subregions differed between preterm and full-term adolescents: preterm individuals had a 14.7% decrease in posterior (P < 0.0001) and an 11.6% decrease in mid-posterior CC quarters (P = 0.029). Preterm individuals who had experienced periventricular haemorrhage and ventricular dilatation in the neonatal period showed the greatest decrease in CC area. In very preterm boys only, verbal IQ and verbal fluency scores were positively associated with total mid-sagittal CC size and mid-posterior surface area. These results suggest that very preterm birth adversely affects the development of the CC, particularly its posterior quarter, and this impairs verbal skills in boys.

The structural brain correlates of neurological soft signs in ÆSOP first-episode psychoses study

Abstract: Patients with schizophrenia and related psychoses have an excess of minor neurological abnormalities (neurological soft signs) of unclear neuropathological origin. These include poor motor coordination, sensory perceptual difficulties and difficulties in sequencing complex motor tasks. Neurological soft signs seem not to reflect primary tract or nuclear pathology. It still has to be established whether neurological soft signs result from specific or diffuse brain structural abnormalities. Studying their anatomical correlates can provide not only a better understanding of the aetiopathogenesis of soft signs, but also of the pathophysiology of schizophrenia. Surprisingly few studies have investigated the brain correlates of neurological soft signs. In the present study, we investigated the relationship between brain structure and neurological soft signs in an epidemiologically based sample of 77 first-episode psychosis patients. We used the Neurological Evaluation Scale for neurological assessment and high-resolution MRI and voxel-based methods of image analysis to investigate brain structure. Higher rates of soft neurological signs (both motor and sensory) were associated with a reduction of grey matter volume of subcortical structures (putamen, globus pallidus and thalamus). Signs of sensory integration deficits were additionally associated with volume reduction in the cerebral cortex, including the precentral, superior and middle temporal, and lingual gyri. Neurological soft signs and their associated brain changes were independent of antipsychotic exposure. We conclude that neurological soft signs are associated with regional grey matter volume changes and that they may represent a clinical sign of the perturbed cortical-subcortical connectivity that putatively underlies psychotic disorders.

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.