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.

A family study of HLA antigens and other genetic markers in schizophrenia

Abstract: HLA antigens and 19 other genetic marker systems were studied in 12 families containing 2 or more members diagnosed as schizophrenic. In contrast with previous reports we could exclude linkage between the disorder and HLA, and we found no evidence of linkage with Gm or Ge. The disagreement between our results and those of a previous study, which suggested linkage between HLA and schizophrenia, could not be explained on the basis of genetic heterogeneity within the disorder. The problems of performing a linkage study in the face of diagnostic uncertainty and an unknown mode of inheritance of the main trait are discussed, and the measures taken in attempts to overcome these difficulties are described. Despite present drawbacks, genetic marker studies hold future promise as a means of detecting major gene effects in schizophrenia and other familial psychiatric disorders.

Magnetic Resonance Imaging of the Thalamus and Adhesio Interthalamica in Twins With Schizophrenia

Abstract: Context Abnormalities of the thalamus are thought to be central to the pathophysiology of schizophrenia. These abnormalities include altered structure and shape of the thalamus itself and possibly changes to the adhesio interthalamica (or massa intermedia), the gray matter bridge connecting the 2 thalamic lobes. However, it is not clear to what extent these abnormalities are determined by the genetic liability for schizophrenia. Objective To investigate thalamic volume and the presence of the adhesio interthalamica in monozygotic (MZ) twins concordant or discordant for schizophrenia. Design Study of MZ twins. Setting Patients were drawn from inpatient and outpatient clinics. Twin controls were recruited from a volunteer twin register and through media advertisements. Participants A total of 123 twins participated: 19 MZ twin pairs concordant for schizophrenia, 15 MZ schizophrenic twins and 16 MZ nonschizophrenic twins drawn from 17 pairs discordant for schizophrenia, and 27 MZ twin pairs without schizophrenia. Groups were matched for age, sex, handedness, level of education, parental socioeconomic status, and ethnicity. Main Outcome Measures The volume of the thalamus (including right and left hemispheres) was measured (in cubic centimeters) and the presence of the adhesio interthalamica was ascertained from structural magnetic resonance images. Results Concordant twin pairs displayed significantly reduced thalamic volume compared with control twins, even when covarying for effects of whole-brain volume, age, and sex. There was a significant linear decrease in thalamic volume (control greater than discordant nonschizophrenic greater than discordant schizophrenic greater than concordant). In all groups, right thalamus was larger than left thalamus. There was no difference across groups in the frequency of the adhesio interthalamica. Conclusions Volumetric thalamic abnormalities in schizophrenia occur in twin pairs concordant for schizophrenia. These abnormalities may mark the substantial genetic contribution to the illness seen in concordant twin pairs, whereas the adhesio interthalamica is unlikely to be affected in schizophrenia.

The National Adult Reading Test as a measure of premorbid IQ in schizophrenia

Abstract: Objectives. To investigate the validity of the NART as an estimate of premorbid IQ in schizophrenia. Design. A within-in participants, follow-back design was adopted. Methods. A sample of adults with schizophrenia who had presented to psychiatric services and had a measure of IQ routinely taken during childhood were traced and subject to follow-up WAIS-R and NART IQ assessment (N = 24). Measures of current IQ and NART estimated premorbid IQ were compared with the measure of IQ taken ‘premorbidly’, i.e. in childhood. Results. There were no significant differences between childhood and adult measures of IQ. However there were significant differences between these two indices and NART estimated IQ, particularly where IQ deviated from general population means. The Vocabulary subtest of the WAIS-R performed better as an estimate of both premorbid and current IQ in the sample. Conclusion. Use of a word-reading test such as the NART to predict past levels of intellectual function should proceed with caution, particularly where IQ does not fall in the ‘average category’. Use of more than one index of prior level of function is recommended.

Diagnostic change 10 years after a first episode of psychosis.

Abstract: Background A lack of an aetiologically based nosology classification has contributed to instability in psychiatric diagnoses over time. This study aimed to examine the diagnostic stability of psychosis diagnoses using data from an incidence sample of psychosis cases, followed up after 10 years and to examine those baseline variables which were associated with diagnostic change. Method Data were examined from the AESOP and AESOP-10 studies, an incidence and follow-up study, respectively, of a population-based cohort of first-episode psychosis cases from two sites. Diagnosis was assigned using ICD-10 and DSM-IV-TR. Diagnostic change was examined using prospective and retrospective consistency. Baseline variables associated with change were examined using logistic regression and likelihood ratio tests. Results Slightly more (59.6%) cases had the same baseline and lifetime ICD-10 diagnosis compared with DSM-IV-TR (55.3%), but prospective and retrospective consistency was similar. Schizophrenia, psychotic bipolar disorder and drug-induced psychosis were more prospectively consistent than other diagnoses. A substantial number of cases with other diagnoses at baseline (ICD-10, n = 61; DSM-IV-TR, n = 76) were classified as having schizophrenia at 10 years. Many variables were associated with change to schizophrenia but few with overall change in diagnosis. Conclusions Diagnoses other than schizophrenia should to be regarded as potentially provisional.

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.