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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.


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TL;DR: The debate between the protagonists and prohibitionists has recently been re-ignited, but unfortunately this debate continues mainly in ignorance of the new understanding of the effects of cannabis on the brain and of studies that have quantified the extent of the risks of long-term use.
Abstract: Cannabis has been known for at least 4,000 years to have profound effects on the mind--effects that have provoked dramatically divergent attitudes towards it. Some societies have regarded cannabis as a sacred boon for mankind that offers respite from the tribulations of everyday life, whereas others have demonized it as inevitably leading to 'reefer madness'. The debate between the protagonists and prohibitionists has recently been re-ignited, but unfortunately this debate continues mainly in ignorance of our new understanding of the effects of cannabis on the brain and of studies that have quantified the extent of the risks of long-term use.

323 citations

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TL;DR: The results suggest that schizophrenia is associated with a reduced left and increased right temporal cortical response to auditory perception of speech, with little distinction between patients who differ in their vulnerability to hallucinations.
Abstract: OBJECTIVE: The authors explored whether abnormal functional lateralization of temporal cortical language areas in schizophrenia was associated with a predisposition to auditory hallucinations and whether the auditory hallucinatory state would reduce the temporal cortical response to external speech. METHOD: Functional magnetic resonance imaging was used to measure the blood-oxygenation-level-dependent signal induced by auditory perception of speech in three groups of male subjects: eight schizophrenic patients with a history of auditory hallucinations (trait-positive), none of whom was currently hallucinating; seven schizophrenic patients without such a history (trait-negative); and eight healthy volunteers. Seven schizophrenic patients were also examined while they were actually experiencing severe auditory verbal hallucinations and again after their hallucinations had diminished. RESULTS: Voxel-by-voxel comparison of the median power of subjects' responses to periodic external speech revealed that this ...

319 citations

Journal ArticleDOI
TL;DR: Data provide further support for elevated amygdala activity in depression and suggest that anterior cingulate activity may be a predictor of treatment response to both pharmacotherapy and CBT.

312 citations

Journal ArticleDOI
13 Feb 1999-BMJ
TL;DR: A few specific pregnancy and perinatal factors were associated with the subsequent development of psychotic disorder, particularly schizophrenia, in early adult life.
Abstract: # Prenatal and perinatal risk factors for schizophrenia, affective psychosis, and reactive psychosis of early onset: case-control study {#article-title-2} Objective: To examine prenatal and perinatal risk factors for subsequent development of schizophrenia and affective and reactive psychosis. Design: Three population based, case-control studies conducted within a Sweden-wide cohort of all children born during 1973-9.This was done by linking individual data from the Swedish birth register, which represents 99% of all births in Sweden, to the Swedish inpatient register. Subjects: Patients listed in inpatient register as having been first admitted to hospital aged 15-21years with a main diagnosis of schizophrenia (n=167), affective psychosis (n=198), or reactive psychosis (n=292). For each case, five controls were selected. Main outcome measures: Risks of schizophrenia and affective and reactive psychosis in relation to pregnancy and perinatal characteristics. Results: Schizophrenia was positively associated with multiparity (odds ratio 2.0), maternal bleeding during pregnancy (odds ratio 3.5), and birth in late winter (odds ratio 1.4). Affective psychosis was associated with uterine atony (odds ratio 2.2) and late winter birth (odds ratio 1.5). Reactive psychosis was related to multiparity (odds ratio 2.1). An increased risk for schizophrenia was found in boys who were small for their gestational age at birth (odds ratio 3.2), who were number four or more in birth order (odds ratio 3.6), and whose mothers had had bleeding during late pregnancy (odds ratio 4.0). Conclusions: A few specific pregnancy and perinatal factors were associated with the subsequent development of psychotic disorder, particularly schizophrenia, in early adult life. The association of small size for gestational age and bleeding during pregnancy with increased risk of early onset schizophrenia among males could reflect placental insufficiency. # Prenatal and perinatal risk factors for early onset schizophrenia, affective psychosis, and reactive psychosis {#article-title-34}

310 citations

Journal ArticleDOI
TL;DR: In some patients with schizophrenia, antipsychotic treatment may be ineffective because they do not exhibit the elevation in dopamine synthesis capacity that is classically associated with the disorder; this may reflect a different underlying pathophysiology or a differential effect of antipsychotics.
Abstract: ObjectiveElevated presynaptic striatal dopaminergic function is a robust feature of schizophrenia. However, the relationship between this dopamine abnormality and the response to dopamine-blocking antipsychotic treatments is unclear. The authors tested the hypothesis that in patients with schizophrenia the response to antipsychotic treatment would be related to the severity of presynaptic dopamine dysfunction, as indexed using [18F]-DOPA uptake positron emission tomography (PET).MethodTwelve patients with treatment-resistant schizophrenia, twelve patients with schizophrenia who had responded to antipsychotics, and twelve healthy volunteers matched for gender, age, ethnicity, weight, and cigarette smoking underwent [18F]-DOPA PET scanning. [18F]-DOPA influx rate constants (Kicer values) were measured in the striatum and its functional subdivisions.ResultsPatients who had responded to antipsychotic treatment showed significantly higher Kicer striatal values than did patients with treatment-resistant illness...

309 citations


Cited by
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TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).

13,246 citations

Journal ArticleDOI
TL;DR: The 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.
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.

11,042 citations

Journal Article
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
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

9,847 citations

Journal ArticleDOI
TL;DR: This article reviews studies investigating complex brain networks in diverse experimental modalities and provides an accessible introduction to the basic principles of graph theory and highlights the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
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.

9,700 citations

01 Jan 2014
TL;DR: 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.
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.

9,618 citations