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.

Does socioeconomic status predict course and outcome in patients with psychosis

Abstract: Background We examined the relationship between socioeconomic status (SES) and course and outcome of patients with psychosis. Two hypotheses were examined: a) patients with higher best-ever SES will have better course and outcome than those with lower best-ever SES, and b) patients with greater downward drift in SES will have poorer course and outcome than those with less downward drift. Method Data were drawn from the baseline and 2-year follow-up assessments of the UK700 Case Management Trial of 708 patients with severe psychosis. The indicators of SES used were occupational status and educational achievement. Drift in SES was defined as change from best-ever occupation to occupation at baseline. Results For the baseline data highly significant differences were found between best-ever groups and negative symptoms (non-manual vs. unemployed – coef −10.5, p=0.000, 95 % CIs 5.1–15.8), functioning (non-manual vs. unemployed – coef −0.6, p=0.000, 95 % CIs 0.3 to −0.8) and unmet needs (manual vs. unemployed – coef 0.5, p=0.004, 95 % CIs 0.2–0.9). No significant differences between best-ever groups were found for days in hospital, symptoms, perceived quality of life and dissatisfaction with services. Significant differences for clinical and social variables were found between drift and non-drift SES groups. There were no significant findings between educational groups and clinical and social variables. Conclusions Best-ever occupation, but not educational qualifications, appeared to predict prognosis in patients with severe psychosis. Downward drift in occupational status did not result in poorer illness course and outcome.

Altered resting-state functional connectivity in emotion-processing brain regions in adults who were born very preterm

Abstract: Background Very preterm birth (VPT; <32 weeks of gestation) has been associated with impairments in emotion regulation, social competence and communicative skills. However, the neuroanatomical mechanisms underlying such impairments have not been systematically studied. Here we investigated the functional integrity of the amygdala connectivity network in relation to the ability to recognize emotions from facial expressions in VPT adults. Method Thirty-six VPT-born adults and 38 age-matched controls were scanned at rest in a 3-T MRI scanner. Resting-state functional connectivity (rs-fc) was assessed with SPM8. A seed-based analysis focusing on three amygdalar subregions (centro-medial/latero-basal/superficial) was performed. Participants’ ability to recognize emotions was assessed using dynamic stimuli of human faces expressing six emotions at different intensities with the Emotion Recognition Task (ERT). Results VPT individuals compared to controls showed reduced rs-fc between the superficial subregion of the left amygdala, and the right posterior cingulate cortex (p = 0.017) and the left precuneus (p = 0.002). The VPT group further showed elevated rs-fc between the left superficial amygdala and the superior temporal sulcus (p = 0.008). Performance on the ERT showed that the VPT group was less able than controls to recognize anger at low levels of intensity. Anger scores were significantly associated with rs-fc between the superficial amygdala and the posterior cingulate cortex in controls but not in VPT individuals. Conclusions These findings suggest that alterations in rs-fc between the amygdala, parietal and temporal cortices could represent the mechanism linking VPT birth and deficits in emotion processing.

Life events before psychotic episodes: do clinical and social variables affect the relationship?

Abstract: We have previously used data from the Camberwell Collaborative Psychosis Study to demonstrate a strong relationship between life events and subsequent episodes of schizophrenic, manic and depressive psychoses. In the current paper, we confirmed the robustness of this relationship, which was not vitiated by controlling for clinical and social variables. Thus, the event-onset association was not affected by the type of onset or the number of previous episodes. The influences of social variables, such as social class, ethnicity and marital status, did not seriously diminish the importance of events, although there may be a role for other forms of social disadvantage as reflected in these variables.

Premorbid tobacco smoking is associated with later age at onset in schizophrenia

Abstract: Rates of cigarette smoking in individuals with schizophrenia well exceed those in the general population and in other mental illnesses. In the present study, we examined the relationship between smoking status, clinical characteristics and cognitive functions in 230 male Chinese schizophrenia patients. They were interviewed by experienced psychiatrists using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) (SCID-P). Clinical symptoms were rated using the Positive and Negative Syndrome Scale (PANSS), and the Revised Tolerance Questionnaire (RTQ) used to evaluate the severity of nicotine dependence. Nine neuropsychological tests were used to assess cognitive function. We found that never-smokers had a younger age at examination and earlier onset and longer duration of illness than smokers and ex-smokers. The age of initiation of regular smoking in patients was significantly earlier than their age of illness onset. We found that longer duration of illness was significantly associated with higher RTQ scores. Ex-smokers with schizophrenia performed significantly more poorly on the Stroop C test than smokers. The results imply that smoking may affect cognitive function and illness onset time in patients with schizophrenia.

Duration of prodromal phase and severity of volumetric abnormalities in first-episode psychosis

Abstract: Background First-episode psychosis is typically preceded by a prodrome in which there is deterioration in global and social functioning. Aims To examine whether the duration ofthe prodromal phase influences grey and white matter volumes at the onset of psychosis. Methods Eighty-two people were scanned using magnetic resonance imaging when they developed a first episode of psychosis. The duration of the prodromal phase was estimated from detailed interviews and medical records. Voxel-based morphometry was used to assess neuroanatomical abnormalities. Results A long prodromal phase was associated with smaller grey matter volumes in the cingulate, frontal and left insular cortex, and with less white matter volume bilaterally in the superior longitudinal and uncinate fasciculi and the cingulum. Conclusions The severity of volumetric abnormalities in first-episode psychosis was greater in those with a long prodrome.

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.