Biomedical subjects

Liam M O'Brien

Publications and source records attributed to Liam M O'Brien.

6 recordsLinked to original sources

PubMed2006

Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability.

BACKGROUND: This is a unique hypothalamic magnetic resonance imaging (MRI) study in schizophrenia, an important region in the limbic system. We hypothesized abnormal volumetric increases, with greater severity in multiplex families (more than one ill member) compared with simplex families (one ill). We tested the hypothesis that normal hypothalamic sexual dimorphism is disrupted in schizophrenia. METHODS: Eighty-eight DSM-III-R schizophrenia cases (40 simplex and 48 multiplex), 43 first-degree nonpsychotic relatives, and 48 normal comparisons systematically were compared. A 1.5-Tesla General Electric scanner was used to acquire structural MRI scans, and contiguous 3.1-mm slices were used to segment anterior and posterior hypothalamus. General linear model for correlated data and generalized estimating equations were used to compare cases, relatives, and controls on right and left hypothalamus, controlled for age, sex, and total cerebral volume. Spearman's correlations of hypothalamic volumes with anxiety were calculated to begin to examine arousal correlates with structural abnormalities. RESULTS: Findings demonstrated significantly increased hypothalamic volume in cases and nonpsychotic relatives, particularly in regions of paraventricular and mammillary body nuclei, respectively. This increase was linear from simplex to multiplex cases, was positively correlated with anxiety, and had a greater propensity in women. CONCLUSIONS: Findings suggest important implications for understanding genetic vulnerability of schizophrenia and the high rate of endocrine abnormalities.

Adult↗

PubMed2006

Move & Improve: a worksite wellness program in Maine.

BACKGROUND: We describe the evaluation process and outcomes of Move & Improve, a worksite wellness program in Maine. The evaluation process was based on the Centers for Disease Control and Prevention's Framework for Program Evaluation in Public Health and community-based participatory research principles. Innovative approaches are required to address burgeoning chronic disease trends and risk factors. Worksites are an ideal setting in which to affect working adults and high-risk individuals. Using community-based participatory research methodology increases community capacity for evaluation, dissemination, and use of evaluation results. CONTEXT: Move & Improve is an ongoing program that was implemented in 1996. Although evaluation data have been collected since the program's inception, a more systematic evaluation based on community-based participatory research principles was undertaken in 2003 and 2004 with the technical assistance of the Maine-Harvard Prevention Research Center and Colby College. METHODS: The Maine-Harvard Prevention Research Center facilitated the development of a program logic model, evaluation questions, data collection instruments, an analysis plan, presentations, and reports. We used a cross-sectional study design with nonparticipant comparison groups. CONSEQUENCES: Data indicate possible program improvement strategies and substantial improvements in lifestyle factors among participants. INTERPRETATION: Limitations of the evaluation include participant self-selection, cross-sectional study design, a lack of adequate resources for evaluation, and the challenges of using community-based participatory research methods. Despite these limitations, Move & Improve program staff consider the evaluation of the program a success and have learned ways to improve the program and future evaluation efforts. Overall satisfaction with the process has been nurtured through community-based participatory research methods. This approach also enabled us to meet key evaluation standards.

Exercise↗

PubMed2006

Maximum likelihood estimation of marginal pairwise associations with multiple source predictors.

Researchers interested in the association of a predictor with an outcome will often collect information about that predictor from more than one source. Standard multiple regression methods allow estimation of the effect of each predictor on the outcome while controlling for the remaining predictors. The resulting regression coefficient for each predictor has an interpretation that is conditional on all other predictors. In settings in which interest is in comparison of the marginal pairwise relationships between each predictor and the outcome separately (e.g., studies in psychiatry with multiple informants or comparison of the predictive values of diagnostic tests), standard regression methods are not appropriate. Instead, the generalized estimating equations (GEE) approach can be used to simultaneously estimate, and make comparisons among, the separate pairwise marginal associations. In this paper, we consider maximum likelihood (ML) estimation of these marginal relationships when the outcome is binary. ML enjoys benefits over GEE methods in that it is asymptotically efficient, can accommodate missing data that are ignorable, and allows likelihood-based inferences about the pairwise marginal relationships. We also explore the asymptotic relative efficiency of ML and GEE methods in this setting.

Depression↗

PubMed2005

Regression models for the analysis of longitudinal Gaussian data from multiple sources.

We present a regression model for the joint analysis of longitudinal multiple source Gaussian data. Longitudinal multiple source data arise when repeated measurements are taken from two or more sources, and each source provides a measure of the same underlying variable and on the same scale. This type of data generally produces a relatively large number of observations per subject; thus estimation of an unstructured covariance matrix often may not be possible. We consider two methods by which parsimonious models for the covariance can be obtained for longitudinal multiple source data. The methods are illustrated with an example of multiple informant data arising from a longitudinal interventional trial in psychiatry.

Adolescent↗

PubMed2002

Impact of normal sexual dimorphisms on sex differences in structural brain abnormalities in schizophrenia assessed by magnetic resonance imaging.

BACKGROUND: Previous studies suggest that the impact of early insults predisposing to schizophrenia may have differential consequences by sex. We hypothesized that brain regions found to be structurally different in normal men and women (sexual dimorphisms) and abnormal in schizophrenia would show significant sex differences in brain abnormalities, particularly in the cortex, in schizophrenia. METHODS: Forty outpatients diagnosed as having schizophrenia by DSM-III-R were systematically sampled to be comparable within sex with 48 normal comparison subjects on the basis of age, ethnicity, parental socioeconomic status, and handedness. A comprehensive assessment of the entire brain was based on T1-weighted 3-dimensional images acquired from a 1.5-T magnet. Multivariate general linear models for correlated data were used to test for sex-specific effects regarding 22 hypothesized cortical, subcortical, and cerebrospinal fluid brain volumes, adjusted for age and total cerebrum size. Sex x group interactions were also tested on asymmetries of the planum temporale, Heschl's gyrus, and superior temporal gyrus, additionally controlled for handedness. RESULTS: Normal patterns of sexual dimorphisms were disrupted in schizophrenia. Sex-specific effects were primarily evident in the cortex, particularly in the frontomedial cortex, basal forebrain, cingulate and paracingulate gyri, posterior supramarginal gyrus, and planum temporale. Normal asymmetry of the planum was also disrupted differentially in men and women with schizophrenia. There were no significant differential sex effects in subcortical gray matter regions or cerebrospinal fluid. CONCLUSION: Factors that produce normal sexual dimorphisms may be associated with modulating insults producing schizophrenia, particularly in the cortex.

Adult↗

PubMedDate not supplied

Adjustment for whole brain and cranial size in volumetric brain studies: a review of common adjustment factors and statistical methods.

In this article we address analytic challenges inherent in brain volumetrics (i.e., the study of volumes of brains and brain regions). It has sometimes been assumed in the literature that deviations in regional brain size in clinical samples are directly related to maldevelopment or pathogenesis. However, this assumption may be incorrect; such volume differences may, instead, be wholly or partly attributable to individual differences in overall dimension (e.g., for head, brain, or body size). What quantitative approaches can be used to take these factors into account? Here, we provide a review of volumetric and nonvolumetric adjustment factors. We consider three examples of common statistical methods by which one can adjust for the effects of body, head, or brain size on regional volumetric measures: the analysis of covariance, the proportion, and the residual approaches. While the nature of the adjustment will help dictate which method is most appropriate, the choice is context sensitive, guided by numerous considerations-chiefly the experimental hypotheses, but other factors as well (including characteristic features of the disorder and sample size). These issues come into play in logically framing the assessment of putative abnormalities in regional brain volumes.

Analysis of Variance↗