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      Sex differences in the interacting roles of impulsivity and positive alcohol expectancy in problem drinking: A structural brain imaging study

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          Abstract

          Alcohol expectancy and impulsivity are implicated in alcohol misuse. However, how these two risk factors interact to determine problem drinking and whether men and women differ in these risk processes remain unclear. In 158 social drinkers (86 women) assessed for Alcohol Use Disorder Identification Test (AUDIT), positive alcohol expectancy, and Barratt impulsivity, we examined sex differences in these risk processes. Further, with structural brain imaging, we examined the neural bases underlying the relationship between these risk factors and problem drinking. The results of general linear modeling showed that alcohol expectancy best predicted problem drinking in women, whereas in men as well as in the combined group alcohol expectancy and impulsivity interacted to best predict problem drinking. Alcohol expectancy was associated with decreased gray matter volume (GMV) of the right posterior insula in women and the interaction of alcohol expectancy and impulsivity was associated with decreased GMV of the left thalamus in women and men combined and in men alone, albeit less significantly. These risk factors mediated the correlation between GMV and problem drinking. Conversely, models where GMV resulted from problem drinking were not supported. These new findings reveal distinct psychological factors that dispose men and women to problem drinking. Although mediation analyses did not determine a causal link, GMV reduction in the insula and thalamus may represent neural phenotype of these risk processes rather than the consequence of alcohol consumption in non-dependent social drinkers. The results add to the alcohol imaging literature which has largely focused on dependent individuals and help elucidate alterations in brain structures that may contribute to the transition from social to habitual drinking.

          Highlights

          • Alcohol expectancy (AE) and impulsivity are risk factors for problem drinking.

          • AE mediates the correlation between right insula GMV and problem drinking in women.

          • AE and impulsivity interacts to mediate left thalamus GMV and problem drinking in all.

          • Models where changes in GMV as a result of problem drinking are not supported.

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          Adaptive non-local means denoising of MR images with spatially varying noise levels.

          To adapt the so-called nonlocal means filter to deal with magnetic resonance (MR) images with spatially varying noise levels (for both Gaussian and Rician distributed noise). Most filtering techniques assume an equal noise distribution across the image. When this assumption is not met, the resulting filtering becomes suboptimal. This is the case of MR images with spatially varying noise levels, such as those obtained by parallel imaging (sensitivity-encoded), intensity inhomogeneity-corrected images, or surface coil-based acquisitions. We propose a new method where information regarding the local image noise level is used to adjust the amount of denoising strength of the filter. Such information is automatically obtained from the images using a new local noise estimation method. The proposed method was validated and compared with the standard nonlocal means filter on simulated and real MRI data showing an improved performance in all cases. The new noise-adaptive method was demonstrated to outperform the standard filter when spatially varying noise is present in the images. (c) 2009 Wiley-Liss, Inc.
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            Sex and gender-related differences in alcohol use and its consequences: Contemporary knowledge and future research considerations.

            To review the contemporary evidence reflecting male/female differences in alcohol use and its consequences along with the biological (sex-related) and psycho-socio-cultural (gender-related) factors associated with those differences.
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              Fast and robust parameter estimation for statistical partial volume models in brain MRI.

              Due to the finite spatial resolution of imaging devices, a single voxel in a medical image may be composed of mixture of tissue types, an effect known as partial volume effect (PVE). Partial volume estimation, that is, the estimation of the amount of each tissue type within each voxel, has received considerable interest in recent years. Much of this work has been focused on the mixel model, a statistical model of PVE. We propose a novel trimmed minimum covariance determinant (TMCD) method for the estimation of the parameters of the mixel PVE model. In this method, each voxel is first labeled according to the most dominant tissue type. Voxels that are prone to PVE are removed from this labeled set, following which robust location estimators with high breakdown points are used to estimate the mean and the covariance of each tissue class. Comparisons between different methods for parameter estimation based on classified images as well as expectation--maximization-like (EM-like) procedure for simultaneous parameter and partial volume estimation are reported. The robust estimators based on a pruned classification as presented here are shown to perform well even if the initial classification is of poor quality. The results obtained are comparable to those obtained using the EM-like procedure, but require considerably less computation time. Segmentation results of real data based on partial volume estimation are also reported. In addition to considering the parameter estimation problem, we discuss differences between different approximations to the complete mixel model. In summary, the proposed TMCD method allows for the accurate, robust, and efficient estimation of partial volume model parameters, which is crucial to a variety of brain MRI data analysis procedures such as the accurate estimation of tissue volumes and the accurate delineation of the cortical surface.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                31 March 2017
                2017
                31 March 2017
                : 14
                : 750-759
                Affiliations
                [a ]Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, United States
                [b ]Department of Psychology, State University of New York at Oswego, Oswego, NY 13126, United States
                [c ]Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, United States
                [d ]Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT 06520, United States
                Author notes
                [* ]Corresponding author at: Connecticut Mental Health Center, S112, Department of Psychiatry, Yale University School of Medicine, 34 Park Street, New Haven, CT 06519, United States.Connecticut Mental Health CenterS112Department of PsychiatryYale University School of Medicine34 Park StreetNew HavenCT06519United States chiang-shan.li@ 123456yale.edu
                Article
                S2213-1582(17)30074-8
                10.1016/j.nicl.2017.03.015
                5385596
                28413777
                f75a7365-c7c9-4994-a18b-9c6ea31cf727
                © 2017 Published by Elsevier Inc.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 28 February 2017
                : 20 March 2017
                : 30 March 2017
                Categories
                Regular Article

                cerebral morphometry,vbm,thalamus,insula,gender difference,disinhibition,alcohol expectancy,social drinking

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