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

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Protocol publication

[…] The aim of VBM is to identify differences in the local composition of brain tissue and its association with behavioral and cognitive measures, while discounting large scale differences in gross anatomy and position. This can be achieved by spatially normalizing individuals' structural images to the same stereotactic space, segmenting the normalized images into distinct brain tissues, smoothing the gray-matter images, and performing a statistical test to localize significant associations between anatomical and behavioral measures ().VBM was performed using the Computational Anatomy Toolbox (CAT 12 r933, packaged in Statistical Parametric Mapping 12 (Wellcome Department of Imaging Neuroscience, University College London, U.K.). CAT12 provides several components optimized for morphometry, including internal interpolation, affine preprocessing (affine registration of bias-corrected images), partial volume segmentation, denoising, DARTEL normalization, local adaptive segmentation, skull-stripping, adaptive maximum a posteriori segmentation, and a final “clean-up”. In short, T1 images were first co-registered to the Montreal Neurological Institute or MNI template space (1.5 mm3 isotropic voxels) using a multiple stage affine transformation, during which the 12 parameters were estimated. Co-registration started with a coarse affine registration using mean square differences, followed by affine registration using mutual information. In this step, coefficients of the basis functions that minimize the residual square difference between individual image and the template were estimated. Tissue probability maps constructed from 452 healthy subjects were used in affine transformation, and affine regularization was performed with ICBM space template – European brains. Affine preprocessing was performed with default parameter ‘light’. After affine transformation, a spatial-adaptive non-local means denoising filter () with default parameter 0.5 was applied, to account for intensity variations (inhomogeneity) and noise caused by different positions of cranial structures within the MRI coil; and, finally, they were segmented into cerebrospinal fluid, gray matter and white matter, using an adaptive maximum a posteriori method () with k-means initializations. In segmentation, partial volume estimation was performed with a simplified mixed model of at most two tissue types (), and a local adaptive segmentation was executed with default parameter 0.5 to account for GM inhomogeneity prior to the final adaptive maximum a posteriori estimation. Segmented and the initially registered tissue class maps were normalized using DARTEL (), a fast diffeomorphic image registration algorithm of SPM. As a high-dimensional non-linear spatial normalization method, DARTEL generates mathematically consistent inverse spatial transformations. We used the standard DARTEL template in MNI space, constructed from 555 healthy subjects of the IXI-database (, to drive the DARTEL normalization. Skull-stripping and final clean up (to remove remaining meninges and correct for volume effects in some regions) were performed with default parameters of 0.5′s. Normalized GM maps were modulated to obtain the absolute volume of GM tissue corrected for individual brain sizes. Finally, the GM maps were smoothed by convolving with an isotropic Gaussian kernel. Smoothing helps in compensating for the inexact nature of spatial normalization and reduces the number of statistical comparisons; however, it reduces the accuracy of localization. Most VBM studies used a kernel size of FWHM = 12 mm. We used a smaller kernel size of FWHM = 8 mm to enhance localization accuracy.In group analyses, we employed multiple regressions as informed by the best GLMs of the clinical data. The rationale was to identify the neural correlates of variables that best predicted AUDIT within each group; that is, GP for women, and BI × GP for men and for women and men combined (please see Results). Therefore, we regressed the GM volumes of the whole brain against GP score for women; and GM volumes against BI × GP score for men and for men and women combined, all with age as a covariate, as with GLM analysis of the clinical data. […]

Pipeline specifications

Software tools CAT, SPM
Application Magnetic resonance imaging
Organisms Homo sapiens
Chemicals Ethanol