Computational protocol: The left superior temporal gyrus is a shared substrate for auditory short-term memory and speech comprehension: evidence from 210 patients with stroke

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Protocol publication

[…] Structural images were pre-processed with Statistical Parametric Mapping software (SPM5: Wellcome Trust Centre for Neuroimaging: http://www.fil.ion.ucl.ac.uk/spm) running under MATLAB 7.0.4 (MathWorks, Natick, MA, USA). The images were spatially normalized into standard Montreal Neurological Institute (MNI) space using a unified segmentation algorithm optimized for use in patients with focal brain lesions. The unified segmentation algorithm is a generative model that combines tissue segmentation, bias correction and spatial normalization in the inversion of a single unified model (Ashburner and Friston, ). This algorithm was developed to deal with normal subjects’ brains but with patients it outperforms the previous ‘gold-standard’ of cost-function masking (Crinion et al., ). More recently, a modified version of the tissue segmentation component has been developed to further improve identification and spatial normalization of ‘brain’ as opposed to ‘non-brain’ components by adding in an extra tissue class, ‘lesion’, into which outlier voxels can be classified (Seghier et al., ). These images were then smoothed with an isotropic kernel of 8 mm at full-width half maximum to increase the chance that regional effects are expressed at a spatial scale in which homologies in structural anatomy are shared over subjects. After smoothing, the value in each voxel represents the probability that the tissue belongs to the grey matter class and not one of the three others (white matter, non-brain or lesion). Higher scores indicate higher (normal) grey matter density. [...] All statistical analyses used voxel-based morphometry; that is a whole-brain, unbiased, semi-automated technique for characterizing regional differences in structural magnetic resonance images (Ashburner and Friston, ). Statistical analyses were performed on the smoothed grey matter images using the general linear model as implemented in SPM5. The grey matter images from the 210 patients were entered into a multiple regression model. In all analyses the linear and nonlinear effects of age were excluded by including them as covariates. Time since stroke (in months) was also included as a covariate of no interest. In three of our analyses, we also entered the estimated volume of tissue loss (stroke volume) as a regressor. This was measured using the automated lesion-identification algorithm cited earlier (Seghier et al., ). These variables are likely to impact on cognitive performance and we wished to minimize their associated variance so that we could focus our analyses on the relationship between digit-span score and grey matter density. Tests of regression coefficients in multiple regression models are equivalent to testing corresponding partial correlations. illustrates the design matrices of three of the four statistical analyses conducted here. Analysis 1 was designed to show where cortical damage leads to reduced digit span. We included digit span as the sole behavioural measure. Analysis 2 was designed to identify neural structures that determine auditory short-term memory capacity. We included five extra regressors: four scores from the comprehensive aphasia test (auditory word repetition, auditory non-word repetition, verbal fluency and picture naming) and one structural parameter (stroke volume). Analyses 3 and 4 were designed to test the structural relationship between auditory short-term memory and language comprehension. Analysis 3 included the same regressors as Analysis 2, with the addition of two comprehension scores; spoken single word comprehension and written sentence comprehension. Finally Analysis 4 (not shown) included the same regressors as Analysis 3, with the addition of spoken sentence comprehension. Figure 2 We only report and discuss regions that showed significant positive effects at P < 0.05 after correction for multiple comparisons across the whole brain at the height (voxel) level as well as an extent threshold for each cluster of 25 voxels (i.e. a minimum cortical volume of 0.2 cm3). […]

Pipeline specifications

Software tools SPM, ALI
Applications Magnetic resonance imaging, Functional magnetic resonance imaging
Organisms Homo sapiens
Diseases Stroke