For each of the six emotions, four trials representing that emotion were administered; stimuli that were most consistently identified as representing that vocal emotion by the previous group of healthy control subjects (Sauter, 2006) were selected. The task on each trial was to decide which of the six basic emotions was represented in the vocalisation. The modality specificity of any affective prosodic deficit was investigated using the same task for a parallel set of 24 facial expression stimuli [four trials representing each of the same six canonical emotions, derived from the set created by Ekman
and Friesen (1976), which has been widely assessed in both healthy and clinical populations]. Dasatinib in vivo These facial expression stimuli were administered to 13 of the 19 patients (as part of a separate study) in the timeframe of the prosody assessment; these patients represented each of the PPA subgroups (six PNFA, five LPA, two GRN-PPA). Facial emotion
recognition in patients was assessed in relation to a group of 15 healthy age-matched control subjects. Behavioural data were analysed statistically using STATA 10.0 (Stata Corporation, College Talazoparib datasheet Station, TX). Linear regression models were used to compare performance on the tests between groups after adjusting for age. 95% bias-corrected bootstrap confidence intervals with 1000 replicates were used (these methods
make fewer assumptions about the underlying structure of the data than conventional analytical parametric tests). To look at within disease group comparisons Wilcoxon signed-rank tests were used to assess differences between patient scores as a percentage of the control mean. To investigate the neuroanatomical associations of receptive prosody in the PPA group, a VBM analysis was performed using SPM5 Mirabegron software (http://www.fil.ion.ucl.ac.uk/spm) with default settings for all parameters. The patients’ MR brain images underwent an initial segmentation process in SPM5 which simultaneously estimated transformation parameters for warping grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) tissue probability maps (TPMs) onto the images. The native space GM segments were then rigidly spatially normalised, using just the rotations and translations from the inverse of the TPM transformation, and resampled to 1.5 mm isotropic resolution. These “imported” images were then iteratively warped to an evolving estimate of their group-wise GM average template using the DARTEL toolbox (Ashburner, 2007 and Ashburner and Friston, 2009). The GM segmentations were then normalised using the final DARTEL transformations and modulated to account for volume changes. Finally, the images were smoothed using a 6 mm full-width at half-maximum (FWHM) Gaussian kernel.