Neural organization of speech production: A lesion-based study of error patterns in connected speech

Brielle C Stark; Alexandra Basilakos; Gregory Hickok; Chris Rorden; Leonardo Bonilha; Julius Fridriksson

Lesions were manually drawn on the T2-weighted image by a neurologist (author LB), who was blinded to the participant's language scores at the time of the lesion drawing. The T2 image was co-registered to the T1 image, and these parameters were used to re-slice the lesion into the native T1 space. The resliced lesion maps were smoothed with a 3mm full-width half maximum Gaussian kernel to remove jagged edges associated with manual drawing. We then performed enantiomorphic segmentation-normalization (Nachev et al., 2008) using SPM12 and MATLAB scripts we developed (Rorden et al., 2012) as follows: first, a . CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/544841 doi: bioRxiv preprint mirrored image of the T1 image (reflected around the midline) was created, and this mirrored image was co-registered to the native T1 image. We then created a chimeric image based on the native T1 image with the lesioned tissue replaced by tissue from the mirrored scan (using the smoothed lesion map to modulate this blending). SPM12's unified segmentation-normalization (Ashburner and Friston, 2005) was used to warp this chimeric image to standard (MNI) space, with the resulting spatial transform applied to the native T1 image as well as the lesion map and the T2 image (which used the T1 segmentation parameters to mask non-brain signal). The normalized lesion map was then binarized, using a 50% probability threshold. Figure 1A shows the overlap of lesions for our sample of participants, separated by task group.