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Microstructural white matter correlates of performance on a speech adaptation task
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Poster E70 in Poster Session E, Thursday, October 26, 10:15 am - 12:00 pm CEST, Espace Vieux-Port
Deryk S. Beal1,2,3, Kieran Wheatley2,3, Trina Mitchell3, Mohammad Naderi3; 1Department of Speech-Language Pathology, Temerty Faculty of Medicine, University of Toronto, 2Rehabilitation Sciences Institute, University of Toronto, 3Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital
The advancement of in-vivo neuroimaging and neuromodulation has furthered refined understanding of the speech-related brain networks and the mechanisms underlying various components of the speech process, including speech adaptation. We aimed to define the associations of key nodes in the speech neural network with performance on a speech adaptation task using multimodal structural neuroimaging. 39 participants completed an auditory perturbation task and underwent MRI imaging at Holland Bloorview Kids Rehabilitation Hospital. Audapter and MATLAB were used to administer the speech perturbation protocol in a sound isolation booth. The auditory perturbation paradigm involved a start phase (30 trials), ramp phase (30 trials), stay phase (60 trials), and end phase (30 trials). During the start phase, participants performed the task while listening to unaltered auditory feedback of their own voices. During the ramp phase, perturbation of the auditory feedback was gradually shifted until it reached a maximum of a 25% increase in the F1 formant and 12.5% decrease in the F2 formant. During the stay phase, this maximum perturbation was maintained, and then removed again in the end phase. The stay phase responses represent the participants’ online compensation for the perturbation, whereas the end phase responses represent lingering adaptive effects of motor learning from the previous perturbed state. T1-weighted and Multi-shell diffusion (b=1000, b=1600, b=2600) MRI sequences were acquired using a 3 Tesla Siemens scanner. T1 images were analyzed in Freesurfer version 7.3.2 to obtain mean cortical thickness in the temporal plane of the superior temporal gyrus, superior temporal sulcus, opercular part of the inferior frontal gyrus, and inferior part of the precentral sulcus. Fixel-based analysis was applied to multi-shell diffusion images using MRtrix3. Mean fiber density (FD), cross-section (FC), as well as combined FDC in the bilateral arcuate fasciculus (AF), inferior fronto-occipital fasciculus (IFO), inferior longitudinal fasciculus (ILF), middle longitudinal fasciculus (MLF), and uncinate fasciculus (UF). Tracts were delineated by applying TractSeg to the study-specific population template. Cortical thickness, as well as FD, FC, and FDC were correlated with performance on the speech motor adaptation task, measured by the F1 and F2 mean formant ratio. N=32 of the participants were responders, n=2 were non-responders, and n=5 were followers. Across all participants, increased FDC in the bilateral AF (Spearman Rho=.40, P<0.05, FDR corrected), MLF (Spearman Rho=.43, P<0.05, FDR corrected), and UF (Spearman Rho=.46, P<0.05, FDR corrected), correlated with increased mean formant ratio in the F1 vowel formant. This was driven by differences in fiber cross-section. Increased log-FC in the AF (Spearman Rho=.46, P<0.05, FDR corrected), IFO (Spearman Rho=.37, P<0.05, FDR corrected), ILF (Spearman Rho=.39, P<0.05, FDR corrected), MLF (Spearman Rho=.45, P<0.05, FDR corrected), and UF (Spearman Rho=.51, P<0.01, FDR corrected) correlated with increased mean formant ratio in the F1 formant. There were no correlations with cortical thickness any of the speech-related gray matter regions (P>0.05, FDR corrected) with either F1 or F2 mean formant ratios. These results from the fixel-based analysis highlight key regions of the neural network supporting speech adaptation that are related to performance on a speech motor adaptation task.
Topic Areas: Speech Motor Control,