Diffusion imaging of cerebral white matter in persons who stutter: evidence for network-level anomalies
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Cai-2014-Diffusion imaging.pdf
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Author(s)
Cai, Shanqing
Tourville, Jason A.
Beal, Deryk S.
Perkell, Joseph S.
Guenther, Frank H.
Ghosh, Satrajit S.
Date Issued
February 2014
Journal
Frontiers in Human Neuroscience
Publisher
Frontiers Research Foundation
Citation
Cai, Shanqing, Jason A. Tourville, Deryk S. Beal, Joseph S. Perkell, Frank H. Guenther, and Satrajit S. Ghosh. “Diffusion Imaging of Cerebral White Matter in Persons Who Stutter: Evidence for Network-Level Anomalies.” Frontiers in Human Neuroscience 8 (2014).
Version
Final published version
Abstract
Deficits in brain white matter have been a main focus of recent neuroimaging studies on stuttering. However, no prior study has examined brain connectivity on the global level of the cerebral cortex in persons who stutter (PWS). In the current study, we analyzed the results from probabilistic tractography between regions comprising the cortical speech network. An anatomical parcellation scheme was used to define 28 speech production-related ROIs in each hemisphere. We used network-based statistic (NBS) and graph theory to analyze the connectivity patterns obtained from tractography. At the network-level, the probabilistic corticocortical connectivity from the PWS group were significantly weaker than that from persons with fluent speech (PFS). NBS analysis revealed significant components in the bilateral speech networks with negative correlations with stuttering severity. To facilitate comparison with previous studies, we also performed tract-based spatial statistics (TBSS) and regional fractional anisotropy (FA) averaging. Results from tractography, TBSS and regional FA averaging jointly highlight the importance of several regions in the left peri-Rolandic sensorimotor and premotor areas, most notably the left ventral premotor cortex (vPMC) and middle primary motor cortex, in the neuroanatomical basis of stuttering.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.3389/fnhum.2014.00054
