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Poster A34, Wednesday, November 8, 10:30 – 11:45 am, Harborview and Loch Raven Ballrooms

White Matter Connectivity and Lexical Access in Aphasia

William Hula1,2, Juan Fernandez-Miranda3, David Fernandes-Cabral3, Michelle Gravier1, Michael Walsh Dickey1,2, Fang-Cheng Yeh3, Sandip Panesar3, Vijay Rowthu3, Sudhir Pathak2, Patrick Doyle1,2;1VA Pittsburgh Healthcare System, 2University of Pittsburgh, 3University of Pittsburgh Medical Center

INTRODUCTION: Neurolinguistic models have coalesced around the view that two distinct pathways support different kinds of processing (Hickok & Poeppel ,2004; Saur et al., 2008): A ventral stream (VS) maps sound to meaning, while a dorsal stream (DS) maps sound to articulation. Dell, Schwartz, Nozari, Faseyitan, & Coslett (2013) found correspondences between this framework and the interactive two-step model of lexical access (Foygel & Dell, 2000) such that integrity of VS cortical areas predicted the semantic (s) connection weight parameter and DS areas predicted phonological (p) weight. However, there is evidence that the arcuate fascicle (AF), a major component of the DS, also contributes to semantic processing (Glasser & Rilling, 2008; Fernandez-Miranda et al., 2014). We present updated results from an ongoing study (Hula Fernandez-Miranda, et al., 2014) applying High Definition Fiber Tractography (Fernandez-Miranda, Pathak, Engh, et al., 2012) to investigate white matter correlates of word production in aphasia. We hypothesize that both left DS and VS connectivity predict s-weight, while only the former predicts p-weight. METHOD: Thirty-three participants with aphasia of at least six months post-onset due to unilateral left hemisphere stroke were given the Philadelphia Naming Test (mean %correct = 64, sd = 23). Error-type counts were used to estimate the s-weight and p-weight weight parameters of the two-step model. Diffusion spectrum imaging data were reconstructed by generalized q-sampling (Yeh, Wedeen, & Tseng, 2010). Orientation distribution functions (directional probability of diffusion) were used to calculate quantitative anisotropy (QA) values for each tract using whole-brain seeding and defined ROIs for each tract. We estimated two regression models using Bayesian methods with s-weight and p-weight as dependent variables and QA values for the left VS (VSQA; inferior fronto-occipital fascicle, uncinate fascicle) and DS (DSQA; AF, superior longitudinal fascicle) as predictors. RESULTS: Markov chain Monte Carlo convergence and model fit were acceptable. VSQA and DSQA were not significantly correlated (r = 0.083, 95%CI: -0.25, 0.39). VSQA and DSQA were both significant positive predictors of s-weight (both ps < 0.02; VSQA beta = 0.39, 95%CI: 0.09, 0.62; DSQA beta = 0.44, 95%CI: 0.11, 0.67; multiple r-squared = 0.35, 95%CI: 0.132 0.56) Only VSQA was a significant predictor of p-weight (p = 0.026; VSQA beta = 0.38, 95%CI: 0.05, 0.61; r-squared = 0.14, 95%CI = 0.004, 0.37). DISCUSSION: As predicted, we found significant relationships between VS and DS white matter connectivity and semantic word production ability in aphasia, consistent with involvement of the AF in semantic processing (Glasser & Rilling, 2008; Fernandez-Miranda et al., 2014). Contrary our hypothesis, we found that only VS connectivity predicted phonological ability. One potential explanation is related to prior reports that recruitment of perilesional cortex may be associated with better recovery (Saur, Lange, et al, 2006; Szaflarski, Allendorfer, et al., 2013). It is possible that in this sample of persons with chronic aphasia who have recovered to varying degrees, neuroplastic recruitment of perilesional areas that include cortical terminations of the ventral stream may be associated with increased connectivity of these tracts and better phonological function.

Topic Area: Language Disorders

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