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Left Ventral Stream White Matter Connectivity Predicts Response to Semantic Feature Analysis Treatment in Chronic Aphasia

William Hula1, 2*, Juan Fernandez-Miranda3, Fang-Cheng Yeh3, David Fernandes-Cabral3, Michael W. Dickey1, 2, Michelle Gravier1, Sandip Panesar3, Vijay Rowthu3 and Patrick J. Doyle1, 2
  • 1 VA Pittsburgh Healthcare System, GRECC, United States
  • 2 University of Pittsburgh, Communication Science and Disorders, United States
  • 3 University of Pittsburgh Medical Center, Neurological Surgery, United States

INTRODUCTION Identifying predictors of treatment response in aphasia is important for understanding the mechanisms underlying particular interventions and informing treatment candidacy. Aphasia severity is one predictor of outcomes (Dignam et al., 2017; Lambon Ralph, Snell, Fillingham, Conroy, & Sage, 2010; Winans-Mitrik et al., 2014), and gray matter correlates have also been identified (Fridriksson, 2010; Meinzer, Harnish, Conway, & Crosson, 2011; Parkinson, Raymer, Chang, FitzGerald, & Crosson, 2009). However, only one study has reported associations between treatment response and white matter integrity (Meinzer et al., 2010). We used high definition fiber tractography (Fernandez-Miranda et al., 2012) to investigate whether integrity of language-related white matter tracts predicts improvement in overall language function associated with intensive semantic feature analysis treatment for anomia (SFA; Boyle, 2010). Our approach was oriented toward dual-stream neurolinguistic models in which a ventral stream maps sound to meaning, and a dorsal stream maps sound to articulation (Hickok & Poeppel, 2004; Saur et al., 2008). The arcuate and superior longitudinal fasciculi have been proposed as white matter substrates for the dorsal stream (Saur et al., 2008), while the inferior fronto-occipital and uncinate fasciculi have been identified as potential components of the ventral stream (Parker et al., 2005). METHOD Thirteen participants with aphasia due to left-hemisphere stroke received four weeks of intensive SFA as part of a larger ongoing trial. The Comprehensive Aphasia Test (CAT; Swinburn, Porter, & Howard, 2004) was administered pre- and post-treatment. Aphasia severity was estimated using the CAT Modality Mean T-score, and the difference between post-test and pre-test was taken as treatment-related change (ΔCAT). Diffusion spectrum imaging data were acquired pre-treatment via Siemens 3T Tim Trio Scanner using a 2D EPI diffusion sequence and reconstructed by q-space diffeomorphic reconstruction (Yeh, Wedeen, & Tseng, 2010). Orientation distribution functions quantifying directional probability of diffusion were used to calculate quantitative anisotropy (QA) values for dorsal and ventral stream tracts using whole-brain seeding and defined ROIs. Correlations and a path model were estimated using Bayesian methods. Spin distribution functions were also used to construct a connectometry analysis (Yeh, Badre, & Verstynen, 2016) (Figure, Panel B), which shows white matter pathways correlated with ΔCAT. RESULTS The zero-order correlations (95% CIs) between ΔCAT and baseline CAT, left ventral QA, and left dorsal QA were 0.57 (0.04, 0.83), 0.80 (0.35, 0.95), and 0.29 (-0.41, 0.78), respectively. Baseline CAT correlated 0.75 (0.24, 0.94) with ventral QA and 0.29 (-0.42, 0.78) with dorsal QA. Ventral and dorsal QA correlated 0.56 (-0.11, 0.88). A path model regressing ΔCAT on ventral QA with baseline CAT as a mediator (see Figure, Panel B) obtained a robust direct effect and a null indirect effect. The r-squared estimate for ΔCAT was 0.51 (0.11, 0.76). Model fit was acceptable. DISCUSSION These results suggest that the integrity of left-hemisphere ventral pathways may account for the relationship between baseline aphasia severity and treatment-related change, which constrains the interpretation of baseline severity as a prognostic indicator. The results are also consistent with dual-stream models of language and the hypothesized mechanisms underlying response to SFA.

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Acknowledgements

This research was supported by the National Institute on Deafness and Other Communication Disorders of the National Institutes of Health under award number R01DC013803. The authors gratefully acknowledge the assistance of Angela Grzybowski, Rebecca Owens, Alyssa Verlinich, and Emily Boss. The contents of this paper do not represent the views of the Department of Veterans Affairs of the United States Government.

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Keywords: Aphasia, Treatment outcomes, Semantic Feature Analysis, white matter, tractography, connectome mapping

Conference: Academy of Aphasia 55th Annual Meeting , Baltimore, United States, 5 Nov - 7 Nov, 2017.

Presentation Type: poster or oral

Topic: General Submission

Citation: Hula W, Fernandez-Miranda J, Yeh F, Fernandes-Cabral D, Dickey MW, Gravier M, Panesar S, Rowthu V and Doyle PJ (2019). Left Ventral Stream White Matter Connectivity Predicts Response to Semantic Feature Analysis Treatment in Chronic Aphasia. Conference Abstract: Academy of Aphasia 55th Annual Meeting . doi: 10.3389/conf.fnhum.2017.223.00038

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Received: 02 May 2017; Published Online: 25 Jan 2019.

* Correspondence: Dr. William Hula, VA Pittsburgh Healthcare System, GRECC, Pittsburgh, United States, william.hula@va.gov