The utility of lesion classification models in predicting language abilities and treatment outcomes in persons with aphasia

Event Abstract Back to Event The utility of lesion classification models in predicting language abilities and treatment outcomes in persons with aphasia Erin L. Meier1*, Jeffrey P. Johnson1, Yue Pan1 and Swathi Kiran1 1 Boston University, United States Middle cerebral artery (MCA) infarct that results in aphasia is often qualified in terms of cortical damage to anterior and/or posterior left hemisphere (LH) regions. Two problems with descriptive lesion classification exist. First, infarct results in damage along vascular territories that spreads beyond the borders of atlas-based anatomical regions, rendering discrete lesion classification difficult. Second, MCA stroke typically extends beyond the cortical surface into subcortical white matter (WM) pathways critical for language processing. Therefore, two primary aims were addressed: (1) to generate two lesion classification systems, one based on gray matter (GM) integrity metrics alone and the other based on combined GM+WM integrity and (2) to evaluate the utility of both systems in predicting aphasia severity, naming abilities and naming treatment outcomes in persons with chronic aphasia (PWA). Thirty-three PWA (23M, mean age=62.18 years, mean months post-stroke=48.88) participated in the study. Language testing was used to obtain severity measures of aphasia and anomia per the Western Aphasia Battery-Revised Aphasia Quotient (AQ)[1] and the Boston Naming Test (BNT)[2], respectively. A subset of patients (n=30) received up to 12 weeks of semantic feature analysis-based naming treatment[3]. Therapy success was determined by the proportion of potential maximal gain (PMG)[4], which reflected the degree of improved naming of trained items, accounting for pre-treatment abilities. All PWA completed T1-weighted and DTI scans. MR data preprocessing was performed via a bespoke pipeline[5] created for processing stroke data. Non-lesioned voxels were retained from the intersection of manually-drawn lesion maps and atlas-based LH GM (i.e., dorsolateral prefrontal cortex, inferior frontal, anterior and posterior temporal and parietal) and WM tract (i.e., arcuate, inferior fronto-occipital, inferior longitudinal and uncinate fasciculi ) masks. Fractional anisotropy (of WM) and spared voxels per mask were extracted from all participants’ lesioned masks. K-medoids analyses were used to determine lesion group membership based on GM only versus GM+WM metrics. Lesion classifications were subsequently used in regressions predicting AQ, BNT and PMG. Both k-medoids analyses captured two lesion dimensions—extent and location—that resulted in four lesion groups. In the GM only model, clusters 1 and 2 included PWA with small and large lesions, respectively. PWA in clusters 3 and 4 had medium-sized, primarily ventral and dorsal lesions, respectively. The GM+WM model resulted in more nuanced clustering wherein cluster 1=small, primarily dorsal lesions; cluster 2=large, primarily dorsal lesions; cluster 3=small, primarily ventral lesions and cluster 4=large, primarily ventral lesions (see Figure 1). GM only lesion classification significantly predicted AQ (t=-3.254, p=0.003, R-squared=0.231) and BNT (t=-2.058, p=0.048, R-squared=0.092) but not PMG (t=-0.738, p=0.466, R-squared=-0.016). By contrast, GM+WM lesion classification not only significantly predicted AQ (t=-2.641, p=0.013, R-squared=0.157) and BNT (t=-2.308, p=0.028, R-squared=0.119) but also treatment success per PMG (t=-2.143, p=0.041, R-squared=0.110). In conclusion, while GM structural integrity adequately predicts language impairment (i.e., overall aphasia and anomia severity), combined GM+WM metrics are stronger predictors of baseline naming skills and better indicators of the potential to regain language function after treatment. Thus, quantification of both cortical and subcortical structural integrity is essential to build more robust structure-behavior prediction models. Figure 1 Acknowledgements Funding: This work was supported by the National Institutes of Health/National Institute on Deafness and Other Communication Disorders (1P50DC012283 and 1F31DC015940). References 1. Kertesz, A. (2007). Western Aphasia Battery (Revised) PsychCorp. San Antonio, Tx. 2. Kaplan, E., Goodglass, H., Weintraub, S., Segal, O., & van Loon-Vervoorn, A. (2001). Boston naming test. Pro-ed. 3. Kiran, S., & Thompson, C. K. (2003). The role of semantic complexity in treatment of naming deficits: Training semantic categories in fluent aphasia by controlling exemplar typicality. Journal of Speech, Language, and Hearing Research, 46(4), 773–787. 4. Lambon Ralph, M. A., Snell, C., Fillingham, J. K., Conroy, P., & Sage, K. (2010). Predicting the outcome of anomia therapy for people with aphasia post CVA: Both language and cognitive status are key predictors. Neuropsychological Rehabilitation, 20(2), 289–305. 5. Advanced Diffusion Preprocessing Pipeline from the Northwestern University Neuroimaging Data Archive (NUNDA;http://niacal.northwestern.edu/nunda_pipelines/18) Keywords: Lesion classification, DTI analyses, Neuroimaging, Aphasia, Neural predictors Conference: Academy of Aphasia 56th Annual Meeting, Montreal, Canada, 21 Oct - 23 Oct, 2018. Presentation Type: oral presentation Topic: Eligible for a student award Citation: Meier EL, Johnson JP, Pan Y and Kiran S (2019). The utility of lesion classification models in predicting language abilities and treatment outcomes in persons with aphasia. Conference Abstract: Academy of Aphasia 56th Annual Meeting. doi: 10.3389/conf.fnhum.2018.228.00083 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 01 May 2018; Published Online: 22 Jan 2019. * Correspondence: Ms. Erin L Meier, Boston University, Boston, United States, e.meier@northeastern.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Erin L Meier Jeffrey P Johnson Yue Pan Swathi Kiran Google Erin L Meier Jeffrey P Johnson Yue Pan Swathi Kiran Google Scholar Erin L Meier Jeffrey P Johnson Yue Pan Swathi Kiran PubMed Erin L Meier Jeffrey P Johnson Yue Pan Swathi Kiran Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.