Poster B8, Tuesday, August 20, 2019, 3:15 – 5:00 pm, Restaurant Hall
White Matter Hyperintensity predicts naming treatment outcomes in aphasia
Claudia Penaloza1, Maria Varkanitsa1,2, Andreas Charidimou2, David Caplan2, Swathi Kiran1;1Boston University, 2Massachusetts General Hospital
Introduction: Predicting treatment-induced recovery in people with aphasia (PWA) is essential in rehabilitation research given the large outcome variability observed and the complex interplay between multiple factors modulating treatment response. Although stroke-related factors including lesion site and volume, and aphasia severity substantially predict aphasia recovery, general brain health markers may independently modulate individual response to language therapy. Here, we examined whether white matter hyperintensity (WMH) predicts treatment outcomes in aphasia beyond stroke-related factors. Methods: Participants were 30 chronic PWA (10F, age: mean=61years, range=40–80 years, education: mean=15 years, range=12–18, time post stroke: mean=52 months, range=8–170 months) with a single left hemisphere stroke (volume: mean=135.21cm3, range=11.66–317.07cm3) who completed up to 12 weeks of semantic feature analysis treatment. Mean baseline aphasia severity (AQ quotient) from the Western Aphasia Battery–Revised was 59.83 (range=11.7–95.2). T2–FLAIR MRI scans at baseline were scored for WMH severity on the right hemisphere using the Fazekas scale. Periventricular WMH (PVWMH), deep WMH (DWMH), and deep WMH lesion count (DWMHcount) were rated on a 4–point scale (0=absent–3=severe) by two independent raters (inter-rater reliability κ: PVWMH=0.79, DWMH=0.90, DWMHcount=0.95). We additionally computed two composite scores, WMH global load (PVWMH score + DWMH score) and the WMH count-based load (PVWMH score + DWMH score + DWMHcount score). The proportion of the potential maximal gain (PMG; Lambon Ralph et al., 2010), assessed immediately after treatment [(mean post-treatment score – mean pre-treatment score)/(total number of trained items – mean pre-treatment score)] was the primary dependent variable, and WMH scores the predictors. Because none of the measures was normally distributed, we dichotomized the WMH scores in mild and moderate/severe cases and split PMG into quartiles to develop four ordinal regression models using STATA, one for each WMH score, including AQ, total lesion volume and age as covariates. Results: Both DWMH severity and DWMH lesion count independently predicted treatment outcome; going from mild to moderate/severe DWMH and DWMH lesion count were associated with lower odds of moving to a higher PMG quartile (DWMH: odds ratio=0.11, SE=0.11, z=-2.23, p=0.02; DWMHcount: odds ratio=0.13, SE=0.11, z=-2.35, p=0.01). Similar results were found for the two composite scores (WMH global load: odds ratio=0.14, SE=0.13, z=-2.03, p=0.04; WMH count-based load: odds ratio= 0.19, SE= 0.16, z= -2.00, p=0.04). PVWMH severity was not a significant predictor. Conclusion: Our findings indicate that WMH in the RH predicts language treatment outcomes in PWA above and beyond age and stroke-related indexes of brain damage. WMH has been associated with varying neuropathological processes including demyelination and axonal loss. Because neural structural integrity is essential for brain plasticity, our findings suggest that individual differences in treatment response may depend on white matter integrity in the contralesional (RH) hemisphere. In addition, given that WMH is a chronic progressive brain pathology that tends to be symmetrical and exists even in stroke-free healthy individuals, our findings suggest that pre-morbid markers of brain health may affect treatment and functional outcome. These results highlight the utility of examining biomarkers of neural integrity in aphasia recovery and rehabilitation research.
Themes: Disorders: Acquired, Language Therapy
Method: White Matter Imaging (dMRI, DSI, DKI)