Abstract
The role of the right hemisphere (RH) in post-stroke aphasia (PSA) has not been completely understood. In general, the language alterations in PSA are normally evaluated from the perspective of the language processing models developed from Western languages such as English. However, the successful application of the models for assessing Chinese-language functions in patients with PSA has not been reported. In this study, the features of specific language-related lesion distribution and early variations of structure in RH in Chinese patients with PSA were investigated. Forty-two aphasic patients (female: 13, male: 29, mean age: 58 ± 12 years) with left hemisphere (LH) injury between 1 and 6 months after stroke were included. The morphological characteristics, both at the levels of gray matter (GM) and white matter (WM), were quantified by 3T multiparametric brain MRI. The Fridriksson et al.’s dual-stream model was used to compare language-related lesion regions. Voxel-based lesion-symptom mapping (VLSM) analysis has been performed. Our results showed that lesions in the precentral, superior frontal, middle frontal, and postcentral gyri were responsible for both the production and comprehension dysfunction of Chinese patients with PSA and were quite different from the lesions described by using the dual-stream model of Fridriksson et al. Furthermore, gray matter volume (GMV) was found significantly decreased in RH, and WM integrity was disturbed in RH after LH injury in Chinese patients with PSA. The different lesion patterns between Chinese patients with PSA and English-speaking patients with PSA may indicate that the dual-stream model of Fridriksson et al. is not suitable for the assessment of Chinese-language functions in Chinese patients with PSA in subacute phase of recovery. Moreover, decreased structural integrity in RH was found in Chinese patients with PSA.
Highlights
Stroke is one of the leading causes of aphasia, accounting for approximately 30–40% of stroke survivors with aphasia (Engelter et al, 2006; Marebwa et al, 2017; Nouwens et al, 2018)
Inclusion criteria were as follows: first-ever left hemisphere (LH) ischemic stroke that happened between 1 and 6 months before language testing and MRI examination; aphasia was confirmed by a research speech-language pathologist; and all patients were right-handed based on the Edinburgh Handedness Inventory (EHI) test since the EHI test has the advantage of being a simple and brief method of evaluating laterality using a quantitative scale (Veale, 2014) and the hemispheric lateralization was assessed by using functional MRI according to a previous publication (Wilke and Lidzba, 2007)
Previous studies performed by Ivanova et al (2016) in native Russiaspeaking patients with post-stroke aphasia (PSA) and Rosso et al (2015) in native French-speaking patients with PSA have shown some variabilities in the results as compared with the study results in English speakers (Hickok and Poeppel, 2004; Forkel et al, 2014), suggesting that different native languages might influence the dual-stream model English, French, and Russian belong to a family of Indo-European languages
Summary
Stroke is one of the leading causes of aphasia, accounting for approximately 30–40% of stroke survivors with aphasia (Engelter et al, 2006; Marebwa et al, 2017; Nouwens et al, 2018). The mechanisms promoting the recovery and reorganization of language in post-stroke aphasia (PSA) are still unresolved. The role of the right hemisphere (RH) in the recovery of PSA remains controversial. Previous studies suggested an important role of RH in the recovery from PSA due to left hemisphere (LH) stroke (Barlow, 1877; Weiller et al, 1995; Musso et al, 1999; Abo et al, 2004; Winhuisen et al, 2005; Turkeltaub et al, 2012). Some studies suggest that increased RH activation may result in excessive right-to-left suppression, which may hinder language performance (Belin et al, 1996; Heiss et al, 2003; Naeser et al, 2004; Thiel et al, 2006). For a description of specific language functions related to RH structures or brain networks, local gray matter volume (GMV) is commonly measured to explain additional variance in language outcome based on T1weighted images (Butler et al, 2014; Xing et al, 2016) along with white matter (WM) regions detected by diffusion tensor imaging (DTI) (Forkel et al, 2014; Ivanova et al, 2016; Pani et al, 2016)
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