Abstract
There is a need to identify biomarkers that predict degree of chronic speech fluency/language impairment and potential for improvement after stroke. We previously showed that the Arcuate Fasciculus lesion load (AF-LL), a combined variable of lesion site and size, predicted speech fluency in patients with chronic aphasia. In the current study, we compared lesion loads of such a structural map (i.e., AF-LL) with those of a functional map [i.e., the functional gray matter lesion load (fGM-LL)] in their ability to predict speech fluency and naming performance in a large group of patients. The fGM map was constructed from functional brain images acquired during an overt speaking task in a group of healthy elderly controls. The AF map was reconstructed from high-resolution diffusion tensor images also from a group of healthy elderly controls. In addition to these two canonical maps, a combined AF-fGM map was derived from summing fGM and AF maps. Each canonical map was overlaid with individual lesion masks of 50 chronic aphasic patients with varying degrees of impairment in speech production and fluency to calculate a functional and structural lesion load value for each patient, and to regress these values with measures of speech fluency and naming. We found that both AF-LL and fGM-LL independently predicted speech fluency and naming ability; however, AF lesion load explained most of the variance for both measures. The combined AF-fGM lesion load did not have a higher predictability than either AF-LL or fGM-LL alone. Clustering and classification methods confirmed that AF lesion load was best at stratifying patients into severe and non-severe outcome groups with 96% accuracy for speech fluency and 90% accuracy for naming. An AF-LL of greater than 4 cc was the critical threshold that determined poor fluency and naming outcomes, and constitutes the severe outcome group. Thus, surrogate markers of impairments have the potential to predict outcomes and can be used as a stratifier in experimental studies.
Highlights
Aphasia is a common symptom after left hemisphere stroke, and affected individuals often experience incomplete recovery despite receiving intense speech therapy after the acute stroke phase (Kertesz and McCabe, 1977; Wade et al, 1986; Pedersen et al, 1995; Engelter et al, 2006)
A comparison of Arcuate Fasciculus lesion load (AF-LL) with extreme capsule (EMC) and uncinate fasciculus (UF) lesion loads confirmed our previous finding that arcuate fasciculus (AF)-LL was the only significant predictor of speech fluency and naming (Table 2); lesion volume was not significant in any multiple regression models relative to AF, EMC, and UF lesion loads (p > 0.05)
AF-LL provided the best classification of speech fluency and naming outcomes with >94 and 90% accuracy, respectively
Summary
Aphasia is a common symptom after left hemisphere stroke, and affected individuals often experience incomplete recovery despite receiving intense speech therapy after the acute stroke phase (Kertesz and McCabe, 1977; Wade et al, 1986; Pedersen et al, 1995; Engelter et al, 2006). Factors that can determine a patient’s recovery from aphasia include lesion size and lesion site (Lazar and Antoniello, 2008; Marchina et al, 2011), as well as the initial level of impairment (Lazar et al, 2010) Other factors such as age, gender, degree of hemispheric language laterality, and small vessel ischemic lesion burden are likely to play a role, but their significance in explaining some of the variance in outcome has not been well examined in larger-scale studies. The method entails overlapping canonical probabilistic maps of a white matter tract (derived from diffusion tensor imaging) with patients’ stroke lesion masks One such speech-related tract is the arcuate fasciculus (AF), known from previous studies to play a critical role in the feedforward and feedback control of speech production (Breier et al, 2008; Hosomi et al, 2009; Saur et al, 2010b). The AF may have direct components (i.e., connections between temporal and inferior frontal brain regions) as well as indirect components
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