The utility of lesion classification in predicting language and treatment outcomes in chronic stroke-induced aphasia

Abstract

Stroke recovery models can improve prognostication of therapy response in patients with chronic aphasia, yet quantifying the effect of lesion on recovery is challenging. This study aimed to evaluate the utility of lesion classification via gray matter (GM)-only versus combined GM plus white matter (WM) metrics and to determine structural measures associated with aphasia severity, naming skills, and treatment outcomes. Thirty-four patients with chronic aphasia due to left hemisphere infarct completed T1-weighted and DTI scans and language assessments prior to receiving a 12-week naming treatment. GM metrics included the amount of spared tissue within five cortical masks. WM integrity was indexed by spared tissue and fractional anisotropy (FA) from four homologous left and right association tracts. Clustering of GM-only and GM + WM metrics via k-medoids yielded four patient clusters that captured two lesion characteristics, size and location. Linear regression models revealed that both GM-only and GM + WM clustering predicted baseline aphasia severity and naming skills, but only GM + WM clustering predicted treatment outcomes. Spearman correlations revealed that without controlling for lesion volume, the majority of left hemisphere metrics were related to language measures. However, adjusting for lesion volume, no relationships with aphasia severity remained significant. FA from two ventral left WM tracts was related to naming and treatment success, independent of lesion size. In sum, lesion volume and GM metrics are sufficient predictors of overall aphasia severity in patients with chronic stroke, whereas diffusion metrics reflecting WM tract integrity may add predictive power to language recovery outcomes after rehabilitation.

Keywords: Aphasia; Diffusion-weighted imaging; Lesion size and location; Treatment outcomes.

Figures

Figure 1.

Anatomical masks. (A) Gray matter (GM) regions of interest (ROIs), including DLPFC (purple), iFrontal (green), aTemporal (red), pTemporal (navy blue), Parietal (cyan) and Occipital (yellow). Numbers denote ROIs in the AAL atlas, listed in Table 1. (B) Bilateral white matter (WM) tract masks, including—from left to right—the arcuate (in red), inferior fronto-occipital (in green), inferior longitudinal (in blue) and uncinate (in violet) fasciculi. (C) From left to right, the normalized T1 image, lesion map, and subject-specific GM and WM masks shown for a sample patient. Matrices below the masks represent 4×4mm swathes of tissue as a toy example of the procedure used to calculate spared tissue and fractional anisotropy (FA) per each subject-specific mask. In the far left and middle matrices, 1 = spared and 0 = lesioned voxels in the GM and WM masks, respectively; the font color corresponds to the mask from which spared voxels were extracted. The numbers in the third matrix (at right) reflect FA values per voxel in the same 4×4mm of WM tissue shown in the middle matrix.

Figure 2.

Patient lesion data. Lesion overlay and lesion volume mean (denoted by x‒) in cubic centimeters (cc) and distribution for n = 34 patients.

Figure 3.

GM-only clustering. (A) Results of the k-medoids analysis using GM-only ROI metrics. P1-34 correspond to single patients, Dim = Dimension. (B) Lesion volume per cluster in cubic centimeters (cc). (C) Lesion subtraction plots of patients in cluster 3 (in orange) versus cluster 4 (in green). Brighter colors reflect greater overlap of patients’ lesions.

Figure 4.

GM+WM clustering. (A) Results of the k-medoids analysis using combined GM and WM metrics, Dim = Dimension. (B) Lesion volume per cluster in cubic centimeters (cc). (C) Lesion subtraction plots of patients in cluster 1 (in yellow) versus cluster 3 (in vermillion). (D) Lesion subtraction plots of patients in cluster 2 (in blue) versus cluster 4 (in pink). Brighter colors reflect greater overlap of patients’ lesions.

Figure 5.

Language measures, including A) aphasia severity per WAB-R AQ, B) naming skills per BNT total correct and C) treatment gains per (PMG) plotted by GM-only and GM+WM clusters. *** p

Figure 6.

Associations between language measures and structural metrics, controlling for lesion volume, including (A) BNT total correct and fractional anisotropy (FA) in the left inferior fronto-occipital fasciculus (IFOF) and inferior longitudinal fasciculus (ILF) and (B) proportion of potential maximal gain (PMG) and FA in the left IFOF and ILF.