Predicting aphasia type from brain damage measured with structural MRI

Abstract

Chronic aphasia is a common consequence of a left-hemisphere stroke. Since the early insights by Broca and Wernicke, studying the relationship between the loci of cortical damage and patterns of language impairment has been one of the concerns of aphasiology. We utilized multivariate classification in a cross-validation framework to predict the type of chronic aphasia from the spatial pattern of brain damage. Our sample consisted of 98 patients with five types of aphasia (Broca's, Wernicke's, global, conduction, and anomic), classified based on scores on the Western Aphasia Battery (WAB). Binary lesion maps were obtained from structural MRI scans (obtained at least 6 months poststroke, and within 2 days of behavioural assessment); after spatial normalization, the lesions were parcellated into a disjoint set of brain areas. The proportion of damage to the brain areas was used to classify patients' aphasia type. To create this parcellation, we relied on five brain atlases; our classifier (support vector machine - SVM) could differentiate between different kinds of aphasia using any of the five parcellations. In our sample, the best classification accuracy was obtained when using a novel parcellation that combined two previously published brain atlases, with the first atlas providing the segmentation of grey matter, and the second atlas used to segment the white matter. For each aphasia type, we computed the relative importance of different brain areas for distinguishing it from other aphasia types; our findings were consistent with previously published reports of lesion locations implicated in different types of aphasia. Overall, our results revealed that automated multivariate classification could distinguish between aphasia types based on damage to atlas-defined brain areas.

Keywords: Aphasia typology; Chronic aphasia; Multivariate classification.

Figure 1

Overlap of lesions across patients of each aphasia type. A voxel with overlap = 1 indicates that this voxel is lesioned in 100% of the patients.

Figure 2

Brain loadings computed from predicting one aphasia type versus another. See Figure 3 for the remaining 5 pairings of aphasia types.

Figure 3

Brain loadings computed from predicting one aphasia type versus another. See Figure 2 for the remaining 5 pairings of aphasia types.

Figure 4

Areas that are most predictive of each aphasia type (see Table 3) mapped onto a standard brain template. The colour indicates the predictive relevance of a brain area (red being the least predictive, and yellow being the most predictive).