Intrahemispheric White Matter Asymmetries and Interhemispheric Connections Underlying the Lateralization of Language Production and Spatial Attention in Left-Handers

Abstract

Leftward language production and rightward spatial attention are salient features of functional organization in most humans, but their anatomical basis remains unclear. Interhemispheric connections and intrahemispheric white matter asymmetries have been proposed as important factors underlying functional lateralization. To investigate the role of white matter connectivity in functional lateralization, we first identified 96 left-handers using visual half field naming tasks. They were then divided into atypical and typical functional dominance based on the lateralization of brain activation in a word generation task (for language production) and a landmark task (for spatial attention). Using a novel fixel-based framework, we obtained fiber-specific properties of white matter pathways. Results showed, first, that differences between two language dominance groups occurred in the asymmetry of the superior longitudinal fasciculus-III (SLF-III), whereas differences between two spatial attention dominance groups occurred in the rostrum and rostral body of the corpus callosum. However, the directions of functional lateralization were not associated with the directions of white matter asymmetries. Second, the degree of language lateralization was predicted by SLF-III asymmetry and the rostral body of the corpus callosum, whereas the degree of spatial attention lateralization was predicted by the rostrum of the corpus callosum. Notably, the degree of each functional lateralization was negatively correlated with the anterior and middle callosal connections, supporting the excitatory model of the corpus callosum. The results suggest that language lateralization is shaped by a combined effect of intra- and interhemispheric connections, whereas spatial attention lateralization relies more on interhemispheric connections.

Keywords: corpus callosum; functional lateralization; language lateralization; language production; spatial attention; superior longitudinal fasciculus.

Figure 1.

Correlations between functional lateralization index (LI) in language production and reaction time (RT) differences between two visual half fields (VHFs) in word and picture naming tasks. A. Both the LI for language production and spatial attention present a non-normally distribution. B. RT differences in the word naming task displays a normal distribution, whereas those in the picture naming task displays a non-normal distribution. C–D. The LI of brain activation based on Broca’s area is positively correlated with RT differences in two naming tasks. Despite the high false alarm rate, the behavioral screening identified atypical lateralization individuals with a high hit rate and enough individuals with weak laterality. RT_LVF-RT_RVF indicates reaction time differences between the left and right VHF; LI indicates language dominance: below 0 for right, above 0 for left. The closer to −1 or 1, the stronger the dominance.

Figure 2.

The relationship between functional lateralization in language production and the asymmetry of superior longitudinal fasciculus-III (SLF-III). A. The SLF reconstructed used TractSeg. B. The lateralization of language production is positively associated with the asymmetry of SLF-III. C. Individuals with right language dominance (RLD) showed a less leftward SLF-III in fiber density cross-section (FDC) compared to those with left language dominance (LLD). D. The interaction plot of the Hemisphere * Lateralization Group ANOVA. A negative asymmetry index in the plots signifies right lateralization.

Figure 3.

Difference in the asymmetry of SLF_III between two groups of spatial attention lateralization. A. Rostrum. B. Rostral body. The left spatial attention dominance (LSD) group showed a higher FDC of the rostrum and rostral body compared to the right spatial attentional dominance (RSD) group.

Figure 4.

The subdivisions of the corpus callosum (CC) reconstructed using TractSeg.