Functional differentiation between cerebral and cerebellar white matter in word decoding and automaticity: a diffusion MRI study

Abstract

The involvement of both the cerebrum and the cerebellum in reading processes has been acknowledged in previous research, yet their specific contributions remained unclear. In this study, we employed machine learning techniques and diffusion tensor imaging (DTI) to elucidate the respective roles of the cerebrum and the cerebellum in reading in adult readers (n = 109, 63 females, mean age = 21 years). We discovered that fractional anisotropy (FA) across the entire brain effectively differentiated good readers from those with poorer reading abilities. Furthermore, compared to the FA within the cerebellum, FA within the cerebrum demonstrated superior performance in identifying readers with better word decoding abilities. In contrast, compared to FA within the cerebellum, the model based on cerebro-cerebellar FA was more effective in distinguishing readers with varying levels of automaticity. These findings were validated through diverse methods, including brain-behavioral association analysis, support vector machine algorithms, and logistic regression. Our results provide evidence for a functional differentiation between the cerebrum and the cerebellum in word reading. Specifically, cerebral white matters are closely associated with word decoding abilities, whereas cerebro-cerebellar connections appear to play a role in supporting automatized skills.