We propose a deep learning-based cerebellar pathway parcellation approach that integrates diffusion magnetic resonance imaging tractography with individual cognitive and motor functional performance measures. We show that our method can identify unique cerebellar parcels with unique structure–function saliency patterns. We publish a four-parcel parcellation and analyse its motor and cognitive saliency patterns, as well as the way in which it subdivides major cerebellar white matter tracts.
