  Parcellation of human cerebellar pathways is essential for advancing our
understanding of the human brain. Existing diffusion MRI tractography
parcellation methods have been successful in defining major cerebellar fibre
tracts, while relying solely on fibre tract structure. However, each fibre
tract may relay information related to multiple cognitive and motor functions
of the cerebellum. Hence, it may be beneficial for parcellation to consider the
potential importance of the fibre tracts for individual motor and cognitive
functional performance measures. In this work, we propose a multimodal
data-driven method for cerebellar pathway parcellation, which incorporates both
measures of microstructure and connectivity, and measures of individual
functional performance. Our method involves first training a multitask deep
network to predict various cognitive and motor measures from a set of fibre
tract structural features. The importance of each structural feature for
predicting each functional measure is then computed, resulting in a set of
structure-function saliency values that are clustered to parcellate cerebellar
pathways. We refer to our method as Deep Multimodal Saliency Parcellation
(DeepMSP), as it computes the saliency of structural measures for predicting
cognitive and motor functional performance, with these saliencies being applied
to the task of parcellation. Applying DeepMSP we found that it was feasible to
identify multiple cerebellar pathway parcels with unique structure-function
saliency patterns that were stable across training folds.
