  Recent advancements in understanding the brain's functional organization
related to behavior have been pivotal, particularly in the development of
predictive models based on brain connectivity. Traditional methods in this
domain often involve a two-step process by first constructing a connectivity
matrix from predefined brain regions, and then linking these connections to
behaviors or clinical outcomes. However, these approaches with unsupervised
node partitions predict outcomes inefficiently with independently established
connectivity. In this paper, we introduce the Supervised Brain Parcellation
(SBP), a brain node parcellation scheme informed by the downstream predictive
task. With voxel-level functional time courses generated under resting-state or
cognitive tasks as input, our approach clusters voxels into nodes in a manner
that maximizes the correlation between inter-node connections and the
behavioral outcome, while also accommodating intra-node homogeneity. We
rigorously evaluate the SBP approach using resting-state and task-based fMRI
data from both the Adolescent Brain Cognitive Development (ABCD) study and the
Human Connectome Project (HCP). Our analyses show that SBP significantly
improves out-of-sample connectome-based predictive performance compared to
conventional step-wise methods under various brain atlases. This advancement
holds promise for enhancing our understanding of brain functional architectures
with behavior and establishing more informative network neuromarkers for
clinical applications.
