Brain parcellation is important for exploiting neuroimaging data. Variability in physiology between individuals has led to the need for data-driven approaches to parcellation, with recent research focusing on simultaneously estimating and partitioning the network structure of the brain. We view data preprocessing, parcellation, and parcel validation from the perspective of predictive modeling. The goal is to identify parcels in a way that best generalizes to unseen data. We utilize an uncertainty quantification approach from image science to define parcels as groups of unresolvable variables in the predictive model.
Model parameters are chosen via cross-validation. Parcellation results are compared based on both their repeatability as well as their ability to describe held-out data. The approach provides insight and strategies for open questions such as the choice of evaluation metrics, selection of model order, and the optimal tuning of preprocessing steps for functional imaging data. We compare new and established approaches using functional imaging data, where we find the proposed approach produces parcellations which are both more accurate and more repeatable than the current baseline clustering method. The metrics also demonstrate potential problems with overfitting for the baseline method, and with bias for other methods.