Bagging Improves Reproducibility of Functional Parcellation of the Human Brain

Increasing the reproducibility of neuroimaging measurement addresses a central impediment to the clinical impact of human neuroscience. Recent efforts demonstrating variance in functional brain organization within and between individuals shows a need for improving reproducibility of functional parcellations without long scan times. We apply bootstrap aggregation, or bagging, to the problem of improving reproducibility in functional parcellation. We use two large datasets to demonstrate that compared to a standard clustering framework, bagging improves the reproducibility and test-retest reliability of both cortical and subcortical functional parcellations across a range of sites, scanners, samples, scan lengths, and clustering parameters. With as little as six minutes of scan time bagging creates more reproducible parcellations than standard approaches with twice as much data. This suggests bagging may be a key method for improving functional parcellation and bringing functional neuroimaging-based measurement closer to clinical impact.