Bagging Improves Reproducibility of Functional Parcellation

Developing methods that increase the reproducibility and reliability of neuroimaging measurement is an important challenge in clinical and cognitive neuroscience neuroimaging. One particular area of importance is estimation of functional areal organization, often studied through functional parcellation of the brain. Functional areal organization shows substantial variance across individuals, and creating more reproducible and reliable functional areal parcellations would allow for more generalizable estimates of brain organization. We apply bootstrap aggregation, or bagging, to the problem of improving reproducibility in functional parcellation. We use two test-retest datasets, one of 30 young adults scanned 10 times for ten minutes per scan, and another of 300 young adults scanned twice for six minutes per scan, to demonstrate that bagging provides functional parcellations with higher reproducibility and reliability compared to non-bagged functional parcellation. While increasing scan length and sample size have been regarded as the main methods of improving robustness of estimating functional organization, our results demonstrate that bagging can be used to boost the robustness of functional parcellation with as little as five minutes of scan time in as few as thirty subjects. These results imply bagging can be used to improve the robustness in acquisitions with a short scan time, which is commonplace in many established and ongoing studies and open source datasets. By testing an array of different reproducibility metrics, datasets, cluster levels, and acquisition lengths, we show where bagging can improve the reproducibility and reliability of functional parcellations. Overall, it seems that the use of this approach is beneficial in creating more reproducible clusters, and bagging should be applied when reproducibility of functional parcellations are under consideration.