RT Journal Article
SR Electronic
T1 Improving Corticostriatal Parcellation Through Multilevel Bagging with PyBASC
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 343392
DO 10.1101/343392
A1 Aki Nikolaidis
A1 Joshua Vogelstein
A1 Pierre Bellec
A1 Michael P. Milham
YR 2018
UL http://biorxiv.org/content/early/2018/06/11/343392.abstract
AB Understanding the functional organization the brain is a centrally important theme of human neuroscience. Ideally, these organizational maps uncover the underlying structure of the brain’s functional architecture, and group-level maps are accurate representations of the individuals in the sample. Using simulated fMRI data, we demonstrate that bagging improves the ability of clustering to uncover the data’s underlying structure. We show that the group-level maps become more correlated to the individual-level maps with more bootstrap aggregates, suggesting bagging improves the representativeness of the group-level solution. Using a test-retest dataset of 30 young adults, we confirm these findings. More specifically, we see bagging improves the test-retest correlation between cluster maps, and increases correlation between group-level and individual-level cluster maps, and these effects are robust to number of clusters and length of scan used. These results suggest bagging is an important method for increasing reliability and validity of functional parcellation approaches.