Multi-way Regression Reveals Backbone of Macaque Structural Brain Connectivity in Longitudinal Datasets

摘要

Brain development has been an intriguing window to study the dynamic formation of brain features at a variety of scales, such as cortical convolution and white matter wiring diagram. However, recent studies only focused on a few specific fasciculus or several region. Very few studies focused on the development of macro-scale wiring diagrams due to the lack of longitudinal datasets and associated methods. In this work, we take the advantage of recently released longitudinal macaque MRI and DTI datasets and develop a novel multi-way regression method to model such datasets. By this method, we extract the backbone of structural connectome of macaque brains and study the trajectory of its development over time. Graphic statistics of these backbone connectomes demonstrate their soundness. Our findings are consistent with the reports in the literatures, suggesting the effectiveness and promise of this framework.