Developmental Connectomics from a “Big Data” Perspective

From infancy through adulthood, the human brain undergoes profound structural and functional maturation that supports the development of complex cognitive, social, and behavioral abilities. The advent of multi-modal neuroimaging techniques has enabled non-invasive mapping of the developing structural and functional connectivity, namely the developmental connectome. Recent advances in large-scale, high-resolution, and multi-site neuroimaging have ushered developmental connectomics into the era of big data. This shift is characterized by large sample sizes, both longitudinal and cross-sectional designs, and the integration of cognitive, biological, and environmental measures. These data-rich resources have not only overcome previous limitations but also expanded upon earlier findings, advancing the field beyond descriptive observations towards mechanistic insights into brain development. In this review, we highlight recent advances in developmental connectomics from the prenatal period to early adulthood, with a focus on the big data perspective enabled by multi-modal magnetic resonance imaging. We first introduce major large-scale neuroimaging datasets that provide comprehensive, multi-dimensional data on brain development. Next, we review cutting-edge connectome-based approaches, including graph-based and network communication models, along with statistical methods such as growth curve modeling and multivariate analysis. Finally, we summarize key findings on developmental principles, derived from both prior studies and recent large-scale efforts, and their associations with cognitive, behavioral, and genetic factors, and outline emerging challenges and future directions in the field.