  The human brains are organized into hierarchically modular networks
facilitating efficient and stable information processing and supporting diverse
cognitive processes during the course of development. While the remarkable
reconfiguration of functional brain network has been firmly established in
early life, all these studies investigated the network development from a
"single-scale" perspective, which ignore the richness engendered by its
hierarchical nature. To fill this gap, this paper leveraged a longitudinal
infant resting-state functional magnetic resonance imaging dataset from birth
to 2 years of age, and proposed an advanced methodological framework to
delineate the multi-scale reconfiguration of functional brain network during
early development. Our proposed framework is consist of two parts. The first
part developed a novel two-step multi-scale module detection method that could
uncover efficient and consistent modular structure for longitudinal dataset
from multiple scales in a completely data-driven manner. The second part
designed a systematic approach that employed the linear mixed-effect model to
four global and nodal module-related metrics to delineate scale-specific
age-related changes of network organization. By applying our proposed
methodological framework on the collected longitudinal infant dataset, we
provided the first evidence that, in the first 2 years of life, the brain
functional network is co-evolved at different scales, where each scale displays
the unique reconfiguration pattern in terms of modular organization.
