Motivation: The development of in vivo brain connectomics field has heavily relied on using functional and diffusion Magnetic Resonance Imaging (MRI) modalities, which have fundamentally focused on studying the functional and structural relationships between pairs of anatomical regions in the brain. However, works on brain morphological (i.e., shape-to-shape) connections, which can be derived from T1 and T2 MR images, in both typical and atypical development or ageing are almost absent. Furthermore, the brain cannot be only regarded as a static shape, since it is a dynamic complex system that changes at functional, structural and morphological levels. Hence, examining the ‘connection’ between brain shape and its changes with time (e.g., growth or atrophy) may help advance our understanding of brain dynamics as well as disorders that may affect it.
Innovation: We introduce in this work three population-based shape-growth connectivity analysis tools that further extend the field of connectomics to brain morphology and dynamics: the morphome, kinectome and morpho-kinectome.
Brain network atlas estimation
The developmental functional baby network atlas
Motivation: Many methods have been developed to spatially normalize a population of brain images for estimating a mean image as a population- average atlas. However, methods for deriving a network atlas from a set of brain networks sitting on a complex manifold are still absent.
Innovation: We propose a novel network atlas estimation framework from a population of brain networks estimated from a single neuroimaging modality (e.g., functional MRI).