“Older circuits get richer”: neurogenesis timeline shapes hubness and neurogenetic profiles of the adult human brain

Abstract

ABSTRACTUnderstanding the architectural principle that shapes the topology of the human connectome at its multiple spatial scales is a major challenge for systems neuroscience. This would provide key fundamental principles and a theory for browsing brain’s networks, to ultimately generate hypothesis and approach to which extent key structures might impact different brain pathologies. In this work, we propose the hypothesis that the centrality of the different brain nodes in the human connectome is a product of their embryogenic age, and accordingly, early-born nodes should display higher hubness, and viceversa for late-born nodes. We tested our hypothesis by identifying and segmenting eighteen macroregions with a well-known embryogenic age, over which we calculated nodes’ centrality in the structural and functional networks at different spatial resolutions. First, nodes’ structural centrality correlated with their embryogenic age, fully confirming our working hypothesis. However, at the functional level, distinct trends were found at different resolutions. Secondly, the difference in embryonic age between nodes inversely correlated with the probability of existence and the weights of the links. This indicated the presence of a temporal developmental gradient that shapes connectivity and where nodes connect more to nodes with a similar age. Finally, brain transcriptomic analysis revealed high association between embryonic age, structural-functional centrality and the expression of genes related to nervous system development, synapse regulation, and human neurological diseases. Overall, these results support the hypothesis that the embryogenic age of brain regions shapes the topology of adult brain networks. Our results show two key principles, “preferential age attachment” and “older gets richer” on the wiring of the human brain, thus shedding new light on the relationship between brain development, transcriptomics, node centrality, and neurological diseases.