ABSTRACT
Networks are present in many aspects of our lives, and networks in neuroscience have recently gained much attention leading to novel representations of brain connectivity. Indeed, there is still room for investigation of the genetic contribution to brain connectivity. The integration of neuroimaging and genetics allows a better understanding of the effects of the genetic variations on brain structural and functional connections, but few studies have successfully investigated the longitudinal association of such a mutual interplay. Nevertheless, several Alzheimer’s disease-associated genetic variants have been identified through omic studies, and the current work uses whole-brain tractography in a longitudinal case-control study design and measures the structural connectivity changes of brain networks to study the neurodegeneration of Alzheimer’s. This is performed by examining the effect of targeted genetic risk factors on local and global brain connectivity. We investigated the degree to which changes in brain connectivity are affected by gene expression. More specifically, we used the most common brain connectivity measures such as efficiency, characteristic path length, betweenness centrality, Louvain modularity and transitivity (a variation of clustering coefficient). Furthermore, we examined the extent to which Clinical Dementia Rating reflects brain connections longitudinally and genetic variation. Here, we show that the expression of PLAU and HFE genes increases the change in betweenness centrality related to the fusiform gyrus and clustering coefficient of cingulum bundle over time, respectively. APP and BLMH gene expression associates with local connectivity. We also show that betweenness centrality has a high contribution to dementia in distinct brain regions. Our findings provide insights into the complex longitudinal interplay between genetics and neuroimaging characteristics and highlight the role of Alzheimer’s genetic risk factors in the estimation of regional brain connection alterations. These regional relationship patterns can be useful for early disease treatment and neurodegeneration prediction.
