Morphological brain networks of white matter: mapping, evaluation, characterization and application

Abstract

Neuroimaging-based connectomics studies have long focused on the wiring patterns between gray matter regions. In recent years, increasing evidence emerges that neural activity in specific sets of white matter (WM) tracts dynamically fluctuates in a coordinated manner. However, the structural basis underlying the coordination is poorly understood largely due to the lack of approaches for estimating structural relations between WM regions. Here, we developed an approach to construct morphological WM networks based on structural magnetic resonance imaging. We found that the morphological WM networks exhibited nontrivial organizational principles, presented good to excellent short- and long-term reliability, accounted for phenotypic interindividual differences (Motor and Cognition), and were under genetic control. Interestingly, highly heritable edges contributed largely to interindividual differences in phenotype. Through integration with other multimodal and multiscale data, we further showed that the morphological WM networks were able to predict regional profiles of hamodynamic coherence, metabolic synchronization, gene co-expression and chemoarchitectonic covariance. Moreover, the prediction followed functional connectomic hierarchy of WM for hamodynamic coherence, was driven by genes enriched in the forebrain neuron development and differentiation for gene co-expression, and was attributed to serotonergic system-related receptors and transporters for chemoarchitectonic covariance. Finally, applying our approach to multiple sclerosis and neuromyelitis optica spectrum disorders, we found that both diseases were associated with morphological WM dysconnectivity, which was correlated with clinical variables and able to diagnose and differentiate the diseases. Altogether, our findings indicate that morphological WM networks provide a reliable and meaningful means to explore WM architecture in health and disease.