The brain can be understood as a complex network with interconnected brain regions linked by white matter pathways on the macroscale. In this thesis, we explored the connectivity between these regions and its disruption in brain disorders. By employing a relational approach that examines interactions and relationships across different scales, modalities, and domains, we aimed to gain a more comprehensive understanding of the brain. Our findings highlight a common pattern of disconnectivity across neurological and psychiatric disorders, showing that connections central in the network architecture are particularly vulnerable to disruptions. In an in-depth examination of insomnia, depression, and anxiety disorders, we identified multimodal brain correlates associated with symptoms of these conditions. We distinguished between common and unique associations of brain structure and function with the severity of symptoms characteristic of each disorder, providing insights into both shared and specific brain circuits. Additionally, this thesis contributes to the development and application of tools for reconstructing and analyzing brain networks. We introduced the CATO toolbox, an open-source software tool for structural and functional connectivity reconstruction. We also proposed the normalized Laplacian eigenvalue spectrum as a method for comparing neuronal networks across species and assessing differences in connectome organization. Overall, the research presented contributes to our methodologies for reconstructing and analyzing brain networks and provides insights into the organization of neuronal networks and the disruptions in network organization in brain disorders.
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