Graph theoretical methods have emerged as crucial tools for exploring the intricate networks within the human brain, spanning disciplines such as neuroscience, cognitive science, psychiatry, psychology, and the study of brain disorders and development. However, research in this realm has traditionally concentrated on assessing local and global graph metrics, inadvertently neglecting the rich information embedded within the intricate paths that interconnect distinct brain regions. This gap in knowledge motivated the development of an innovative algorithm aimed at identifying multi-step paths in patient groups by comparing them to control cohorts. Following path identification, a covariance decomposition approach is employed to delve into the connections shared between pairs of brain nodes, offering a deeper understanding of network dynamics. The application of this methodology is exemplified through the analysis of resting-state functional MRI data from individuals with schizophrenia, yielding valuable insights into the presence of disconnectors within and between specific functional domains, with a particular focus on the default mode and cognitive control networks. Additionally, an extensive longitudinal study investigates the processes associated with healthy aging, employing advanced neuroimaging techniques and cognitive assessments. This comprehensive approach spans from individuals in their mid-30s to centenarians, revealing dynamic changes in brain networks. These findings underscore the importance of considering both static and dynamic network characteristics and highlight specific graph metrics that hold relevance in elucidating the cognitive changes associated with the aging process. Furthermore, the proposed path analysis algorithm detects disrupted pathways, shedding light on potential path-based biomarkers. Altogether, these research endeavors expand our understanding of brain network dynamics in health and disease, with implications for both clinical applications and the broader study of brain function.