Title:  Path-based Differential Algorithm and Graph Theory-based Analysis on Neuroimaging Data

Committee: 

Dr. Calhoun, Advisor    

Dr. Anderson, Chair

Dr. Rahnev

Abstract: The objective of the proposed research is to show that a comprehensive path (as opposed to individual edge) analysis in brain graphs of control and patient groups may help identify putative path-based biomarkers from neuroimaging data. We compare and analyze paths between the brain graphs of control and patient groups. Details of the disruption of paths in the patient group graph may be highly informative for understanding disease mechanisms. To detect the absence or addition of multi-step paths in the patient group, we provide an algorithm estimating edges that contribute to these paths with reference to the control group. We next examine where pairs of nodes were connected through paths in both groups using a covariance decomposition method. We apply our method to study resting-state fMRI data in schizophrenia versus controls. Results show several disconnectors in schizophrenia within and between functional domains, particularly within the default mode and cognitive control networks. Additionally, we identify new edges generating additional paths. Moreover, although paths exist in both groups, these paths take unique trajectories and have a significant contribution to the decomposition. The proposed path analysis provides a way to characterize individuals by evaluating changes in paths, rather than just focusing on the pair-wise relationships. Our results show promise for identifying path-based metrics in neuroimaging data.