Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs

Qingbao Yu, Yuhui Du, Jiayu Chen, Jing Sui, Tulay Adali, Godfrey D. Pearlson, Vince D. Calhoun
2018 Proceedings of the IEEE  
Human brain connectivity is complex. Graph theory based analysis has become a powerful and popular approach for analyzing brain imaging data, largely because of its potential to quantitatively illuminate the networks, the static architecture in structure and function, the organization of dynamic behavior over time, and disease related brain changes. The first step in creating brain graphs is to define the nodes and edges connecting them. We review a number of approaches for defining brain nodes
more » ... including fixed versus data-driven nodes. Expanding the narrow view of most studies which focus on static and/or single modality brain connectivity, we also survey advanced approaches and their performances in building dynamic and multi-modal brain graphs. We show results from both simulated and real data from healthy controls and patients with mental illnesse. We outline the advantages and challenges of these various techniques. By summarizing and inspecting recent studies which analyzed brain imaging data based on graph theory, this article provides a guide for developing new powerful tools to explore complex brain networks. defined by AAL atlas, 110, 220, 440, and 880 ROIs defined by the Harvard-Oxford Atlas (HO), and 54, 108, 216 and 432 ROIs defined by the LONI Probabilistic Brain Atlas (LPBA40) were used as nodes respectively. Basic connectivity properties and several graph metrics displayed high reproducibility and low variability in both DSI and DTI networks [182] . An impressive property of previous diffusion brain image-based graphs is the so-called "rich club" organization initially identified by a study using 82 ROIs defined by Freesurfer suite as nodes. A rich club organization is characterized by a tendency for high-degree nodes to be more densely connected among themselves than nodes of a lower degree in the graph [183] . A following study which used 1170 ROIs as nodes showed that the set of pathways linking rich club regions formed a central high-cost, high-capacity backbone for global brain communication [184]. A wide application of brain graph analysis is for the detection of potential biomarkers of mental illness such as schizophrenia. Altered brain graph properties have been revealed in schizophrenia using DTI data with different nodes. When using 82 ROIs [104] and 108 ROIs [185] as nodes to build the graphs respectively, it was shown that though small-world attributes were conserved in schizophrenia, the cortex was interconnected more sparsely and up to 20% less efficiently [104] , and node specific path lengths were longer in patients [185] . Another DTI study which used 90 ROIs defined by AAL as nodes discovered decreased global efficiency in schizophrenia [186] . Yu et al.
doi:10.1109/jproc.2018.2825200 pmid:30364630 pmcid:PMC6197492 fatcat:uh6tvaymifgh3cubnkemqgcd5a