Cortical thickness and structural covariance network alterations in cerebral amyloid angiopathy: A graph theoretical analysis

This study investigates large-scale brain network alterations in cerebral amyloid angiopathy (CAA) using structural covariance network (SCN) analysis and graph theory based on 7 T MRI. We employed structural covariance network (SCN) analysis based on cortical thickness data from ultra-high field 7 T MRI to investigate network alterations in CAA patients. Graph theoretical analysis was applied to quantify topological properties, including small-worldness, nodal centrality, and network efficiency. Between-group differences were assessed using permutation tests and false discovery rate (FDR) correction. CAA patients exhibited significant alterations in small-world properties, with decreased Gamma (p = 0.002) and Sigma (p < 0.001), suggesting a shift toward a less optimal network configuration. Local efficiency was significantly different between groups (p = 0.045), while global efficiency remained unchanged (p = 0.127), indicating regionally disrupted rather than globally impaired network efficiency. At the nodal level, the right superior frontal gyrus exhibited increased betweenness centrality (p = 0.013), whereas the right banks of the superior temporal sulcus, left postcentral gyrus, and left superior temporal gyrus showed significantly reduced centrality (all p < 0.05). Additionally, nodal degree and efficiency were altered in key memory-related and association regions, including the entorhinal cortex, fusiform gyrus, and temporal pole. SCN analysis combined with graph theory offers a valuable approach for understanding disease-related connectivity disruptions and may contribute to the development of network-based biomarkers for CAA.