Chun-Ju Kao1, Vincent Chin-Hung Chen2,3, Yuan-Hsiung Tsai3,4, and Jun-Cheng Weng1,2,5
1Department of Medical Imaging and Radiological Sciences, and Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan, 2Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan, 3School of Medicine, Chang Gung University, Taoyuan, Taiwan, 4Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi, Taiwan, 5Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
1Department of Medical Imaging and Radiological Sciences, and Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan, 2Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan, 3School of Medicine, Chang Gung University, Taoyuan, Taiwan, 4Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi, Taiwan, 5Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
SA group showed lower global integration and higher local segregation compared to D and HC groups. Furthermore, SA and D groups had significant subnetwork connections in frontal and parietal lobes than HC group.
Figure 3. NBS results. (a) Compared with the HC group, the SA group demonstrated significantly stronger subnetwork connections in the frontal and parietal lobe. (b) Compared with the HC group, the D group demonstrated significantly stronger subnetwork connections in the parietal lobe (p < 0.05).
Figure 2. Topological parameters of graph theoretical analysis. Topological parameters including (a) global efficiency, (b) normalized shortest path length (λ), (c) characteristic path length, (d) normalized clustering coefficient (γ), (e) modularity and (f) small-worldness index among three groups (SA, D and HC).