Module control of network analysis in psychopathology
Chunyu Pan,
Quan Zhang,
Yue Zhu,
Shengzhou Kong,
Juan Liu,
Changsheng Zhang,
Fei Wang,
Xizhe Zhang
Affiliations
Chunyu Pan
Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, China; Northeastern University, Shenyang, Liaoning 110169, China
Quan Zhang
Vanke School of Public Health, Tsinghua University, Beijing 100084, China; Institute for Healthy China, Tsinghua University, Beijing 100084, China
Yue Zhu
Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu 210024, China
Shengzhou Kong
Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, China
Juan Liu
Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu 210024, China
Changsheng Zhang
Northeastern University, Shenyang, Liaoning 110169, China
Fei Wang
Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu 210024, China; Department of Mental Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Corresponding author
Xizhe Zhang
School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210033, China; Corresponding author
Summary: The network approach to characterizing psychopathology departs from traditional latent categorical and dimensional approaches. Causal interplay among symptoms contributed to dynamic psychopathology system. Therefore, analyzing the symptom clusters is critical for understanding mental disorders. Furthermore, despite extensive research studying the topological features of symptom networks, the control relationships between symptoms remain largely unclear. Here, we present a novel systematizing concept, module control, to analyze the control principle of the symptom network at a module level. We introduce Module Control Network (MCN) to identify key modules that regulate the network’s behavior. By applying our approach to a multivariate psychological dataset, we discover that non-emotional modules, such as sleep-related and stress-related modules, are the primary controlling modules in the symptom network. Our findings indicate that module control can expose central symptom cluster governing psychopathology network, offering novel insights into the underlying mechanisms of mental disorders and individualized approach to psychological interventions.
