Subgroup Identification Through Multiplex Community Structure Within Functional Connectivity Networks

• Published in: Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 2141 - 2145
• Main Authors: Yang, H., Ortiz-Bouza, M., Vu, T., Laport, F., Calhoun, V. D., Aviyente, S., Adali, T.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.04.2024
• Subjects: blind source separation; community detection; Correlation; Functional magnetic resonance imaging; Mental disorders; multiplex network; Multiplexing; resting-state fMRI; Robustness; Signal processing; Speech processing; Subgroup identification
• DOI: 10.1109/ICASSP48485.2024.10446076

Subgroup identification is a fundamental step in precision medicine. Recent research applying data-driven methods such as independent component/vector analysis to multi-subject functional magnetic resonance imaging (fMRI) data has effectively revealed meaningful subgroups. These methods typically focus on single-dimensional information, such as individual functional networks or assuming uniform subgroup structures across networks. Given the complex nature of psychiatric disorders, considering the relationships among subjects across different functional networks can offer valuable insights into diagnostic heterogeneity. We introduce a novel subgroup identification method that leverages multiplex community detection to identify subgroups from multi-subject resting-state fMRI data. The proposed method models subject correlations across functional networks as a multiplex network and identifies common communities across multiple networks and unique communities specific to each functional network. Results from applying the proposed method to 464 psychotic patients show that the identified subgroups exhibit significant group differences on multiple meaningful functional networks as well as the clinical scores, which demonstrate the effectiveness of our method on identifying meaningful subgroups.