6533b858fe1ef96bd12b62ce
RESEARCH PRODUCT
Dynamic Community Detection for Brain Functional Networks during Music Listening with Block Component Analysis
Yongjie ZhuJia LiuFengyu Congsubject
Brain modelingmodule detectionBiomedical EngineeringTensorsblock term decompositiondynamic community detectiontensor decompositiontensorsInternal MedicineAnalytical modelsgenerative modelHidden Markov modelsaivotutkimusEEGhidden Markov modelsGeneral Neurosciencefeature extractionbrain connectivityRehabilitation3112 Neurosciencesanalytical modelsElectroencephalographybrain modeling113 Computer and information sciencesTask analysistask analysisFeature extractionaivotelectroencephalographydescription
Publisher Copyright: Author The human brain can be described as a complex network of functional connections between distinct regions, referred to as the brain functional network. Recent studies show that the functional network is a dynamic process and its community structure evolves with time during continuous task performance. Consequently, it is important for the understanding of the human brain to develop dynamic community detection techniques for such time-varying functional networks. Here, we propose a temporal clustering framework based on a set of network generative models and surprisingly it can be linked to Block Component Analysis to detect and track the latent community structure in dynamic functional networks. Specifically, the temporal dynamic networks are represented within a unified three-way tensor framework for simultaneously capturing multiple types of relationships between a set of entities. The multi-linear rank-(Lr,Lr,1) block term decomposition (BTD) is adopted to fit the network generative model to directly recover underlying community structures with the specific evolution of time from the temporal networks. We apply the proposed method to the study of the reorganization of the dynamic brain networks from electroencephalography (EEG) data recorded during free music listening. We derive several network structures (Lr communities in each component) with specific temporal patterns (described by BTD components) significantly modulated by musical features, involving subnetworks of frontoparietal, default mode, and sensory-motor networks. The results show that the brain functional network structures are dynamically reorganized and the derived community structures are temporally modulated by the music features. The proposed generative modeling approach can be an effective tool for describing community structures in brain networks that go beyond static methods and detecting the dynamic reconfiguration of modular connectivity elicited by continuously naturalistic tasks. Peer reviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2023-01-01 |