A1 Journal article (refereed)
Dynamic Community Detection for Brain Functional Networks during Music Listening with Block Component Analysis (2023)

Zhu, Y., Liu, J., & Cong, F. (2023). Dynamic Community Detection for Brain Functional Networks during Music Listening with Block Component Analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 2438-2447. https://doi.org/10.1109/tnsre.2023.3277509

JYU authors or editors

Publication details

All authors or editors: Zhu, Yongjie; Liu, Jia; Cong, Fengyu

Journal or series: IEEE Transactions on Neural Systems and Rehabilitation Engineering

ISSN: 1534-4320

eISSN: 1558-0210

Publication year: 2023

Volume: 31

Pages range: 2438-2447

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/tnsre.2023.3277509

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/87149

Abstract

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-(L r ,L r ,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 (L r 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.

Keywords: brain; brain research; EEG

Free keywords: brain modeling; tensors; hidden Markov models; electroencephalography; feature extraction; analytical models; task analysis

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2023

Preliminary JUFO rating: 2