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- Liu, Jia, et al.
(författare)
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Analysis of modulations of mental fatigue on intra-individual variability from single-trial event related potentials
- 2024
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Ingår i: JOURNAL OF NEUROSCIENCE METHODS. - 0165-0270 .- 1872-678X. ; 406
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Tidskriftsartikel (refereegranskat)abstract
- Background: Intra-individual variability (IIV), a measure of variance within an individual's performance, has been demonstrated as metrics of brain responses for neural functionality. However, how mental fatigue modulates IIV remains unclear. Consequently, the development of robust mental fatigue detection methods at the single-trial level is challenging. New methods: Based on a long-duration flanker task EEG dataset, the modulations of mental fatigue on IIV were explored in terms of response time (RT) and trial-to-trial latency variations of event-related potentials (ERPs). Specifically, latency variations were quantified using residue iteration decomposition (RIDE) to reconstruct latency-corrected ERPs. We compared reconstructed ERPs with raw ERPs by means of temporal principal component analysis (PCA). Furthermore, a single-trial classification pipeline was developed to detect the changes of mental fatigue levels. Results: We found an increased IIV in the RT metric in the fatigue state compared to the alert state. The same sequence of ERPs (N1, P2, N2, P3a, P3b, and slow wave, or SW) was separated from both raw and reconstructed ERPs using PCA, whereas differences between raw and reconstructed ERPs in explained variances for separated ERPs were found owing to IIV. Particularly, a stronger N2 was detected in the fatigue than alert state after RIDE. The single-trial fatigue detection pipeline yielded an acceptable accuracy of 73.3%. Comparison with existing methods: The IIV has been linked to aging and brain disorders, and as an extension, our finding demonstrates IIV as an efficient indicator of mental fatigue. Conclusions: This study reveals significant modulations of mental fatigue on IIV at the behavioral and neural levels and establishes a robust mental fatigue detection pipeline.
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| 2. |
- Liu, Jia, et al.
(författare)
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Reconfiguration of cognitive control networks during a long-duration flanker task
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Ingår i: IEEE Transactions on Cognitive and Developmental Systems. - 2379-8920. ; , s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- Continuous task engagement generally leads to vigilance decrement and deteriorates task performance. However, how conflict effect is modulated by vigilance decrement has no consistent evidence, and little is known about the underlying neural mechanisms. Here we adopted an electroencephalogram dataset collected during a prolonged flanker task to examine the interactions between vigilance and congruency on behavioral performance and neural measures. Specifically, we extracted a sequence of ERPs using temporal principal component analysis (PCA) and performed functional network analysis with graph measures. Behavioral analysis results showed that behavioral performance deteriorated due to vigilance decrement, but the capability of conflict processing was maintained over time. Regarding the neural analysis results, the conflict effect reflected in P3a and P3b was changed and maintained respectively when affected by vigilance decrement. The theta band frontoparietal network was observed in the face of conflicting interference and the conflict effect for graph measures disappeared over time. These results demonstrated deteriorated task performance, impaired cognitive functions, and the reconfiguration of cognitive control networks during a prolonged flanker task. Our findings also support the evidence that temporal PCA and event-related network analysis might be efficient for the investigation of the neural dynamics of complex cognitive processes.
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| 3. |
- Zhu, Yongjie, et al.
(författare)
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Dynamic Community Detection for Brain Functional Networks During Music Listening With Block Component Analysis
- 2023
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1534-4320. ; 31, s. 2438-2447
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Tidskriftsartikel (refereegranskat)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.
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