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publications
EGCN-TSD: Explainable GCN for Time Series Data and Its Applications to the Study of Brain Development
Published in Neurocomputing, 2023
Functional connectivity is vital in understanding the dynamic process of the brain function network during its development. At present, graph convolutional networks (GCNs) have received extensive attention in the study of brain connectivity during development. Approaches of GCNs utilizing spatio-temporal data generated by medical technologies have been employed for the study of brain development and brain diseases. However, many of these studies disregard both the time-series information and the high-dimension but small-sample-size characteristics of the data, in addition to the explainability of the models used. In this study, we present an explainable GCN for time series data. The regions of interest of human brain are regarded as GCN nodes, and frequency-domain embedding is derived using fast Fourier transform and dictionary learning to effectively extract distinct frequency features and reduce the dimensionality of data. The functional connectivity matrix obtained with truncated nuclear norm regularization is used as the initial value of GCN edges, and attention mechanism is incorporated to capture the dynamic connections, thus to improve the feature extraction and learning abilities of GCN, and reduce the model complexity. For explainability, gradient-weighted class activation mapping and the transition matrix of importance are introduced to identify the key brain regions and connections. Experimental results show that adults’ resting-state networks (RSNs) exhibit denser within-network connectivity, while children’s connections between RSNs are more dispersed. During development, functional partitions of the human brain network will move from segregated to integrated, i.e., gradually become aggregated and modular. The discoveries reflect progressively maturing brain functions, which further uncovers significant changes occur in functional connectivity that are likely related to deduction, reasoning and flexible abilities during development.
A Deep Spatio-Temporal Architecture for Dynamic ECN Analysis with Granger Causality based Causal Discovery Permalink
Published in Pattern Recognition, 2023
Neurobrain science provides the motivation for research on causal modeling. The existing causal discovery methods have shown promising results in effective connectivity network analysis, however, they often overlook the dynamics of causality, in addition to the incorporation of spatio-temporal information in data. Dynamic effective connectivity networks (dECNs) reveal the changing directed brain activity and the dynamic causal influences among brain regions, which facilitate the identification of individual differences and enhance the understanding of human brain. To learn dynamic causality, we propose a deep spatio-temporal fusion architecture, which employs a dynamic causal deep encoder to incorporate spatio-temporal information into dynamic causality modeling, and a dynamic causal deep decoder to verify the discovered causality. The effectiveness of the proposed method is first illustrated with simulated data. Then, experimental results from Philadelphia Neurodevelopmental Cohort (PNC) demonstrate the superiority of the proposed method in inferring dECNs, which reveal the dynamic evolution of directed flow between brain regions. The analysis shows the difference of dECNs between young adults and children. Specifically, the directed brain functional networks transit from fluctuating undifferentiated systems to more stable specialized networks as one grows. This observation provides further evidence on the modularization and adaptation of brain networks during development, leading to higher cognitive abilities observed in young adults.
Time-Reversal Enhanced Dynamic Causality Distribution Learning and Its Application in Identifying Dynamic ECNs in MCI Patients Permalink
Published in IEEE Transactions on Biomedical Engineering, 2024
Objective: Dynamic causal influences between brain regions are crucial for understanding the temporal variation and fluctuation of the interaction in human brain. However, recent causal discovery approaches often focus on fixed causality under directed acyclic graph constraints, and do not infer the dynamic and fluctuating nature of causality, which commonly exists in the brain. Methods: We propose a causality learning framework with evolving distribution for non-stationary and non-linear systems. Based on this framework, a time-reversal enhanced dynamic causality distribution learning (TRDCDL) model is constructed, which integrates spatio-temporal information to identify evolving distributional sparse interactions in data. Results: TRDCDL is validated in two synthetic models, which show the accuracy in learning both linear and non-linear causality within synthetic data. We further apply TRDCDL to the Alzheimer’s Disease Neuroimaging Initiative dataset and infer dynamic effective connectivity networks (dECNs) among two stages of mild cognitive impairment (MCI). Conclusion: The results reveal significant differences in dECNs between brain regions across the these stages, indicating that dECNs can serve as reliable neuromarkers for distinguishing different stages of MCI. Significance: Significant reductions in dynamic causal influences within the default mode network and bilateral limbic network, along with few increased connectivity, reflect neurodegeneration and changing patterns of dECNs as MCI progresses.
Continuous-Time Causal Distribution Learning with Identifiability for Brain dEC Inference
Published in IEEE Transactions on Medical Imaging, 2025
Dynamic effective connectivity (dEC) analysis provides an approach for revealing the causal mechanism of information transmission in human brain. However, existing discrete-time dEC learning models suffer a mismatch between sampling and causal frequencies as well as low efficiency in data utilization. Based on a continuous-time dynamic causality distribution learning framework, we propose a dEC distribution learning model, which uses free energy principle to ensure more proper parameter learning than maximum likelihood estimation. Theoretically, the identifiability of the proposed model is proved, which guarantees the uniqueness of the learned dEC given the same data distribution. Results from the synthetic dataset show that the ability in both causality learning and time series reconstruction of the proposed model outperforms the comparison methods. The dEC results learned from the real-world data reveal the changing information transmission patterns in brain when dealing with picture-sentence matching tasks. Additionally, during the transition from resting state to task state, information flow in the brain’s dEC network becomes increasingly organized around the left supramarginal gyrus, a hub known for the language processing and information integration in parietal lobe.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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