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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
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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
Exciting Work!
A Deep Spatio-Temporal Architecture for Dynamic ECN Analysis with Granger Causality based Causal Discovery
Published in Pattern Recognition, 2023
Exciting Work!
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.
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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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.