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NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes

Autor: Wei, Ziquan, Dan, Tingting, Ding, Jiaqi, Wu, Guorong

Although modern imaging technologies allow us to study connectivity between two distinct brain regions in-vivo, an in-depth understanding of how anatomical structure supports brain function and how spontaneous functional fluctuations emerge remarkabl

Externí odkaz: http://arxiv.org/abs/2409.17510

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Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations

Autor: Ding, Jiaqi, Dan, Tingting, Wei, Ziquan, Cho, Hyuna, Laurienti, Paul J., Kim, Won Hwa, Wu, Guorong

An unprecedented amount of existing functional Magnetic Resonance Imaging (fMRI) data provides a new opportunity to understand the relationship between functional fluctuation and human cognition/behavior using a data-driven approach. To that end, tre

Externí odkaz: http://arxiv.org/abs/2409.11377

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Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals

Autor: Dan, Tingting, Ding, Jiaqi, Wei, Ziquan, Kovalsky, Shahar Z, Kim, Minjeong, Kim, Won Hwa, Wu, Guorong

Graph neural networks (GNNs) are widely used in domains like social networks and biological systems. However, the locality assumption of GNNs, which limits information exchange to neighboring nodes, hampers their ability to capture long-range depende

Externí odkaz: http://arxiv.org/abs/2307.00222

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