Chendi Wang

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Cortical parcellation of the human brain typically serves as a basis for higher-level analyses such as connectivity analysis and investigation of brain network properties. Inferences drawn from such analyses can be significantly confounded if the brain parcels are inaccurate. In this paper, we propose a novel affinity matrix structure based on multiple(More)
Functional subnetwork extraction is commonly employed to study the brain's modular structure. However, reliable extraction from functional magnetic resonance imaging (fMRI) data remains challenging. As representations of brain networks, brain graph estimates are typically noisy due to the pronounced noise in fMRI data. Also, confounds, such as region size(More)
Reliable cortical parcellation is a crucial step in human brain network analysis since incorrect definition of nodes may invalidate the inferences drawn from the network. Cortical parcellation is typically cast as an unsupervised clustering problem on functional magnetic resonance imaging (fMRI) data, which is particularly challenging given the pronounced(More)
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