6 papers
Position: AI for Science Should Treat Measurement-to-Dataset Pipelines as Inference Components
Ling Zhan, Xiaoyao Yu, Tao Jia
AI for Science (AI4Science) workflows often treat the released dataset as a fixed interface to the underlying system. However, in domains relying on \emph{indirect observation}, th…
Accelerating Benchmarking of Functional Connectivity Modeling via Structure-aware Core-set Selection
Ling Zhan, Zhen Li, Junjie Huang +1
Benchmarking the hundreds of functional connectivity (FC) modeling methods on large-scale fMRI datasets is critical for reproducible neuroscience. However, the combinatorial explos…
Beyond Pairwise Connections: Extracting High-Order Functional Brain Network Structures under Global Constraints
Ling Zhan, Junjie Huang, Xiaoyao Yu +2
Functional brain network (FBN) modeling often relies on local pairwise interactions, whose limitation in capturing high-order dependencies is theoretically analyzed in this paper.…
Quantifying sex differences in brain network topology by aggregating nodal centrality rankings
Wenyu Chen, Ling Zhan, Yunsong Luo +2
Although numerous studies report significant sex differences in functional connectivity, these differences do not sufficient to reveal specific functional disparities among brain r…
Sex Differences in Hierarchical and Modular Organization of Functional Brain Networks: Insights from Hierarchical Entropy and Modularity Analysis
Wenyu Chen, Ling Zhan, Tao Jia
Existing studies have demonstrated significant sex differences in the neural mechanisms of daily life and neuropsychiatric disorders. The hierarchical organization of the functiona…
Multi-feature concatenation and multi-classifier stacking: an interpretable and generalizable machine learning method for MDD discrimination with rsfMRI
Yunsong Luo, Wenyu Chen, Ling Zhan +2
Major depressive disorder is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspect…