5 papers
FlexiBrain: Resolution-Agnostic Voxel-Level Encoding for Native fMRI
Mo Wang, Wenhao Ye, Junfeng Xia +3
The success of large-scale deep learning models in neuroscience is fundamentally constrained by severe data heterogeneity. Native fMRI data aggregated from diverse sources exhibit…
Omni-fMRI: A Universal Atlas-Free fMRI Foundation Model
Mo Wang, Wenhao Ye, Junfeng Xia +6
Self-supervised fMRI foundation models have shown promising transfer performance, yet most rely on predefined region-level parcellations that discard fine-grained voxel information…
SLIM-Brain: A Data- and Training-Efficient Foundation Model for fMRI Data Analysis
Mo Wang, Junfeng Xia, Wenhao Ye +5
Foundation models are emerging as a powerful paradigm for fMRI analysis, but current approaches face a dual bottleneck of data- and training-efficiency. Atlas-based methods aggrega…
Personalized Transcranial Electrical Stimulation: A Review of Computational Modeling and Optimization
Mo Wang, Kexin Zheng, Yingyue Xin +8
Objective. Personalized transcranial electrical stimulation (tES) has gained growing attention due to the substantial inter-individual variability in brain anatomy and physiology.…
DCA: Graph-Guided Deep Embedding Clustering for Brain Atlases
Mo Wang, Kaining Peng, Jingsheng Tang +2
Brain atlases are essential for reducing the dimensionality of neuroimaging data and enabling interpretable analysis. However, most existing atlases are predefined, group-level tem…