8 papers
Brain-DiT: A Universal Multi-state fMRI Foundation Model with Metadata-Conditioned Pretraining
Junfeng Xia, Wenhao Ye, Xuanye Pan +3
Current fMRI foundation models primarily rely on a limited range of brain states and mismatched pretraining tasks, restricting their ability to learn generalized representations ac…
BrainWorld: A Structural-Prior-Conditioned Generative Model for Whole-Brain 4D fMRI Dynamics
Junfeng Xia, Wenhao Ye, Junxiang Zhang +3
Whole-brain 4D fMRI generation is valuable for modeling functional brain dynamics, yet existing fMRI foundation models mainly target representation learning and downstream predicti…
OpTI-Mouse: Optimization for Targeted Temporal Interference Stimulation in the Mouse Brain
Jingsheng Tang, Zhengkang Zhou, Yingyue Xin +4
Temporal Interference (TI) stimulation enables deep brain targeting, yet precise optimization tools for mouse models remain limited. We developed a computational optimization tool…
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…
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 optimization of pediatric HD-tDCS for dose consistency and target engagement
Zeming Liu, Mo Wang, Xuanye Pan +3
High-definition transcranial direct current stimulation (HD-tDCS) dosing in children remains largely empirical, relying on one-size-fits-all protocols despite rapid developmental c…