Current methodologies typically integrate biophysical brain models with functional magnetic resonance imaging (fMRI) data - while offering millimeter-scale spatial resolution (0. 5-2 mm³ voxels), these approaches suffer from limited temporal resolution (>0. 5 Hz) for tracking rapid neural dynamics during continuous tasks. Conversely, Electroencephalogram (EEG) provides millisecond-scale temporal precision (<=1 ms sampling rate) for real-time guidance of continuous task execution, albeit constrained by low spatial resolution. To reconcile these complementary modalities, we present a generalizable Bayesian inference framework that integrates high-spatial-resolution structural MRI (sMRI) with high-temporal-resolution EEG to construct a biologically realistic digital twin brain (DTB) model. The framework establishes voxel-wise mappings between millisecond-scale EEG and sMRI-derived spiking networks, while demonstrating its translational potential through a brain-inspired autonomous driving simulation. Our EEG-DTB model achieves capabilities: (1) Biologically-plausible EEG signal generation (0. 88 resting-state, 0. 60 task-state correlation), with simulated signals in task-state yielding steering predictions outperforming both chance and empirical signals (p<0. 05) ; (2) Successful autonomous driving in the CARLA simulator using decoded steering angles. The proposed approach pioneers a new paradigm for studying sensorimotor integration and for mechanistic studies of perception-action cycles and the development of brain-inspired control systems.
Hou et al. (Wed,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: