EEG-fMRI 联合约束的数字孪生脑与 AD

Current digital twin brain (DTB) models are typically optimized using single-modality functional neuroimaging data, which restricts their ability to simulate brain dynamics across multiple spatiotemporal scales. Here, we show that a two-stage DTB (TS-DTB) modeling framework jointly constrained by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data. Validation in both the healthy young and Alzheimer's disease (AD) cohorts demonstrates that the TS-DTB model simultaneously captures subject-specific features of multi-scale brain dynamics. In particular, the TS-DTB models of AD patients successfully recapitulate spectral-temporal signatures of cognitive decline, suggesting a model-inferred association between these deficits and simulated excitation-inhibition (E-I) imbalances. By simulating responses to repetitive transcranial magnetic stimulation (rTMS), we further reveal that acute EEG normalization in AD patients is computationally associated with putative E-I rebalancing via model-inferred synaptic reconfiguration and background suppression. Overall, these findings underscore the potential of the TS-DTB to advance the mechanistic understanding of the brain and inform personalized digital therapeutics.