An EEG-fMRI Jointly Constrained Digital Twin Brain and Its Application in Alzheime's Disease

Current digital twin brain (DTB) models are typically optimized using single-modality functional magnetic resonance imaging (fMRI) data, which restricts their ability to simulate brain dynamics across multiple spatiotemporal scales. Here, we bridged this gap by developing a two-stage DTB (TS-DTB) modeling framework jointly constrained by fMRI and electroencephalography (EEG) data. Validation in both the healthy young and Alzheimer's disease (AD) cohorts demonstrated 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 recapitulated spectral-temporal signatures of cognitive decline, mechanistically linking these deficits to excitation-inhibition (E-I) imbalances. By simulating responses to repetitive transcranial magnetic stimulation (rTMS), we further revealed that cognitive recovery in AD patients can be driven by E-I rebalancing via 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.