The integration of an individual’s brain imaging data in VBMs has improved patient-specific predictivity, although Bayesian
estimation of spatially distributed parameters remains challenging even with state-
of-the-art Monte Carlo sampling. VBMs imply latent nonlinear state space models
driven by noise and network input, necessitating advanced probabilistic machine
learning techniques for widely applicable Bayesian estimation. Here we present
Simulation-based Inference on Virtual Brain Models (SBI-VBMs), and demonstrate
that training deep neural networks on both spatio-temporal and functional fea-
tures allows for accurate estimation of generative parameters in brain disorders.
The systematic use of brain stimulation provides an effective remedy for the non-
identifiability issue in estimating the degradation limited to smaller subset of con-
nections. By prioritizing model structure over data, we show that the hierarchical
structure in SBI-VBMs renders the inference more effective, precise and biologically
plausible. This approach could broadly advance precision medicine by enabling fast
and reliable prediction of patient-specific brain disorders.

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Ref: https://iopscience.iop.org/article/10.1088/2632-2153/ad6230