Virtual Brain Models 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 features allows for accurate estimation of generative parameters in brain disorders.

Code: https://github.com/ins-amu/SBI-VBMs

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