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2 -{{{The integration of an individual’s brain imaging data in VBMs has improved patient-specific predictivity, although Bayesian
3 -estimation of spatially distributed parameters remains challenging even with state-
4 -of-the-art Monte Carlo sampling. VBMs imply latent nonlinear state space models
5 -driven by noise and network input, necessitating advanced probabilistic machine
6 -learning techniques for widely applicable Bayesian estimation. Here we present
7 -Simulation-based Inference on Virtual Brain Models (SBI-VBMs), and demonstrate
8 -that training deep neural networks on both spatio-temporal and functional fea-
9 -tures allows for accurate estimation of generative parameters in brain disorders.
10 -The systematic use of brain stimulation provides an effective remedy for the non-
11 -identifiability issue in estimating the degradation limited to smaller subset of con-
12 -nections. By prioritizing model structure over data, we show that the hierarchical
13 -structure in SBI-VBMs renders the inference more effective, precise and biologically
14 -plausible. This approach could broadly advance precision medicine by enabling fast
15 -and reliable prediction of patient-specific brain disorders.
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22 -Ref: [[https:~~/~~/iopscience.iop.org/article/10.1088/2632-2153/ad6230>>https://iopscience.iop.org/article/10.1088/2632-2153/ad6230]]
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