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1		-{{{The integration of an individual’s brain
2		-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.
	1	+{{{Virtual Brain Models imply latent nonlinear state space models
	2	+driven by noise and network input, necessitating advanced probabilistic
	3	+machinelearning techniques for widely applicable Bayesian estimation.
	4	+Here we present Simulation-based Inference on Virtual Brain Models (SBI-VBMs),
	5	+and demonstrate that training deep neural networks on both spatio-temporal and
	6	+functional features allows for accurate estimation of generative parameters in brain disorders.
16	16
17		-Code:
18		-}}}
	8	+Code: }}}
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	11	+(% style="text-align: justify;" %)
21	21	Ref: [[https:~~/~~/iopscience.iop.org/article/10.1088/2632-2153/ad6230>>https://iopscience.iop.org/article/10.1088/2632-2153/ad6230]]