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1		-{{{Virtual Brain Models imply latent nonlinear state space models
2		-driven by noise and network input, necessitating advanced probabilistic
3		-machine learning 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.
	1	+(% style="text-align: justify;" %)
	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.
7	7
	17	+Code:
8	8	}}}
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11		-
12		-Code: [[https:~~/~~/github.com/ins-amu/SBI-VBMs>>https://github.com/ins-amu/SBI-VBMs]]
13		-
14	14	(% style="text-align: justify;" %)
15	15	Ref: [[https:~~/~~/iopscience.iop.org/article/10.1088/2632-2153/ad6230>>https://iopscience.iop.org/article/10.1088/2632-2153/ad6230]]