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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.
	1	+This tool was developed at INS in Marseille.
	2	+Authors: M Hashemi, A Ziaeemehr, MM Woodman, J Fousek, S Petkoski, VK Jirsa
16	16
17		-Code:
18		-}}}
	4	+Virtual Brain Models imply latent nonlinear state space models
	5	+driven by noise and network input, necessitating advanced probabilistic
	6	+machine learning techniques for widely applicable Bayesian estimation.
	7	+Here we present Simulation-based Inference on Virtual Brain Models (SBI-VBMs),
	8	+and demonstrate that training deep neural networks on both spatio-temporal and
	9	+functional features allows for accurate estimation of generative parameters in brain disorders.
19	19
20	20
	12	+Code: [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/ebrains-task-3-3/Drive#notebooks/SBI-VBM>>https://wiki.ebrains.eu/bin/view/Collabs/ebrains-task-3-3/Drive#notebooks/SBI-VBM]]
	13	+
21	21	(% style="text-align: justify;" %)
22	22	Ref: [[https:~~/~~/iopscience.iop.org/article/10.1088/2632-2153/ad6230>>https://iopscience.iop.org/article/10.1088/2632-2153/ad6230]]
	16	+
	17	+{{{
	18	+@article{SBI-VBM,
	19	+ title={Simulation-based inference on virtual brain models of disorders},
	20	+ author={Hashemi, Meysam and Ziaeemehr, Abolfazl and Woodman, Marmaduke M and Fousek, Jan and Petkoski, Spase and Jirsa, Viktor K},
	21	+ journal={Machine Learning: Science and Technology},
	22	+ volume={5},
	23	+ number={3},
	24	+ pages={035019},
	25	+ year={2024},
	26	+ publisher={IOP Publishing}
	27	+}
	28	+}}}