Last modified by mhashemi on 2024/12/03 18:26

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1 This tool was developed at INS in Marseille.
2 Authors: M Hashemi, A Ziaeemehr, MM Woodman, J Fousek, S Petkoski, VK Jirsa
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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.
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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]]
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15 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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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 }}}