Journal
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
Volume 3, Issue 3, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/2632-2153/ac9037
Keywords
Bayesian inference; brain-network modelling; epilepsy; Hamiltonian Monte Carlo; personalized medicine
Categories
Funding
- French National Research Agency (ANR), second `Investissements d'Avenir' program [ANR-17-RHUS-0004]
- European Union [785907, 945539, 826421]
- VirtualBrainCloud
- PHRC-I 2013 EPISODIUM [2014-27]
- Fondation pour la Recherche Medicale [DIC20161236442]
- SATT Sud-Est [827-SA-16-UAM]
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Virtual brain models integrate individual brain imaging data with neural mass modeling to generate autonomous brain activity. In this study, a Bayesian methodology is proposed to estimate parameters related to the extent of the epileptogenic zone. The method improves computational efficiency and convergence diagnostics, allowing for personalized medicine applications.
Virtual brain models are data-driven patient-specific brain models integrating individual brain imaging data with neural mass modeling in a single computational framework, capable of autonomously generating brain activity and its associated brain imaging signals. Along the example of epilepsy, we develop an efficient and accurate Bayesian methodology estimating the parameters linked to the extent of the epileptogenic zone. State-of-the-art advances in Bayesian inference using Hamiltonian Monte Carlo (HMC) algorithms have remained elusive for large-scale differential-equations based models due to their slow convergence. We propose appropriate priors and a novel reparameterization to facilitate efficient exploration of the posterior distribution in terms of computational time and convergence diagnostics. The methodology is illustrated for in-silico dataset and then, applied to infer the personalized model parameters based on the empirical stereotactic electroencephalography recordings of retrospective patients. This improved methodology may pave the way to render HMC methods sufficiently easy and efficient to use, thus applicable in personalized medicine.
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