P331 Vitrual Brain Inference(VBI): A Toolkit for Probabilistic Inference in Virtual Brain Models
Abolfazl Ziaeemehr*¹, Marmaduke Woodman¹, Lia Domide², Spase Petkoski¹, Viktor Jirsa¹, Meysam Hashemi¹
¹ Aix Marseille Univ, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France ² Codemart, Cluj-Napoca, Romania*Email: abolfazl.ziaee-mehr@univmail.com
IntroductionUnderstanding brain dynamics requires accurate models that integrate neural activity and neuroimaging data. Virtual brain modeling has emerged as a powerful approach to simulate brain signals based on neurobiological mechanisms. However, solving the inverse problem of inferring brain dynamics from observed neuroimaging data remains a challenge. The Virtual Brain Inference (VBI) [1] toolkit addresses this need by offering a probabilistic framework for parameter estimation in large-scale brain models. VBI combines neural mass modeling with simulation-based inference (SBI) [2] to efficiently estimate generative model parameters and uncover underlying neurophysiological mechanisms.MethodsVBI integrates structural and functional neuroimaging data to build personalized virtual brain models. The toolkit supports various neural mass models, including Wilson-Cowan, Montbrió, Jansen-Rit, Stuart-Landau, Wong-Wang, and Epileptor. Using GPU-accelerated simulations, VBI extracts key statistical features such as functional connectivity (FC), functional connectivity dynamics (FCD), and power spectral density (PSD). Deep neural density estimators, such as Masked Autoregressive Flows (MAFs) and Neural Spline Flows (NSFs), are trained to approximate posterior distributions. This SBI approach allows efficient inference of neural parameters without reliance on traditional sampling-based methods. ResultsWe demonstrate VBI’s capability by applying it to simulated and real neuroimaging datasets. The probabilistic inference framework accurately reconstructs neural parameters and identifies inter-individual variability in brain dynamics. Compared to traditional methods like Markov Chain Monte Carlo (MCMC) [3] and Approximate Bayesian Computation (ABC), VBI achieves superior scalability and efficiency. Performance evaluations highlight its robustness across different brain models and noise conditions. The ability to generate personalized inferences makes VBI a valuable tool for both research and clinical applications [4], aiding in the study of neurological disorders and cognitive function. Look at Fig.1 for the workflow. DiscussionVBI provides an efficient and scalable solution for inferring neural parameters from brain signals, addressing a critical gap in computational neuroscience. By leveraging SBI and deep learning, VBI enhances the interpretability and applicability of virtual brain models. This open-source toolkit offers researchers a flexible platform for modeling, simulation, and inference, fostering advancements in neuroscience and neuroimaging research.
Figure 1. Overview of the VBI workflow: (A) A personalized connectome is constructed using diffusion tensor imaging and a brain parcellation atlas. (B) This serves as the foundation for building a virtual brain model, with control parameters sampled from a prior distribution. (C) VBI simulates time series data corresponding to neuroimaging recordings. (D) Summary statistics, including functional connectivit Acknowledgements This research was funded by the EU’s Horizon 2020 Programme under Grant Agreements No. 101147319 (EBRAINS 2.0), No. 101137289 (Virtual Brain Twin), No. 101057429 (environMENTAL), and ANR grant ANR-22-PESN-0012 (France 2030). We acknowledge Fenix Infrastructure resources, partially funded by the EU’s Horizon 2020 through the ICEI project (Grant No. 800858).
