The process of inference across spatiotemporalscalesis essential toidentifythe underlyingcausalmechanisms of brain computation and (dys)function. However, there remains a critical need for automated model inversion tools to estimate control (bifurcation) parameters from recordings acrossbrainscales, ideally including uncertainty.
Methods In this work, we attempt to bridge this gap by providing efficient and automatic Bayesian inference operating across scales. We usethestate-of-the-art probabilistic machine learning tools employing likelihood-based (MCMC sampling [1, 2]) and likelihood-free (a.k.a. simulation-based inference [3, 4]) approaches.
Results We demonstrate inference on the parameters and dynamics of spiking neurons, their mean-field approximation at the regional level, and brain network models. We show the benefits of incorporatingprior andinference diagnostics, leveraging self-tuning Monte Carlo strategies for unbiased sampling, and deep density estimators for efficient transformations[5]. The performance of these methods is then demonstratedfor causal inference inepilepsy [6], multiple sclerosis [7], focal intervention [8], healthy aging [9], and social facilitation [10].
Discussion This work shows potential to improve hypothesis evaluation in across brain scales through uncertainty quantification, and contribute to advances in precision medicine by enhancing the predictive power of brain models. Figure 1. Bayesian inference across brain scales. (A) Based on Bayes’ theorem, background knowledge about control parameters (expressed as a prior distribution), is combined with information from observed data (in the form of a likelihood function) to determine the posterior distribution. (B) Examples of the observed and predicted data features. Acknowledgements This research has received funding from EU’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreements No. 101147319 (EBRAINS 2.0 Project), No. 101137289 (Virtual Brain Twin Project), and government grant managed by the Agence Nationale de la Recherche reference ANR-22-PESN-0012 (France 2030 program). References
