Lectures and seminars Cognitive Neuroscience Club: "Probabilistic Inference Toward Precision Medicine"

Abstract

Uncertainty-aware inference from brain recordings is essential for identifying the underlying causal mechanisms of brain (dys)function, thereby advancing precision medicine, eg., through Virtual Brain Twins¹.

In this talk, I will first provide an overview of the state-of-the-art probabilistic machine learning approaches for inference, including likelihood-based methods (such as MCMC sampling) and likelihood-free methods (also known as simulation-based inference), along with the challenges involved in generalisation. I will then present our recently developed tools, the flexible Virtual Brain Inference² automatic Dynamic Causal Modeling³,  and Cohort Amortized Inference⁴, which are available in the cloud through EBRAINS services.

I will highlight the benefits of incorporating prior knowledge, reparameterization, and inference diagnostics for reliable causal inference. The performance of these methods will be demonstrated in epilepsy, multiple sclerosis, focal intervention, healthy ageing, and social facilitation⁵. Finally, I will move beyond model-based inference and discuss learning brain dynamics directly from detailed simulations using deep neural networks. Overall, these results contribute to advances in precision medicine by enhancing the predictive power of Virtual Brain Twins.

References

1. Hashemi et al. "Principles and operation of virtual brain twins." IEEE Reviews in Biomedical Engineering (2025).
2. Ziaeemehr, et al. "Virtual Brain Inference (VBI), a flexible and integrative toolkit for efficient probabilistic inference on whole-brain models." Elife 14 (2025).
3. Baldy et al. "Dynamic causal modelling in probabilistic programming languages." Journal of the Royal Society Interface 22.227 (2025).
4. Esmaeili et al, Cohort-amortized personalization: navigating the privacy-utility frontier for virtual brain twins.
5. Esmaeili, et al. "Probabilistic inference of social presence across brain scales reveals enhanced synaptic efficacy." Communications Biology 8.1 (2025): 1608.