Mentors: Meysam Hashemi <meysam.HASHEMI@univ-amu.fr> and Daniele Marinazzo <daniele.marinazzo@gmail.com>
Skill level: Intermediate/advanced
Required skills: At least intermediate coding skills. Comfortable with Python and JAX.
Experience with probabilistic inference and tools such as NumPyro or PyMC would be beneficial.
Time commitment: Full time (350 hours)
About: Bayesian inference is indispensable for hypothesis testing and uncertainty quantification in understanding complex brain (dys)function and cognition. Meanwhile, Virtual Brain Models, implemented through neuroinformatic tools like The Virtual Brain (TVB), have gained significant popularity due to their potential for clinical translation. Bayesian inference on Virtual Brain Models translates into probabilistic estimation of latent and observed states within systems driven by network input and stimuli, modeled by high-dimensional nonlinear differential equations, with potentially correlated parameters. To address these challenges, advanced MCMC sampling and inference algorithms embedded in Probabilistic Programming Languages (PPLs) have shown remarkable results. In particular, gradient-based algorithms, such as the No-U-Turn Sampler, and automatic Laplace approximation, have demonstrated effectiveness in achieving reliable Bayesian inference even in the presence of multimodal parameter distributions. These methods have been successfully applied in contexts like dynamical causal modeling of evoked-related potentials (see here) and inferring seizure propagation at the whole-brain scale (see here). However, significant challenges persist for fMRI BOLD data inference at whole-brain scales, e.g., in resting-state. Addressing these challenges requires reparameterization techniques to improve convergence and implementation in high-level tools like NumPyro or PyMC, streamlining the inference process. The aim of this project is to extend the automatic Bayesian estimation methods available in PPLs such as NumPyro or PyMC to enable Bayesian estimation of bifurcation parameters from fMRI data at large scales. The project leverages existing Python packages and prior expertise to advance inference methods for large scales dynamical models. By leveraging existing Python packages and prior expertise, the expected outcomes aim to significantly advance Bayesian inference for VBMs, addressing current limitations and enhancing their scalability for studying brain dynamics and supporting clinical applications.
Aims:
- Improving the implementation of Virtual Brain Models using JAX-based frameworks, such as NumPyro or PyMC, to improve efficiency and scalability, validated in-silico
- Develop reparameterization techniques to decorrelate parameters, facilitating improved gradient calculations for convergence, and visualizing outcomes
- Benchmark existing algorithms within NumPyro or PyMC to systematically identify their strengths and weaknesses in handling high-dimensional, multimodal problems
- Monitor algorithm convergence and provide comprehensive guidelines for ensuring reliable inference in the presence of multimodal parameter distributions
Website: The Virtual Brain: Delivering practical results. For novel clinical applications.
Tech keywords: Python, probabilistic ML/AI