Papers › Deep Jansen-Rit Parameter Inference for Model-Driven Analysis of Brain Activity
Deep Jansen-Rit Parameter Inference for Model-Driven Analysis of Brain Activity
Deepa Tilwani, Christian O'Reilly
Accurately modeling effective connectivity (EC) is critical for understanding how the brain processes and integrates sensory information. Yet, it remains a formidable challenge due to complex neural dynamics and noisy measurements such as those obtained from the electroencephalogram (EEG). Model-driven EC infers local (within a brain region) and global (between brain regions) EC parameters by fitting a generative model of neural activity onto experimental data. This approach offers a promising route for various applications, including investigating neurodevelopmental disorders. However, current approaches fail to scale to whole-brain analyses and are highly noise-sensitive. In this work, we employ three deep-learning architectures--a transformer, a long short-term memory (LSTM) network, and a convolutional neural network and bidirectional LSTM (CNN-BiLSTM) network--for inverse modeling and compare their performance with simulation-based inference in estimating the Jansen-Rit neural mass model (JR-NMM) parameters from simulated EEG data under various noise conditions. We demonstrate a reliable estimation of key local parameters, such as synaptic gains and time constants. However, other parameters like local JR-NMM connectivity cannot be evaluated reliably from evoked-related potentials (ERP). We also conduct a sensitivity analysis to characterize the influence of JR-NMM parameters on ERP and evaluate their learnability. Our results show the feasibility of deep-learning approaches to estimate the subset of learnable JR-NMM parameters.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections