  The study of effective connectivity (EC) is essential in understanding how
the brain integrates and responds to various sensory inputs. Model-driven
estimation of EC is a powerful approach that requires estimating global and
local parameters of a generative model of neural activity. Insights gathered
through this process can be used in various applications, such as studying
neurodevelopmental disorders. However, accurately determining EC through
generative models remains a significant challenge due to the complexity of
brain dynamics and the inherent noise in neural recordings, e.g., in
electroencephalography (EEG). Current model-driven methods to study EC are
computationally complex and cannot scale to all brain regions as required by
whole-brain analyses. To facilitate EC assessment, an inference algorithm must
exhibit reliable prediction of parameters in the presence of noise. Further,
the relationship between the model parameters and the neural recordings must be
learnable. To progress toward these objectives, we benchmarked the performance
of a Bi-LSTM model for parameter inference from the Jansen-Rit neural mass
model (JR-NMM) simulated EEG under various noise conditions. Additionally, our
study explores how the JR-NMM reacts to changes in key biological parameters
(i.e., sensitivity analysis) like synaptic gains and time constants, a crucial
step in understanding the connection between neural mechanisms and observed
brain activity. Our results indicate that we can predict the local JR-NMM
parameters from EEG, supporting the feasibility of our deep-learning-based
inference approach. In future work, we plan to extend this framework to
estimate local and global parameters from real EEG in clinically relevant
applications.
