1Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia 2Jozef Stefan International Postgraduate School, Ljubljana, Slovenia 3Department of Mathematics, Faculty of Mathematics and Physics, Ljubljana, Slovenia
*Email: nina.omejc@ijs.si Introduction
Electroencephalography (EEG) is a clinical, non-invasive, high-temporal resolution technique for measuring whole-brain activity. However, the underlying mechanisms that give rise to the observed high-level rhythmic activity remain incompletely understood. Various neural population and network models attempt to explain these dynamics [1], but, to our knowledge, they have not been systematically explored or evaluated. Methods To explore the space of proposed and potential models, we represent brain networks as graphs, where nodes correspond to brain sources obtained via EEG source analysis, in our case the dipole fitted independent components (Figure 1). Each node’s dynamics are further categorized into three subdynamics: synapto-dendritic dynamics (input transformation), intrinsic dynamic, and firing response (output transformation). These subdynamics are defined by a bounded set of functions derived from the literature [1], or generated by an unbounded probabilistic context-free grammar [2]. Such a modular and unbounded specification allows for flexible and physiologically valid construction of the network. Results We are currently utilizing our Julia-based framework and are in the model evaluation phase. The dataset we use consists of 64-channel EEG recordings from 50 participants performing a visual flickering task, designed to induce steady-state visual evoked potentials [3]. We repeatedly sample potential EEG models using Markov Chain Monte Carlo and optimize the model parameters using CMA-ES algorithm. By the time of the conference, we aim to determine which established and previously unexamined whole-brain activity models can reproduce the observed oscillations, and, more importantly, which can also accurately capture the harmonics of the flickering stimulation frequency, a robust and interesting feature observed in this dataset. Discussion The presence of these harmonic components is a well-documented but not yet fully understood phenomenon in EEG research [4]. By systematically exploring different model configurations, we aim to assess which types of nonlinear models and which features (for example, recurrent connectivity, nonlinear synaptic integration, parallel computations, delays) play a crucial role in shaping these spectral patterns. Exploring the set of valid models to understand these mechanisms could have broader implications for theories of whole-brain neural activity and improve our understanding of EEG measurements.
Figure 1. Figure 1: A data-driven framework for exploring whole-brain network EEG models. Acknowledgements We would like to thank our department's SHED group for equation discovery for the fruitful discussions regarding our work. References
