CNS*2025 Florence

P045 Cortical Microcircuit Modeling for Concurrent EEG-fMRI Recordings

Shih-Cheng Chien*1, Stanislav Jiříček1,2,3, Thomas Knösche4, Jaroslav Hlinka1,2, Helmut Schmidt1


1Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
2National Institute of Mental Health, Klecany, Czech Republic
3Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
4Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany


*Email:chien@cs.cas.cz
Introduction

EEG and fMRI are widely used noninvasive methods for human brain imaging. Concurrent EEG-fMRI recordings help answer fundamental questions about the functional roles of EEG rhythms, their origin, and their relationship with the BOLD signal. Given the fact that both EEG and BOLD signals predominantly originate from postsynaptic potentials (PSPs) [1,2], and considering that distinct inhibitory neuron types influence EEG rhythms differently [3] and possess varied neurovascular coupling properties [4], a cortical microcircuit model incorporating multiple inhibitory neuron types would offer a promising framework for investigating local neural dynamics underlying EEG rhythms and their relationship with BOLD signals.

Methods
We developed a cortical microcircuit model that incorporates excitatory (E) and inhibitory (PV, SOM, and VIP) populations across cortical layers (L2/3, L4, L5, and L6) with realistic configurations, including connection probabilities, synaptic strengths, neuronal densities, and firing rate functions for each neuron type. The model receives three types of external inputs: (1) lateral input, (2) modulatory input, and (3) thalamic input. We characterized the spectral properties of EEG rhythms across a range of external inputs, explored EEG-BOLD correlations under constant and varying input conditions, and analyzed how neuronal populations contribute to EEG rhythms and the EEG-BOLD correlation.
Results
The model generates EEG rhythms, with increased power in the alpha (8-12 Hz), beta (13-30 Hz), and gamma bands (30-50 Hz) at low modulatory input and increased delta (0.5-4 Hz) and theta (4-7 Hz) powers at high modulatory input. We found low-frequency EEG activity (from delta to low beta band) was driven more strongly by infragranular than supragranular populations. Conversely, supragranular populations drive high-frequency EEG activity (high beta and gamma band) more intensely. As to EEG-BOLD correlations, we found that alpha-BOLD correlation is almost exclusively driven by fluctuations (i.e., standard deviation of firing rates) in infragranular populations, with little contribution from the supragranular layer.

Discussion
Our cortical microcircuit model generates EEG rhythms based on a generic mechanism involving the nonlinear amplification and filtering of synaptic noise. Our investigation focused on different forms of long-range external input, which targets distinct neuronal populations. The model could be used to help design optimal stimulation protocols for various applications, including the effect of specific neuronal populations on EEG and BOLD.




Acknowledgements
The publication was supported by a Lumina-Quaeruntur fellowship (LQ100302301) by the Czech Academy of Sciences (awarded to HS) and ERDF-Project Brain Dynamics, No. CZ.02.01.01/00/22_008/0004643. We acknowledge the core facility MAFIL supported by the Czech-BioImaging large RI project (LM2018129 funded by MEYS CR) for their support in obtaining scientific data presented in this work.
References
[1]https://doi.org/10.1016/j.brainresrev.2009.12.004
[2]https://doi.org/10.1016/j.cub.2018.11.052
[3]https://doi.org/10.1016/j.tins.2003.09.016
[4]https://doi.org/10.1523/JNEUROSCI.3065-04.2004