A Minimal Biophysical Model of Neocortical Pyramidal Cells: Implications for Frontal Cortex Microcircuitry and Field Potential Generation

Abstract

Ca 21 spikes initiated in the distal trunk of layer 5 pyramidal cells (PCs) underlie nonlinear dynamic changes in the gain of cellular response, critical for top-down control of cortical processing. Detailed models with many compartments and dozens of ionic channels can account for this Ca 21 spike-dependent gain and associated critical frequency. However, current models do not account for all known Ca 21 -dependent features. Previous attempts to include more features have required increasing complexity, limiting their interpretability and utility for studying large population dynamics. We overcome these limitations in a minimal two-compartment biophysical model. In our model, a basal-dendrites/somatic compartment included fast-inactivating Na 1 and delayed-rectifier K 1 conductances, while an apical-dendrites/trunk compartment included persistent Na 1 , hyperpolarization-activated cation (I h ), slow-inactivating K 1 , muscarinic K 1 , and Ca 21 L-type. The model replicated the Ca 21 spike morphology and its critical frequency plus three other defining features of layer 5 PC synaptic integration: linear frequency-current relationships, back-propagation-activated Ca 21 spike firing, and a shift in the critical frequency by blocking I h . Simulating 1000 synchronized layer 5 PCs, we reproduced the current source density patterns evoked by Ca 21 spikes and describe resulting medial-frontal EEG on a male macaque monkey. We reproduced changes in the current source density when I h was blocked. Thus, a two-compartment model with five crucial ionic currents in the apical dendrites reproduces all features of these neurons. We discuss the utility of this minimal model to study the microcircuitry of agranular areas of the frontal lobe involved in cognitive control and responsible for event-related potentials, such as the error-related negativity.

Cortical Contributions to Medial Frontal β-Bursts during Executive Control

“…LFP β-bursts were most prominent in the upper cortical layers but were not synchronized across the cortical column. Although SEF exhibits the functional, biophysical, and anatomical properties required to contribute to EEG signals (Sajad et al 2019(Sajad et al , 2022Herrera et al 2020), we found no evidence that EEG β-bursts could be caused by LFP β-bursts. These findings challenge a prevailing hypothesis and constrain models of the functional microcircuitry of β-burst generation during cognitive control.…”

“…Although the relationship between intracortical LFP and EEG signals is uncertain, we know that EEG signals measured over the scalp are generated by microcircuits within the cortex (e.g., Herrera et al 2020;2022) such that associations between the activity of particular neurons and EEG signatures can be observed (e.g., Sajad et al 2019Sajad et al , 2022. Biophysical modeling has suggested that cortical β-.…”

Resolving the mesoscopic missing link: Biophysical modeling of EEG from cortical columns in primates

“…The current dipole moment is assumed to originate from postsynaptic currents caused mainly by the activation of pyramidal cells perpendicular to the cortical surface (Hämäläinen et al, 1993) and from nonlinear processes taking place in their dendrites (Herrera et al, 2020; Suzuki and Larkum, 2017). Therefore, we can write as , with and representing its orientation and time-varying amplitude, respectively (Riera et al, 2012).…”

“…To compare the estimated EEG derived from these two approaches, we calculated the resulting EEG (ground-truth EEG) directly mapping the transmembrane currents into the scalp potentials described previously (Herrera et al, 2020). We quantified the mismatch error of each dipolar approach by calculating the magnitude (MAG) and the relative difference (RDM) measures (Meijs et al, 1989) between them and the ground-truth EEG at 15 randomly selected positions, designated as the center of the cortical column, throughout the monkey brain.…”

“…We employed the point-source approximation, implemented in LFPy, to compute the extracellular potentials with low-pass bioRxiv filtering at 100 Hz to obtain the LFP. The CSD patterns of the synthetic data sets were calculated using the spline-iCSD method (Pettersen et al, 2006) as implemented in the CSDplotter toolbox (https://github.com/espenhgn/CSDplotter) with custom MATLAB (R2021b, The MathWorks) scripts (Herrera et al, 2020).…”