CNS*2026 Halifax

Introduction
While the fields of extracellular neural recordings are well understood and mostly dominated by the somatic spikes and dendritic activity [1,2], there are some unnecessarily neglected sources. One of these is axonal branching patterns, that can under correct circumstances make a large contribution extracellularly to both near and far fields. These circumstances include, e.g., a synchronous volley of spikes in a branching axonal bundle, as often observed in the auditory brainstem [3]. I address the question under which circumstances the fields from axonal branching patterns can be non-negligible, and whether their fields are fully explained by their dipole contribution.

Methods
I simulate single multi-compartment cells with NEURON and LFPy packages to study their extracellular potentials at distances relevant for EEGs, often referred as far fields. I furthermore analytically study the extracellular fields of axonal branching patterns, singling out their relative dipole and quadrupole contributions to the extracellular field both along and perpendicular to the dipole axis.

Results
As expected, the simulations show that the dipole between apical dendrites and the soma can determined the extracellular far fields in pyramidal-like cell morphologies. Additionally, both the simulations and the analytics show that axonal branching patterns can create similarly extracellular far fields that are similarly large in amplitude. Furthermore, these axonal fields cannot be explained by the dipole contribution alone.


Discussion
As conventionally assumed, the dipole spanned between the dendrites and soma is the main source of the electro-encephalography (EEG) signals of cortical pyramidal neurons [e.g. 4]. This assumption does not necessarily hold for neurons with a large axonal branching zone, particularly when embedded in a population of neurons with similar morphologies and with synchronous population activation. These results have consequences e.g. for the interpretation of evoked somatosensory potentials, such as the auditory brainstem response.

References

1. Gold, C., et al. (2006). On the origin of the extracellular action potential waveform: A modeling study. 95(5), 3113–3128. https://doi.org/10.1152/jn.00979.2005
2. Næss, S., et al. (2021). Biophysically detailed forward modeling of the neural origin of EEG and MEG signals. NeuroImage, 225, 117467. https://doi.org/10.1016/j.neuroimage.2020.117467
3. McColgan, T., et al. (2017). Dipolar extracellular potentials generated by axonal projections. eLife, 6, 343. https://doi.org/10.7554/eLife.26106
4. Neymotin, S. A., et al. (2020). Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data. eLife, 9, e51214. https://doi.org/10.7554/eLife.51214

Acknowledgement
I thank Catherine Carr, Christine Köppl, Richard Kempter and Ghadi El Hasbani for helpful discussions, and Hannah Schultheiss for preliminary modeling.
This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) grant nr. 502188599.