Biophysical modeling of electric and magnetic brain signals

Abstract

When we think and feel, the nerve cells (neurons) in the brain communicate by means of electric messages. We can listen to these neural conversations by recording resulting electric and magnetic signals on the outside of the head. The electric signals can be measured with small electrodes placed on the scalp, a method known as electroencephalography (EEG), while magnetic fields can be recorded with magnetoencephalography (MEG). Even though EEG and MEG are widely used techniques for studying cognition and disease in the human brain, we know surprisingly little about the neural origin of these signals. We can get an overview of the electrical activity in the neural symphony by studying the current dipole moment capturing the melody of the network. To illustrate: if you know which tune is played on stage, you will have a good idea of what one can hear from the outside of the concert hall. Correspondingly, the current dipole moment can be applied for modeling EEG and MEG signals measured outside of the head. Furthermore, it is possible to simulate neural activity with detailed neuron models reconstructed from experimental data. However, the possibility to predict non-invasive brain recordings by calculating the current dipole moment from detailed neural activity has not yet been taken full advantage of. This thesis presents a forward modeling framework for computing EEG and MEG signals, with methods firmly grounded in the underlying biophysics. Specifically, In Paper I, we present analytical formulas and available python code for computing electric brain signals from a current dipole moment in a simplified head consisting of four concentric spheres. In Paper II, we expand the open-source python-package LFPy, allowing for current dipole calculations from morphologically reconstructed neurons and neural populations. LFPy 2.0 includes methods for computing electric potentials on top of the brain (electrocorticography), as well as EEG and MEG signals. In Paper III, we apply methods from Paper I and II to compute the current dipole moment and the resulting electric brain signals from biophysically detailed single cells and existing neural simulations. We demonstrate how the presented modeling framework opens the door for exploring the neural origin of electric and magnetic brain signals.