Biophysically detailed forward modeling of the neural origin of EEG and MEG signals

Næss, Solveig; Halnes, Geir; Hagen, Espen; Hagler, Donald J.; Dale, Anders M.; Einevoll, Gaute T.; Ness, Torbjørn V.

doi:10.1016/j.neuroimage.2020.117467

Cited by 47 publications

(79 citation statements)

References 80 publications

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“…We then computed a “ground-truth” EEG (referred to simply as “EEG” in the paper), following the hybrid modelling scheme [30, 35, 42, 43], and used this ground-truth EEG to compare the performance of the different proxies. To do so, we created a network of unconnected multicompartment neuron models with realistic morphologies and homogeneous distribution within the circular section of a cylinder of radius r = 0.5 mm (Fig 1 C), which roughly approximates the spatial extension of a layer in a cortical column.…”

Section: Resultsmentioning

confidence: 99%

“…We focused on computing the EEG generated by neurons with somas positioned in layer 2/3, so that somas of the multicompartment neurons are aligned in the Z-axis (150 μm below the reference point Z = 8.5 mm). We chose to position somas in layer 2/3 based on previous computational work suggesting that this layer gives a large contribution to extracellular potentials [30, 35]. The reference point Z = 8.5 mm was chosen to approximate the radial distance between the center of a spherical rodent head model and the brain tissue [46].…”

Section: Resultsmentioning

confidence: 99%

“…EEGs were then generated from transmembrane currents of multicompartment neurons in combination with a forward-modelling scheme based on volume conduction theory [6]. To approximate the different geometries and electrical conductivities of the head, we computed the EEG using the four-layered spherical head model described in [35, 49]. In this model, the different layers represent the brain tissue, cerebrospinal fluid (CSF), skull, and scalp, with radii 9, 9.5, 10 and 10.5 mm respectively, which approximate the dimensions of a rodent head model [46].…”

Section: Resultsmentioning

confidence: 99%

“…However, the limitations and caveats of using such ad-hoc simplifications to compute the EEG have been rarely considered and tested. As a result, it is still unclear how best to compute EEGs directly from output from point-like neuron network models [35, 36].…”

Section: Introductionmentioning

confidence: 99%

“…This approach is however computationally cumbersome, and it does not allow an easily tractable and exhaustive analysis of the dynamics of such networks. One alternative could be using a hybrid scheme [30, 35, 42, 43] that projects the spike times generated by the LIF point-neuron network onto morphologically detailed 3D neuron models and then computing the electric field that the currents flowing through these 3D networks generate. This scheme provides a simplification by separating the study of the network dynamics (described by the point-neuron network model) from that of field generation (described by the multicompartment neuron model), but still requires running cumbersome multicompartment model simulations for each simulation of the LIF network.…”

Section: Introductionmentioning

confidence: 99%

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Computation of the electroencephalogram (EEG) from network models of point neurons

Martínez‐Cañada

Ness

Einevoll

et al. 2020

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The electroencephalogram (EEG) is one of the main tools for non-invasively studying brain function and dysfunction. To better interpret EEGs in terms of neural mechanisms, it is important to compare experimentally recorded EEGs with the output of neural network models. Most current neural network models use networks of simple point neurons. They capture important properties of cortical dynamics, and are numerically or analytically tractable. However, point neuron networks cannot directly generate an EEG, since EEGs are generated by spatially separated transmembrane currents. Here, we explored how to compute an accurate approximation of the EEG with a combination of quantities defined in point-neuron network models. We constructed several different candidate approximations (or proxies) of the EEG that can be computed from networks of leaky integrate-and-fire (LIF) point neurons, such as firing rates, membrane potentials, and specific combinations of synaptic currents. We then evaluated how well each proxy reconstructed a realistic ground-truth EEG obtained when the synaptic input currents of the LIF network were fed into a three-dimensional (3D) network model of multi-compartmental neurons with realistic cell morphologies. We found that a new class of proxies, based on an optimized linear combination of time-shifted AMPA and GABA currents, provided the most accurate estimate of the EEG over a wide range of network states of the LIF point-neuron network. The new linear proxies explained most of the variance (85-95%) of the ground-truth EEG for a wide range of cell morphologies, distributions of presynaptic inputs, and position of the recording electrode. Non-linear proxies, obtained using a convolutional neural network (CNN) to predict the EEG from synaptic currents, increased proxy performance by a further 2-8%. Our proxies can be used to easily calculate a biologically realistic EEG signal directly from point-neuron simulations and thereby allow a quantitative comparison between computational models and experimental EEG recordings.

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