Assessing human brain impedance using simultaneous surface and intracerebral recordings

How to assess the accuracy of volume conduction models? A validation study with stereotactic EEG data

Introduction

Volume conduction models of the human head are used in various neuroscience fields, such as for source reconstruction in EEG and MEG, and for modeling the effects of brain stimulation. Numerous studies have quantified the accuracy and sensitivity of volume conduction models by analyzing the effects of the geometrical and electrical features of the head model, the sensor model, the source model, and the numerical method. Most studies are based on simulations as it is hard to obtain sufficiently detailed measurements to compare to models. The recording of stereotactic EEG during electric stimulation mapping provides an opportunity for such empirical validation.

Methods

In the study presented here, we used the potential distribution of volume-conducted artifacts that are due to cortical stimulation to evaluate the accuracy of finite element method (FEM) volume conduction models. We adopted a widely used strategy for numerical comparison, i.e., we fixed the geometrical description of the head model and the mathematical method to perform simulations, and we gradually altered the head models, by increasing the level of detail of the conductivity profile. We compared the simulated potentials at different levels of refinement with the measured potentials in three epilepsy patients.

Results

Our results show that increasing the level of detail of the volume conduction head model only marginally improves the accuracy of the simulated potentials when compared to in-vivo sEEG measurements. The mismatch between measured and simulated potentials is, throughout all patients and models, maximally 40 microvolts (i.e., 10% relative error) in 80% of the stimulation-recording combination pairs and it is modulated by the distance between recording and stimulating electrodes.

Discussion

Our study suggests that commonly used strategies used to validate volume conduction models based solely on simulations might give an overly optimistic idea about volume conduction model accuracy. We recommend more empirical validations to be performed to identify those factors in volume conduction models that have the highest impact on the accuracy of simulated potentials. We share the dataset to allow researchers to further investigate the mismatch between measurements and FEM models and to contribute to improving volume conduction models.

Theoretical considerations and supporting evidence for the primary role of source geometry on field potential amplitude and spatial extent

Field potential (FP) recording is an accessible means to capture the shifts in the activity of neuron populations. However, the spatial and composite nature of these signals has largely been ignored, at least until it became technically possible to separate activities from co-activated sources in different structures or those that overlap in a volume. The pathway-specificity of mesoscopic sources has provided an anatomical reference that facilitates transcending from theoretical analysis to the exploration of real brain structures. We review computational and experimental findings that indicate how prioritizing the spatial geometry and density of sources, as opposed to the distance to the recording site, better defines the amplitudes and spatial reach of FPs. The role of geometry is enhanced by considering that zones of the active populations that act as sources or sinks of current may arrange differently with respect to each other, and have different geometry and densities. Thus, observations that seem counterintuitive in the scheme of distance-based logic alone can now be explained. For example, geometric factors explain why some structures produce FPs and others do not, why different FP motifs generated in the same structure extend far while others remain local, why factors like the size of an active population or the strong synchronicity of its neurons may fail to affect FPs, or why the rate of FP decay varies in different directions. These considerations are exemplified in large structures like the cortex and hippocampus, in which the role of geometrical elements and regional activation in shaping well-known FP oscillations generally go unnoticed. Discovering the geometry of the sources in play will decrease the risk of population or pathway misassignments based solely on the FP amplitude or temporal pattern.

Why Should Natural Principles Be Simple?

One of the criteria to a strong principle in natural sciences is simplicity. The conventional view holds that the world is provided with natural laws that must be simple. This common-sense approach is a modern rewording of the medieval philosophical/theological concept of the Multiple arising from (and generated by) the One. Humans need to pursue unifying frameworks, classificatory criteria and theories of everything. Still, the fact that our cognitive abilities tend towards simplification and groupings does not necessarily entail that this is the way the world works. Here we ask: what if singularity does not pave the way to multiplicity? How will we be sure if the Ockham's razor holds in real life? We will show in the sequel that the propensity to reduce to simplicity the relationships among the events leads to misleading interpretations of scientific issues. We are not going to take a full sceptic turn: we will engage in active outreach, suggesting examples from biology and physics to demonstrate how a novel methodological antiunitary approach might help to improve our scientific attitude towards world affairs. We will provide examples from aggregation of SARS-Cov-2 particles, unclassified extinct creatures, pathological brain stiffness. Further, we will describe how antiunitary strategies, plagiarising medieval concepts from William od Ockham and Gregory of Rimini, help to explain novel relational approaches to quantum mechanics and the epistemological role of our mind in building the real world.

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

Electroencephalography (EEG) and magnetoencephalography (MEG) are among the most important techniques for non-invasively studying cognition and disease in the human brain. These signals are known to originate from cortical neural activity, typically described in terms of current dipoles. While the link between cortical current dipoles and EEG/MEG signals is relatively well understood, surprisingly little is known about the link between different kinds of neural activity and the current dipoles themselves. Detailed biophysical modeling has played an important role in exploring the neural origin of intracranial electric signals, like extracellular spikes and local field potentials. However, this approach has not yet been taken full advantage of in the context of exploring the neural origin of the cortical current dipoles that are causing EEG/MEG signals. Here, we present a method for reducing arbitrary simulated neural activity to single current dipoles. We find that the method is applicable for calculating extracranial signals, but less suited for calculating intracranial electrocorticography (ECoG) signals. We demonstrate that this approach can serve as a powerful tool for investigating the neural origin of EEG/MEG signals. This is done through example studies of the single-neuron EEG contribution, the putative EEG contribution from calcium spikes, and from calculating EEG signals from large-scale neural network simulations. We also demonstrate how the simulated current dipoles can be used directly in combination with detailed head models, allowing for simulated EEG signals with an unprecedented level of biophysical details. In conclusion, this paper presents a framework for biophysically detailed modeling of EEG and MEG signals, which can be used to better our understanding of non-inasively measured neural activity in humans.

Fast simulation of extracellular action potential signatures based on a morphological filtering approximation

Simulating extracellular recordings of neuronal populations is an important and challenging task both for understanding the nature and relationships between extracellular field potentials at different scales, and for the validation of methodological tools for signal analysis such as spike detection and sorting algorithms. Detailed neuronal multicompartmental models with active or passive compartments are commonly used in this objective. Although using such realistic NEURON models could lead to realistic extracellular potentials, it may require a high computational burden making the simulation of large populations difficult without a workstation. We propose in this paper a novel method to simulate extracellular potentials of firing neurons, taking into account the NEURON geometry and the relative positions of the electrodes. The simulator takes the form of a linear geometry based filter that models the shape of an action potential by taking into account its generation in the cell body / axon hillock and its propagation along the axon. The validity of the approach for different NEURON morphologies is assessed. We demonstrate that our method is able to reproduce realistic extracellular action potentials in a given range of axon/dendrites surface ratio, with a time-efficient computational burden.

A multi-channel approach for cortical stimulation artefact suppression in depth EEG signals using time-frequency and spatial filtering

OBJECTIVE

The stereo electroencephalogram (SEEG) recordings are the sate of the art tool used in pre-surgical evaluation of drug-unresponsive epileptic patients. Coupled with SEEG, electrical cortical stimulation (CS) offer a complementary tool to investigate the lesioned/healthy brain regions and to identify the epileptic zones with precision. However, the propagation of this stimulation inside the brain masks the cerebral activity recorded by nearby multi-contact SEEG electrodes. The objective of this paper is to propose a novel filtering approach for suppressing the CS artifact in SEEG signals using time, frequency as well as spatial information.

METHODS

The method combines spatial filtering with tunable-Q wavelet transform (TQWT). SEEG signals are spatially filtered to isolate the CS artifacts within a few number of sources/components. The artifacted components are then decomposed into oscillatory background and sharp varying transient signals using tunable-Q wavelet transform (TQWT). The CS artifact is assumed to lie in the transient part of the signal. Using prior known time-frequency information of the CS artifacts, we selectively mask the wavelet coefficients of the transient signal and extract out any remaining significant electrophysiological activity.

RESULTS

We have applied our proposed method of CS artifact suppression on simulated and real SEEG signals with convincing performance. The experimental results indicate the effectiveness of the proposed approach.

CONCLUSION

The proposed method suppresses CS artifacts without affecting the background SEEG signal.

SIGNIFICANCE

The proposed method can be applied for suppressing both low and high frequency CS artifacts and outperforms current methods from the literature.

Features of Simultaneous Scalp and Intracranial EEG That Predict Localization of Ictal Onset Zone

OBJECTIVE

To assess the utility of simultaneous scalp EEG in patients with focal epilepsy undergoing intracranial EEG evaluation after a detailed presurgical testing, including an inpatient scalp video EEG evaluation.

METHODS

Patients who underwent simultaneous scalp and intracranial EEG (SSIEEG) monitoring were classified into group 1 or 2 depending on whether the seizure onset zone was delineated or not. Seizures were analyzed using the following 3 EEG features at the onset of seizures latency, location, and pattern.

RESULTS

The criteria showed at least one of the following features when comparing SSIEEG: prolonged latency, absence of anatomical congruence, lack of concordance of EEG pattern in 11.11% (1/9) of the patients in group 1 and 75 % (3/4) of the patients in group 2. These 3 features were not present in any of the 5 patients who had Engel class I outcome compared with 1 of the 2 patients (50%) who had seizure recurrence after resective surgery. The mean latency of seizure onset in scalp EEG compared with intracranial EEG of patients in group 1 was 17.48 seconds (SD = 16.07) compared with 4.33 seconds (SD = 11.24) in group 2 ( P = .03). None of the seizures recorded in patients in group 1 had a discordant EEG pattern in SSIEEG.

CONCLUSION

Concordance in EEG features like latency, location, and EEG pattern, at the onset of seizures in SSIEEG is associated with a favorable outcome after epilepsy surgery in patients with intractable focal epilepsy.

SIGNIFICANCE

Simultaneous scalp EEG complements intracranial EEG evaluation even after a detailed inpatient scalp video EEG evaluation and could be part of standard intracranial EEG studies in patients with intractable focal epilepsy.

Magnitude and behavior of cross-talk effects in multichannel electrophysiology experiments

Modern neurophysiological experiments frequently involve multiple channels separated by very small distances. A unique methodological concern for multiple-electrode experiments is that of capacitive coupling (cross-talk) between channels. Yet the nature of the cross-talk recording circuit is not well known in the field, and the extent to which it practically affects neurophysiology experiments has never been fully investigated. Here we describe a simple electrical circuit model of simultaneous recording and stimulation with two or more channels and experimentally verify the model using ex vivo brain slice and in vivo whole-brain preparations. In agreement with the model, we find that cross-talk amplitudes increase nearly linearly with the impedance of a recording electrode and are larger for higher frequencies. We demonstrate cross-talk contamination of action potential waveforms from intracellular to extracellular channels, which is observable in part because of the different orders of magnitude between the channels. This contamination is electrode impedance-dependent and matches predictions from the model. We use recently published parameters to simulate cross-talk in high-density multichannel extracellular recordings. Cross-talk effectively spatially smooths current source density (CSD) estimates in these recordings and induces artefactual phase shifts where underlying voltage gradients occur; however, these effects are modest. We show that the effects of cross-talk are unlikely to affect most conclusions inferred from neurophysiology experiments when both originating and receiving electrode record signals of similar magnitudes. We discuss other types of experiments and analyses that may be susceptible to cross-talk, techniques for detecting and experimentally reducing cross-talk, and implications for high-density probe design.NEW & NOTEWORTHY We develop and experimentally verify an electrical circuit model describing cross-talk that necessarily occurs between two channels. Recorded cross-talk increased with electrode impedance and signal frequency. We recorded cross-talk contamination of spike waveforms from intracellular to extracellular channels. We simulated high-density multichannel extracellular recordings and demonstrate spatial smoothing and phase shifts that cross-talk enacts on CSD measurements. However, when channels record similar-magnitude signals, effects are modest and unlikely to affect most conclusions.