Comparative power spectral analysis of simultaneous elecroencephalographic and magnetoencephalographic recordings in humans suggests non-resistive extracellular media

The resistive or non-resistive nature of the extracellular space in the brain is still debated, and is an important issue for correctly modeling extracellular potentials. Here, we first show theoretically that if the medium is resistive, the frequency scaling should be the same for electroencephalogram (EEG) and magnetoencephalogram (MEG) signals at low frequencies (<10 Hz). To test this prediction, we analyzed the spectrum of simultaneous EEG and MEG measurements in four human subjects. The frequency scaling of EEG displays coherent variations across the brain, in general between 1/f and 1/f^2, and tends to be smaller in parietal/temporal regions. In a given region, although the variability of the frequency scaling exponent was higher for MEG compared to EEG, both signals consistently scale with a different exponent. In some cases, the scaling was similar, but only when the signal-to-noise ratio of the MEG was low. Several methods of noise correction for environmental and instrumental noise were tested, and they all increased the difference between EEG and MEG scaling. In conclusion, there is a significant difference in frequency scaling between EEG and MEG, which can be explained if the extracellular medium (including other layers such as dura matter and skull) is globally non-resistive.Comment: Submitted to Journal of Computational Neuroscienc

Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study

International audienceElectrophysiological signals (electroencephalography, EEG, and magnetoencephalography , MEG), as many natural processes, exhibit scale-invariance properties resulting in a power-law (1/f) spectrum. Interestingly, EEG and MEG differ in their slopes, which could be explained by several mechanisms, including non-resistive properties of tissues. Our goal in the present study is to estimate the impact of space/frequency structure of source signals as a putative mechanism to explain spectral scaling properties of neuroimaging signals. We performed simulations based on the summed contribution of cortical patches with different sizes (ranging from 0.4 to 104.2 cm 2). Small patches were attributed signals of high frequencies, whereas large patches were associated with signals of low frequencies, on a logarithmic scale. The tested parameters included i) the space/frequency structure (range of patch sizes and frequencies) and ii) the amplitude factor c parametrizing the spatial scale ratios. We found that the space/frequency structure may cause differences between EEG and MEG scale-free spectra that are compatible with real data findings reported in previous studies. We also found that below a certain spatial scale, there were no more differences between EEG and MEG, suggesting a limit for the resolution of both methods. Our work provides an explanation of experimental findings. This does not rule out other mechanisms for differences between EEG and MEG, but suggests an important role of spatio-temporal structure of neural dynamics. This can help the analysis and interpretation of power-law measures in EEG and MEG, and we believe our results can also impact computational modeling of brain dynamics, where different local connectivity structures could be used at different frequencies

Propriétés de l'activité de décharge neuronale de masse chez les humains mesurée par EEG non invasive

Abstract : Electroencephalography (EEG) is a non-invasive neuroimaging modality that was first introduced over 80 years ago. Surface EEG does not directly measure neuronal activity, and it is often assumed that it cannot provide indications on the underlying neuronal firing. However, recent studies based on invasive measurements in monkeys have shown that the coupling between two EEG frequency bands, namely the Gamma (25-45 Hz) and Delta (2-4 Hz) bands, is a good predictor of underlying mass-spiking activity. Specifically, when the Delta signal is in its trough and Gamma power is high, the probability of mass- firing of neurons is large. Here, we investigate this property in healthy human EEG acquired during resting-state. Using the interaction between Delta phase and Gamma power, we derived a modeled spike signal (MSS) from the recorded EEG. We found the power spectrum density (PSD) pattern of the MSS to be similar to that observed in animal studies. Specifically, between 1-10 Hz that the PSD deviates from a 1/[florin] trend and exhibits a small peak at about 2-3Hz. In addition, an inter-hemispheric correlation was found between the MSS of the different pairs of electrode in opposite hemispheres. Our results open the possibility of studying underlying neuronal output with non-invasive EEG. // Résumé : L'électroencéphalographie (EEG) est une modalité de neuro-imagerie non invasive qui a été introduite il y a plus de 80 ans. L’EEG de surface ne mesure pas directement l’activité neuronale et il est généralement supposé qu’elle ne donne pas d’indications sur la décharge neuronale sous-jacente. Cependant des études récentes ont montré à l’aide de mesures invasives que le couplage entre deux bandes de fréquences EEG, soit les bandes Gamma (25-45 Hz) et Delta (2-4 Hz), est un bon indicateur de l’activité neuronale de masse sous-jacente chez les singes. Plus précisément, lorsque le signal Delta est dans un creux (phase de π) et que la puissance dans le signal Gamma est élevée, la probabilité de décharge de masse des neurones est grande. Cette propriété est ici étudiée dans les signaux EEG d’humains sains en état de repos. En se basant sur l'interaction entre la phase du signal Delta et la puissance du signal Gamma, nous avons dérivé un modèle de l’activité neuronale de masse sous-jacente (modeled spike signal-MSS) obtenu à partir du signal l'EEG enregistrée. On trouve que la densité spectrale de puissance (power spectal density-PSD) du MSS est similaire à celle observée dans les études animales. Plus spécifiquement, entre 1-10 Hz la PSD s’écarte d’une tendance en 1 / [florin] et présente un pic de faible amplitude à environ 2-3Hz. En outre, une corrélation inter-hémisphérique a été observée entre les MSS de différentes paires d'électrodes positionnées sur les hémisphères opposés. Nos résultats ouvrent la possibilité d'étudier l’activité neuronale sous-jacente par EEG non-invasive

A unifying principle underlying the extracellular field potential spectral responses in the human cortex

Electrophysiological mass potentials show complex spectral changes upon neuronal activation. However, it is unknown to what extent these complex band-limited changes are interrelated or, alternatively, reflect separate neuronal processes. To address this question, intracranial electrocorticograms (ECoG) responses were recorded in patients engaged in visuomotor tasks. We found that in the 10- to 100-Hz frequency range there was a significant reduction in the exponent chi of the 1/f(chi) component of the spectrum associated with neuronal activation. In a minority of electrodes showing particularly high activations the exponent reduction was associated with specific band-limited power modulations: emergence of a high gamma (80-100 Hz) and a decrease in the alpha (9-12 Hz) peaks. Importantly, the peaks\u27 height was correlated with the 1/f(chi) exponent on activation. Control simulation ruled out the possibility that the change in 1/f(chi) exponent was a consequence of the analysis procedure. These results reveal a new global, cross-frequency (10-100 Hz) neuronal process reflected in a significant reduction of the power spectrum slope of the ECoG signal

Measurement of excitation-inhibition ratio in autism spectrum disorder using critical brain dynamics

cited By 0Balance between excitation (E) and inhibition (I) is a key principle for neuronal network organization and information processing. Consistent with this notion, excitation-inhibition imbalances are considered a pathophysiological mechanism in many brain disorders including autism spectrum disorder (ASD). However, methods to measure E/I ratios in human brain networks are lacking. Here, we present a method to quantify a functional E/I ratio (fE/I) from neuronal oscillations, and validate it in healthy subjects and children with ASD. We define structural E/I ratio in an in silico neuronal network, investigate how it relates to power and long-range temporal correlations (LRTC) of the network's activity, and use these relationships to design the fE/I algorithm. Application of this algorithm to the EEGs of healthy adults showed that fE/I is balanced at the population level and is decreased through GABAergic enforcement. In children with ASD, we observed larger fE/I variability and stronger LRTC compared to typically developing children (TDC). Interestingly, visual grading for EEG abnormalities that are thought to reflect E/I imbalances revealed elevated fE/I and LRTC in ASD children with normal EEG compared to TDC or ASD with abnormal EEG. We speculate that our approach will help understand physiological heterogeneity also in other brain disorders.Peer reviewe

Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): Comparing multi-electrode recordings from simulated and biological mammalian cortical tissue

Local field potentials (LFPs) sampled with extracellular electrodes are frequently used as a measure of population neuronal activity. However, relating such measurements to underlying neuronal behaviour and connectivity is non-trivial. To help study this link, we developed the Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX). We first identified a reduced neuron model that retained the spatial and frequency filtering characteristics of extracellular potentials from neocortical neurons. We then developed VERTEX as an easy-to-use Matlab tool for simulating LFPs from large populations (>100 000 neurons). A VERTEX-based simulation successfully reproduced features of the LFPs from an in vitro multi-electrode array recording of macaque neocortical tissue. Our model, with virtual electrodes placed anywhere in 3D, allows direct comparisons with the in vitro recording setup. We envisage that VERTEX will stimulate experimentalists, clinicians, and computational neuroscientists to use models to understand the mechanisms underlying measured brain dynamics in health and disease.Comment: appears in Brain Struct Funct 201

Psyche, Signals and Systems

For a century or so, the multidisciplinary nature of neuroscience has left the field fractured into distinct areas of research. In particular, the subjects of consciousness and perception present unique challenges in the attempt to build a unifying understanding bridging between the micro-, meso-, and macro-scales of the brain and psychology. This chapter outlines an integrated view of the neurophysiological systems, psychophysical signals, and theoretical considerations related to consciousness. First, we review the signals that correlate to consciousness during psychophysics experiments. We then review the underlying neural mechanisms giving rise to these signals. Finally, we discuss the computational and theoretical functions of such neural mechanisms, and begin to outline means in which these are related to ongoing theoretical research