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

Sparse wavelet-based solutions for the M/EEG inverse problem

This paper is concerned with variational and Bayesian approaches to neuro-electromagnetic inverse problems (EEG and MEG). The strong indeterminacy of these problems is tackled by introducing sparsity inducing regularization/priors in a transformed domain, namely a spatial wavelet domain. Sparsity in the wavelet domain allows to reach ''data compression'' in the cortical sources domain. Spatial wavelets defined on the mesh graph of the triangulated cortical surface are used, in combination with sparse regression techniques, namely LASSO regression or sparse Bayesian learning, to provide localized and compressed estimates for brain activity from sensor data. Numerical results on simulated and real MEG data are provided, which outline the performances of the proposed approach in terms of localization

Independent component analysis reveals the unity of cognitive control

ISBN : 978-2-9532965-0-1In reaction time tasks, when subjects commit an error, a negative wave peaking approximatively 70-100ms after the erroneous response is recorded with EEG. This negativity, called "Error (Related) Negativity" (Ne or ERN[1, 2]), is maximal fronto-centrally, above the anterior cingulate cortex and/or SMA and was first interpreted as reflecting an error detection mechanism. However, after Laplacian estimation, a similar component was later observed on correct trials [3]. If this component on correct trials were to be the same as the one observed on errors, this would put important constraints on computational models of cognitive control. To address this issue we used Independent Com- ponent Analysis (ICA) to evaluate whether a single component (in ICA terms) could account for the waves observed in both erroneous and correct trials. For all the participants, a single component that accounts for the waves observed in the three categories of trials was found. The localisation of the sources is consistent with a rostral-cingulate zone origin, where control mechanisms are likely implemented [4]. This novel use of ICA allowed us to conclude that the negativities observed on error and correct trials are reflecting the same physiological mechanism whose amplitude is modulated as function of the performance

Studying connectivity between time-series using an interactive application

[Otros] Connectivity is a complex concept whose meaning varies for each domain. In a broad sense, it is possible to divide the connectivity in two main groups: physical and statistical. The former represents whether there is a physical link connecting the elements, like the telephone line, while the latter indicates a relation between the dynamics of the elements, as the number of sales and the income of a company. In several scientific fields, including economics, physics and neuroscience, the statistical connectivity is computed based on the analysis of time-series. Several different methods can be applied on the data, using different features of the signals, like covariance and frequency, to estimate the connectivity, yielding different results and interpretations. The covariance indicates if two elements follow the same dynamics, i.e., if they increase or decrease at the same time. On the other hand, the frequency is related to the time required for the elements to change, which can be daily in the case of the tides, or milliseconds if referred to the computations in a microchip. A third factor is the directionality, with one element depending on the other, but not in the opposite case. This work proposes an interactive computer-based application to generate a three-node network and estimate its connectivity following different approaches. We describe how the application is structured, from the selection of the parameters to the interpretation of the output results, detailing the skills in connectivity that can be developed by the students.López-Madrona, VJ.; Moratal, D.; Bénar, CG. (2021). Studying connectivity between time-series using an interactive application. IATED Academy. 6635-6638. https://doi.org/10.21125/edulearn.2021.1342S6635663

Studying memory processes at different levels with simultaneous depth and surface EEG recordings

Investigating cognitive brain functions using non-invasive electrophysiology can be challenging due to the particularities of the task-related EEG activity, the depth of the activated brain areas, and the extent of the networks involved. Stereoelectroencephalographic (SEEG) investigations in patients with drug-resistant epilepsy offer an extraordinary opportunity to validate information derived from non-invasive recordings at macro-scales. The SEEG approach can provide brain activity with high spatial specificity during tasks that target specific cognitive processes (e.g., memory). Full validation is possible only when performing simultaneous scalp SEEG recordings, which allows recording signals in the exact same brain state. This is the approach we have taken in 12 subjects performing a visual memory task that requires the recognition of previously viewed objects. The intracranial signals on 965 contact pairs have been compared to 391 simultaneously recorded scalp signals at a regional and whole-brain level, using multivariate pattern analysis. The results show that the task conditions are best captured by intracranial sensors, despite the limited spatial coverage of SEEG electrodes, compared to the whole-brain non-invasive recordings. Applying beamformer source reconstruction or independent component analysis does not result in an improvement of the multivariate task decoding performance using surface sensor data. By analyzing a joint scalp and SEEG dataset, we investigated whether the two types of signals carry complementary information that might improve the machine-learning classifier performance. This joint analysis revealed that the results are driven by the modality exhibiting best individual performance, namely SEEG

Interictal Functional Connectivity of Human Epileptic Networks Assessed by Intracerebral EEG and BOLD Signal Fluctuations

In this study, we aimed to demonstrate whether spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal derived from resting state functional magnetic resonance imaging (fMRI) reflect spontaneous neuronal activity in pathological brain regions as well as in regions spared by epileptiform discharges. This is a crucial issue as coherent fluctuations of fMRI signals between remote brain areas are now widely used to define functional connectivity in physiology and in pathophysiology. We quantified functional connectivity using non-linear measures of cross-correlation between signals obtained from intracerebral EEG (iEEG) and resting-state functional MRI (fMRI) in 5 patients suffering from intractable temporal lobe epilepsy (TLE). Functional connectivity was quantified with both modalities in areas exhibiting different electrophysiological states (epileptic and non affected regions) during the interictal period. Functional connectivity as measured from the iEEG signal was higher in regions affected by electrical epileptiform abnormalities relative to non-affected areas, whereas an opposite pattern was found for functional connectivity measured from the BOLD signal. Significant negative correlations were found between the functional connectivities of iEEG and BOLD signal when considering all pairs of signals (theta, alpha, beta and broadband) and when considering pairs of signals in regions spared by epileptiform discharges (in broadband signal). This suggests differential effects of epileptic phenomena on electrophysiological and hemodynamic signals and/or an alteration of the neurovascular coupling secondary to pathological plasticity in TLE even in regions spared by epileptiform discharges. In addition, indices of directionality calculated from both modalities were consistent showing that the epileptogenic regions exert a significant influence onto the non epileptic areas during the interictal period. This study shows that functional connectivity measured by iEEG and BOLD signals give complementary but sometimes inconsistent information in TLE

Asymmetric function of theta and gamma activity in syllable processing: an intra-cortical study

Low-gamma (25-45 Hz) and theta (4-8 Hz) oscillations are proposed to underpin the integration of phonemic and syllabic information, respectively. How these two scales of analysis split functions across hemispheres is unclear. We analyzed cortical responses from an epileptic patient with a rare bilateral electrode implantation (stereotactic EEG) in primary (A1/BA41 and A2/BA42) and association auditory cortices (BA22). Using time-frequency analyses, we confirmed the dominance of a 5-6 Hz theta activity in right and of a low-gamma (25-45 Hz) activity in left primary auditory cortices (A1/A2), during both resting state and syllable processing. We further detected high-theta (7-8 Hz) resting activity in left primary, but also associative auditory regions. In left BA22, its phase correlated with high-gamma induced power. Such a hierarchical relationship across theta and gamma frequency bands (theta/gamma phase-amplitude coupling) could index the process by which the neural code shifts from stimulus feature- to phonological- encoding, and is associated with the transition from evoked to induced power responses. These data suggest that theta and gamma activity in right and left auditory cortices bear different functions. They support a scheme where slow parsing of the acoustic information dominates in right-hemisphere at a syllabic (5-6 Hz) rate, and left auditory cortex exhibits a more complex cascade of oscillations, reflecting the possible extraction of transient acoustic cues at a fast (~25-45 Hz) rate, subsequently integrated at a slower, e.g. syllabic one. Slow oscillations could functionally participate to speech processing by structuring gamma activity in left BA22, where abstract percepts emerge

Despiking SEEG signals reveals dynamics of gamma band preictal activity

International audienceInterictal epileptiform discharges, or "interictal spikes", are the hallmark of epilepsy. Still, there is growing evidence that oscillatory activity - whether in the gamma band (30-120 Hz) or at higher frequencies is another important marker of hyperexcitable tissues. A major difficulty arises from the fact that interictal spikes and oscillations overlap in the frequency domain. This hampers the correct delineation of the cortex producing pathological oscillations by simple filtering. Here, we propose a nonlinear technique for fitting the spike waveform in order to remove it, resulting in a "despiked" signal. This strategy was first applied to simulated data inspired from real stereo-electroencephalographic (SEEG) signals, then to real data. We show that despiking leads to a better space-time-frequency analysis of the oscillatory part of the signal. Thus, in the real SEEG signals, the spatio-temporal maps show a buildup of gamma oscillations during the preictal period in the despiked signals, whereas in the original signals this activity is masked by spikes. Despiking is thus a promising venue for a better characterization of oscillatory activity in electrophysiology of epilepsy

A systematic framework for functional connectivity measures

Various methods have been proposed to characterize the functional connectivity between nodes in a network measured with different modalities (electrophysiology, functional magnetic resonance imaging etc.). Since different measures of functional connectivity yield different results for the same dataset, it is important to assess when and how they can be used. In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures – based upon a comprehensive portfolio of models generating measurable responses. Specifically, we benchmarked 42 methods using 10,000 simulated datasets from 5 different types of generative models with different connectivity structures. Since all functional connectivity methods require the setting of some parameters (window size and number, model order etc.), we first optimized these parameters using performance criteria based upon (threshold free) ROC analysis. We then evaluated the performance of the methods on data simulated with different types of models. Finally, we assessed the performance of the methods against different levels of signal-to-noise ratios and network configurations. A MATLAB toolbox is provided to perform such analyses using other methods and simulated datasets

Rostral Cingulate Zone and correct response monitoring: ICA and source localization evidences for the unicity of correct- and error-negativities

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