Neurofeedback and epilepsy: Renaissance of an old self-regulation method?

Neurofeedback is a brain-computer interface tool enabling the user to self-regulate their neuronal activity, and ultimately, induce long-term brain plasticity, making it an interesting instrument to cure brain disorders. Although this method has been used successfully in the past as an adjunctive therapy in drug-resistant epilepsy, this approach remains under-explored and deserves more rigorous scientific inquiry. In this review, we present early neurofeedback protocols employed in epilepsy and provide a critical overview of the main clinical studies. We also describe the potential neurophysiological mechanisms through which neurofeedback may produce its therapeutic effects. Finally, we discuss how to innovate and standardize future neurofeedback clinical trials in epilepsy based on evidence from recent research studies.

Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy.

Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence and big data will offer unprecedented opportunities to further advance the field in the near future, ultimately resulting in improved quality of life for many patients with drug-resistant epilepsy. This article summarizes selected presentations from Day 1 of the two-day symposium "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". Day 1 was dedicated to highlighting and honoring the work of Dr. Jean Gotman, a pioneer in EEG, intracranial EEG, simultaneous EEG/ functional magnetic resonance imaging, and signal analysis of epilepsy. The program focused on two main research directions of Dr. Gotman, and was dedicated to "High-frequency oscillations, a new biomarker of epilepsy" and "Probing the epileptic focus from inside and outside". All talks were presented by colleagues and former trainees of Dr. Gotman. The extended summaries provide an overview of historical and current work in the neurophysiology of epilepsy with emphasis on novel EEG biomarkers of epilepsy and source imaging and concluded with an outlook on the future of epilepsy research, and what is needed to bring the field to the next level.

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

Electrophysiological 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 cm2). 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.

The role of the dorsal anterior insula in ecstatic sensation revealed by direct electrical brain stimulation.

BACKGROUND: An ecstatic phenomenon is an altered state of consciousness with a sense of "hyper-reality", and a complete present-moment awareness with a feeling of union with the Universe. A better understanding of the network mechanisms underlying this fascinating subjective experience may help to unravel some mysteries of human consciousness. Insula has been recently proposed to be a key region to elicit these symptoms. OBJECTIVE/HYPOTHESIS: We studied functional connectivity changes in several brain areas during the induction of ecstatic auras by direct electrical stimulation of the dorsal anterior insular cortex in patients with refractory focal epilepsy implanted with intracerebral electrodes (stereotactic-EEG, SEEG) in the context of their pre-surgical evaluation. METHODS: Three patients were selected on the basis of the occurrence of ecstatic symptoms triggered by direct intracerebral electrical stimulation (ES) of the antero-dorsal part of the insula. ES was performed (50 Hz, 1.5-2.1 mA, in a bipolar fashion to each contact in the gray matter during a 3 s period) to map functional cortices and trigger habitual seizures. One stimulation inducing ecstatic changes in each patient was analyzed. Functional connectivity analysis was performed by measuring interdependencies (nonlinear regression analysis based on the h2 coefficient) between SEEG signals before and after stimulations. RESULTS: In all patients, only the stimulation of dorsal anterior insula was able to reproduce an ecstatic aura. We observed a significant increase of functional connectivity values between several brain regions in the immediate period following stimulations. The most commonly implicated region was the dorsal anterior insula. Out-degrees (a measure intended to identify leading structures in a network) identified the dorsal anterior insula as the most common leading region in the induced networks. CONCLUSION(S): Our findings bring additional support in favor of a major role played by the dorsal anterior insula in ecstatic experiences.

Deep brain activities can be detected with magnetoencephalography.

The hippocampus and amygdala are key brain structures of the medial temporal lobe, involved in cognitive and emotional processes as well as pathological states such as epilepsy. Despite their importance, it is still unclear whether their  neural activity can be recorded non-invasively. Here, using simultaneous intracerebral and magnetoencephalography (MEG) recordings in patients with focal drug-resistant epilepsy, we demonstrate a direct contribution of amygdala and hippocampal activity to surface MEG recordings. In particular, a method of blind source separation, independent component analysis, enabled activity arising from large neocortical networks to be disentangled from that of deeper structures, whose amplitude at the surface was small but significant. This finding is highly relevant for our understanding of hippocampal and amygdala brain activity as it implies that their activity could potentially be measured non-invasively.

EpiTools, A software suite for presurgical brain mapping in epilepsy: Intracerebral EEG.

BACKGROUND: In pharmacoresistant epilepsy, exploration with depth electrodes can be needed to precisely define the epileptogenic zone. Accurate location of these electrodes is thus essential for the interpretation of Stereotaxic EEG (SEEG) signals. As SEEG analysis increasingly relies on signal processing, it is crucial to make a link between these results and patient's anatomy. Our aims were thus to develop a suite of software tools, called "EpiTools", able to i) precisely and automatically localize the position of each SEEG contact and ii) display the results of signal analysis in each patient's anatomy. NEW METHOD: The first tool, GARDEL (GUI for Automatic Registration and Depth Electrode Localization), is able to automatically localize SEEG contacts and to label each contact according to a pre-specified nomenclature (for instance that of FreeSurfer or MarsAtlas). The second tool, 3Dviewer, enables to visualize in the 3D anatomy of the patient the origin of signal processing results such as rate of biomarkers, connectivity graphs or Epileptogenicity Index. RESULTS: GARDEL was validated in 30 patients by clinicians and proved to be highly reliable to determine within the patient's individual anatomy the actual location of contacts. COMPARISON WITH EXISTING METHODS: GARDEL is a fully automatic electrode localization tool needing limited user interaction (only for electrode naming or contact correction). The 3Dviewer is able to read signal processing results and to display them in link with patient's anatomy. CONCLUSION: EpiTools can help speeding up the interpretation of SEEG data and improving its precision.

Technical solutions for simultaneous MEG and SEEG recordings: towards routine clinical use.

OBJECTIVE: The simultaneous recording of intracerebral EEG (stereotaxic EEG, SEEG) and magnetoencephalography (MEG) is a promising strategy that provides both local and global views on brain pathological activity. Yet, acquiring simultaneous signals poses difficult technical issues that hamper their use in clinical routine. Our objective was thus to develop a set of solutions for recording a high number of SEEG channels while preserving signal quality. APPROACH: We recorded data in a patient with drug resistant epilepsy during presurgical evaluation. We used dedicated insertion screws and optically insulated amplifiers. We recorded 137 SEEG contacts on 10 depth electrodes (5-15 contacts each) and 248 MEG channels (magnetometers). Signal quality was assessed by comparing the distribution of RMS values in different frequency bands to a reference set of MEG acquisitions. MAIN RESULTS: The quality of signals was excellent for both MEG and SEEG; for MEG, it was comparable to that of MEG signals without concurrent SEEG. Discharges involving several structures on SEEG were visible on MEG, whereas discharges limited in space were not seen at the surface. SIGNIFICANCE: SEEG can now be recorded simultaneously with whole-head MEG in routine. This opens new avenues, both methodologically for understanding signals and improving signal processing methods, and clinically for future combined analyses.

AnyWave: a cross-platform and modular software for visualizing and processing electrophysiological signals.

BACKGROUND: The importance of digital signal processing in clinical neurophysiology is growing steadily, involving clinical researchers and methodologists. There is a need for crossing the gap between these communities by providing efficient delivery of newly designed algorithms to end users. We have developed such a tool which both visualizes and processes data and, additionally, acts as a software development platform. NEW METHOD: AnyWave was designed to run on all common operating systems. It provides access to a variety of data formats and it employs high fidelity visualization techniques. It also allows using external tools as plug-ins, which can be developed in languages including C++, MATLAB and Python. RESULTS: In the current version, plug-ins allow computation of connectivity graphs (non-linear correlation h2) and time-frequency representation (Morlet wavelets). The software is freely available under the LGPL3 license. COMPARISON WITH EXISTING METHODS: AnyWave is designed as an open, highly extensible solution, with an architecture that permits rapid delivery of new techniques to end users. CONCLUSIONS: We have developed AnyWave software as an efficient neurophysiological data visualizer able to integrate state of the art techniques. AnyWave offers an interface well suited to the needs of clinical research and an architecture designed for integrating new tools. We expect this software to strengthen the collaboration between clinical neurophysiologists and researchers in biomedical engineering and signal processing.

EEG extended source localization: tensor-based vs. conventional methods.

The localization of brain sources based on EEG measurements is a topic that has attracted a lot of attention in the last decades and many different source localization algorithms have been proposed. However, their performance is limited in the case of several simultaneously active brain regions and low signal-to-noise ratios. To overcome these problems, tensor-based preprocessing can be applied, which consists in constructing a space-time-frequency (STF) or space-time-wave-vector (STWV) tensor and decomposing it using the Canonical Polyadic (CP) decomposition. In this paper, we present a new algorithm for the accurate localization of extended sources based on the results of the tensor decomposition. Furthermore, we conduct a detailed study of the tensor-based preprocessing methods, including an analysis of their theoretical foundation, their computational complexity, and their performance for realistic simulated data in comparison to conventional source localization algorithms such as sLORETA, cortical LORETA (cLORETA), and 4-ExSo-MUSIC. Our objective consists, on the one hand, in demonstrating the gain in performance that can be achieved by tensor-based preprocessing, and, on the other hand, in pointing out the limits and drawbacks of this method. Finally, we validate the STF and STWV techniques on real measurements to demonstrate their usefulness for practical applications.

Pitfalls of high-pass filtering for detecting epileptic oscillations: a technical note on "false" ripples.

OBJECTIVES: To analyze interictal High frequency oscillations (HFOs) as observed in the medial temporal lobe of epileptic patients and animals (ripples, 80-200Hz and fast ripples, 250-600Hz). To show that the identification of interictal HFOs raises some methodological issues, as the filtering of sharp transients (e.g., epileptic spikes or artefacts) or signals with harmonics can result in "false" ripples. To illustrate and quantify the occurrence of false ripples on filtered EEG traces. METHODS: We have performed high-pass filtering on both simulated and real data. We have also used two alternate methods: time-frequency analysis and matching pursuit. RESULTS: Two types of events were shown to produce oscillations after filtering that could be confounded with actual oscillatory activity: sharp transients and harmonics of non-sinusoidal signals. CONCLUSIONS: High-pass filtering of EEG traces for detection of oscillatory activity should be performed with great care. Filtered traces should be compared to original traces for verification of presence of transients. Additional techniques such as time-frequency transforms or sparse decompositions are highly beneficial. SIGNIFICANCE: Our study draws the attention on an issue of great importance in the marking of HFOs on EEG traces. We illustrate complementary methods that can help both researchers and clinicians.

Evaluation of EEG localization methods using realistic simulations of interictal spikes.

Performing an accurate localization of sources of interictal spikes from EEG scalp measurements is of particular interest during the presurgical investigation of epilepsy. The purpose of this paper is to study the ability of six distributed source localization methods to recover extended sources of activated cortex. Due to the frequent lack of a gold standard to evaluate source localization methods, our evaluation was performed in a controlled environment using realistic simulations of EEG interictal spikes, involving several anatomical locations with several spatial extents. Simulated data were corrupted by physiological EEG noise. Simulations involving pairs of sources with the same amplitude were also studied. In addition to standard validation criteria (e.g., geodesic distance or mean square error), we proposed an original criterion dedicated to assess detection accuracy, based on receiver operating characteristic (ROC) analysis. Six source localization methods were evaluated: the minimum norm, the minimum norm weighted by multivariate source prelocalization (MSP), cortical LORETA with or without additional minimum norm regularization, and two derivations of the maximum entropy on the mean (MEM) approach. Results showed that LORETA-based and MEM-based methods were able to accurately recover sources of different spatial extents, with the exception of sources in temporo-mesial and fronto-mesial regions. Several spurious sources were generated by those methods, however, whereas methods using the MSP always located very accurately the maximum of activity but not its spatial extent. These findings suggest that one should always take into account the results from different localization methods when analyzing real interictal spikes.

Cortical and thalamic fMRI responses in partial epilepsy with focal and bilateral synchronous spikes.

OBJECTIVE: To determine the blood oxygen level-dependent (BOLD) responses to epileptic discharges in the thalamus and cerebral cortex in patients with partial epilepsy. METHODS: Among 64 tested patients, 40 had EEG spikes during scanning and were divided in two groups: unilateral or bilateral independent spikes (29 patients) and bilaterally synchronous spikes (11 patients). Each spike topography was analyzed separately, yielding 40 studies in the first group and 17 in the second. RESULTS: Forty-five percent of focal spike studies showed significant BOLD responses. Cortical activation (positive BOLD) represented the dominant response and had a better correlation with spike location than cortical deactivation (negative BOLD). In the second group, all patients had significant BOLD responses; they were more widespread compared to the first group, and deactivated areas were as important as activated regions. A thalamic response was seen in 12.5% of studies in the first group and 55% in the second. CONCLUSIONS: The thalamus is involved in partial epilepsy during interictal discharges. This involvement and also cortical deactivation are more commonly seen with bilateral spikes than focal discharges. SIGNIFICANCE: These findings show evidence for a role for the thalamus and a more important role for inhibition in secondary bilateral synchrony.

fMRI activation during spike and wave discharges in idiopathic generalized epilepsy.

The objectives of this study were to evaluate the haemodynamic response of the cerebral cortex and thalamus during generalized spike and wave or polyspike and wave (GSW) bursts in patients with idiopathic generalized epilepsy (IGE). The haemodynamic response is measured by fMRI [blood oxygenation level-dependent (BOLD) effect]. We used combined EEG-functional MRI, a method that allows the unambiguous measurement of the BOLD effect during bursts, compared with measurements during the inter-burst interval. Fifteen patients with IGE had GSW bursts during scanning and technically acceptable studies. fMRI cortical changes as a result of GSW activity were present in 14 patients (93%). Changes in the form of activation (increased BOLD) or deactivation (decreased BOLD) occurred symmetrically in the cortex of both hemispheres, involved anterior as much as posterior head regions, but were variable across patients. Bilateral thalamic changes were also found in 12 patients (80%). Activation predominated over deactivation in the thalamus, whereas the opposite was seen in the cerebral cortex. These results bring a new light to the pathophysiolocal mechanisms generating GSW. The spatial distribution of BOLD responses to GSW was unexpected: it involved as many posterior as anterior head regions, contrary to the usual fronto-central predominance seen in EEG. The presence of a thalamic BOLD response in most patients provided, for the first time in a group of human patients, confirmation of the evidence of thalamic involvement seen in animal models. The possible mechanisms underlying these phenomena are discussed.

Modeling of post-surgical brain and skull defects in the EEG inverse problem with the boundary element method.

OBJECTIVES: In order to obtain accurate EEG inverse solutions in patients subjected to surgery, we have studied the feasibility and influence of incorporating brain and skull defects in realistic head models. METHODS: We first measured the conductivity of the methacrylate used for cranioplasty. Then, we designed realistic boundary element method head models with a skull burr hole, a methacrylate plug or a temporal-lobe resection. We simulated the potentials that would be produced at 71 electrode locations (10/10 system) by dipoles located near the defects. Then, we fitted dipoles on these potentials using a defect-free head model. We also ran simulations in a noisy situation and with higher skull and cerebrospinal fluid (CSF) conductivity. RESULTS: The largest errors were found for burr holes, with a localization error up to 20 mm for a radial dipole located 30 mm below the hole and an amplification factor of 8. Methacrylate plugs lead to errors up to 5 mm and 0.5; the resection only lead to errors of 2 mm and 1.3. Results obtained with noise were consistent with those obtained without noise. Doubling the skull conductivity led to errors that were reduced by 10-20%, while doubling CSF conductivity increased the errors by up to 31%. CONCLUSIONS: We have shown that it is important to incorporate skull defects in realistic head models when sources are located near the defects and precision is sought. Brain cavities of the size of a typical anterior temporal lobe resection may be omitted without a significant impact on dipole localization.