Magnetoencephalographic signatures of insular epileptic spikes based on functional connectivity.

Failure to recognize insular cortex seizures has recently been identified as a cause of epilepsy surgeries targeting the temporal, parietal, or frontal lobe. Such failures are partly due to the fact that current noninvasive localization techniques fare poorly in recognizing insular epileptic foci. Our group recently demonstrated that magnetoencephalography (MEG) is sensitive to epileptiform spikes generated by the insula. In this study, we assessed the potential of distributed source imaging and functional connectivity analyses to distinguish insular networks underlying the generation of spikes. Nineteen patients with operculo-insular epilepsy were investigated. Each patient underwent MEG as well as T1-weighted magnetic resonance imaging (MRI) as part of their standard presurgical evaluation. Cortical sources of MEG spikes were reconstructed with the maximum entropy on the mean algorithm, and their time courses served to analyze source functional connectivity. The results indicate that the anterior and posterior subregions of the insula have specific patterns of functional connectivity mainly involving frontal and parietal regions, respectively. In addition, while their connectivity patterns are qualitatively similar during rest and during spikes, couplings within these networks are much stronger during spikes. These results show that MEG can establish functional connectivity-based signatures that could help in the diagnosis of different subtypes of insular cortex epilepsy. Hum Brain Mapp 37:3250-3261, 2016. © 2016 Wiley Periodicals, Inc.

Capturing with EEG the neural entrainment and coupling underlying sensorimotor synchronization to the beat.

Synchronizing movements with rhythmic inputs requires tight coupling of sensory and motor neural processes. Here, using a novel approach based on the recording of steady-state-evoked potentials (SS-EPs), we examine how distant brain areas supporting these processes coordinate their dynamics. The electroencephalogram was recorded while subjects listened to a 2.4-Hz auditory beat and tapped their hand on every second beat. When subjects tapped to the beat, the EEG was characterized by a 2.4-Hz SS-EP compatible with beat-related entrainment and a 1.2-Hz SS-EP compatible with movement-related entrainment, based on the results of source analysis. Most importantly, when compared with passive listening of the beat, we found evidence suggesting an interaction between sensory- and motor-related activities when subjects tapped to the beat, in the form of (1) additional SS-EP appearing at 3.6 Hz, compatible with a nonlinear product of sensorimotor integration; (2) phase coupling of beat- and movement-related activities; and (3) selective enhancement of beat-related activities over the hemisphere contralateral to the tapping, suggesting a top-down effect of movement-related activities on auditory beat processing. Taken together, our results are compatible with the view that rhythmic sensorimotor synchronization is supported by a dynamic coupling of sensory and motor related activities.

MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy.

The purpose of this study is to develop and quantitatively assess whether fusion of EEG and MEG (MEEG) data within the maximum entropy on the mean (MEM) framework increases the spatial accuracy of source localization, by yielding better recovery of the spatial extent and propagation pathway of the underlying generators of inter-ictal epileptic discharges (IEDs). The key element in this study is the integration of the complementary information from EEG and MEG data within the MEM framework. MEEG was compared with EEG and MEG when localizing single transient IEDs. The fusion approach was evaluated using realistic simulation models involving one or two spatially extended sources mimicking propagation patterns of IEDs. We also assessed the impact of the number of EEG electrodes required for an efficient EEG-MEG fusion. MEM was compared with minimum norm estimate, dynamic statistical parametric mapping, and standardized low-resolution electromagnetic tomography. The fusion approach was finally assessed on real epileptic data recorded from two patients showing IEDs simultaneously in EEG and MEG. Overall the localization of MEEG data using MEM provided better recovery of the source spatial extent, more sensitivity to the source depth and more accurate detection of the onset and propagation of IEDs than EEG or MEG alone. MEM was more accurate than the other methods. MEEG proved more robust than EEG and MEG for single IED localization in low signal-to-noise ratio conditions. We also showed that only few EEG electrodes are required to bring additional relevant information to MEG during MEM fusion.

Magnetoencephalography resting-state correlates of executive and language components of verbal fluency.

Verbal fluency (VF) is a heterogeneous cognitive function that requires executive as well as language abilities. The purpose of this study was to elucidate the specificity of the resting state MEG correlates of the executive and language components. To this end, we administered a VF test, another verbal test (Vocabulary), and another executive test (Trail Making Test), and we recorded 5-min eyes-open resting-state MEG data in 28 healthy participants. We used source-reconstructed spectral power estimates to compute correlation/anticorrelation MEG clusters with the performance at each test, as well as with the advantage in performance between tests, across individuals using cluster-level statistics in the standard frequency bands. By obtaining conjunction clusters between verbal fluency scores and factor loading obtained for verbal fluency and each of the two other tests, we showed a core of slow clusters (delta to beta) localized in the right hemisphere, in adjacent parts of the premotor, pre-central and post-central cortex in the mid-lateral regions related to executive monitoring. We also found slow parietal clusters bilaterally and a cluster in the gamma 2 and 3 bands in the left inferior frontal gyrus likely associated with phonological processing involved in verbal fluency.

Large-Scale Desynchronization During Interictal Epileptic Discharges Recorded With Intracranial EEG.

It is increasingly recognized that deep understanding of epileptic seizures requires both localizing and characterizing the functional network of the region where they are initiated, i. e., the epileptic focus. Previous investigations of the epileptogenic focus' functional connectivity have yielded contrasting results, reporting both pathological increases and decreases during resting periods and seizures. In this study, we shifted paradigm to investigate the time course of connectivity in relation to interictal epileptiform discharges. We recruited 35 epileptic patients undergoing intracranial EEG (iEEG) investigation as part of their presurgical evaluation. For each patient, 50 interictal epileptic discharges (IEDs) were marked and iEEG signals were epoched around those markers. Signals were narrow-band filtered and time resolved phase-locking values were computed to track the dynamics of functional connectivity during IEDs. Results show that IEDs are associated with a transient decrease in global functional connectivity, time-locked to the peak of the discharge and specific to the high range of the gamma frequency band. Disruption of the long-range connectivity between the epileptic focus and other brain areas might be an important process for the generation of epileptic activity. Transient desynchronization could be a potential biomarker of the epileptogenic focus since 1) the functional connectivity involving the focus decreases significantly more than the connectivity outside the focus and 2) patients with good surgical outcome appear to have a significantly more disconnected focus than patients with bad outcomes.

Insular Epilepsy: Semiology and Noninvasive Investigations.

In this review, authors discuss the semiology and noninvasive investigations of insular epilepsy, an underrecognized type of epilepsy, which may mimic other focal epilepsies. In line with the various functions of the insula and its widespread network of connections, insular epilepsy may feature a variety of early ictal manifestations from somatosensory, visceral, olfactory, gustatory, or vestibular manifestations. Depending on propagation pathways, insular seizures may also include altered consciousness, dystonic posturing, complex motor behaviors, and even autonomic features. Considering the variability in seizure semiology, recognition of insular epilepsy may be challenging and confirmation by noninvasive tests is warranted although few studies have assessed their value. Detection of an insular lesion on MRI greatly facilitates the diagnosis. Scalp EEG findings in frontocentral and/or temporal derivations will generally allow lateralization of the seizure focus. Ictal single-photon computed tomography has moderate sensitivity, whereas positron emission tomography has lower sensitivity. Among newer techniques, magnetoencephalography is highly beneficial, whereas proton magnetic resonance spectroscopy currently has limited value.

Spontaneous brain oscillations as neural fingerprints of working memory capacities: A resting-state MEG study.

Short-term storage and mental information manipulation capacities in the human brain are key to healthy cognition. These brain processes collectively known as working memory (WM) are associated with modulations of rhythmic brain activity across multiple brain areas and frequencies. Yet, it is not clear whether - and, if so, how-intrinsic resting-state neuronal oscillations are related to individual WM capacities, as measured by standard neuropsychological tests. We addressed this question by probing the correlation between resting-state brain activity, recorded with magnetoencephalography (MEG), and verbal and visuo-spatial WM indices obtained from the standardized Wechsler Adult Intelligence Scale (WAIS-IV) and the Wechsler Memory Scale (WMS-IV). To this end, 5-min eyes-open resting-state MEG data were acquired in 28 healthy participants. Source-reconstructed spectral power estimates were then computed in standard frequency bands and their correlation with neuropsychological indices across individuals was assessed using Pearson correlation and cluster-level statistics. We found statistically significant positive correlations between spectral amplitudes measured at rest and standardized scores on both verbal and visuo-spatial WM performance. The correlation clusters primarily involved key medial and dorsolateral components within the parietal and prefrontal regions. In addition, while the correlation in some clusters was frequency selective (e.g., alpha-band oscillations), other areas showed correlations with WM across a wide range of frequencies reflecting a broadband effect. These results provide the first evidence for a positive correlation between neuromagnetic signals measured at rest and WM performance separately assessed by standardized neuropsychological tests. Our results advance our understanding of the link between WM capacities and intrinsic oscillatory dynamics networks. They also suggest that individual differences in baseline spectral power might need to be taken into account when probing differences in brain responses during the execution of WM tasks.

A time-frequency analysis of the dynamics of cortical networks of sleep spindles from MEG-EEG recordings.

Sleep spindles are a hallmark of NREM sleep. They result from a widespread thalamo-cortical loop and involve synchronous cortical networks that are still poorly understood. We investigated whether brain activity during spindles can be characterized by specific patterns of functional connectivity among cortical generators. For that purpose, we developed a wavelet-based approach aimed at imaging the synchronous oscillatory cortical networks from simultaneous MEG-EEG recordings. First, we detected spindles on the EEG and extracted the corresponding frequency-locked MEG activity under the form of an analytic ridge signal in the time-frequency plane (Zerouali et al., 2013). Secondly, we performed source reconstruction of the ridge signal within the Maximum Entropy on the Mean framework (Amblard et al., 2004), yielding a robust estimate of the cortical sources producing observed oscillations. Lastly, we quantified functional connectivity among cortical sources using phase-locking values. The main innovations of this methodology are (1) to reveal the dynamic behavior of functional networks resolved in the time-frequency plane and (2) to characterize functional connectivity among MEG sources through phase interactions. We showed, for the first time, that the switch from fast to slow oscillatory mode during sleep spindles is required for the emergence of specific patterns of connectivity. Moreover, we show that earlier synchrony during spindles was associated with mainly intra-hemispheric connectivity whereas later synchrony was associated with global long-range connectivity. We propose that our methodology can be a valuable tool for studying the connectivity underlying neural processes involving sleep spindles, such as memory, plasticity or aging.

Optimal eye-gaze fixation position for face-related neural responses.

It is generally agreed that some features of a face, namely the eyes, are more salient than others as indexed by behavioral diagnosticity, gaze-fixation patterns and evoked-neural responses. However, because previous studies used unnatural stimuli, there is no evidence so far that the early encoding of a whole face in the human brain is based on the eyes or other facial features. To address this issue, scalp electroencephalogram (EEG) and eye gaze-fixations were recorded simultaneously in a gaze-contingent paradigm while observers viewed faces. We found that the N170 indexing the earliest face-sensitive response in the human brain was the largest when the fixation position is located around the nasion. Interestingly, for inverted faces, this optimal fixation position was more variable, but mainly clustered in the upper part of the visual field (around the mouth). These observations extend the findings of recent behavioral studies, suggesting that the early encoding of a face, as indexed by the N170, is not driven by the eyes per se, but rather arises from a general perceptual setting (upper-visual field advantage) coupled with the alignment of a face stimulus to a stored face template.

Localization of synchronous cortical neural sources.

Neural synchronization is a key mechanism to a wide variety of brain functions, such as cognition, perception, or memory. High temporal resolution achieved by EEG recordings allows the study of the dynamical properties of synchronous patterns of activity at a very fine temporal scale but with very low spatial resolution. Spatial resolution can be improved by retrieving the neural sources of EEG signal, thus solving the so-called inverse problem. Although many methods have been proposed to solve the inverse problem and localize brain activity, few of them target the synchronous brain regions. In this paper, we propose a novel algorithm aimed at localizing specifically synchronous brain regions and reconstructing the time course of their activity. Using multivariate wavelet ridge analysis, we extract signals capturing the synchronous events buried in the EEG and then solve the inverse problem on these signals. Using simulated data, we compare results of source reconstruction accuracy achieved by our method to a standard source reconstruction approach. We show that the proposed method performs better across a wide range of noise levels and source configurations. In addition, we applied our method on real dataset and identified successfully cortical areas involved in the functional network underlying visual face perception. We conclude that the proposed approach allows an accurate localization of synchronous brain regions and a robust estimation of their activity.

The effects of early and late night partial sleep deprivation on automatic and selective attention: An ERP study.

The link between decrease in levels of attention and total sleep deprivation is well known but the respective contributions of slow wave sleep (SWS) and rapid eye movement sleep (REM) is still largely unknown. The aim of this study was to characterize the effects of sleep deprivation during the SWS phase (i.e., early night sleep) and the REM phase (i.e., late night sleep) on tasks that tap automatic and selective attention; these two forms of attention were indexed respectively by "mismatch negativity" (MMN) and "negative difference" (Nd) event-related potential (ERP) difference waves. Ten young adult participants were subjected to a three-night sleep protocol. They were each received one night of full sleep (F), one night of sleep deprivation during the first half of the night (H1), and one night of sleep deprivation during the second half of the night (H2). MMN and Nd were recorded the following morning of each night during two auditory oddball tasks that tapped automatic and selective attention. The effect of sleep deprivation condition was assessed using ERP amplitude measures and standardized low-resolution electromagnetic tomography method (sLORETA). ERP results revealed significant MMN amplitude reduction over frontal and temporal recording areas following the H2 night compared to F and H1, indicating reductions in levels of automatic attention. In addition, Nd amplitude over the parietal recording area was significantly increased following the H2 night compared to F and H1. sLORETA findings show significant changes from F to H2 night in frontal cortex activity, decreasing during the automatic attention task but increasing during the selective attention task. No significant change in brain activity is observed after H1 night. The restoration of attention processes is mainly achieved during REM sleep, which confirms results from previous studies in rat models. The anterior cortex seems to be more sensitive to sleep loss, while the parietal cortex acts as a compensatory resource to restore cognitive performance in a task context.