Exploring the Electrophysiological Correlates of the Default-Mode Network with Intracerebral EEG

While functional imaging studies allow for a precise spatial characterization of resting state networks, their neural correlates and thereby their fine-scale temporal dynamics remain elusive. A full understanding of the mechanisms at play requires input from electrophysiological studies. Here, we discuss human and non-human primate electrophysiological data that explore the neural correlates of the default-mode network. Beyond the promising findings obtained with non-invasive approaches, emerging evidence suggests that invasive recordings in humans will be crucial in order to elucidate the neural correlates of the brain's default-mode function. In particular, we contend that stereotactic-electroencephalography, which consists of implanting multiple depth electrodes for pre-surgical evaluation in drug-resistant epilepsy, is particularly suited for this endeavor. We support this view by providing rare data from depth recordings in human posterior cingulate cortex and medial prefrontal cortex that show transient neural deactivation during task-engagement

Magnetoencephalography as a tool in psychiatric research: current status and perspective

The application of neuroimaging to provide mechanistic insights into circuit dysfunctions in major psychiatric conditions and the development of biomarkers are core challenges in current psychiatric research. In this review, we propose that recent technological and analytic advances in Magnetoencephalography (MEG), a technique which allows the measurement of neuronal events directly and non-invasively with millisecond resolution, provides novel opportunities to address these fundamental questions. Because of its potential in delineating normal and abnormal brain dynamics, we propose that MEG provides a crucial tool to advance our understanding of pathophysiological mechanisms of major neuropsychiatric conditions, such as Schizophrenia, Autism Spectrum Disorders, and the dementias. In our paper, we summarize the mechanisms underlying the generation of MEG signals and the tools available to reconstruct generators and underlying networks using advanced source-reconstruction techniques. We then survey recent studies that have utilized MEG to examine aberrant rhythmic activity in neuropsychiatric disorders. This is followed by links with preclinical research, which have highlighted possible neurobiological mechanisms, such as disturbances in excitation/inhibition parameters, which could account for measured changes in neural oscillations. In the final section of the paper, challenges as well as novel methodological developments are discussed which could pave the way for a widespread application of MEG in translational research with the aim of developing biomarkers for early detection and diagnosis

Changes in brain network activity during working memory tasks: a magnetoencephalography study.

In this study, we elucidate the changes in neural oscillatory processes that are induced by simple working memory tasks. A group of eight subjects took part in modified versions of the N-back and Sternberg working memory paradigms. Magnetoencephalography (MEG) data were recorded, and subsequently processed using beamformer based source imaging methodology. Our study shows statistically significant increases in θ oscillations during both N-back and Sternberg tasks. These oscillations were shown to originate in the medial frontal cortex, and further to scale with memory load. We have also shown that increases in θ oscillations are accompanied by decreases in β and γ band oscillations at the same spatial coordinate. These decreases were most prominent in the 20–40 Hz frequency range, although spectral analysis showed that γ band power decrease extends up to at least 80 Hz. β/γ Power decrease also scales with memory load. Whilst θ increases were predominately observed in the medial frontal cortex, β/γ decreases were associated with other brain areas, including nodes of the default mode network (for the N-back task) and areas associated with language processing (for the Sternberg task). These observations are in agreement with intracranial EEG and fMRI studies. Finally, we have shown an intimate relationship between changes in β/γ band oscillatory power at spatially separate network nodes, implying that activity in these nodes is not reflective of uni-modal task driven changes in spatially separate brain regions, but rather represents correlated network activity. The utility of MEG as a non-invasive means to measure neural oscillatory modulation has been demonstrated and future studies employing this technology have the potential to gain a better understanding of neural oscillatory processes, their relationship to functional and effective connectivity, and their correspondence to BOLD fMRI

Category-Specific Visual Responses: An Intracranial Study Comparing Gamma, Beta, Alpha, and ERP Response Selectivity

The specificity of neural responses to visual objects is a major topic in visual neuroscience. In humans, functional magnetic resonance imaging (fMRI) studies have identified several regions of the occipital and temporal lobe that appear specific to faces, letter strings, scenes, or tools. Direct electrophysiological recordings in the visual cortical areas of epileptic patients have largely confirmed this modular organization, using either single-neuron peri-stimulus time-histogram or intracerebral event-related potentials (iERP). In parallel, a new research stream has emerged using high-frequency gamma-band activity (50–150 Hz) (GBR) and low-frequency alpha/beta activity (8–24 Hz) (ABR) to map functional networks in humans. An obvious question is now whether the functional organization of the visual cortex revealed by fMRI, ERP, GBR, and ABR coincide. We used direct intracerebral recordings in 18 epileptic patients to directly compare GBR, ABR, and ERP elicited by the presentation of seven major visual object categories (faces, scenes, houses, consonants, pseudowords, tools, and animals), in relation to previous fMRI studies. Remarkably both GBR and iERP showed strong category-specificity that was in many cases sufficient to infer stimulus object category from the neural response at single-trial level. However, we also found a strong discrepancy between the selectivity of GBR, ABR, and ERP with less than 10% of spatial overlap between sites eliciting the same category-specificity. Overall, we found that selective neural responses to visual objects were broadly distributed in the brain with a prominent spatial cluster located in the posterior temporal cortex. Moreover, the different neural markers (GBR, ABR, and iERP) that elicit selectivity toward specific visual object categories present little spatial overlap suggesting that the information content of each marker can uniquely characterize high-level visual information in the brain

Localization of deep brain activity with scalp and subdural EEG

To what extent electrocorticography (ECoG) and electroencephalography (scalp EEG) differ in their capability to locate sources of deep brain activity is far from evident. Compared to EEG, the spatial resolution and signal- to-noise ratio of ECoG is superior but its spatial coverage is more restricted, as is arguably the volume of tissue activity effectively measured from. Moreover, scalp EEG studies are providing evidence of locating activity from deep sources such as the hippocampus using high-density setups during quiet wakefulness. To address this question, we recorded a multimodal dataset from 4 patients with refractory epilepsy during quiet wakefulness. This data comprises simultaneous scalp, subdural and depth EEG electrode recordings. The latter was located in the hippocampus or insula and provided us with our "ground truth" for source localization of deep activity. We ap- plied independent component analysis (ICA) for the purpose of separating the independent sources in theta, alpha and beta frequency band activity. In all patients subdural- and scalp EEG components were observed which had a significant zero-lag correlation with one or more contacts of the depth electrodes. Subsequent dipole modeling of the correlating components revealed dipole locations that were significantly closer to the depth electrodes compared to the dipole location of non-correlating components. These findings support the idea that components found in both recording modalities originate from neural activity in close proximity to the depth electrodes. Sources localized with subdural electrodes were similar to 70% closer to the depth electrode than sources localized with EEG with an absolute improvement of around similar to 2cm. In our opinion, this is not a considerable improvement in source localization accuracy given that, for clinical purposes, ECoG electrodes were implanted in close proximity to the depth electrodes. Furthermore, the ECoG grid attenuates the scalp EEG, due to the electrically isolating silastic sheets in which the ECoG electrodes are embedded. Our results on dipole modeling show that the deep source localization accuracy of scalp EEG is comparable to that of ECoG. Significance Statement Deep and subcortical regions play an important role in brain function. However, as joint recordings at multiple spatial scales to study brain function in humans are still scarce, it is still unresolved to what extent ECoG and EEG differ in their capability to locate sources of deep brain activity. To the best of our knowledge, this is the first study presenting a dataset of simultaneously recorded EEG, ECoG and depth electrodes in the hippocampus or insula, with a focus on non-epileptiform activity (quiet wakefulness). Furthermore, we are the first study to provide experimental findings on the comparison of source localization of deep cortical structures between invasive and non-invasive brain activity measured from the cortical surface

Frequency and spatial characteristics of highfrequency neuromagnetic signals in childhood epilepsy

Purpose. Invasive intracranial recordings have suggested that high-frequency oscillation is involved in epileptogenesis and is highly localized to epileptogenic zones. The aim of the present study is to characterize the frequency and spatial patterns of high-frequency brain signals in childhood epilepsy using a non-invasive technology. Methods. Thirty children with clinically diagnosed epilepsy were studied using a whole head magnetoencephalography (MEG) system. MEG data were digitized at 4 000 Hz. The frequency and spatial characteristics of high-frequency neuromagnetic signals were analyzed using continuous wavelet transform and beamformer. Threedimensional magnetic resonance imaging (MRI) was obtained for each patient to localize magnetic sources. Results. Twenty-six patients showed highfrequency (100-1 000 Hz) components (26/30, 86%). Nineteen patients showed more than one high-frequency component (19/30, 63%). The frequency range of high-frequency components varied across patients. The highest frequency band was identified around 910 Hz. The loci of high-frequency epileptic activities were concordant with the lesions identified by magnetic resonance imaging for 21 patients (21/30, 70%). The MEG source localizations of high-frequency components were found to be concordant with intracranial recordings for nine of the eleven patients who underwent epilepsy surgery (9/11, 82%). Conclusion. The results have demonstrated that childhood epilepsy was associated with high-frequency epileptic activity in a wide frequency range. The concordance of MEG source localization, MRI and intracranial recordings suggests that measurement of high-frequency neuromagnetic signals might provide a novel approach for clinical management of childhood epilepsy

Deficits in high- (>60 Hz) gamma-band oscillations during visual processing in schizophrenia

Current theories of the pathophysiology of schizophrenia have focused on abnormal temporal coordination of neural activity. Oscillations in the gamma-band range (>25 Hz) are of particular interest as they establish synchronization with great precision in local cortical networks. However, the contribution of high gamma (>60 Hz) oscillations toward the pathophysiology is less established. To address this issue, we recorded magnetoencephalographic (MEG) data from 16 medicated patients with chronic schizophrenia and 16 controls during the perception of Mooney faces. MEG data were analysed in the 25–150 Hz frequency range. Patients showed elevated reaction times and reduced detection rates during the perception of upright Mooney faces while responses to inverted stimuli were intact. Impaired processing of Mooney faces in schizophrenia patients was accompanied by a pronounced reduction in spectral power between 60–120 Hz (effect size: d = 1.26) which was correlated with disorganized symptoms (r = −0.72). Our findings demonstrate that deficits in high gamma-band oscillations as measured by MEG are a sensitive marker for aberrant cortical functioning in schizophrenia, suggesting an important aspect of the pathophysiology of the disorder

A new insight into sentence comprehension : the impact of word associations in sentence processing as shown by invasive EEG recording

The effect of word association on sentence processing is still a matter of debate. Some studies observe no effect while others found a dependency on sentence congruity or an independent effect. In an attempt to separate the effects of sentence congruity and word association in the spatio-temporal domain, we jointly recorded scalp- and invasive-EEG (iEEG). The latter provides highly localized spatial (unlike scalp-EEG) and high temporal (unlike fMRI) resolutions. We recorded scalp- and iEEG in three patients with refractory epilepsy. The stimuli consisted of 280 sentences with crossed factors of sentence congruity and within sentence word-association. We mapped semantic retrieval processes involved in sentence comprehension onto the left temporal cortex and both hippocampi, and showed for the first time that certain localized regions participate in the processing of word association in sentence context. Furthermore, simultaneous recording of scalp- and iEEG gave us a direct overview of signal change due to its propagation across the head tissues

Detrended fluctuation analysis in the presurgical evaluation of parietal lobe epilepsy patients

Objective: To examine the usability of long-range temporal correlations (LRTCs) in non-invasive localization of the epileptogenic zone (EZ) in refractory parietal lobe epilepsy (RPLE) patients. Methods: We analyzed 10 RPLE patients who had presurgical MEG and underwent epilepsy surgery. We quantified LRTCs with detrended fluctuation analysis (DFA) at four frequency bands for 200 cortical regions estimated using individual source models. We correlated individually the DFA maps to the distance from the resection area and from cortical locations of interictal epileptiform discharges (IEDs). Additionally, three clinical experts inspected the DFA maps to visually assess the most likely EZ locations. Results: The DFA maps correlated with the distance to resection area in patients with type II focal cortical dysplasia (FCD) (p < 0:05), but not in other etiologies. Similarly, the DFA maps correlated with the IED locations only in the FCD II patients. Visual analysis of the DFA maps showed high interobserver agreement and accuracy in FCD patients in assigning the affected hemisphere and lobe. Conclusions: Aberrant LRTCs correlate with the resection areas and IED locations. Significance: This methodological pilot study demonstrates the feasibility of approximating cortical LRTCs from MEG that may aid in the EZ localization and provide new non-invasive insight into the presurgical evaluation of epilepsy. (c) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG.

NUTMEG is a source analysis toolbox geared towards cognitive neuroscience researchers using MEG and EEG, including intracranial recordings. Evoked and unaveraged data can be imported to the toolbox for source analysis in either the time or time-frequency domains. NUTMEG offers several variants of adaptive beamformers, probabilistic reconstruction algorithms, as well as minimum-norm techniques to generate functional maps of spatiotemporal neural source activity. Lead fields can be calculated from single and overlapping sphere head models or imported from other software. Group averages and statistics can be calculated as well. In addition to data analysis tools, NUTMEG provides a unique and intuitive graphical interface for visualization of results. Source analyses can be superimposed onto a structural MRI or headshape to provide a convenient visual correspondence to anatomy. These results can also be navigated interactively, with the spatial maps and source time series or spectrogram linked accordingly. Animations can be generated to view the evolution of neural activity over time. NUTMEG can also display brain renderings and perform spatial normalization of functional maps using SPM's engine. As a MATLAB package, the end user may easily link with other toolboxes or add customized functions