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Composite diagram of averaged EEG K-complex waveforms recorded in patients from scalp, frontal lobe cortex, frontal lobe white matter, thalamus, and medial occipital lobe. Surface negative scalp EEG peak maximal at Fz > F3,4 > Cz corresponds to synchronous surface negative intracranial peak recorded with widespread bilateral frontal lobe cortical distribution. Inverted (positive polarity) waveforms recorded synchronously from intracranial electrode contacts situated in frontal white matter, thalamus and medial occipital region and from occipital scalp electrode (O2). Waveforms plotted for illustrative purposes on 3D rendering of MNI averaged MRI dataset. MRI insets show locations of patients’ intracranial subdural and depth electrodes. Scalp and intracranial EEG depicted in common average (12 scalp electrodes) referential montage; LFF = 0.5 Hz; HFF = 70 Hz. (Adapted from Figs. 1, 4, 6-9 in Wennberg, 2010).
Source publication
Objective:
To assess whether existing noninvasive source localization techniques can provide valid solutions for large extended cortical sources we tested the capability of various methods of EEG source imaging (ESI) and magnetic source imaging (MSI) to localize the large superficial cortical generator of the human K-complex.
Methods:
We recentl...
Similar publications
Lately, Ensemble Empirical Mode Decomposition (EEMD) techniques receive growing interest in biomedical data analysis. Event-Related Modes (ERMs) represent features extracted by an EEMD from electroencephalographic (EEG) recordings. We present a new approach for source localization of EEG data based on combining ERMs with inverse models. As the firs...
Citations
... Examples of combined EEG-MEG recordings in stage 2 and stage 3 sleep are shown in Figs. 15 and 16, demonstrating the imperfect spatial and temporal correlations between EEG and MEG representations of sleep spindles, K-complexes, and slow oscillations, related in large part to differing sensitivities of EEG and MEG to radial and tangential source orientations (reflecting primarily gyral and sulcal activity, respectively) [10,11]. ...
Recording of the intracranial electrical fields associated with scalp EEG sleep waveforms can provide unique information on the cortical generators of these potentials. Intracranial EEG recordings obtained using intraparenchymal depth or subdural electrodes provide a means to gather such information at the level of large field potentials, which may be either locally generated or volume conducted from a distant source. In this chapter, different intracranial EEG examples are presented in various combinations of bipolar and referential montages, across different sleep stages, with the goal of demonstrating the morphology, topography, and synchrony of the intracranial field correlates of the typical sleep potentials recorded with scalp EEG. In the last section of the chapter, the corresponding magnetic fields that can be recorded during sleep using MEG are shown, along with some examples of electromagnetic inverse source modeling of benign sleep variants.
... Although the results of such studies are compelling, it is difficult to assess the quality of the reconstructed waves because the true generator locations are usually unknown. In Wennberg and Cheyne (2013) , source-modeling of K -complexes was carried out using high-density EEG and MEG recordings in epilepsy patients for which the true generator locations were known from intracranial recordings. Several different forward models and inverse methods were compared and it was found that all combinations of forward models and inverse methods incorrectly localized K -complexes to deep midline structures. ...
... Several different forward models and inverse methods were compared and it was found that all combinations of forward models and inverse methods incorrectly localized K -complexes to deep midline structures. The authors of Wennberg and Cheyne (2013) note that these findings are relevant not only to K -complexes, but to extended superficial cortical sources in general, and that source-modeling of such activity should be conducted with circumspection. Methodolog-ical studies might help in resolving these conflicting experimental findings and, more generally, inform us about the quality with which propagating cortical activity can be reconstructed from EEG/MEG sensordata. ...
The prevailing view on the dynamics of large-scale electrical activity in the human cortex is that it constitutes a functional network of discrete and localized circuits. Within this view, a natural way to analyse magnetoencephalographic (MEG) and electroencephalographic (EEG) data is by adopting methods from network theory. Invasive recordings, however, demonstrate that cortical activity is spatially continuous, rather than discrete, and exhibits propagation behavior. Furthermore, human cortical activity is known to propagate under a variety of conditions such as non-REM sleep, general anesthesia, and coma. Although several MEG/EEG studies have investigated propagating cortical activity, not much is known about the conditions under which such activity can be successfully reconstructed from MEG/EEG sensor-data. This study provides a methodological framework for inverse-modeling of propagating cortical activity. Within this framework, cortical activity is represented in the spatial frequency domain, which is more natural than the dipole domain when dealing with spatially continuous activity.
We define angular power spectra, which show how the power of cortical activity is distributed across spatial frequencies, angular gain/phase spectra, which characterize the spatial filtering properties of linear inverse operators, and angular resolution matrices, which summarize how linear inverse operators leak signal within and across spatial frequencies. We adopt the framework to provide insight into the performance of several linear inverse operators in reconstructing propagating cortical activity from MEG/EEG sensor-data. We also describe how prior spatial frequency information can be incorporated into the inverse-modeling to obtain better reconstructions.
... 17,18 Furthermore, studies on ESI and simultaneous intracranial recordings demonstrated that such deep mesial source solutions were typical errors occurring because of incorrect modeling of large, bilateral cortical sources. 19 More direct study of the origin of these particular EEG graphoelements using advanced source imaging methods in a population of patients has not been performed. ...
... 23 We normalized the results to the global field maxima and we used a cut-off threshold of 50%. 19 We opted for this method because it allowed us to construct plausible source models of large bilateral sources: ESI of K-complexes were consistent with localization previously reported in studies using intracranial recordings, 24 namely bilateral fronto-temporal structures, with the field reversing medially above the cingulate gyrus and laterally above the inferior temporal gyrus (see document 1, ...
... Previous reports 16 suggesting deep mesial structures (cingulate cortices) are probably because of errors of the inverse solutions not suitable for modeling large, bilateral cortical sources. 19 Although anterior mesial frontal structures were also part of our source solutions, polar and antero-lateral frontal areas and anterior temporal structures were involved too. Using source montage, we were able to separate in time the activity in the frontal and temporal areas: the depolarization started in the frontal regions and propagated then to the temporal regions. ...
Purpose:
Triphasic waves (TWs) have been observed in the EEG recorded in patients with various types of encephalopathy, yet their genesis and significance is still debated. The aim of this study was to elucidate the localization of the cortical generators of TWs using EEG source imaging.
Methods:
In 20 consecutive patients who had encephalopathy with TWs, EEG source imaging of the first negative and the positive phases of the TW was performed. Three different approaches were used: equivalent current dipoles, a distributed source model, and a recently described spatial filtration method for visualizing EEG in source space.
Results:
Equivalent current dipole models failed to provide valid solutions. The distributed source model and the spatial filtration method suggested that TWs were generated by large, bilateral cortical networks, invariably involving the anterior frontal and the temporo-polar areas.
Conclusions:
Source imaging localized TWs to anterior frontal and temporo-frontal structures. Involvement of these regions is consistent with the typical pathophysiological changes of altered consciousness and cognitive changes observed in patients with TW encephalopathy.
... 38 Some studies found that ESI applied to widespread spike and wave discharges and K-complex tends to favour localization in deep sources due to the constraints applied to source localization algorithms. 39 Further investigations are needed with regard to source localizations of generalized activity such as GPD. One possible approach could be simultaneous EEG-fMRI to complement the localization of the regions involved in GPD. ...
Aim
Generalized periodic discharge (GPD) is an EEG pattern of poor neurological outcome, frequently observed in comatose patients after cardiac arrest. The aim of our study was to identify the neuronal network generating ≤2.5 Hz GPD using EEG source localization and connectivity analysis.
Methods
We analyzed 40 comatose adult patients with anoxic-ischemic encephalopathy, who had 19 channel-EEG recording. We computed electric source analysis based on distributed inverse solution (LAURA) and we estimated cortical activity in 82 atlas-based cortical brain regions. We applied directed connectivity analysis (Partial Directed Coherence) on these sources to estimate the main drivers.
Results
Source analysis suggested that the GPD are generated in the cortex of limbic system in the majority of patients (87.5%). Connectivity analysis revealed main drivers located in thalamus and hippocampus for the large majority (80%) of patients, together with important activation also in amygdala (70%).
Conclusions
We hypothesize that the anoxic-ischemic dysfunction, leading to hyperactivity of the thalamo-cortical (limbic presumably) circuit, can result in an oscillatory thalamic activity capable of inducing periodic cortical (limbic, mostly medial-temporal and orbitofrontal) discharges, similarly to the case of generalized rhythmic spike-wave discharge in convulsive or non-convulsive status epilepticus.
... When averaged, time-locked signals can be processed in both time-and frequency-domains and allow the reconstruction of estimated sources. Source reconstruction is more accurate for focal sources in the superficial regions of the cortex than it is for extended sources (Wennberg & Cheyne, 2013) or for sources in medial or subcortical regions (Koessler et al., 2014). The source of the brain's electrical signal can Title Marcelo Bigliassi, Michael J. Wright, Costas I. Karageorghis & Alexander V. Nowicky subsequently be reconstructed by applying different methods, such as the minimum norm method (wMNE; i.e. an inverse solution method; Grech et al., 2008) or standardised low resolution brain electromagnetic tomography (sLORETA; Pascual-Marqui, 2002). ...
Elite youth sport environments present unique psychological and social challenges as well as physical challenges to young athletes. In recent years there has been a growing interest in the impact of the psychological and social context across sport including elite football development. However, relatively little has been shared about how these ideas can be put into practice in real world football academies. This paper aims to illustrate concepts and values that underpin the psychological and social environment in a premier league football academy. It describes the work of the academies ‘psych-social team’ (PST), who work with, and within, the wider academy. The aims of the PST are to integrate these ideas across the whole multidisciplinary team (MDT) to create an environment that is both safe and psychologically informed to enhance performance and development of young players. By sharing this developing model, we hope to spark discussion, and collaboration across football about how best to create practical and operational solutions to holistic psychological and social development.
... The great practical value of KC is its use to unequivocally mark sleep onset, along with spindles, a demand for all quantitative studies of sleep in health and disease. Beyond that, several important roles for KCs in brain functions have been proposed by both animal experiments (Amzica and Steriade, 2002;Marini et al., 2004;Ji and Wilson, 2007;Cruz-Aguilar et al., 2015) and human brain metabolism (Larson-Prior et al., 2009;Maquet, 2010;Caporro et al., 2012;Jahnke et al., 2012) and EEG/MEG electrophysiological studies (Lu et al., 1992;Numminen et al., 1996;Yoshida et al., 1996;Murphy et al., 2009;Wennberg and Cheyne, 2013;Ioannides et al., 2017). One of these roles of KC is a sentinel role protecting sleep by preventing waking to normal stimuli, which do not present a danger (Jahnke et al., 2012;Halász, 2016). ...
The large multicomponent K-complex (KC) and the rhythmic spindle are the hallmarks of non-rapid eye movement (NREM)-2 sleep stage. We studied with magnetoencephalography (MEG) the progress of light sleep (NREM-1 and NREM-2) and emergence of KCs and spindles. Seven periods of interest (POI) were analyzed: wakefulness, the two quiet “core” periods of light sleep (periods free from any prominent phasic or oscillatory events) and four periods before and during spindles and KCs. For each POI, eight 2-s (1250 time slices) segments were used. We employed magnetic field tomography (MFT) to extract an independent tomographic estimate of brain activity from each MEG data sample. The spectral power was then computed for each voxel in the brain for each segment of each POI. The sets of eight maps from two POIs were contrasted using a voxel-by-voxel t-test. Only increased spectral power was identified in the four key contrasts between POIs before and during spindles and KCs versus the NREM2 core. Common increases were identified for all four subjects, especially within and close to the anterior cingulate cortex (ACC). These common increases were widespread for low frequencies, while for higher frequencies they were focal, confined to specific brain areas. For the pre-KC POI, only one prominent increase was identified, confined to the theta/alpha bands in a small area in the dorsal caudal part of ACC (dcACC). During KCs, the activity in this area grows in intensity and extent (in space and frequency), filling the space between the areas that expanded their low frequency activity (in the delta band) during NREM2 compared to NREM1. Our main finding is that prominent spectral power increases before NREM2 graphoelements are confined to the dcACC, and only for KCs, sharing common features with changes of activity in dcACC of the well-studied error related negativity (ERN). ERN is seen in awake state, in perceptual conflict and situations where there is a difference between expected and actual environmental or internal events. These results suggest that a KC is the sleep side of the awake state ERN, both serving their putative sentinel roles in the frame of the saliency network.
... Combining EEG and fMRI, KC-associated positive blood oxygen level dependent (BOLD) signal changes have been found in several subcortical (brainstem and thalamus), and cortical areas, mainly paracentral, posterior and inferior parieto-occipital, superior temporal, midline cingulate, and paracingulate regions [32,69]. Albeit the temporal resolution of fMRI does not consent to identify the neural activity associated with the specific KC components, it has been proposed that the subcortical and cingulate BOLD changes may be associated with the production of the negative component of the KC, whereas BOLD changes in posterior and inferior cortical areas may be associated with the subsequent positivity [70]. Then, we cannot exclude that the reduced evidence for a genetic control on KC features in the frontal area reflects a higher genetic influence on the late positivity of the KC than on the classical frontal negativity. ...
Sleep electroencephalogram (EEG) has a trait-like nature. Several findings highlighted the heritability of spectral power in specific frequency ranges and sleep spindles during NREM sleep. However, a genetic influence on the K-complex (KC), one of the electrophysiological hallmarks of NREM sleep, has never been assessed. Here, we investigated the heritability of the KC detected during NREM stage 2 comparing 10 monozygotic (MZ) and 10 dizygotic (DZ) twin pairs. Genetic variance analysis (GVA) and Intraclass Correlation Coefficients (ICCs) were performed to assess the genetic effect and within-pair similarity for KC density, amplitude, and for the area-under-the-curve (AUC) of the KC average waveform at Fz, Cz and Pz scalp locations. Moreover, cluster analysis was performed on the KC average waveform profile. We observed a significant genetic effect on KC AUC at Cz and Pz, and on amplitude at Pz. Within-pair similarity (ICCs) was always significant for MZ twins except for KC density at Fz, while DZ twins always exhibited ICCs below the significance threshold, with the exception of density at Pz. The largest differences in within-pair similarity between MZ and DZ groups were observed again for AUC at Cz and Pz. MZ pairs accurately clustered for the KC average waveform with a higher frequency (successful clustering rate for MZ pairs: Fz=60%; Cz=80%; Pz=90%) compared to DZ pairs (successful clustering rate for DZ pairs: Fz=10%; Cz=10%; Pz=none). Our results suggest the existence of a genetic influence on the human KC, particularly related to its morphology and maximally observable in central and parietal locations.
... In contrast to some of these studies, however, our recordings indicate that spindles are present in multiple locations instead of a few focal deep sources. This might result from the known difficulties of performing cortical localization with EEG/MEG for highly distributed sources (Wennberg and Cheyne, 2013). ...
Since their discovery almost one century ago, sleep spindles, 0.5–2 s long bursts of oscillatory activity at 9–16 Hz during NREM sleep, have been thought to be global and relatively uniform throughout the cortex. Recent work, however, has brought this concept into question but it remains unclear to what degree spindles are global or local and if their properties are uniform or location-dependent. We addressed this question by recording sleep in eight patients undergoing evaluation for epilepsy with intracranial electrocorticography, which combines high spatial resolution with extensive cortical coverage.
We find that spindle characteristics are not uniform but are strongly influenced by the underlying cortical regions, particularly for spindle density and fundamental frequency. We observe both highly isolated and spatially distributed spindles, but in highly skewed proportions: while most spindles are restricted to one or very few recording channels at any given time, there are spindles that occur over widespread areas, often involving lateral prefrontal cortices and superior temporal gyri. Their co-occurrence is affected by a subtle but significant propagation of spindles from the superior prefrontal regions and the temporal cortices towards the orbitofrontal cortex.
This work provides a brain-wide characterization of sleep spindles as mostly local graphoelements with heterogeneous characteristics that depend on the underlying cortical area. We propose that the combination of local characteristics and global organization reflects the dual properties of the thalamo-cortical generators and provides a flexible framework to support the many functions ascribed to sleep in general and spindles specifically.
... This explains that there can be multiple generators for these PCs and thalami and/or insula have maximum propensity for generating these slow PCs in MEG-EEG. Very few studies had identified subcortical source locations in MEG-EEG due to low SNR and other technical issues [29][30][31]. The peak of a waveform succeeding the onset represents the propagation of a brain Source localization results are expressed as cumulative frequency i.e. percentage of each PC localization in the brain was grouped according to the regions and expressed as mean. ...
To study the genesis and propagation patterns of periodic complexes (PCs) associated with myoclonic jerks in sub-acute sclerosing pan-encephalitis (SSPE) using magnetoencephalography (MEG) and electroencephalography (EEG). Simultaneous recording of MEG (306 channels) and EEG (64 channels) in five patients of SSPE (M:F = 3:2; age 10.8 ± 3.2 years; symptom-duration 6.2 ± 10 months) was carried out using Elekta Neuromag® TRIUX™ system. Qualitative analysis of 80–160 PCs per patient was performed. Ten isomorphic classical PCs with significant field topography per patient were analysed at the ‘onset’ and at ‘earliest significant peak’ of the burst using discrete and distributed source imaging methods. MEG background was asymmetrical in 2 and slow in 3 patients. Complexes were periodic (3) or quasi-periodic (2), occurring every 4–16 s and varied in morphology among patients. Mean source localization at onset of bursts using discrete and distributed source imaging in magnetic source imaging (MSI) was in thalami and or insula (50 and 50 %, respectively) and in electric source imaging (ESI) was also in thalami and or insula (38 and 46 %, respectively). Mean source localization at the earliest rising phase of peak in MSI was in peri-central gyrus (49 and 42 %) and in ESI it was in frontal cortex (52 and 56 %). Further analysis revealed that PCs were generated in thalami and or insula and thereafter propagated to anterolateral surface of the cortices (viz. sensori-motor cortex and frontal cortex) to same side as that of the onset. This novel MEG–EEG based case series of PCs provides newer insights for understanding the plausible generators of myoclonus in SSPE and patterns of their propagation.
... This location suggested by EEG source localization appeared to be plausible, given the general predominance of EEG in fronto-central region (Aghakhani, 2004;Montalenti et al., 2001). However, a few recently published works suggested that potential spurious results may be yielded when applying source estimation methods to wide spread spike and wave discharges Kobayashi et al., 2005;Wennberg and Cheyne, 2013). Wennberg and Cheyne (2013) reported that despite intracranial evidence of cortical origins, the scalp EEG during Kcomplex was localized to deep brain regions using either dipole localization or distributed current density source imaging. ...
... However, a few recently published works suggested that potential spurious results may be yielded when applying source estimation methods to wide spread spike and wave discharges Kobayashi et al., 2005;Wennberg and Cheyne, 2013). Wennberg and Cheyne (2013) reported that despite intracranial evidence of cortical origins, the scalp EEG during Kcomplex was localized to deep brain regions using either dipole localization or distributed current density source imaging. While this finding was based on a low-density scalp electrode configuration (27 electrodes) in patients in whom intracranial EEG was available, Wennberg and Cheyne3s results suggested the possibility of mislocalization of widespread bi-hemispheric activities to mid-deep brain (these authors also examined 87-ch scalp EEG in one healthy human volunteer without iEEG recordings). ...
Unlike focal or partial epilepsy, which has a confined range of influence, idiopathic generalized epilepsy (IGE) often affects the whole or a larger portion of the brain without obvious, known cause. It is important to understand the underlying network which generates epileptic activity and through which epileptic activity propagates. The aim of the present study was to investigate the thalamocortical relationship using non-invasive imaging modalities in a group of IGE patients. We specifically investigated the roles of the mediodorsal nuclei in the thalami and the medial frontal cortex in generating and spreading IGE activities. We hypothesized that the connectivity between these two structures is key in understanding the generation and propagation of epileptic activity in brains affected by IGE. Using three imaging techniques of EEG, fMRI and EEG-informed fMRI, we identified important players in generation and propagation of generalized spike-and-wave discharges (GSWDs). EEG-informed fMRI suggested multiple regions including the medial frontal area near to the anterior cingulate cortex, mediodorsal nuclei of the thalamus, caudate nucleus among others that related to the GSWDs. The subsequent seed-based fMRI analysis revealed a reciprocal cortical and bi-thalamic functional connection. Through EEG-based Granger Causality analysis using (DTF) and adaptive DTF, within the reciprocal thalamocortical circuitry, thalamus seems to serve as a stronger source in driving cortical activity from initiation to the propagation of a GSWD. Such connectivity change starts before the GSWDs and continues till the end of the slow wave discharge. Thalamus, especially the mediodorsal nuclei, may serve as potential targets for deep brain stimulation to provide more effective treatment options for patients with drug-resistant generalized epilepsy.
