Verena Brodbeck

Electroencephalographic source imaging: a prospective study of 152 operated epileptic patients

Electroencephalography is mandatory to determine the epilepsy syndrome. However, for the precise localization of the irritative zone in patients with focal epilepsy, costly and sometimes cumbersome imaging techniques are used. Recent small studies using electric source imaging suggest that electroencephalography itself could be used to localize the focus. However, a large prospective validation study is missing. This study presents a cohort of 152 operated patients where electric source imaging was applied as part of the pre-surgical work-up allowing a comparison with the results from other methods. Patients (n = 152) with >1 year postoperative follow-up were studied prospectively. The sensitivity and specificity of each imaging method was defined by comparing the localization of the source maximum with the resected zone and surgical outcome. Electric source imaging had a sensitivity of 84% and a specificity of 88% if the electroencephalogram was recorded with a large number of electrodes (128–256 channels) and the individual magnetic resonance image was used as head model. These values compared favourably with those of structural magnetic resonance imaging (76% sensitivity, 53% specificity), positron emission tomography (69% sensitivity, 44% specificity) and ictal/interictal single-photon emission-computed tomography (58% sensitivity, 47% specificity). The sensitivity and specificity of electric source imaging decreased to 57% and 59%, respectively, with low number of electrodes (<32 channels) and a template head model. This study demonstrated the validity and clinical utility of electric source imaging in a large prospective study. Given the low cost and high flexibility of electroencephalographic systems even with high channel counts, we conclude that electric source imaging is a highly valuable tool in pre-surgical epilepsy evaluation.

Resting electroencephalogram alpha-power over posterior sites indexes baseline visual cortex excitability

Variations of oscillatory brain activity have been related to distinct functional states depending on the frequency of oscillations. In the &agr;-band (about 8–14 Hz), decreased oscillatory activity is thought to reflect a state of enhanced cortical excitability, and increased activity to reflect a state of cortical idling or inhibition in which excitability is reduced, but the &agr;/excitability link has not been probed directly. Here, we studied the relationship between resting oscillatory activity and visual cortex excitability across participants using electroencephalography and transcranial magnetic stimulation to the occipital pole. We found individual posterior &agr;-band power to correlate with the individual threshold for eliciting illusory, transcranial magnetic stimulation-induced visual percepts. This provides direct support for an &agr;/excitability link and for baseline states of the visual brain to vary across individuals.

Dynamic BOLD functional connectivity in humans and its electrophysiological correlates

Neural oscillations subserve many human perceptual and cognitive operations. Accordingly, brain functional connectivity is not static in time, but fluctuates dynamically following the synchronization and desynchronization of neural populations. This dynamic functional connectivity has recently been demonstrated in spontaneous fluctuations of the Blood Oxygen Level-Dependent (BOLD) signal, measured with functional Magnetic Resonance Imaging (fMRI). We analyzed temporal fluctuations in BOLD connectivity and their electrophysiological correlates, by means of long (≈50 min) joint electroencephalographic (EEG) and fMRI recordings obtained from two populations: 15 awake subjects and 13 subjects undergoing vigilance transitions. We identified positive and negative correlations between EEG spectral power (extracted from electrodes covering different scalp regions) and fMRI BOLD connectivity in a network of 90 cortical and subcortical regions (with millimeter spatial resolution). In particular, increased alpha (8–12 Hz) and beta (15–30 Hz) power were related to decreased functional connectivity, whereas gamma (30–60 Hz) power correlated positively with BOLD connectivity between specific brain regions. These patterns were altered for subjects undergoing vigilance changes, with slower oscillations being correlated with functional connectivity increases. Dynamic BOLD functional connectivity was reflected in the fluctuations of graph theoretical indices of network structure, with changes in frontal and central alpha power correlating with average path length. Our results strongly suggest that fluctuations of BOLD functional connectivity have a neurophysiological origin. Positive correlations with gamma can be interpreted as facilitating increased BOLD connectivity needed to integrate brain regions for cognitive performance. Negative correlations with alpha suggest a temporary functional weakening of local and long-range connectivity, associated with an idling state.

Neuronal networks in children with continuous spikes and waves during slow sleep

Sir, We read with great interest the paper by Siniatchkin et al. (2010), in which they report the results of electroencephalogram (EEG) combined with functional MRI studies performed in children with epileptic encephalopathies with continuous spike-waves during sleep (CSWS). This paper undoubtedly brings novel and valuable data on the pathophysiology of epileptic encephalopathies with CSWS, pointing out the implication of subcortical structures such as the striatum and the thalamus in these disorders. This study actually supports the hypothesis that the neurological regression in CSWS is not only related to the neurophysiological impairment at the site of the epileptic focus but also to epilepsy-induced changes in distant and connected brain areas with a particular involvement of the default mode network (De Tiège et al., 2009). Nevertheless, we would like to address some limitations regarding the methodology used in this work. The EEG-functional MRI methodology enhances the statistical model used for functional MRI data analysis thanks to various EEG features such as timing, duration, amplitude, morphology and topography of the epileptic activity. In preliminary analyses, Siniatchkin et al. (2010) found a poor correlation between functional MRI results and electrical source imaging results obtained after averaging all the spike-wave discharges. The authors attributed this poor correspondence to discordance between the brain areas generating the first and the subsequent spike-wave discharges in a sequence. Based on this assumption and preliminary electrical source imaging analysis, the authors used the averaged first spike of every spike-wave discharge sequence to characterize the chronology of neuronal recruitment within the identified functional MRI neuronal network. This approach raises an important pathophysiological issue that the authors did not explore—would a functional MRI statistical model integrating separately initial and subsequent spikes of spike-wave discharge sequences evidence different neuronal networks? Further, to support their assumption, the authors should have directly compared electrical source imaging based on averages of initial versus subsequent spikes of spike-wave discharge sequences. If present, differences in shape and topography between those spike-wave discharges would have supported the assumption made, and these differences should have been accessible from the semi-automatic method used for spike-wave discharge classification. These additional pieces of information are essential; they would not only justify the methodological approach but also improve our understanding of the disorder. In fact, experimental confirmation is required for the assumed difference between generators of first and subsequent spikes in spike-wave discharge sequences because the theoretical justification derived from observations made on seizure activity (Ebersole, 2000) might not be extended to CSWS in which bursts and sequences have no recognized significance in pathophysiological and electrophysiological terms. In our opinion, the unmatched functional MRI and electrical source imaging results obtained on spike-wave discharge averaged on whole spike-wave discharge sequences might actually reflect the existence of multiple independent spike-wave discharge generators or propagation pathways as frequently found in CSWS (Fig. 1). Under this alternative hypothesis, which precludes the averaging approach adopted by the authors, single spike-wave discharge source reconstruction would have been required. This latter methodology is actually preferred for magnetic source imaging investigations to characterize the neuronal networks involved in CSWS activity at the individual level (Paetau, 2009; De Tiège et al., 2010). doi:10.1093/brain/awq389 Brain 2011: 134; 1–3 | e177

Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep

The integration of segregated brain functional modules is a prerequisite for conscious awareness during wakeful rest. Here, we test the hypothesis that temporal integration, measured as long-term memory in the history of neural activity, is another important quality underlying conscious awareness. For this aim, we study the temporal memory of blood oxygen level-dependent signals across the human nonrapid eye movement sleep cycle. Results reveal that this property gradually decreases from wakefulness to deep nonrapid eye movement sleep and that such decreases affect areas identified with default mode and attention networks. Although blood oxygen level-dependent spontaneous fluctuations exhibit nontrivial spatial organization, even during deep sleep, they also display a decreased temporal complexity in specific brain regions. Conversely, this result suggests that long-range temporal dependence might be an attribute of the spontaneous conscious mentation performed during wakeful rest.

Electrical source imaging for presurgical focus localization in epilepsy patients with normal MRI.

Purpose:  Patients with magnetic resonance (MR)–negative focal epilepsy (MRN‐E) have less favorable surgical outcomes (between 40% and 70%) compared to those in whom an MRI lesion guides the site of surgical intervention (60–90%). Patients with extratemporal MRN‐E have the worst outcome (around 50% chance of seizure freedom). We studied whether electroencephalography (EEG) source imaging (ESI) of interictal epileptic activity can contribute to the identification of the epileptic focus in patients with normal MRI.

Combination of EEG-fMRI and EEG source analysis improves interpretation of spike-associated activation networks in paediatric pharmacoresistant focal epilepsies.

Simultaneous recording of EEG and functional MRI (EEG-fMRI) is a promising tool that may be applied in patients with epilepsy to investigate haemodynamic changes associated with interictal epileptiform discharges (IED). As the yield of the EEG-fMRI technique in children with epilepsy is still unclear, the aim of this study was to evaluate whether the combination of EEG-fMRI and EEG source analysis could improve localization of epileptogenic foci in children. Six children with an unambiguous focus localization were selected based on the criterion of the consistency of ictal EEG, PET and ictal SPECT. IEDs were taken as time series for fMRI analysis and as averaged sweeps for the EEG source analysis based on the distributed linear local autoregressive average (LAURA) solution. In four patients, the brain area with haemodymanic changes corresponded to the epileptogenic zone. However, additional distant regions with haemodynamic response were observed. Source analysis located the source of the initial epileptic activity in all cases in the presumed epileptogenic zone and revealed propagation in five cases. In three cases there was a good correspondence between haemodynamic changes and source localization at both the beginning and the propagation of IED. In the remaining three cases, at least one area of haemodynamic changes corresponded to either the beginning or the propagation. In most children analysed, EEG-fMRI revealed extended haemodynamic response, which were difficult to interpret without an appropriate reference, i.e. a priori hypothesis about epileptogenic zone. EEG source analysis may help to differentiate brain areas with haemodynamic response.

EEG microstates of wakefulness and NREM sleep

EEG-microstates exploit spatio-temporal EEG features to characterize the spontaneous EEG as a sequence of a finite number of quasi-stable scalp potential field maps. So far, EEG-microstates have been studied mainly in wakeful rest and are thought to correspond to functionally relevant brain-states. Four typical microstate maps have been identified and labeled arbitrarily with the letters A, B, C and D. We addressed the question whether EEG-microstate features are altered in different stages of NREM sleep compared to wakefulness. 32-channel EEG of 32 subjects in relaxed wakefulness and NREM sleep was analyzed using a clustering algorithm, identifying the most dominant amplitude topography maps typical of each vigilance state. Fitting back these maps into the sleep-scored EEG resulted in a temporal sequence of maps for each sleep stage. All 32 subjects reached sleep stage N2, 19 also N3, for at least 1 min and 45 s. As in wakeful rest we found four microstate maps to be optimal in all NREM sleep stages. The wake maps were highly similar to those described in the literature for wakefulness. The sleep stage specific map topographies of N1 and N3 sleep showed a variable but overall relatively high degree of spatial correlation to the wake maps (Mean: N1 92%; N3 87%). The N2 maps were the least similar to wake (mean: 83%). Mean duration, total time covered, global explained variance and transition probabilities per subject, map and sleep stage were very similar in wake and N1. In wake, N1 and N3, microstate map C was most dominant w.r.t. global explained variance and temporal presence (ratio total time), whereas in N2 microstate map B was most prominent. In N3, the mean duration of all microstate maps increased significantly, expressed also as an increase in transition probabilities of all maps to themselves in N3. This duration increase was partly--but not entirely--explained by the occurrence of slow waves in the EEG. The persistence of exactly four main microstate classes in all NREM sleep stages might speak in favor of an in principle maintained large scale spatial brain organization from wakeful rest to NREM sleep. In N1 and N3 sleep, despite spectral EEG differences, the microstate maps and characteristics were surprisingly close to wakefulness. This supports the notion that EEG microstates might reflect a large scale resting state network architecture similar to preserved fMRI resting state connectivity. We speculate that the incisive functional alterations which can be observed during the transition to deep sleep might be driven by changes in the level and timing of activity within this architecture.

Large-scale brain functional modularity is reflected in slow electroencephalographic rhythms across the human non-rapid eye movement sleep cycle

Large-scale brain functional networks (measured with functional magnetic resonance imaging, fMRI) are organized into separated but interacting modules, an architecture supporting the integration of distinct dynamical processes. In this work we study how the aforementioned modular architecture changes with the progressive loss of vigilance occurring in the descent to deep sleep and we examine the relationship between the ensuing slow electroencephalographic rhythms and large-scale network modularity as measured with fMRI. Graph theoretical methods are used to analyze functional connectivity graphs obtained from fifty-five participants at wakefulness, light and deep sleep. Network modularity (a measure of functional segregation) was found to increase during deeper sleep stages but not in light sleep. By endowing functional networks with dynamical properties, we found a direct link between increased electroencephalographic (EEG) delta power (1-4 Hz) and a breakdown of inter-modular connectivity. Both EEG slowing and increased network modularity were found to quickly decrease during awakenings from deep sleep to wakefulness, in a highly coordinated fashion. Studying the modular structure itself by means of a permutation test, we revealed different module memberships when deep sleep was compared to wakefulness. Analysis of node roles in the modular structure revealed an increase in the number of locally well-connected nodes and a decrease in the number of globally well-connected hubs, which hinders interactions between separated functional modules. Our results reveal a well-defined sequence of changes in brain modular organization occurring during the descent to sleep and establish a close parallel between modularity alterations in large-scale functional networks (accessible through whole brain fMRI recordings) and the slowing of scalp oscillations (visible on EEG). The observed re-arrangement of connectivity might play an important role in the processes underlying loss of vigilance and sensory awareness during deep sleep.

Electric source imaging of interictal activity accurately localises the seizure onset zone

Objective It remains controversial whether interictal spikes are a surrogate of the seizure onset zone (SOZ). Electric source imaging (ESI) is an increasingly validated non-invasive approach for localising the epileptogenic focus in patients with drug-resistant epilepsy undergoing evaluation for surgery, using high-density scalp EEG and advanced source localisation algorithms that include the patients own MRI. Here we investigate whether localisation of interictal spikes by ESI provides valuable information on the SOZ. Methods In 38 patients with focal epilepsy who later underwent intracranial EEG monitoring, we performed ESI of interictal spikes recorded with 128–256-channel EEG. We measured the distance between the ESI maximum and the nearest intracranial electrodes in the SOZ and irritative zone (IZ, the source of interictal spikes). The resection of the region harbouring the ESI maximum was correlated to surgical outcome. Results The median distance from the ESI maximum to the nearest electrode involved in the SOZ was 17 mm (IQR 8–27). The IZ and SOZ colocalised in most patients (median distance 0 mm, IQR 0–14), supporting the notion that localising interictal spikes is a valid surrogate for the SOZ. There was no difference in accuracy among patients with temporal or extratemporal epilepsy. In the 32 patients who underwent resective surgery, including the ESI maximum in the resection correlated with favourable outcome (p=0.03). Conclusions Localisation of interictal spikes provides an excellent estimate of the SOZ in the majority of patients. ESI should be taken into account for the management of patients undergoing intracranial recordings.