Roman Rodionov

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

Causal Hierarchy within the Thalamo-Cortical Network in Spike and Wave Discharges

Background Generalised spike wave (GSW) discharges are the electroencephalographic (EEG) hallmark of absence seizures, clinically characterised by a transitory interruption of ongoing activities and impaired consciousness, occurring during states of reduced awareness. Several theories have been proposed to explain the pathophysiology of GSW discharges and the role of thalamus and cortex as generators. In this work we extend the existing theories by hypothesizing a role for the precuneus, a brain region neglected in previous works on GSW generation but already known to be linked to consciousness and awareness. We analysed fMRI data using dynamic causal modelling (DCM) to investigate the effective connectivity between precuneus, thalamus and prefrontal cortex in patients with GSW discharges. Methodology and Principal Findings We analysed fMRI data from seven patients affected by Idiopathic Generalized Epilepsy (IGE) with frequent GSW discharges and significant GSW-correlated haemodynamic signal changes in the thalamus, the prefrontal cortex and the precuneus. Using DCM we assessed their effective connectivity, i.e. which region drives another region. Three dynamic causal models were constructed: GSW was modelled as autonomous input to the thalamus (model A), ventromedial prefrontal cortex (model B), and precuneus (model C). Bayesian model comparison revealed Model C (GSW as autonomous input to precuneus), to be the best in 5 patients while model A prevailed in two cases. At the group level model C dominated and at the population-level the p value of model C was ∼1. Conclusion Our results provide strong evidence that activity in the precuneus gates GSW discharges in the thalamo-(fronto) cortical network. This study is the first demonstration of a causal link between haemodynamic changes in the precuneus - an index of awareness - and the occurrence of pathological discharges in epilepsy.

Simultaneous intracranial EEG and fMRI of interictal epileptic discharges in humans

Simultaneous scalp EEG-fMRI measurements allow the study of epileptic networks and more generally, of the coupling between neuronal activity and haemodynamic changes in the brain. Intracranial EEG (icEEG) has greater sensitivity and spatial specificity than scalp EEG but limited spatial sampling. We performed simultaneous icEEG and functional MRI recordings in epileptic patients to study the haemodynamic correlates of intracranial interictal epileptic discharges (IED). Two patients undergoing icEEG with subdural and depth electrodes as part of the presurgical assessment of their pharmaco-resistant epilepsy participated in the study. They were scanned on a 1.5 T MR scanner following a strict safety protocol. Simultaneous recordings of fMRI and icEEG were obtained at rest. IED were subsequently visually identified on icEEG and their fMRI correlates were mapped using a general linear model (GLM). On scalp EEG-fMRI recordings performed prior to the implantation, no IED were detected. icEEG-fMRI was well tolerated and no adverse health effect was observed. intra-MR icEEG was comparable to that obtained outside the scanner. In both cases, significant haemodynamic changes were revealed in relation to IED, both close to the most active electrode contacts and at distant sites. In one case, results showed an epileptic network including regions that could not be sampled by icEEG, in agreement with findings from magneto-encephalography, offering some explanation for the persistence of seizures after surgery. Hence, icEEG-fMRI allows the study of whole-brain human epileptic networks with unprecedented sensitivity and specificity. This could help improve our understanding of epileptic networks with possible implications for epilepsy surgery.

The spatio-temporal mapping of epileptic networks: Combination of EEG–fMRI and EEG source imaging

Simultaneous EEG–fMRI acquisitions in patients with epilepsy often reveal distributed patterns of Blood Oxygen Level Dependant (BOLD) change correlated with epileptiform discharges. We investigated if electrical source imaging (ESI) performed on the interictal epileptiform discharges (IED) acquired during fMRI acquisition could be used to study the dynamics of the networks identified by the BOLD effect, thereby avoiding the limitations of combining results from separate recordings. Nine selected patients (13 IED types identified) with focal epilepsy underwent EEG–fMRI. Statistical analysis was performed using SPM5 to create BOLD maps. ESI was performed on the IED recorded during fMRI acquisition using a realistic head model (SMAC) and a distributed linear inverse solution (LAURA). ESI could not be performed in one case. In 10/12 remaining studies, ESI at IED onset (ESIo) was anatomically close to one BOLD cluster. Interestingly, ESIo was closest to the positive BOLD cluster with maximal statistical significance in only 4/12 cases and closest to negative BOLD responses in 4/12 cases. Very small BOLD clusters could also have clinical relevance in some cases. ESI at later time frame (ESIp) showed propagation to remote sources co-localised with other BOLD clusters in half of cases. In concordant cases, the distance between maxima of ESI and the closest EEG–fMRI cluster was less than 33 mm, in agreement with previous studies. We conclude that simultaneous ESI and EEG–fMRI analysis may be able to distinguish areas of BOLD response related to initiation of IED from propagation areas. This combination provides new opportunities for investigating epileptic networks.

Independent component analysis of interictal fMRI in focal epilepsy: Comparison with general linear model-based EEG-correlated fMRI

The general linear model (GLM) has been used to analyze simultaneous EEG-fMRI to reveal BOLD changes linked to interictal epileptic discharges (IED) identified on scalp EEG. This approach is ineffective when IED are not evident in the EEG. Data-driven fMRI analysis techniques that do not require an EEG derived model may offer a solution in these circumstances. We compared the findings of independent components analysis (ICA) and EEG-based GLM analyses of fMRI data from eight patients with focal epilepsy. Spatial ICA was used to extract independent components (IC) which were automatically classified as either BOLD-related, motion artefacts, EPI-susceptibility artefacts, large blood vessels, noise at high spatial or temporal frequency. The classifier reduced the number of candidate IC by 78%, with an average of 16 BOLD-related IC. Concordance between the ICA and GLM-derived results was assessed based on spatio-temporal criteria. In each patient, one of the IC satisfied the criteria to correspond to IED-based GLM result. The remaining IC were consistent with BOLD patterns of spontaneous brain activity and may include epileptic activity that was not evident on the scalp EEG. In conclusion, ICA of fMRI is capable of revealing areas of epileptic activity in patients with focal epilepsy and may be useful for the analysis of EEG-fMRI data in which abnormalities are not apparent on scalp EEG.

Epileptic networks in focal cortical dysplasia revealed using electroencephalography–functional magnetic resonance imaging

Surgical treatment of focal epilepsy in patients with focal cortical dysplasia (FCD) is most successful if all epileptogenic tissue is resected. This may not be evident on structural magnetic resonance imaging (MRI), so intracranial electroencephalography (icEEG) is needed to delineate the seizure onset zone (SOZ). EEG‐functional MRI (fMRI) can reveal interictal discharge (IED)‐related hemodynamic changes in the irritative zone (IZ). We assessed the value of EEG‐fMRI in patients with FCD‐associated focal epilepsy by examining the relationship between IED‐related hemodynamic changes, icEEG findings, and postoperative outcome.

Feasibility of simultaneous intracranial EEG-fMRI in humans: A safety study

In epilepsy patients who have electrodes implanted in their brains as part of their pre-surgical assessment, simultaneous intracranial EEG and fMRI (icEEG-fMRI) may provide important localising information and improve understanding of the underlying neuropathology. However, patient safety during icEEG-fMRI has not been addressed. Here the potential health hazards associated with icEEG-fMRI were evaluated theoretically and the main risks identified as: mechanical forces on electrodes from transient magnetic effects, tissue heating due to interaction with the pulsed RF fields and tissue stimulation due to interactions with the switched magnetic gradient fields. These potential hazards were examined experimentally in vitro on a Siemens 3 T Trio, 1.5 T Avanto and a GE 3 T Signa Excite scanner using a Brain Products MR compatible EEG system. No electrode flexion was observed. Temperature measurements demonstrated that heating well above guideline limits can occur. However heating could be kept within safe limits (<1.0 degrees C) by using a head transmit RF coil, ensuring EEG cable placement to exit the RF coil along its central z-axis, using specific EEG cable lengths and limiting MRI sequence specific absorption rates (SARs). We found that the risk of tissue damage due to RF-induced heating is low provided implant and scanner specific SAR limits are observed with a safety margin used to account for uncertainties (e.g. in scanner-reported SAR). The observed scanner gradient switching induced current (0.08 mA) and charge density (0.2 microC/cm(2)) were well within safety limits (0.5 mA and 30 microC/cm(2), respectively). Site-specific testing and a conservative approach to safety are required to avoid the risk of adverse events.

Imaging haemodynamic changes related to seizures: comparison of EEG-based general linear model, independent component analysis of fMRI and intracranial EEG.

BACKGROUND Simultaneous EEG-fMRI can reveal haemodynamic changes associated with epileptic activity which may contribute to understanding seizure onset and propagation. METHODS Nine of 83 patients with focal epilepsy undergoing pre-surgical evaluation had seizures during EEG-fMRI and analysed using three approaches, two based on the general linear model (GLM) and one using independent component analysis (ICA): The results were compared with intracranial EEG. RESULTS The canonical GLM analysis revealed significant BOLD signal changes associated with seizures on EEG in 7/9 patients, concordant with the seizure onset zone in 4/7. The Fourier GLM analysis revealed changes in BOLD signal corresponding with the results of the canonical analysis in two patients. ICA revealed components spatially concordant with the seizure onset zone in all patients (8/9 confirmed by intracranial EEG). CONCLUSION Ictal EEG-fMRI visualises plausible seizure related haemodynamic changes. The GLM approach to analysing EEG-fMRI data reveals localised BOLD changes concordant with the ictal onset zone when scalp EEG reflects seizure onset. ICA provides additional information when scalp EEG does not accurately reflect seizures and may give insight into ictal haemodynamics.

Safety of localizing epilepsy monitoring intracranial electroencephalograph electrodes using MRI: Radiofrequency-induced heating

To investigate heating during postimplantation localization of intracranial electroencephalograph (EEG) electrodes by MRI.

Continuous EEG source imaging enhances analysis of EEG-fMRI in focal epilepsy ☆

INTRODUCTION EEG-correlated fMRI (EEG-fMRI) studies can reveal haemodynamic changes associated with Interictal Epileptic Discharges (IED). Methodological improvements are needed to increase sensitivity and specificity for localising the epileptogenic zone. We investigated whether the estimated EEG source activity improved models of the BOLD changes in EEG-fMRI data, compared to conventional << event-related >> designs based solely on the visual identification of IED. METHODS Ten patients with pharmaco-resistant focal epilepsy underwent EEG-fMRI. EEG Source Imaging (ESI) was performed on intra-fMRI averaged IED to identify the irritative zone. The continuous activity of this estimated IED source (cESI) over the entire recording was used for fMRI analysis (cESI model). The maps of BOLD signal changes explained by cESI were compared to results of the conventional IED-related model. RESULTS ESI was concordant with non-invasive data in 13/15 different types of IED. The cESI model explained significant additional BOLD variance in regions concordant with video-EEG, structural MRI or, when available, intracranial EEG in 10/15 IED. The cESI model allowed better detection of the BOLD cluster, concordant with intracranial EEG in 4/7 IED, compared to the IED model. In 4 IED types, cESI-related BOLD signal changes were diffuse with a pattern suggestive of contamination of the source signal by artefacts, notably incompletely corrected motion and pulse artefact. In one IED type, there was no significant BOLD change with either model. CONCLUSION Continuous EEG source imaging can improve the modelling of BOLD changes related to interictal epileptic activity and this may enhance the localisation of the irritative zone.