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. © 2009 Vaudano et al

Thalamocortical relationship in epileptic patients with generalized spike and wave discharges — A multimodal neuroimaging study

AbstractUnlike 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

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): 1. EEGs were divided into up to three phases: early ictal EEG change, clinical seizure onset and late ictal EEG change and convolved with a canonical haemodynamic response function (HRF) (canonical GLM analysis). 2. Seizures lasting three scans or longer were additionally modelled using a Fourier basis set across the entire event (Fourier GLM analysis). 3. Independent component analysis (ICA) was applied to the fMRI data to identify ictal BOLD patterns without EEG. 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

Network Connectivity in Epilepsy: Resting State fMRI and EEG-fMRI Contributions.

There is a growing body of evidence pointing toward large-scale networks underlying the core phenomena in epilepsy, from seizure generation to cognitive dysfunction or response to treatment. The investigation of networks in epilepsy has become a key concept to unlock a deeper understanding of the disease. Functional imaging can provide valuable information to characterize network dysfunction; in particular resting state fMRI (RS-fMRI), which is increasingly being applied to study brain networks in a number of diseases. In patients with epilepsy, network connectivity derived from RS-fMRI has found connectivity abnormalities in a number of networks; these include the epileptogenic, cognitive and sensory processing networks. However, in majority of these studies, the effect of epileptic transients in the connectivity of networks has been neglected. EEG-fMRI has frequently shown networks related to epileptic transients that in many cases are concordant with the abnormalities shown in RS studies. This points toward a relevant role of epileptic transients in the network abnormalities detected in RS-fMRI studies. In this review, we summarize the network abnormalities reported by these two techniques side by side, provide evidence of their overlapping findings, and discuss their significance in the context of the methodology of each technique. A number of clinically relevant factors that have been associated with connectivity changes are in turn associated with changes in the frequency of epileptic transients. These factors include different aspects of epilepsy ranging from treatment effects, cognitive processes, or transition between different alertness states (i.e., awake-sleep transition). For RS-fMRI to become a more effective tool to investigate clinically relevant aspects of epilepsy it is necessary to understand connectivity changes associated with epileptic transients, those associated with other clinically relevant factors and the interaction between them, which represents a gap in the current literature. We propose a framework for the investigation of network connectivity in patients with epilepsy that can integrate epileptic processes that occur across different time scales such as epileptic transients and disease duration and the implications of this approach are discussed

Network Connectivity in Epilepsy: Resting State fMRI and EEG-fMRI Contributions.

There is a growing body of evidence pointing toward large-scale networks underlying the core phenomena in epilepsy, from seizure generation to cognitive dysfunction or response to treatment. The investigation of networks in epilepsy has become a key concept to unlock a deeper understanding of the disease. Functional imaging can provide valuable information to characterize network dysfunction; in particular resting state fMRI (RS-fMRI), which is increasingly being applied to study brain networks in a number of diseases. In patients with epilepsy, network connectivity derived from RS-fMRI has found connectivity abnormalities in a number of networks; these include the epileptogenic, cognitive and sensory processing networks. However, in majority of these studies, the effect of epileptic transients in the connectivity of networks has been neglected. EEG-fMRI has frequently shown networks related to epileptic transients that in many cases are concordant with the abnormalities shown in RS studies. This points toward a relevant role of epileptic transients in the network abnormalities detected in RS-fMRI studies. In this review, we summarize the network abnormalities reported by these two techniques side by side, provide evidence of their overlapping findings, and discuss their significance in the context of the methodology of each technique. A number of clinically relevant factors that have been associated with connectivity changes are in turn associated with changes in the frequency of epileptic transients. These factors include different aspects of epilepsy ranging from treatment effects, cognitive processes, or transition between different alertness states (i.e., awake-sleep transition). For RS-fMRI to become a more effective tool to investigate clinically relevant aspects of epilepsy it is necessary to understand connectivity changes associated with epileptic transients, those associated with other clinically relevant factors and the interaction between them, which represents a gap in the current literature. We propose a framework for the investigation of network connectivity in patients with epilepsy that can integrate epileptic processes that occur across different time scales such as epileptic transients and disease duration and the implications of this approach are discussed

Dynamic brain network states in human generalized spike-wave discharges.

Generalized spike-wave discharges in idiopathic generalized epilepsy are conventionally assumed to have abrupt onset and offset. However, in rodent models, discharges emerge during a dynamic evolution of brain network states, extending several seconds before and after the discharge. In human idiopathic generalized epilepsy, simultaneous EEG and functional MRI shows cortical regions may be active before discharges, and network connectivity around discharges may not be normal. Here, in human idiopathic generalized epilepsy, we investigated whether generalized spike-wave discharges emerge during a dynamic evolution of brain network states. Using EEG-functional MRI, we studied 43 patients and 34 healthy control subjects. We obtained 95 discharges from 20 patients. We compared data from patients with discharges with data from patients without discharges and healthy controls. Changes in MRI (blood oxygenation level-dependent) signal amplitude in discharge epochs were observed only at and after EEG onset, involving a sequence of parietal and frontal cortical regions then thalamus (P < 0.01, across all regions and measurement time points). Examining MRI signal phase synchrony as a measure of functional connectivity between each pair of 90 brain regions, we found significant connections (P < 0.01, across all connections and measurement time points) involving frontal, parietal and occipital cortex during discharges, and for 20 s after EEG offset. This network prominent during discharges showed significantly low synchrony (below 99% confidence interval for synchrony in this network in non-discharge epochs in patients) from 16 s to 10 s before discharges, then ramped up steeply to a significantly high level of synchrony 2 s before discharge onset. Significant connections were seen in a sensorimotor network in the minute before discharge onset. This network also showed elevated synchrony in patients without discharges compared to healthy controls (P = 0.004). During 6 s prior to discharges, additional significant connections to this sensorimotor network were observed, involving prefrontal and precuneus regions. In healthy subjects, significant connections involved a posterior cortical network. In patients with discharges, this posterior network showed significantly low synchrony during the minute prior to discharge onset. In patients without discharges, this network showed the same level of synchrony as in healthy controls. Our findings suggest persistently high sensorimotor network synchrony, coupled with transiently (at least 1 min) low posterior network synchrony, may be a state predisposing to generalized spike-wave discharge onset. Our findings also show that EEG onset and associated MRI signal amplitude change is embedded in a considerably longer period of evolving brain network states before and after discharge events

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

Epileptic encephalopathy with continuous spikes and waves during slow sleep is an age-related disorder characterized by the presence of interictal epileptiform discharges during at least >85% of sleep and cognitive deficits associated with this electroencephalography pattern. The pathophysiological mechanisms of continuous spikes and waves during slow sleep and neuropsychological deficits associated with this condition are still poorly understood. Here, we investigated the haemodynamic changes associated with epileptic activity using simultaneous acquisitions of electroencephalography and functional magnetic resonance imaging in 12 children with symptomatic and cryptogenic continuous spikes and waves during slow sleep. We compared the results of magnetic resonance to electric source analysis carried out using a distributed linear inverse solution at two time points of the averaged epileptic spike. All patients demonstrated highly significant spike-related positive (activations) and negative (deactivations) blood oxygenation-level-dependent changes (P < 0.05, family-wise error corrected). The activations involved bilateral perisylvian region and cingulate gyrus in all cases, bilateral frontal cortex in five, bilateral parietal cortex in one and thalamus in five cases. Electrical source analysis demonstrated a similar involvement of the perisylvian brain regions in all patients, independent of the area of spike generation. The spike-related deactivations were found in structures of the default mode network (precuneus, parietal cortex and medial frontal cortex) in all patients and in caudate nucleus in four. Group analyses emphasized the described individual differences. Despite aetiological heterogeneity, patients with continuous spikes and waves during slow sleep were characterized by activation of the similar neuronal network: perisylvian region, insula and cingulate gyrus. Comparison with the electrical source analysis results suggests that the activations correspond to both initiation and propagation pathways. The deactivations in structures of the default mode network are consistent with the concept of epileptiform activity impacting on normal brain function by inducing repetitive interruptions of neurophysiological functio

BOLD-fMRI activity informed by network variation of scalp EEG in juvenile myoclonic epilepsy

Epilepsy is marked by hypersynchronous bursts of neuronal activity, and seizures can propagate variably to any and all areas, leading to brain network dynamic organization. However, the relationship between the network characteristics of scalp EEG and blood oxygenation level-dependent (BOLD) responses in epilepsy patients is still not well known. In this study, simultaneous EEG and fMRI data were acquired in 18 juvenile myoclonic epilepsy (JME) patients. Then, the adapted directed transfer function (ADTF) values between EEG electrodes were calculated to define the time-varying network. The variation of network information flow within sliding windows was used as a temporal regressor in fMRI analysis to predict the BOLD response. To investigate the EEG-dependent functional coupling among the responding regions, modulatory interactions were analyzed for network variation of scalp EEG and BOLD time courses. The results showed that BOLD activations associated with high network variation were mainly located in the thalamus, cerebellum, precuneus, inferior temporal lobe and sensorimotor-related areas, including the middle cingulate cortex (MCC), supplemental motor area (SMA), and paracentral lobule. BOLD deactivations associated with medium network variation were found in the frontal, parietal, and occipital areas. In addition, modulatory interaction analysis demonstrated predominantly directional negative modulation effects among the thalamus, cerebellum, frontal and sensorimotor-related areas. This study described a novel method to link BOLD response with simultaneous functional network organization of scalp EEG. These findings suggested the validity of predicting epileptic activity using functional connectivity variation between electrodes. The functional coupling among the thalamus, frontal regions, cerebellum and sensorimotor-related regions may be characteristically involved in epilepsy generation and propagation, which provides new insight into the pathophysiological mechanisms and intervene targets for JME

An EEG-fMRI Study on the Termination of Generalized Spike-And-Wave Discharges in Absence Epilepsy

INTRODUCTION: Different studies have investigated by means of EEG-fMRI coregistration the brain networks related to generalized spike-and-wave discharges (GSWD) in patients with idiopathic generalized epilepsy (IGE). These studies revealed a widespread GSWD-related neural network that involves the thalamus and regions of the default mode network. In this study we investigated which brain regions are critically involved in the termination of absence seizures (AS) in a group of IGE patients. METHODS: Eighteen patients (6 male; mean age 25 years) with AS were included in the EEG-fMRI study. Functional data were acquired at 3T with continuous simultaneous video-EEG recording. Event-related analysis was performed with SPM8 software, using the following regressors: (1) GSWD onset and duration; (2) GSWD offset. Data were analyzed at single-subject and at group level with a second level random effect analysis. RESULTS: A mean of 17 events for patient was recorded (mean duration of 4.2 sec). Group-level analysis related to GSWD onset respect to rest confirmed previous findings revealing thalamic activation and a precuneus/posterior cingulate deactivation. At GSWD termination we observed a decrease in BOLD signal over the bilateral dorsolateral frontal cortex respect to the baseline (and respect to GSWD onset). The contrast GSWD offset versus onset showed a BOLD signal increase over the precuneus-posterior cingulate region bilaterally. Parametric correlations between electro-clinical variables and BOLD signal at GSWD offset did not reveal significant effects. CONCLUSION: The role of the decreased neural activity of lateral prefrontal cortex at GSWD termination deserve future investigations to ascertain if it has a role in promoting the discharge offset, as well as in the determination of the cognitive deficits often present in patients with AS. The increased BOLD signal at precuneal/posterior cingulate cortex might reflect the recovery of neural activity in regions that are "suspended" during spike and waves activity, as previously hypothesized