Cortical and subcortical contributions to absence seizure onset examined with EEG/fMRI

Department of Neurology, University of Cincinnati Medical Center, Cincinnati, OH 45267-0525, USA.
Epilepsy & Behavior (Impact Factor: 2.26). 08/2010; 18(4):404-13. DOI: 10.1016/j.yebeh.2010.05.009
Source: PubMed


In patients with idiopathic generalized epilepsies (IGEs), bursts of generalized spike and wave discharges (GSWDs) lasting > or =2 seconds are considered absence seizures. The location of the absence seizures generators in IGEs is thought to involve interplay between various components of thalamocortical circuits; we have recently postulated that medication resistance may, in part, be related to the location of the GSWD generators [Szaflarski JP, Lindsell CJ, Zakaria T, Banks C, Privitera MD. Epilepsy Behav. 2010;17:525-30]. In the present study we hypothesized that patients with medication-refractory IGE (R-IGE) and continued absence seizures may have GSWD generators in locations other than the thalamus, as typically seen in patients with IGE. Hence, the objective of this study was to determine the location of the GSWD generators in patients with R-IGE using EEG/fMRI. Eighty-three patients with IGE received concurrent EEG/fMRI at 4 T. Nine of them (aged 15-55) experienced absence seizures during EEG/fMRI and were included; all were diagnosed with R-IGE. Subjects participated in up to three 20-minute EEG/fMRI sessions (400 volumes, TR=3 seconds) performed at 4 T. After removal of fMRI and ballistocardiographic artifacts, 36 absence seizures were identified. Statistical parametric maps were generated for each of these sessions correlating seizures to BOLD response. Timing differences between brain regions were tested using statistical parametric maps generated by modeling seizures with onset times shifted relative to the GSWD onsets. Although thalamic BOLD responses peaked approximately 6 seconds after the onset of absence seizures, other areas including the prefrontal and dorsolateral cortices showed brief and nonsustained peaks occurring approximately 2 seconds prior to the maximum of the thalamic peak. Temporal lobe peaks occurred at the same time as the thalamic peak, with a cerebellar peak occurring approximately 1 second later. Confirmatory analysis averaging cross-correlation between cortical and thalamic regions of interest across seizures corroborated these findings. Finally, Granger causality analysis showed effective connectivity directed from frontal lobe to thalamus, supporting the notion of earlier frontal than thalamic involvement. The results of this study support our original hypothesis and indicate that in the patients with R-IGE studied, absence seizures may be initiated by widespread cortical (frontal and parietal) areas and sustained in subcortical (thalamic) regions, suggesting that the examined patients have cortical onset epilepsy with propagation to thalamus.

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    • "More specifically, causality may be evaluated by comparing the variance of the residuals after an autoregressive (AR) application to the reference signal “A”, with the same variance being obtained when autoregression is evaluated on the past values of the signal “A” and the past values of the potentially causing signal “B”. GCA has been shown to be a viable technique for analyzing fMRI data [47]–[49] and to not vary after filtering [50]. Analysis of effective connectivity between the independent components was thus conducted using GCA, which models directional causality among multiple time series based on a variable autoregressive model [51]. "
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    ABSTRACT: Memory encoding engages multiple concurrent and sequential processes. While the individual processes involved in successful encoding have been examined in many studies, a sequence of events and the importance of modules associated with memory encoding has not been established. For this reason, we sought to perform a comprehensive examination of the network for memory encoding using data driven methods and to determine the directionality of the information flow in order to build a viable model of visual memory encoding. Forty healthy controls ages 19-59 performed a visual scene encoding task. FMRI data were preprocessed using SPM8 and then processed using independent component analysis (ICA) with the reliability of the identified components confirmed using ICASSO as implemented in GIFT. The directionality of the information flow was examined using Granger causality analyses (GCA). All participants performed the fMRI task well above the chance level (>90% correct on both active and control conditions) and the post-fMRI testing recall revealed correct memory encoding at 86.33±5.83%. ICA identified involvement of components of five different networks in the process of memory encoding, and the GCA allowed for the directionality of the information flow to be assessed, from visual cortex via ventral stream to the attention network and then to the default mode network (DMN). Two additional networks involved in this process were the cerebellar and the auditory-insular network. This study provides evidence that successful visual memory encoding is dependent on multiple modules that are part of other networks that are only indirectly related to the main process. This model may help to identify the node(s) of the network that are affected by a specific disease processes and explain the presence of memory encoding difficulties in patients in whom focal or global network dysfunction exists.
    PLoS ONE 10/2014; 9(10):e107761. DOI:10.1371/journal.pone.0107761 · 3.23 Impact Factor
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    • "Recently, in order to characterize the abnormal information flow, some approaches have been used in epilepsy patients or experimental protocols, such as non-linear regression [24]–[28], dynamic causal modeling [29]–[31] and Granger causality analysis (GCA) [32]–[35]. GCA has been proved helpful to identify the direction of seizure propagation [35], [36]. In a region-of-interest (ROI) based research, Morgan et al. [34] performed GCA between bilateral hippocampus in mTLE. "
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    ABSTRACT: Although mesial temporal lobe epilepsy (mTLE) is characterized by the pathological changes in mesial temporal lobe, function alteration was also found in extratemporal regions. Our aim is to investigate the information flow between the epileptogenic zone (EZ) and other brain regions. Resting-state functional magnetic resonance imaging (RS-fMRI) data were recorded from 23 patients with left mTLE and matched controls. We first identified the potential EZ using the amplitude of low-frequency fluctuation (ALFF) of RS-fMRI signal, then performed voxel-wise Granger causality analysis between EZ and the whole brain. Relative to controls, patients demonstrated decreased driving effect from EZ to thalamus and basal ganglia, and increased feedback. Additionally, we found an altered causal relation between EZ and cortical networks (default mode network, limbic system, visual network and executive control network). The influence from EZ to right precuneus and brainstem negatively correlated with disease duration, whereas that from the right hippocampus, fusiform cortex, and lentiform nucleus to EZ showed positive correlation. These findings demonstrate widespread brain regions showing abnormal functional interaction with EZ. In addition, increased ALFF in EZ was positively correlated with the increased driving effect on EZ in patients, but not in controls. This finding suggests that the initiation of epileptic activity depends not only on EZ itself, but also on the activity emerging in large-scale macroscopic brain networks. Overall, this study suggests that the causal topological organization is disrupted in mTLE, providing valuable information to understand the pathophysiology of this disorder.
    PLoS ONE 09/2013; 8(5):e63183. DOI:10.1371/journal.pone.0063183 · 3.23 Impact Factor
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    • "Sources frequently localized to the thalamus at the earliest time point prior to the spike (-50 ms), followed by very little source localization at the time of the spike (0 ms). These results are in agreement with some prior fMRI studies (Aghakhani et al., 2004; Archer et al., 2003; Bai et al., 2010; Carney et al., 2010; Gotman et al., 2005; Moeller et al., 2008, 2011; Nersesyan et al., 2004; Salek-Haddadi et al., 2003; Szaflarski et al., 2010; Tenney et al., 2003, 2004a,b; Tyvaert et al., 2009). The remaining cortical areas (parietal, temporal, occipital ) were gradually ''recruited'' following the spike. "
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