Dataset Information

Independent component analysis reveals dynamic ictal BOLD responses in EEG-fMRI data from focal epilepsy patients.

ABSTRACT:

Introduction

Seizures occur rarely during EEG-fMRI acquisitions of epilepsy patients, but can potentially offer a better estimation of the epileptogenic zone than interictal activity. Independent component analysis (ICA) is a data-driven method that imposes minimal constraints on the hemodynamic response function (HRF). In particular, the investigation of HRFs with clear peaks, but varying latency, may be used to differentiate the ictal focus from propagated activity.

Methods

ICA was applied on ictal EEG-fMRI data from 15 patients. Components related to seizures were identified by fitting an HRF to the component time courses at the time of the ictal EEG events. HRFs with a clear peak were used to derive maps of significant BOLD responses and their associated peak delay. The results were then compared with those obtained from a general linear model (GLM) method. Concordance with the presumed epileptogenic focus was also assessed.

Results

The ICA maps were significantly correlated with the GLM maps for each patient (Spearman's test, p<0.05). The ictal BOLD responses identified by ICA always included the presumed epileptogenic zone, but were also more widespread, accounting for 20.3% of the brain volume on average. The method provided a classification of the components as a function of peak delay. BOLD response clusters associated with early HRF peaks were concordant with the suspected epileptogenic focus, while subsequent HRF peaks may correspond to ictal propagation.

Conclusion

ICA applied to EEG-fMRI can detect areas of significant BOLD response to ictal events without having to predefine an HRF. By estimating the HRF peak time in each identified region, the method could also potentially provide a dynamic analysis of ictal BOLD responses, distinguishing onset from propagated activity.

SUBMITTER: LeVan P

PROVIDER: S-EPMC3779215 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

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Publications

Independent component analysis reveals dynamic ictal BOLD responses in EEG-fMRI data from focal epilepsy patients.

LeVan Pierre P Tyvaert Louise L Moeller Friederike F Gotman Jean J

NeuroImage 20090806 1

<h4>Introduction</h4>Seizures occur rarely during EEG-fMRI acquisitions of epilepsy patients, but can potentially offer a better estimation of the epileptogenic zone than interictal activity. Independent component analysis (ICA) is a data-driven method that imposes minimal constraints on the hemodynamic response function (HRF). In particular, the investigation of HRFs with clear peaks, but varying latency, may be used to differentiate the ictal focus from propagated activity.<h4>Methods</h4>ICA ...[more]

PMID: 19647798

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Project description:BackgroundEarly surgical intervention in children with drug resistant epilepsy has benefits but requires using tolerable and minimally invasive tests. EEG-fMRI studies have demonstrated good sensitivity for the localization of epileptic focus but a poor yield although the reasons for this have not been systematically addressed. While adults EEG-fMRI studies are performed in the "resting state"; children are commonly sedated however, this has associated risks and potential confounds. In this study, we assessed the impact of the following factors on the tolerability and results of EEG-fMRI in children: viewing a movie inside the scanner; movement; occurrence of interictal epileptiform discharges (IED); scan duration and design efficiency. This work's motivation is to optimize EEG-fMRI parameters to make this test widely available to paediatric population.MethodsForty-six children with focal epilepsy and 20 controls (6-18) underwent EEG-fMRI. For two 10 minutes sessions subjects were told to lie still with eyes closed, as it is classically performed in adult studies ("rest sessions"), for another two sessions, subjects watched a child friendly stimulation i.e. movie ("movie sessions"). IED were mapped with EEG-fMRI for each session and across sessions. The resulting maps were classified as concordant/discordant with the presumed epileptogenic focus for each subject.FindingsMovement increased with scan duration, but the movie reduced movement by ~40% when played within the first 20 minutes. There was no effect of movie on the occurrence of IED, nor in the concordance of the test. Ability of EEG-fMRI to map the epileptogenic region was similar for the 20 and 40 minute scan durations. Design efficiency was predictive of concordance.ConclusionsA child friendly natural stimulus improves the tolerability of EEG-fMRI and reduces in-scanner movement without having an effect on IED occurrence and quality of EEG-fMRI maps. This allowed us to scan children as young as 6 and obtain localising information without sedation. Our data suggest that ~20 minutes is the optimal length of scanning for EEG-fMRI studies in children with frequent IED. The efficiency of the fMRI design derived from spontaneous IED generation is an important factor for producing concordant results.

Project description: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.

Project description:Accurately characterising the brain networks involved in seizure activity may have important implications for our understanding of epilepsy. Intracranial EEG-fMRI can be used to capture focal epileptic events in humans with exquisite electrophysiological sensitivity and allows for identification of brain structures involved in this phenomenon over the entire brain. We investigated ictal BOLD networks using the simultaneous intracranial EEG-fMRI (icEEG-fMRI) in a 30 year-old male undergoing invasive presurgical evaluation with bilateral depth electrode implantations in amygdalae and hippocampi for refractory temporal lobe epilepsy. One spontaneous focal electrographic seizure was recorded. The aims of the data analysis were firstly to map BOLD changes related to the ictal activity identified on icEEG and secondly to compare different fMRI modelling approaches. Visual inspection of the icEEG showed an onset dominated by beta activity involving the right amygdala and hippocampus lasting 6.4 s (ictal onset phase), followed by gamma activity bilaterally lasting 14.8 s (late ictal phase). The fMRI data was analysed using SPM8 using two modelling approaches: firstly, purely based on the visually identified phases of the seizure and secondly, based on EEG spectral dynamics quantification. For the visual approach the two ictal phases were modelled as 'ON' blocks convolved with the haemodynamic response function; in addition the BOLD changes during the 30 s preceding the onset were modelled using a flexible basis set. For the quantitative fMRI modelling approach two models were evaluated: one consisting of the variations in beta and gamma bands power, thereby adding a quantitative element to the visually-derived models, and another based on principal components analysis of the entire spectrogram in attempt to reduce the bias associated with the visual appreciation of the icEEG. BOLD changes related to the visually defined ictal onset phase were revealed in the medial and lateral right temporal lobe. For the late ictal phase, the BOLD changes were remote from the SOZ and in deep brain areas (precuneus, posterior cingulate and others). The two quantitative models revealed BOLD changes involving the right hippocampus, amygdala and fusiform gyrus and in remote deep brain structures and the default mode network-related areas. In conclusion, icEEG-fMRI allowed us to reveal BOLD changes within and beyond the SOZ linked to very localised ictal fluctuations in beta and gamma activity measured in the amygdala and hippocampus. Furthermore, the BOLD changes within the SOZ structures were better captured by the quantitative models, highlighting the interest in considering seizure-related EEG fluctuations across the entire spectrum.

Project description:Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal intracranial EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well as the normal confounds of spatiotemporally varying neural activity and connectivity. We propose that comparing intracranial EEG recordings to a normative atlas of intracranial EEG activity and connectivity can reliably map abnormal regions, identify targets for invasive treatment and increase our understanding of human epilepsy. Merging data from the Penn Epilepsy Center and a public database from the Montreal Neurological Institute, we aggregated interictal intracranial EEG retrospectively across 166 subjects comprising >5000 channels. For each channel, we calculated the normalized spectral power and coherence in each canonical frequency band. We constructed an intracranial EEG atlas by mapping the distribution of each feature across the brain and tested the atlas against data from novel patients by generating a z-score for each channel. We demonstrate that for seizure onset zones within the mesial temporal lobe, measures of connectivity abnormality provide greater distinguishing value than univariate measures of abnormal neural activity. We also find that patients with a longer diagnosis of epilepsy have greater abnormalities in connectivity. By integrating measures of both single-channel activity and inter-regional functional connectivity, we find a better accuracy in predicting the seizure onset zones versus normal brain (area under the curve = 0.77) compared with either group of features alone. We propose that aggregating normative intracranial EEG data across epilepsy centres into a normative atlas provides a rigorous, quantitative method to map epileptic networks and guide invasive therapy. We publicly share our data, infrastructure and methods, and propose an international framework for leveraging big data in surgical planning for refractory epilepsy.

Dataset Information

Independent component analysis reveals dynamic ictal BOLD responses in EEG-fMRI data from focal epilepsy patients.

Introduction

Methods

Results

Conclusion

Publications

Independent component analysis reveals dynamic ictal BOLD responses in EEG-fMRI data from focal epilepsy patients.

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