Dynamic Causal Modelling of epileptic seizure propagation pathways: A combined EEG–fMRI study

Murta, Teresa; Leal, Alberto; Garrido, Marta I.; Figueiredo, Patrícia

doi:10.1016/j.neuroimage.2012.05.053

Cited by 66 publications

(72 citation statements)

References 49 publications

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“…While our patient stated that he neither especially liked nor disliked rap music, he indicated that the auras were accompanied by a rather unpleasant feeling, which might find its correlate in the activation of the right mesial temporal lobe during the seizure. DCM for fMRI has already been successfully used to investigate effective connectivity in epileptic networks identified with EEG-fMRI on a single-subject patient-specific level (Murta et al, 2012;Vaudano et al, 2012Vaudano et al, , 2013 as well as in a small group of patients with idiopathic generalized epilepsy (Vaudano et al, 2009). In these cases, the primary source and propagation pathway of epileptic discharges from fMRI data were determined by testing a set of clinically plausible network connectivity models.…”

Section: Dynamic Causal Modelingmentioning

confidence: 99%

Multimodal effective connectivity analysis reveals seizure focus and propagation in musicogenic epilepsy

et al. 2015

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Dynamic causal modeling (DCM) is a method to non-invasively assess effective connectivity between brain regions. 'Musicogenic epilepsy' is a rare reflex epilepsy syndrome in which seizures can be elicited by musical stimuli and thus represents a unique possibility to investigate complex human brain networks and test connectivity analysis tools. We investigated effective connectivity in a case of musicogenic epilepsy using DCM for fMRI, high-density (hd-) EEG and MEG and validated results with intracranial EEG recordings. A patient with musicogenic seizures was examined using hd-EEG/fMRI and simultaneous '256-channel hd-EEG'/ 'whole head MEG' to characterize the epileptogenic focus and propagation effects using source analysis techniques and DCM. Results were validated with invasive EEG recordings. We recorded one seizure with hd-EEG/fMRI and four auras with hd-EEG/MEG. During the seizures, increases of activity could be observed in the right mesial temporal region as well as bilateral mesial frontal regions. Effective connectivity analysis of fMRI and hd-EEG/MEG indicated that right mesial temporal neuronal activity drives changes in the frontal areas consistently in all three modalities, which was confirmed by the results of invasive EEG recordings. Seizures thus seem to originate in the right mesial temporal lobe and propagate to mesial frontal regions. Using DCM for fMRI, hd-EEG and MEG we were able to correctly localize focus and propagation of epileptic activity and thereby characterize the underlying epileptic network in a patient with musicogenic epilepsy. The concordance between all three functional modalities validated by invasive monitoring is noteworthy, both for epileptic activity spread as well as for effective connectivity analysis in general.© 2015 Elsevier Inc. All rights reserved. IntroductionMany physiological and pathological processes of the human brain are driven by networks and connectivity between brain regions. Thus, analysis of these connections is of particular importance in neurosciences and several methods have been proposed to study connectivity in-vivo by means of functional imaging. One of these is dynamic causal modeling (DCM), a method to assess the effective connectivity between brain regions, i.e. the causal influence that one neuronal system exerts over others. It was first developed for fMRI with the aim to estimate the parameters of a neuronal system model from which a blood oxygenation level dependent (BOLD) signal can be predicted that corresponds as closely as possible to the measured BOLD time series (Friston et al., 2003). Later, DCM was extended to the analysis of effective connectivity in EEG and MEG data taking into account the very high temporal resolution of these techniques and thereby combining a spatial forward model with a biologically informed temporal forward model (Kiebel et al., 2008). DCM can be used to test which brain region drives which by constructing different models of interacting regions or nodes and identifying the best one by model comparison. Thi...

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Section: Dynamic Causal Modelingmentioning

confidence: 99%

Multimodal effective connectivity analysis reveals seizure focus and propagation in musicogenic epilepsy

et al. 2015

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“…Prior work has also employed DCM analysis to examine hypothalamic efferents and epileptogenic signal propagation in hamartoma-related seizure (Murta et al, 2012). There, directionality was resolved by measuring ascending and descending pathways, with concurrent consideration of corticocortical connections.…”

Section: Box 2 Ongoing and Completed Trials For The Development Of Dbmentioning

confidence: 99%

“…This recent DCM analysis of seizure progression offers a nice analogy to the questions one could pose regarding abnormal AD pathways that link cortical and/or subcortical regions. Murta et al (2012) tested long-standing and competing hypotheses regarding the pathways and epileptic signal propagation from hypothalamic regions to frontal regions in a single patient with hypothalamic hamartoma-induced seizures. Using DCM for fMRI, they found that the probable connectivity architecture involved a pathway from the hypothalamus to frontal cortex via a cingulate region, rather than a direct projection from hypothalamus to frontal cortex.…”

Section: Box 2 Ongoing and Completed Trials For The Development Of Dbmentioning

confidence: 99%

Deep brain stimulation for neurodegenerative disease

Moran

2015

Progress in Brain Research

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“…Over the past two decades, the simultaneous acquisition of the electroencephalogram (EEG) with functional magnetic resonance imaging (fMRI) has had an important role in the investigation of human brain function, particularly in the clinical context of epilepsy (Ritter and Villringer 2006;Laufs 2008;Vulliemoz et al 2010;Gotman and Pittau 2011;Murta et al 2012;Chaudhary et al 2013). This noninvasive multimodal imaging technique has been particularly useful for the mapping of haemodynamic changes over the whole brain associated with spontaneous activity (including inter-trial variability) observed on EEG, taking advantage of the complementary nature and spatiotemporal properties of the EEG and fMRI signals (Rosa et al 2010a;Jorge et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Characterisation and Reduction of the EEG Artefact Caused by the Helium Cooling Pump in the MR Environment: Validation in Epilepsy Patient Data

et al. 2014

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The EEG acquired simultaneously with fMRI is distorted by a number of artefacts related to the presence of strong magnetic fields, which must be reduced in order to allow for a useful interpretation and quantification of the EEG data. For the two most prominent artefacts, associated with magnetic field gradient switching and the heart beat, reduction methods have been developed and applied successfully. However, a number of artefacts related to the MR-environment can be found to distort the EEG data acquired even without ongoing fMRI acquisition. In this paper, we investigate the most prominent of those artefacts, caused by the Helium cooling pump, and propose a method for its reduction and respective validation in data collected from epilepsy patients. Since the Helium cooling pump artefact was found to be repetitive, an average template subtraction method was developed for its reduction with appropriate adjustments for minimizing the degradation of the physiological part of the signal. The new methodology was validated in a group of 15 EEG-fMRI datasets collected from six consecutive epilepsy patients, where it successfully reduced the amplitude of the artefact spectral peaks by 95 ± 2 % while the background spectral amplitude within those peaks was reduced by only -5 ± 4 %. Although the Helium cooling pump should ideally be switched off during simultaneous EEG-fMRI acquisitions, we have shown here that in cases where this is not possible the associated artefact can be effectively reduced in post processing.

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Dynamic Causal Modelling of epileptic seizure propagation pathways: A combined EEG–fMRI study

Cited by 66 publications

References 49 publications

Multimodal effective connectivity analysis reveals seizure focus and propagation in musicogenic epilepsy

Multimodal effective connectivity analysis reveals seizure focus and propagation in musicogenic epilepsy

Deep brain stimulation for neurodegenerative disease

Characterisation and Reduction of the EEG Artefact Caused by the Helium Cooling Pump in the MR Environment: Validation in Epilepsy Patient Data

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