EEG synchronization measures predict epilepsy-related BOLD-fMRI fluctuations better than commonly used univariate metrics

Abreu, Rodolfo; Leal, Alberto; Silva, Fernando Lopes da; Figueiredo, Patrícia

doi:10.1016/j.clinph.2017.12.038

Cited by 31 publications

(34 citation statements)

References 89 publications

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“…The rationale underlying the choice of method for the extraction of such features mainly depends on the type of activity of interest. Epileptic activity is particularly relevant in the scope of simultaneous EEG-fMRI studies, given the suitability of this technique for the localization of brain networks associated with epileptic discharges, and therefore extensive literature on the extraction of epilepsy-related EEG features predictive of BOLD fluctuations is available ( Gotman et al, 2006 ; Marques et al, 2009 ; Mulert and Lemieux, 2009 ; Gotman and Pittau, 2011 ; Laufs, 2012 ; Jorge et al, 2014 ; Murta et al, 2015 ; Abreu et al, 2018 ).…”

Section: Eeg-informed Fmri Integration Methodsmentioning

confidence: 99%

“…In a resting-state study, GFS measures within the lower (8.5–10.5 Hz) and upper (10.5–12.5 Hz) alpha band were found to be positively correlated with the BOLD-fMRI-derived dorsal attention network and the default mode network, respectively ( Jann et al, 2009 ). More recently, a study by our group applied both PSI and GFS in an EEG-fMRI study of epilepsy ( Abreu et al, 2018 ). We showed that PSI within a frequency band of interest outperformed power-weighted metrics, as well as GFS, in predicting BOLD changes associated with epileptic activity.…”

Section: Eeg-informed Fmri Integration Methodsmentioning

confidence: 99%

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EEG-Informed fMRI: A Review of Data Analysis Methods

Abreu

Leal

Figueiredo

2018

Front. Hum. Neurosci.

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The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.

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Section: Eeg-informed Fmri Integration Methodsmentioning

confidence: 99%

Section: Eeg-informed Fmri Integration Methodsmentioning

confidence: 99%

EEG-Informed fMRI: A Review of Data Analysis Methods

Abreu

Leal

Figueiredo

2018

Front. Hum. Neurosci.

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143

126

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“…In fact, while EEG can sample the relatively fast electrical oscillations abnormally generated by hypersynchronized populations of neurons, the remarkable spatial resolution of fMRI enables their accurate localization in the brain 1–4 . Typically, features representative of epileptic activity are extracted from the EEG data and brain regions exhibiting correlated BOLD signal changes are identified as the epileptic network 5–8 . This is especially important in the case of patients with drug-refractory focal epilepsy underdoing evaluation for surgical treatment 9–13 .…”

Section: Introductionmentioning

confidence: 99%

Identification of epileptic brain states by dynamic functional connectivity analysis of simultaneous EEG-fMRI: a dictionary learning approach

Abreu

Leal

Figueiredo

2019

Sci Rep

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Most fMRI studies of the brain’s intrinsic functional connectivity (FC) have assumed that this is static; however, it is now clear that it changes over time. This is particularly relevant in epilepsy, which is characterized by a continuous interchange between epileptic and normal brain states associated with the occurrence of epileptic activity. Interestingly, recurrent states of dynamic FC (dFC) have been found in fMRI data using unsupervised learning techniques, assuming either their sparse or non-sparse combination. Here, we propose an l1-norm regularized dictionary learning (l1-DL) approach for dFC state estimation, which allows an intermediate and flexible degree of sparsity in time, and demonstrate its application in the identification of epilepsy-related dFC states using simultaneous EEG-fMRI data. With this l1-DL approach, we aim to accommodate a potentially varying degree of sparsity upon the interchange between epileptic and non-epileptic dFC states. The simultaneous recording of the EEG is used to extract time courses representative of epileptic activity, which are incorporated into the fMRI dFC state analysis to inform the selection of epilepsy-related dFC states. We found that the proposed l1-DL method performed best at identifying epilepsy-related dFC states, when compared with two alternative methods of extreme sparsity (k-means clustering, maximum; and principal component analysis, minimum), as well as an l0-norm regularization framework (l0-DL), with a fixed amount of temporal sparsity. We further showed that epilepsy-related dFC states provide novel insights into the dynamics of epileptic networks, which go beyond the information provided by more conventional EEG-correlated fMRI analysis, and which were concordant with the clinical profile of each patient. In addition to its application in epilepsy, our study provides a new dFC state identification method of potential relevance for studying brain functional connectivity dynamics in general.

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“…It has been hypothesized that functional techniques such as arterial spin labelling and blood oxygen-level dependent imaging (BOLD) would work synergistically with PET, which assesses interictal metabolism. To some extent, this has been performed with PET used together with BOLD [13,14] MR imaging during epilepsy imaging evaluation to select patients for resection to lower risk of loss of valuable memory or language function. However, the most common non-conventional imaging MR sequence, diffusion-weighted imaging is rarely utilized [15].…”

Section: Discussionmentioning

confidence: 99%