Dynamic coupling between fMRI local connectivity and interictal EEG in focal epilepsy: A wavelet analysis approach

Abstract

Simultaneous scalp EEG-fMRI recording is a noninvasive neuroimaging technique for combining electrophysiological and hemodynamic aspects of brain function. Despite the time-varying nature of both measurements, their relationship is usually considered as time-invariant. The aim of this study was to detect direct associations between scalp-recorded EEG and regional changes of hemodynamic brain connectivity in focal epilepsy through a time-frequency paradigm. To do so, we developed a voxel-wise framework that analyses wavelet coherence between dynamic regional phase synchrony (DRePS, calculated from fMRI) and band amplitude fluctuation (BAF) of a target EEG electrode with dominant interictal epileptiform discharges (IEDs). As a proof of concept, we applied this framework to seven patients with focal epilepsy. The analysis produced patient-specific spatial maps of DRePS-BAF coupling, which highlight regions with a strong link between EEG power and local fMRI connectivity. Although we observed DRePS-BAF coupling proximate to the suspected seizure onset zone in some patients, our results suggest that DRePS-BAF is more likely to identify wider 'epileptic networks'. We also compared DRePS-BAF with standard EEG-fMRI analysis based on general linear modelling (GLM). There was, in general, little overlap between the DRePS-BAF maps and GLM maps. However, in some subjects the spatial clusters revealed by these two analyses appeared to be adjacent, particularly in medial posterior cortices. Our findings suggest that (1) there is a strong time-varying relationship between local fMRI connectivity and interictal EEG power in focal epilepsy, and (2) that DRePS-BAF reflect different aspects of epileptic network activity than standard EEG-fMRI analysis. These two techniques, therefore, appear to be complementary. Hum Brain Mapp 38:5356-5374, 2017. © 2017 Wiley Periodicals, Inc.

Pushing the Limits of EEG: Estimation of Large-Scale Functional Brain Networks and Their Dynamics Validated by Simultaneous fMRI

“…Although unreported so far, the match between dFC states derived from fMRI and EEG data found in the present study was somewhat expected, considering that a number of studies have already found EEG correlates of dFC fluctuations and brain states measured with simultaneous fMRI (Tagliazucchi and Laufs, 2015), motivated by the yet unclear physiological underpinnings of dFC (Thompson, 2018). These studies were mainly focused on healthy subjects (Chang and Glover, 2010;Allen et al, 2017) and epilepsy patients (Laufs et al, 2014;Lopes et al, 2014;Preti et al, 2014;Omidvarnia et al, 2017;Abreu et al, 2019). Interestingly, when comparing the contrast of interest for mapping the FEPN with the contribution over time of each fMRI and EEG dFC state, we found that the contribution of two matched dFC states based on their spatial correlation were significantly correlated with the FEPN contrast.…”

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

“…For this purpose, long periods in-between epileptic events are necessary in order to define the baseline, which may not be feasible in many cases due to the spontaneous, and often almost continuous, nature of epileptic activity. Only in two reports was the EEG information truly integrated, by comparing an EEG-derived epilepsy-related metric with the dFC time-course computed for each pair of AAL regions, by using Pearson correlation 17 or wavelet coherence 28 . This yields a single correlation/coherence matrix and the regions specifically associated with epileptic activity are then identified through appropriate thresholding.…”

“…In the context of epilepsy, standard EEG-correlated fMRI analyses have provided crucial insights into the brain areas involved in the generation of epileptic activity 2–6,8,24 . Motivated by this observation and the categorization of epilepsy as a network disease 25 , a few studies have integrated simultaneously recorded EEG information onto the dFC analyses in order to identify network connectivity changes associated with epileptic activity 17,26–28 . Apart from two studies 17,28 , the information retrieved from the EEG data has typically only been used to guide the dFC analysis rather than being fully integrated in the dFC estimation 26,27 .…”

Higher Sensitivity and Reproducibility of Wavelet-Based Amplitude of Resting-State fMRI

“…2 http://www.fil.ion.ucl.ac.uk/spm/software/spm8/ where x(t) denotes the time series, ψ k,s (t) denotes the mother wavelet function, s denotes wavelet scale (64 frequency bins in the current study, between 0 and 0.25 Hz at an interval of 0.0039 Hz), k denotes the localized time index (k ∈ [1, 170] and [1,230] for EOEC dataset and ADHD-200 dataset, respectively), and * denotes the complex conjugate (Poza et al, 2014;Morabito et al, 2017). We used five mother wavelets which have been used in previous fMRI literature, including db2 (Bullmore et al, 2004;Salomon et al, 2011;Zhang et al, 2016), bior4.4 (Laine, 2000;Van De Ville et al, 2003;Mutihac, 2006), morl (Chang and Glover, 2010;Bajaj et al, 2013;Omidvarnia et al, 2017;Yaesoubi et al, 2017), meyr (Behjat et al, 2015), and sym3 (Khullar et al, 2011). The traces for the five wavelets are shown in Supplementary Figures S1-S5, respectively.…”

“…In the present study, we applied five mother wavelets, namely, Daubechies 2 (db2) (Bullmore et al, 2004;Salomon et al, 2011;Zhang et al, 2016), biorthogonal 4.4 (bior4.4) (Laine, 2000;Van De Ville et al, 2003;Mutihac, 2006), Morlet (morl) (Chang and Glover, 2010;Bajaj et al, 2013;Omidvarnia et al, 2017;Yaesoubi et al, 2017), Meyer (meyr) (Behjat et al, 2015), and Symlets 3 (sym3) (Khullar et al, 2011) to calculate ALFF and compared the sensitivity and reproducibility between Wavelet-ALFF and FFT-ALFF in multiple frequency bands and multiple cohorts to explore whether Wavelet-ALFF is superior to FFT-ALFF and as to which mother wavelet is more superior.…”