Complementary contributions of concurrent EEG and fMRI connectivity for predicting structural connectivity

Wirsich, Jonathan; Ridley, Ben; Besson, Pierre; Jirsa, Viktor K.; Bénar, Christian; Ranjeva, Jean‐Philippe; Guye, Maxime

doi:10.1016/j.neuroimage.2017.08.055

Cited by 57 publications

(126 citation statements)

References 76 publications

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“…Furthermore, a significant positive interaction term between ED and SC SI contributes to the prediction in both fMRI and EEG. The correlation between these two variables (ED and SC SI ) is 0.48, in line with previous findings (Wirsich et al, 2017) . Since SC and SC SI are negatively correlated (see Methods), this translates to two interpretations: ED has less of an impact on the FC between ROI pairs that have a strong SC-connection (high weight in the SC matrix); and the strength of the SC connection has less of an impact on the FC between ROI pairs that are far apart from each other (high ED).…”

Section: Sc Provides Additional Predictive Power For Eeg-fcsupporting

confidence: 92%

“…The results of the present study suggest that the relationship between SC and FC in EEG can be exploited in order to attenuate spurious statistical dependencies between source-level signals. We first replicate the previous finding (Chu et al, 2015;Finger et al, 2016;Siems et al, 2016;Wirsich et al, 2017) that SC partially shapes FC in EEG, beyond the impact of Euclidean distance induced by volume conduction. Subsequently, rather than applying signal orthogonalization or removing zero-lag-statistical interactions, we favor the approach of combining EEG with diffusion MRI (dMRI) and fMRI, modalities which have higher spatial resolution.…”

Section: Introductionsupporting

confidence: 80%

“…Concurring findings from these studies show that FC between regions of interest (ROIs)/sources located in the gray matter is in part shaped by anatomical connections of the SC, such that the strength of SC (fiber count, density) is predictive to some degree of the strength of FC (correlation, coherence, etc.) This finding has been shown to extend to EEG using data analytical (Chu et al, 2015;Wirsich et al, 2017) and modelling approaches (Bhattacharya, Coyle, & Maguire, 2011;de Haan, Mott, van Straaten, Scheltens, & Stam, 2012;Finger et al, 2016;Pons, Cantero, Atienza, & Garcia-Ojalvo, 2010;Ponten, Daffertshofer, Hillebrand, & Stam, 2010; van Dellen et al, 2013) . However, the details of how SC and FC are related in EEG are unknown, and few tools are available.…”

Section: Introductionmentioning

confidence: 74%

“…EEG-FC is computed between these average time courses of all pairs of ROIs as correlations between low-frequency power modulations (see Methods) in three standard frequency bands: alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-40 Hz). We first characterize the relationship between SC and EEG-FC averaged over all subjects (Chu et al, 2015;Finger et al, 2016;Wirsich et al, 2017) . We fit a stepwise general linear model (GLM) in order to quantify how well the following measures predict the FC: 1) SC in the form of search information (Goñi et al, 2014) , referred to as SC SI , a measure that is derived from fiber counts and that is non-zero for all connections, yielding a connectivity matrix that is dense just like the FC matrix ( Figure S8); the intuitive interpretation of search information is that it measures how "hidden" the shortest path between two ROIs is.…”

Section: Sc Provides Additional Predictive Power For Eeg-fcmentioning

confidence: 99%

“…Characteristics of the whole network or of single nodes can then be extracted, interpreted, and compared across subjects, groups, or between experimental conditions. These methods are now being translated to EEG research (Babiloni et al, 2005;Chen, Ros, & Gruzelier, 2013;Chu et al, 2015;Coito, Michel, Vulliemoz, & Plomp, 2019;Finger et al, 2016;Siems, Pape, Hipp, & Siegel, 2016;Wirsich et al, 2017) , but further validation and methodological developments, especially when it comes to resting state EEG, are necessary to make them more accessible for the community. Compared to fMRI, M/EEG measure neural signals more directly (Buzsáki, Anastassiou, & Koch, 2012) , and do so on a time scale (of milliseconds) much closer to that of neural events than fMRI (typically several 100 milliseconds to 2 seconds).…”

Section: Introductionmentioning

confidence: 99%

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Using structural connectivity to augment community structure in EEG functional connectivity

Glomb

Mullier

Carboni

et al. 2019

Preprint

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Recently, EEG recording techniques (high-density EEG) as well as source analysis have improved markedly. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG signals suffer from volume conduction, introducing spurious functional connections (statistical dependencies) between nearby sources. Genuine functional connectivity (FC) reflecting functional relationships is impossible to disentangle from spurious FC, impairing the applicability of network neuroscience to EEG. Here, we use information from white matter structural connectivity (SC) to attenuate the impact of volume conduction on EEG-FC. We confirm that FC (power envelope correlations) is predicted by the SC beyond the impact of Euclidean distance. We then smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions is increased while FC between non-connected regions remains unchanged. We hypothesize that this results in an increase in genuine relative to spurious FC. We analyze changes in FC due to smoothing by assessing the resemblance between EEG-and fMRI-FC, and find that smoothing increases resemblance, both in terms of overall correlation and community structure. This suggests that information from the SC can indeed be used to attenuate the effect of volume conduction on EEG source signals. Author summaryIn this study, we combine high-density EEG recorded during resting state with white matter connectivity obtained from diffusion MRI and fiber tracking. We leverage the additional information contained in the structural connectome towards augmenting the source level EEG functional connectivity. In particular, it is known -and confirmed in this study -that the activity of brain regions that possess a direct anatomical connection is, on average, more strongly correlated than that of regions that have no such direct link. We use the structural connectome to define a graph and smooth the source reconstructed EEG signal in the space spanned by this graph. We compare the resulting "filtered" signal correlation matrices to those obtained from fMRI and find that such "graph filtering" improves the agreement between EEG and fMRI functional connectivity structure. Thus suggests that structural connectivity can be used to attenuate some of the limitations imposed by volume conduction.

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Section: Sc Provides Additional Predictive Power For Eeg-fcsupporting

confidence: 92%