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Using structural connectivity to augment community structure in EEG functional connectivity.

ABSTRACT: Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.

SUBMITTER: Glomb K

PROVIDER: S-EPMC7462431 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Using structural connectivity to augment community structure in EEG functional connectivity.

Glomb Katharina K Mullier Emeline E Carboni Margherita M Rubega Maria M Iannotti Giannarita G Tourbier Sebastien S Seeber Martin M Vulliemoz Serge S Hagmann Patric P

Network neuroscience (Cambridge, Mass.) 20200801 3

Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-sc ...[more]

PMID: 32885125

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Project description:BACKGROUND:Although numerous electroencephalogram (EEG) studies have described differences in functional connectivity in Alzheimer's disease (AD) compared to healthy subjects, there is no general consensus on the methodology of estimating functional connectivity in AD. Inconsistent results are reported due to multiple methodological factors such as diagnostic criteria, small sample sizes and the use of functional connectivity measures sensitive to volume conduction. We aimed to investigate the reproducibility of the disease-associated effects described by commonly used functional connectivity measures with respect to the amyloid, tau and neurodegeneration (A/T/N) criteria. METHODS:Eyes-closed task-free 21-channel EEG was used from patients with probable AD and subjective cognitive decline (SCD), to form two cohorts. Artefact-free epochs were visually selected and several functional connectivity measures (AEC(-c), coherence, imaginary coherence, PLV, PLI, wPLI) were estimated in five frequency bands. Functional connectivity was compared between diagnoses using AN(C)OVA models correcting for sex, age and, additionally, relative power of the frequency band. Another model predicted the Mini-Mental State Exam (MMSE) score of AD patients by functional connectivity estimates. The analysis was repeated in a subpopulation fulfilling the A/T/N criteria, after correction for influencing factors. The analyses were repeated in the second cohort. RESULTS:Two large cohorts were formed (SCD/AD; n =?197/214 and n =?202/196). Reproducible effects were found for the AEC-c in the alpha and beta frequency bands (p =?6.20 × 10-7, Cohen's d =?-?0.53; p =?5.78 × 10-4, d =?-?0.37) and PLI and wPLI in the theta band (p =?3.81 × 10-8, d =?0.59; p =?1.62 × 10-8, d =?0.60, respectively). Only effects of the AEC-c remained significant after statistical correction for the relative power of the selected bandwidth. In addition, alpha band AEC-c correlated with disease severity represented by MMSE score. CONCLUSION:The choice of functional connectivity measure and frequency band can have a large impact on the outcome of EEG studies in AD. Our results indicate that in the alpha and beta frequency bands, the effects measured by the AEC-c are reproducible and the most valid in terms of influencing factors, correlation with disease severity and preferable properties such as correction for volume conduction. Phase-based measures with correction for volume conduction, such as the PLI, showed reproducible effects in the theta frequency band.

Dataset Information

Using structural connectivity to augment community structure in EEG functional connectivity.

Publications

Using structural connectivity to augment community structure in EEG functional connectivity.

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