Dataset Information

EEG Signatures of Dynamic Functional Network Connectivity States.

ABSTRACT: The human brain operates by dynamically modulating different neural populations to enable goal directed behavior. The synchrony or lack thereof between different brain regions is thought to correspond to observed functional connectivity dynamics in resting state brain imaging data. In a large sample of healthy human adult subjects and utilizing a sliding windowed correlation method on functional imaging data, earlier we demonstrated the presence of seven distinct functional connectivity states/patterns between different brain networks that reliably occur across time and subjects. Whether these connectivity states correspond to meaningful electrophysiological signatures was not clear. In this study, using a dataset with concurrent EEG and resting state functional imaging data acquired during eyes open and eyes closed states, we demonstrate the replicability of previous findings in an independent sample, and identify EEG spectral signatures associated with these functional network connectivity changes. Eyes open and eyes closed conditions show common and different connectivity patterns that are associated with distinct EEG spectral signatures. Certain connectivity states are more prevalent in the eyes open case and some occur only in eyes closed state. Both conditions exhibit a state of increased thalamocortical anticorrelation associated with reduced EEG spectral alpha power and increased delta and theta power possibly reflecting drowsiness. This state occurs more frequently in the eyes closed state. In summary, we find a link between dynamic connectivity in fMRI data and concurrently collected EEG data, including a large effect of vigilance on functional connectivity. As demonstrated with EEG and fMRI, the stationarity of connectivity cannot be assumed, even for relatively short periods.

SUBMITTER: Allen EA

PROVIDER: S-EPMC5568463 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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EEG Signatures of Dynamic Functional Network Connectivity States.

Allen E A EA Damaraju E E Eichele T T Wu L L Calhoun V D VD

Brain topography 20170222 1

The human brain operates by dynamically modulating different neural populations to enable goal directed behavior. The synchrony or lack thereof between different brain regions is thought to correspond to observed functional connectivity dynamics in resting state brain imaging data. In a large sample of healthy human adult subjects and utilizing a sliding windowed correlation method on functional imaging data, earlier we demonstrated the presence of seven distinct functional connectivity states/p ...[more]

PMID: 28229308

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Project description: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.

Dataset Information

EEG Signatures of Dynamic Functional Network Connectivity States.

Publications

EEG Signatures of Dynamic Functional Network Connectivity States.

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