Dataset Information

EEG coherence related to fMRI resting state synchrony in long-term abstinent alcoholics.

ABSTRACT: Recent work suggests that faulty co-activation or synchrony of multiple brain regions comprising "networks," or an imbalance between opposing brain networks, is important in alcoholism. Previous studies showed higher fMRI resting state synchrony (RSS) within the executive control (inhibitory control and emotion regulation) networks and lower RSS within the appetitive drive network in long-term (multi-year) abstinent alcoholics (LTAA) vs. non substance abusing controls (NSAC). Our goal was to identify EEG networks that are correlated with the appetitive drive and executive function networks identified with fMRI in our previous alcohol studies. We used parallel ICA for multimodal data fusion for the 20 LTAA and 21 NSAC that had both usable fMRI and 64-channel EEG data. Our major result was that parallel ICA identified a pair of components that significantly separated NSAC from LTAA and were correlated with each other. Examination of the resting-state fMRI seed-correlation map component showed higher bilateral nucleus accumbens seed-correlation in the dorsolateral prefrontal cortex bilaterally and lower seed-correlation in the thalamus. This single component thus encompassed both the executive control and appetitive drive networks, consistent with our previous work. The correlated EEG coherence component showed mostly higher theta and alpha coherence in LTAA compared to NSAC, and lower gamma coherence in LTAA compared to NSAC. The EEG theta and alpha coherence results suggest enhanced top-down control in LTAA and the gamma coherence results suggest impaired appetitive drive in LTAA. Our results support the notion that fMRI RSS is reflected in spontaneous EEG, even when the EEG and fMRI are not obtained simultaneously.

SUBMITTER: Cardenas VA

PROVIDER: S-EPMC5684581 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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EEG coherence related to fMRI resting state synchrony in long-term abstinent alcoholics.

Cardenas Valerie A VA Price Mathew M Fein George G

NeuroImage. Clinical 20171108

Recent work suggests that faulty co-activation or synchrony of multiple brain regions comprising "networks," or an imbalance between opposing brain networks, is important in alcoholism. Previous studies showed higher fMRI resting state synchrony (RSS) within the executive control (inhibitory control and emotion regulation) networks and lower RSS within the appetitive drive network in long-term (multi-year) abstinent alcoholics (LTAA) vs. non substance abusing controls (NSAC). Our goal was to ide ...[more]

PMID: 29159061

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Project description:Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity.

Dataset Information

EEG coherence related to fMRI resting state synchrony in long-term abstinent alcoholics.

Publications

EEG coherence related to fMRI resting state synchrony in long-term abstinent alcoholics.

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