2018
DOI: 10.1038/s41598-017-18471-y
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The neural correlates of the unified percept of alcohol-related craving: a fMRI and EEG study

Abstract: Alcohol addiction is accompanied by aberrant neural activity. Previously, task-based fMRI and resting-state EEG studies have revealed that craving, a critical component of addiction, is linked to abnormal activity in cortical regions including the dorsal anterior cingulate cortex (dACC), nucleus accumbens (NAcc), posterior cingulate cortex (PCC) and pregenual anterior cingulate cortex (pgACC), etc. In this study, we combine these two imaging techniques to investigate a group of alcohol-addicted patients and pr… Show more

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Cited by 72 publications
(66 citation statements)
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“…Since there is only a single eLORETA study on AUD [59], and it is worth comparing our findings to it. Although both studies have examined resting state EEG source connectivity in AUD using eLORETA and showed similar finding of increased FC network multiple brain regions, Huang et al [59] derived seed regions from fMRI activations during a cue-reactivity task and reported hyperconnectivity only in theta band across reward and executive network regions. Similar findings have also been reported in other addictive behaviors.…”
Section: Predominant Pattern Of Dmn Hyperconnectivity In Audmentioning
confidence: 99%
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“…Since there is only a single eLORETA study on AUD [59], and it is worth comparing our findings to it. Although both studies have examined resting state EEG source connectivity in AUD using eLORETA and showed similar finding of increased FC network multiple brain regions, Huang et al [59] derived seed regions from fMRI activations during a cue-reactivity task and reported hyperconnectivity only in theta band across reward and executive network regions. Similar findings have also been reported in other addictive behaviors.…”
Section: Predominant Pattern Of Dmn Hyperconnectivity In Audmentioning
confidence: 99%
“…At each voxel in the cortical grey matter, a 3-component vector time series is computed, corresponding to the current density vector with dipole moments along axes X, Y, and Z [88]. The values in individual voxel reflect the log-transformed fraction of total power across all 6239 voxels that covers the cortical regions of the brain, separately for specific frequencies [59]. While the original LORETA method used weighted minimum norm solution combined depth weighting and Laplacian smoothing, the latest version eLORETA uses optimal weights to achieve localization accuracy [89].…”
Section: Eeg Based Functional Connectivity Analysis In Eloretamentioning
confidence: 99%
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