Relationship Between Alpha Rhythm and the Default Mode Network: An EEG-fMRI Study. Issue 6 (November 2017)

• Record Type: Journal Article
• Authors: Bowman, Anthony D.; Griffis, Joseph C.; Visscher, Kristina M.; Dobbins, Allan C.; Gawne, Timothy J.; DiFrancesco, Mark W.; Szaflarski, Jerzy P.
• Is Part Of: Journal of clinical neurophysiology. Volume 34:Issue 6(2017)
• Publication Date: 2017-11
• Subjects: EEG/fMRI -- Default mode network -- Alpha power -- Independent component analysis -- Thalamus; Electroencephalography -- Periodicals; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electroencephalography; Periodicals; 612.805
• DOI: 10.1097/WNP.0000000000000411
• Languages: English
• ISSNs: 0736-0258

Abstract

Abstract : Purpose: Reports of the relationship between the default mode network (DMN) and alpha power are conflicting. Our goal was to assess this relationship by analyzing concurrently obtained EEG/functional MRI data using hypothesis-independent methods. Methods: We collected functional MRI and EEG data during eyes-closed rest in 20 participants aged 19 to 37 (10 females) and performed independent component analysis on the functional MRI data and a Hamming-windowed fast Fourier transform on the EEG data. We correlated functional MRI fluctuations in the DMN with alpha power. Results: Of the six independent components found to have significant relationships with alpha, four contained DMN-associated regions: One independent component was positively correlated with alpha power, whereas all others were negatively correlated. Furthermore, two independent components with opposite relationships with alpha had overlapping voxels in the medial prefrontal cortex and posterior cingulate cortex, suggesting that subpopulations of neurons within these classic nodes within the DMN may have different relationships to alpha power. Conclusions: Different parts of the DMN exhibit divergent relationships to alpha power. Our results highlight the relationship between DMN activity and alpha power, indicating that networks, such as the DMN, may have subcomponents that exhibit different behaviors.