Slow EEG pattern predicts reduced intrinsic functional connectivity in the default mode network: an inter-subject...

Area of Science:

• Neuroscience
• Cognitive Neuroscience
• Neuroimaging

Background:

• Resting state networks (RSN) represent spontaneous brain activity, characterized by synchronized low-frequency fluctuations in blood oxygenation level dependent (BOLD) signals between functionally connected areas.
• Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been used to explore the neurophysiological underpinnings of RSNs by correlating EEG power with BOLD signal amplitudes.

Purpose of the Study:

• To investigate the hypothesis that band-limited EEG power is directly associated with network-specific functional connectivity (FC) of BOLD signal time courses.
• To examine the relationship between individual EEG signatures and FC within the default mode network (DMN), a core RSN.

Main Methods:

• Combined EEG/fMRI data from 20 healthy volunteers were analyzed during a 15-minute resting state period.
• An inter-subject analysis design was employed to assess the network- and frequency-specific relationship between RSN FC and EEG.
• Multiple regression modeling was used to determine the extent to which EEG band-powers explain DMN FC variance.

Main Results:

• EEG band-powers explained 70% of the variance in DMN FC.
• Significant partial correlations were found between DMN FC and delta (r=-0.73) and beta (r=0.53) EEG power.
• An established EEG-derived sedation index (spectral edge frequency SEF95) showed a strong correlation with DMN FC.

Conclusions:

• The study provides direct evidence for a distinct neurophysiological correlate of DMN FC, linking specific EEG patterns to intrinsic functional connectivity.
• This finding validates the biological relevance of network-specific intrinsic FC and offers a neurophysiological basis for interpreting alterations in DMN FC studies.
• The approach opens new perspectives for EEG/fMRI correlation studies, enhancing our understanding of brain network dynamics.