Global Data-Driven Analysis of Brain Connectivity During Emotion Regulation by Electroencephalography Neurofeedback

Abstract

Background:

Emotion regulation by neurofeedback involves interactions among multiple brain regions, including prefrontal cortex and subcortical regions. Previous studies focused on connections of specific brain regions such as amygdala with other brain regions.

New method:

Electroencephalography (EEG) neurofeedback is used to upregulate positive emotion by retrieving positive autobiographical memories and functional magnetic resonance imaging (fMRI) data acquired simultaneously. A global data-driven approach, group independent component analysis, is applied to the fMRI data and functional network connectivity (FNC) estimated.

Results:

The proposed approach identified all functional networks engaged in positive autobiographical memories and evaluated effects of neurofeedback. The results revealed two pairs of networks with significantly different functional connectivity among emotion regulation blocks (relative to other blocks of the experiment) and between experimental and control groups (false discovery rate corrected for multiple comparisons, q = 0.05). FNC distribution showed significant connectivity differences between neurofeedback blocks and other blocks, revealing more synchronized brain networks during neurofeedback.

Comparison with Existing Methods:

Although the results are consistent with those of previous model-based studies, some of the connections found in this study were not found previously. These connections are between (a) occipital and other regions including limbic system/sublobar, prefrontal/frontal cortex, inferior parietal, and middle temporal gyrus and (b) posterior cingulate cortex and hippocampus.

Conclusions:

This study provided a global insight into brain connectivity for emotion regulation. The brain network interactions may be used to develop connectivity-based neurofeedback methods and alternative therapeutic approaches, which may be more effective than the traditional activity-based neurofeedback methods.

Impact statement

Previous neurofeedback-based emotion regulation studies focused on connections of specific brain regions. This study provided a global insight into brain connectivity for emotion regulation. To this end, fMRI data was acquired while EEG neurofeedback was used to upregulate positive emotion. Then, a global data-driven approach, group independent component analysis (ICA), was applied to the fMRI data and functional network connectivity was estimated. The proposed approach identified all functional networks engaged in positive autobiographical memories and evaluated effects of neurofeedback. While the results were consistent with those of previous studies, some of the connections were not found in the past.