Dynamic Reorganization of the Cortical Functional Brain Network in Affective Processing and Cognitive Reappraisal

Abstract

Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization of the cortical functional brain network during an affective processing and emotion regulation task is documented using an advanced multi-model electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technique. Sliding time window correlation and [Formula: see text]-means clustering are employed to explore the functional brain connectivity (FC) dynamics during the unaltered perception of neutral (moderate valence, low arousal) and negative (low valence, high arousal) stimuli and cognitive reappraisal of negative stimuli. Betweenness centralities are computed to identify central hubs within each complex network. Results from 20 healthy subjects indicate that the cortical mechanism for cognitive reappraisal follows a ‘top-down’ pattern that occurs across four brain network states that arise at different time instants (0–170[Formula: see text]ms, 170–370[Formula: see text]ms, 380–620[Formula: see text]ms, and 620–1000[Formula: see text]ms). Specifically, the dorsolateral prefrontal cortex (DLPFC) is identified as a central hub to promote the connectivity structures of various affective states and consequent regulatory efforts. This finding advances our current understanding of the cortical response networks of reappraisal-based emotion regulation by documenting the recruitment process of four functional brain sub-networks, each seemingly associated with different cognitive processes, and reveals the dynamic reorganization of functional brain networks during emotion regulation.

Enhancing Emotion Recognition Using Region-Specific Electroencephalogram Data and Dynamic Functional Connectivity

“…This type of analyses failed to take advantage of the region-specific neuronal information, or the spatiotemporal-varying interactions at the network level (e.g., cluster, time-varying pattern) to allow a deeper understanding of the brain-emotion relationship. As indicated in a previous study, emotion processing and regulation is likely to involve complex neural circuits in a time-varying manner rather than any independent brain region ( Mauss and Robinson, 2009 ; Fang et al, 2020 ). Besides, various studies have reported that the frontal cortex seems to play a more essential role in emotion-related activity compared to other brain regions such as temporal, parietal, and occipital ( Sarno et al, 2016 ).…”

“…In this context, it has been showed that increased theta power in parietal area is linked to high arousal ( Aftanas et al, 2002 ). Previous studies also suggested that brain-emotion relationship could be characterized by complex network interactions with more fine-grained spatiotemporal resolution ( Nguyen et al, 2019 ; Fang et al, 2020 ). In sum, the optimal measurement protocol for EEG-based emotion studies remains to be determined in future studies.…”

“…Recognition of emotion are usually divided into two categories: physiological signals and non-physiological signals, depending on how signal is collected. Non-physiological signals include expressions ( Liu et al, 2017 ; Liliana, 2019 ), body postures ( Noroozi et al, 2021 ), voice signals ( Wang et al, 2020 ), etc., while physiological signals include electroencephalogram (EEG) ( Namazi et al, 2020 ; Song et al, 2020 ), electrocardiogram (ECG) ( Al-Sheikh et al, 2019 ; Nguyen et al, 2019 ; Fang et al, 2020 ; Sarkar and Etemad, 2021 ) and electromyography (EMG) ( Hossen et al, 2020 ; Kulke et al, 2020 ), etc. Among the various physiological signals, EEG signals have attracted widespread attention due to its high mobility and close relationship with neural response ( Li et al, 2017 , 2020b ).…”

Dynamic Reorganization of Functional Connectivity During Post-Break Task Reengagement

“…Although various approaches have been proposed to capture the fluctuations in brain activity, the utilization of temporal networks to quantify these fluctuations is only beginning to be studied [25]. Through adding time-varying aspects of FC to static network, the temporal network models could help reveal the dynamic variations of functional coordination between different functional units [26], and thus the changes in network topologies could be investigated on a fine time scale [25]. Most recently, Thompson et al, [27] introduced a temporal network analysis framework and verified the usefulness using resting-state fMRI data.…”

Offline rTMS inhibition of the bilateral dorsolateral prefrontal cortex impairs reappraisal efficacy

“…The DLPFC serves multiple crucial functions whilst being richly functionally connected. It is not surprising that this region is widely acknowledged as playing a key role in reappraisal 26 . However, the evidence to support this claim is mostly correlational and hardly provides direct causal support.…”