Altered dynamic electroencephalography connectome phase-space features of emotion regulation in social anxiety

LGGNet: Learning From Local-Global-Graph Representations for Brain-Computer Interface

Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose local-global-graph network (LGGNet), a novel neurologically inspired graph neural network (GNN), to learn local-global-graph (LGG) representations of electroencephalography (EEG) for brain-computer interface (BCI). The input layer of LGGNet comprises a series of temporal convolutions with multiscale 1-D convolutional kernels and kernel-level attentive fusion. It captures temporal dynamics of EEG which then serves as input to the proposed local- and global-graph-filtering layers. Using a defined neurophysiologically meaningful set of local and global graphs, LGGNet models the complex relations within and among functional areas of the brain. Under the robust nested cross-validation settings, the proposed method is evaluated on three publicly available datasets for four types of cognitive classification tasks, namely the attention, fatigue, emotion, and preference classification tasks. LGGNet is compared with state-of-the-art (SOTA) methods, such as DeepConvNet, EEGNet, R2G-STNN, TSception, regularized graph neural network (RGNN), attention-based multiscale convolutional neural network-dynamical graph convolutional network (AMCNN-DGCN), hierarchical recurrent neural network (HRNN), and GraphNet. The results show that LGGNet outperforms these methods, and the improvements are statistically significant ( ) in most cases. The results show that bringing neuroscience prior knowledge into neural network design yields an improvement of classification performance. The source code can be found at https://github.com/yi-ding-cs/LGG.

Transcranial alternating current stimulation for the treatment of major depressive disorder: from basic mechanisms toward clinical applications

Non-pharmacological treatment is essential for patients with major depressive disorder (MDD) that is medication resistant or who are unable to take medications. Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation method that manipulates neural oscillations. In recent years, tACS has attracted substantial attention for its potential as an MDD treatment. This review summarizes the latest advances in tACS treatment for MDD and outlines future directions for promoting its clinical application. We first introduce the neurophysiological mechanism of tACS and its novel developments. In particular, two well-validated tACS techniques have high application potential: high-definition tACS targeting local brain oscillations and bifocal tACS modulating interarea functional connectivity. Accordingly, we summarize the underlying mechanisms of tACS modulation for MDD. We sort out the local oscillation abnormalities within the reward network and the interarea oscillatory synchronizations among multiple MDD-related networks in MDD patients, which provide potential modulation targets of tACS interventions. Furthermore, we review the latest clinical studies on tACS treatment for MDD, which were based on different modulation mechanisms and reported alleviations in MDD symptoms. Finally, we discuss the main challenges of current tACS treatments for MDD and outline future directions to improve intervention target selection, tACS implementation, and clinical validations.

Differential effects of transcranial current type on heart rate variability during emotion regulation in internalizing psychopathologies

BACKGROUND

Internalizing psychopathologies (IPs) are characterized by disruptions in emotion regulation (ER). A potential target for ER modulation in individuals with IPs is the theta band. We hypothesized that offset theta-tACS (transcranial alternating current stimulation) would result in more enhanced ER, indexed by greater increase in heart rate variability (HRV), than transcranial direct current stimulation (tDCS) in participants with IPs.

METHODS

This pilot study utilized a double-blind, pseudo-counterbalanced design. Participants with internalizing psychopathologies (anxiety and depression) were randomly assigned to receive either offset theta-tACS (n = 14) or tDCS (n = 15) and underwent four sessions of stimulation (two sham). In both arms, there were alternating iterations of an emotion regulation task (ERT) during or immediately after stimulation and rest. Heart rate data were collected during each ERT and rest iteration, and analyses were completed using high-frequency (HF) and root mean square of successive differences (RMSSD) HRV metrics.

RESULTS

tACS participants consistently displayed increases in both HRV metrics from Time 1 to Time 4. Participants receiving tDCS displayed few significant changes in HF-HRV and no significant changes in RMSSD-HRV.

LIMITATIONS

Due to the small sample size, analyses were limited. Additionally, the lack of a baseline ERT makes it difficult to determine overall ER improvement.

CONCLUSIONS

tACS appears to increase ER capacity as reflected in increased HRV in individuals with internalizing psychopathologies, particularly after two sessions of stimulation. This study adds validity to the use of tACS as a neuromodulatory technique in cognitive and clinical research. Additional research is required to better understand potential carry-over effects of multiple sessions of stimulation.

Neuromodulatory effects of transcranial electrical stimulation on emotion regulation in internalizing psychopathologies

OBJECTIVE

We hypothesize offset theta-tACS (transcranial alternating current stimulation) improves emotion regulation (ER) and psychopathology more than transcranial direct current stimulation (tDCS) in participants with internalizing psychopathologies (IPs).

METHODS

This pilot study utilized a double-blind, pseudo-counterbalanced, sham-controlled design with participants with IPs. Participants were assigned to receive tDCS or tACS, underwent four stimulation sessions (two sham), and completed an emotion regulation task (ERT) during or after stimulation. Participants completed the Beck Depression Inventory before/after the study, the Spielberger State and Trait Anxiety Index after each ERT, and rated their arousal, valence, and perceived reappraisal ability during the ERT.

RESULTS

Participants receiving either stimulation type showed a reduction in anxiety, depression, and valence and arousal ratings. We additionally discovered an effect demonstrating those who received sham stimulation first displayed little-to-no change in any score across the study, but tACS participants who received verum stimulation first showed significant improvements in each metric.

CONCLUSIONS

Improving ER capabilities via theta tACS has the potential to yield beneficial clinical effects.

SIGNIFICANCE

This study adds validity to the use of non-invasive neuromodulatory methods, especially tACS, to alleviate IPs. Additional research is needed to better understand the effects of sham stimulation. Careful consideration of sham incorporation should be made in future studies.

Self- and Situation-Focused Reappraisal are not homogeneous: Evidence from behavioral and brain networks

Reappraisal is an effective emotion regulation strategy which can be divided into self- and situation-focused subtypes. Previous studies have produced inconsistent findings on the moderating effects and neural mechanisms of reappraisal; thus, further research is necessary to clarify these inconsistencies. In this study, a total of 44 participants were recruited and randomly assigned to two groups. 23 participants were assigned to the self-focused group, while 21 participants were assigned to the situation-focused group. The participants' resting EEG data were collected for 6 minutes before the experiment began, followed by an emotional regulation task. During this task, participants were asked to view emotion-provoking images under four emotion regulation conditions (View, Watch, Increase, and Decrease). Late positive potential (LPP) was obtained when these emotional images were observed. LPP is an effective physiological indicator of emotion regulation, enabling this study to explore emotion regulation under different reappraisal strategies, as well as the functional connectivity and node efficiency within the brain. It was found that, in terms of the effect on emotion regulation, situation-focused reappraisal was significantly better than self-focused reappraisal at enhancing the valence of negative emotion, while self-focused reappraisal was significantly better than situation-focused reappraisal at increasing the arousal of negative emotion. In terms of neural mechanisms, multiple brain regions such as the anterior cingulate cortex, the frontal lobe, the parahippocampal gyrus, parts of the temporal lobe, and parts of the parietal lobe were involved in both reappraisal processes. In addition, there were some differences in brain regions associated with different forms of cognitive reappraisal. Self-focused reappraisal was associated with the posterior cingulate gyrus, fusiform gyrus, and lingual gyrus, and situation-focused reappraisal was associated with the parietal lobule, anterior central gyrus, and angular gyrus. In conclusion, this research demonstrates that self- and situation-focused reappraisal are not homogenous in terms of their effects and neural mechanisms and clarifies the uncertainties over their regulatory effects. Different types of reappraisal activate different brain regions when used, and the functional connectivity or node efficiency of these brain regions seems to be a suitable indicator for assessing the effects of different types of reappraisal.

A dynamic causal model on self-regulation of aversive emotion

Cognitive regulation of emotion has been proven to be effective to take control the emotional responses. Some cognitive models have also been proposed to explain the neural mechanism that underlies this process. However, some characteristics of the models are still unclear, such as whether the cognitive regulation will be spontaneously employed by participants implicitly. The present study recruited the fMRI experiment to focus on the discomfort induced by viewing aversive pictures, and the emotional self-regulation during picture viewing. By using the dynamic causal modeling (DCM), 50 putative models of brain functional networks were constructed, one optimal model that fitted the real data best won the comparison from the candidates. As a result, the optimal model suggests that both the ventral striatum (VS)-centric bottom-up and the dorsolateral prefrontal cortex (DLPFC)-centric top-down regulations are recruited for self-regulation on negative emotions. The DLPFC will exert modulatory influence on the VS only when the VS fails to suppress the induced emotions by self-inhibition.

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

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.

Linear and Nonlinear EEG-Based Functional Networks in Anxiety Disorders

Electrocortical network dynamics are integral to brain function. Linear and nonlinear connectivity applications enrich neurophysiological investigations into anxiety disorders. Discrete EEG-based connectivity networks are unfolding with some homogeneity for anxiety disorder subtypes. Attenuated delta/theta/beta connectivity networks, pertaining to anterior-posterior nodes, characterize panic disorder. Nonlinear measures suggest reduced connectivity of ACC as an executive neuro-regulator in germane "fear circuitry networks" might be more central than considered. Enhanced network complexity and theta network efficiency at rest define generalized anxiety disorder, with similar tonic hyperexcitability apparent in social anxiety disorder further extending to task-related/state functioning. Dysregulated alpha connectivity and integration of mPFC-ACC/mPFC-PCC relays implicated with attentional flexibility and choice execution/congruence neurocircuitry are observed in trait anxiety. Conversely, state anxiety appears to recruit converging delta and beta connectivity networks as panic, suggesting trait and state anxiety are modulated by discrete neurobiological mechanisms. Furthermore, EEG connectivity dynamics distinguish anxiety from depression, despite prevalent clinical comorbidity. Rethinking mechanisms implicated in the etiology, maintenance, and treatment of anxiety from the perspective of EEG network science across micro- and macroscales serves to shed light and move the field forward.

Deep Learning Classification of Neuro-Emotional Phase Domain Complexity Levels Induced by Affective Video Film Clips

In the present article, a novel emotional complexity marker is proposed for classification of discrete emotions induced by affective video film clips. Principal Component Analysis (PCA) is applied to full-band specific phase space trajectory matrix (PSTM) extracted from short emotional EEG segment of 6 s, then the first principal component is used to measure the level of local neuronal complexity. As well, Phase Locking Value (PLV) between right and left hemispheres is estimated for in order to observe the superiority of local neuronal complexity estimation to regional neuro-cortical connectivity measurements in clustering nine discrete emotions (fear, anger, happiness, sadness, amusement, surprise, excitement, calmness, disgust) by using Long-Short-Term-Memory Networks as deep learning applications. In tests, two groups (healthy females and males aged between 22 and 33 years old) are classified with the accuracy levels of [Formula: see text] and [Formula: see text] through the proposed emotional complexity markers and and connectivity levels in terms of PLV in amusement. The groups are found to be statistically different ( p << 0.5) in amusement with respect to both metrics, even if gender difference does not lead to different neuro-cortical functions in any of the other discrete emotional states. The high deep learning classification accuracy of [Formula: see text] is commonly obtained for discrimination of positive emotions from negative emotions through the proposed new complexity markers. Besides, considerable useful classification performance is obtained in discriminating mixed emotions from each other through full-band connectivity features. The results reveal that emotion formation is mostly influenced by individual experiences rather than gender. In detail, local neuronal complexity is mostly sensitive to the affective valance rating, while regional neuro-cortical connectivity levels are mostly sensitive to the affective arousal ratings.