Source localization and functional network analysis in emotion cognitive reappraisal with EEG-fMRI integration

Neuroenhancement by repetitive transcranial magnetic stimulation (rTMS) on DLPFC in healthy adults

The term "neuroenhancement" describes the enhancement of cognitive function associated with deficiencies resulting from a specific condition. Nevertheless, there is currently no agreed-upon definition for the term "neuroenhancement", and its meaning can change based on the specific research being discussed. As humans, our continual pursuit of expanding our capabilities, encompassing both cognitive and motor skills, has led us to explore various tools. Among these, repetitive Transcranial Magnetic Stimulation (rTMS) stands out, yet its potential remains underestimated. Historically, rTMS was predominantly employed in studies focused on rehabilitation objectives. A small amount of research has examined its use on healthy subjects with the goal of improving cognitive abilities like risk-seeking, working memory, attention, cognitive control, learning, computing speed, and decision-making. It appears that the insights gained in this domain largely stem from indirect outcomes of rehabilitation research. This review aims to scrutinize these studies, assessing the effectiveness of rTMS in enhancing cognitive skills in healthy subjects. Given that the dorsolateral prefrontal cortex (DLPFC) has become a popular focus for rTMS in treating psychiatric disorders, corresponding anatomically to Brodmann areas 9 and 46, and considering the documented success of rTMS stimulation on the DLPFC for cognitive improvement, our focus in this review article centers on the DLPFC as the focal point and region of interest. Additionally, recognizing the significance of theta burst magnetic stimulation protocols (TBS) in mimicking the natural firing patterns of the brain to modulate excitability in specific cortical areas with precision, we have incorporated Theta Burst Stimulation (TBS) wave patterns. This inclusion, mirroring brain patterns, is intended to enhance the efficacy of the rTMS method. To ascertain if brain magnetic stimulation consistently improves cognition, a thorough meta-analysis of the existing literature has been conducted. The findings indicate that, after excluding outlier studies, rTMS may improve cognition when compared to appropriate control circumstances. However, there is also a considerable degree of variation among the researches. The navigation strategy used to reach the stimulation site and the stimulation location are important factors that contribute to the variation between studies. The results of this study can provide professional athletes, firefighters, bodyguards, and therapists-among others in high-risk professions-with insightful information that can help them perform better on the job.

Beyond awareness: the binding of reflexive mechanisms with the conscious mind: a perspective from default space theory

How do reflexes operate so quickly with so much multimodal information on the environment? How might unconscious processes help reveal the nature of consciousness? The Default Space Theory of Consciousness (DST) offers a novel way to interpret these questions by describing how sensory inputs, cognitive functions, emotional states, and unconscious processes are integrated by a single unified internal representation. Recent developments in neuroimaging and electrophysiology, such as fMRI, EEG, and MEG, have improved our knowledge of the brain mechanisms that underpin the conscious mind and have highlighted the importance of neural oscillations and sensory integration in its formation. In this article, we put forth a perspective on an underresearched relationship of reflexes with the dynamic character of consciousness and suggest that future research should focus on the interplay of the unconscious processes of reflexes and correlates of the contents of consciousness to better understand its nature. Existing research on the top-down cortical influence over the subcortical operations of reflexes is severely lacking. This top-down influence has been demonstrated, but how the complex multimodal model of the self and environment is encoded and utilized to produce quick and coordinated reflex responses is not understood. Integrating unconscious/subconscious reflexive mechanisms with models of consciousness may illuminate a boundary between or gradient among conscious and unconscious activity. This perspective in light of the DST's framework may reveal future research avenues aimed at understanding the complexities and physical nature of consciousness.

The Role of Emotion Regulation and Awareness in Psychosocial Stress: An EEG-Psychometric Correlational Study

BACKGROUND

In stressful situations, to overcome unpleasant emotions, individuals try to manage stress through emotion regulation strategies such as cognitive reappraisal, interoception, and mindfulness.

METHOD

26 healthy adults underwent a modified version of the Trier Social Stress Test (named the Social Stress Test, SST) while their electrophysiological (EEG) activity was monitored. Participants also completed self-report questionnaires prior to this, including the Five-Facet Mindfulness Questionnaire (FFMQ), Multidimensional Assessment of Interoceptive Awareness (MAIA), Emotional Regulation of Others and Self (EROS), and the Interpersonal Reactivity Index (IRI). Three brain regions of interest (ROIs) were considered in the EEG data processing: frontal, temporo-central, and parieto-occipital. Correlational analyses were performed between psychometric scales and EEG band power spectral values for each ROI.

RESULTS

The results showed positive correlations between interoceptive awareness, mindfulness, and high-frequency EEG bands (beta, alpha, gamma) over frontal ROI, indicating enhanced cognitive processing and emotional regulation. Conversely, emotion regulation and empathy measures correlated positively with low-frequency EEG bands (delta, theta), associated with improved social cognition and top-down regulatory processes.

CONCLUSIONS

These findings suggest that EEG correlations of the stress response are connected to emotion regulation mechanisms, emphasizing the importance of body state awareness in managing stress and emotions for overall well-being and quality of life.

Exploring changes in brain function in IBD patients using SPCCA: a study of simultaneous EEG-fMRI

Research on functional changes in the brain of inflammatory bowel disease (IBD) patients is emerging around the world, which brings new perspectives to medical research. In this paper, the methods of canonical correlation analysis (CCA), kernel canonical correlation analysis (KCCA), and sparsity preserving canonical correlation analysis (SPCCA) were applied to the fusion of simultaneous EEG-fMRI data from 25 IBD patients and 15 healthy individuals. The CCA, KCCA and SPCCA fusion methods were used for data processing to compare the results obtained by the three methods. The results clearly show that there is a significant difference in the activation intensity between IBD and healthy control (HC), not only in the frontal lobe (p < 0.01) and temporal lobe (p < 0.01) regions, but also in the posterior cingulate gyrus (p < 0.01), gyrus rectus (p < 0.01), and amygdala (p < 0.01) regions, which are usually neglected. The mean difference in the SPCCA activation intensity was 60.1. However, the mean difference in activation intensity was only 36.9 and 49.8 by using CCA and KCCA. In addition, the correlation of the relevant components selected during the SPCCA calculation was high, with correlation components of up to 0.955; alternatively, the correlations obtained from CCA and KCCA calculations were only 0.917 and 0.926, respectively. It can be seen that SPCCA is indeed superior to CCA and KCCA in processing high-dimensional multimodal data. This work reveals the process of analyzing the brain activation state in IBD disease, provides a further perspective for the study of brain function, and opens up a new avenue for studying the SPCCA method and the change in the intensity of brain activation in IBD disease.

Exploring Neural Mechanisms of Reward Processing Using Coupled Matrix Tensor Factorization: A Simultaneous EEG-fMRI Investigation

BACKGROUND

It is crucial to understand the neural feedback mechanisms and the cognitive decision-making of the brain during the processing of rewards. Here, we report the first attempt for a simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) study in a gambling task by utilizing tensor decomposition.

METHODS

First, the single-subject EEG data are represented as a third-order spectrogram tensor to extract frequency features. Next, the EEG and fMRI data are jointly decomposed into a superposition of multiple sources characterized by space-time-frequency profiles using coupled matrix tensor factorization (CMTF). Finally, graph-structured clustering is used to select the most appropriate model according to four quantitative indices.

RESULTS

The results clearly show that not only are the regions of interest (ROIs) found in other literature activated, but also the olfactory cortex and fusiform gyrus which are usually ignored. It is found that regions including the orbitofrontal cortex and insula are activated for both winning and losing stimuli. Meanwhile, regions such as the superior orbital frontal gyrus and anterior cingulate cortex are activated upon winning stimuli, whereas the inferior frontal gyrus, cingulate cortex, and medial superior frontal gyrus are activated upon losing stimuli.

CONCLUSION

This work sheds light on the reward-processing progress, provides a deeper understanding of brain function, and opens a new avenue in the investigation of neurovascular coupling via CMTF.

Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder

Growing evidences indicate that age plays an important role in the development of mental disorders, but few studies focus on the neuro mechanisms of generalized anxiety disorder (GAD) in different age groups. Therefore, this study attempts to reveal the neurodynamics of Young_GAD (patients with GAD under the age of 50) and Old_GAD (patients with GAD over 50 years old) through statistical analysis of multidimensional electroencephalogram (EEG) features and machine learning models. In this study, 10-min resting-state EEG data were collected from 45 Old_GAD and 33 Young_GAD. And multidimensional EEG features were extracted, including absolute power (AP), fuzzy entropy (FE), and phase-lag-index (PLI), on which comparison and analyses were performed later. The results showed that Old_GAD exhibited higher power spectral density (PSD) value and FE value in beta rhythm compared to theta, alpha1, and alpha2 rhythms, and functional connectivity (FC) also demonstrated significant reorganization of brain function in beta rhythm. In addition, the accuracy of machine learning classification between Old_GAD and Young_GAD was 99.67%, further proving the feasibility of classifying GAD patients by age. The above findings provide an objective basis in the field of EEG for the age-specific diagnosis and treatment of GAD.