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Previous neuroimaging studies have revealed abnormal brain networks in patients with major depressive disorder (MDD) in emotional processing. While any cognitive task consists of a series of stages, little is yet known about the topology of functional brain networks in MDD for these stages during emotional face recognition. To address this problem, electroencephalography (EEG)- based functional brain networks of MDD patients at different stages of facial information processing were investigated in this study. First, EEG signals were collected from 16 patients with MDD and 18 age-, gender-, and educationmatched normal subjects when performing an emotional face recognition task. Second, the global field power (GFP) method was employed to divide group-averaged event-related potentials into different stages. Third, using the phase transfer entropy (PTE) approach, the brain networks of MDD patients and normal individuals were constructed for each stage in negative and positive face processing, respectively. Finally, we compared the topological properties of brain networks of each stage between the two groups using graph theory approaches. The results showed that the analyzed three stages of emotional face processing corresponded to specific neurophysiological phases, namely, visual perception, face recognition, and emotional decision-making. It was also demonstrated that depressed patients showed abnormally decreased characteristic path length at the visual perception stage of negative face recognition and normalized characteristic path length in the stage of emotional decision-making during positive face processing compared to healthy subjects. Furthermore, while both the MDD and normal groups' brain networks were found to exhibit small-world network characteristics, the brain network of patients with depression tended to be randomized. Moreover, for patients with MDD, the centro-parietal region may lose its status as a hub in the process of facial expression identification. Together, our findings suggested that altered emotional function in MDD patients might be associated with disruptions in the topological organization of functional brain networks during emotional face recognition, which further deepened our understanding of the emotion processing dysfunction underlying MDD. [ABSTRACT FROM AUTHOR]

To study whether Tai Chi (TC) practice can improve the brain connectivity of the prefrontal lobe of college students, the positive psychological capital questionnaires and resting EEG signals were acquired from 50 college students including 25 TC practitioners and 25 demographically matched TC healthy controls. The results showed that the score of the positive psychological capital questionnaire of the TC group was significantly higher than that of the control group, and the node degree of the frontal lobe and temporal lobe of both groups was positively correlated with the score of the positive psychological capital questionnaire. In addition, the response time of the TC group under auditory stimulation was significantly shorter than that of the control group, and there was a significant positive correlation between response time and its characteristic path length, and a significant negative correlation with global efficiency. Meanwhile, during the selected range of sparsity, the difference in global network parameters between two groups is significant in the alpha band. Under all single sparsity, the clustering coefficient, global efficiency, and local efficiency of the TC group have a higher trend, while the characteristic path length tended to be shorter. In the analysis of the local characteristics of the resting brain functional network, it was found that the node degree of the frontal lobe and temporal lobe of the TC group was higher, and the difference was significant in some nodes. These results all point to the fact that TC practice has a certain impact on specific brain areas of the brain. [ABSTRACT FROM AUTHOR]

To explore the change of brain connectivity in schizophrenics (SCZ), the resting-state EEG source functional connections of SCZ and healthy control (HC) were investigated in this paper. Different band single-layer networks, multilayer networks, and improved multilayer networks were constructed and their topological attributes were extracted. The topological attributes of SCZ and HC were automatically distinguished using ensemble learning methods called Ensemble Learning based on Trees and Soft voting method, and the effectiveness of different network construction methods was compared based on the classification accuracy. The results showed that the classification accuracy was 89.38% for α band network, 82.5% for multilayer network, and 86.88% for improved multilayer network. Comparing patients with SCZ to those with Alzheimer's disease (AD), the classification accuracy of improved multilayer network was the highest, which was 88.12%. The power spectrum in the α band of SCZ was significantly lower than HC, whereas there was no significant difference between SCZ and AD. This indicated that the improved multilayer network can effectively distinguish SCZ and other groups not only when their power spectrum was significantly different. The results also suggested that the improved multilayer topological attributes were regarded as biological markers in the clinical diagnosis of patients with schizophrenia and even other mental disorders. [ABSTRACT FROM AUTHOR]

To explore the change of brain connectivity in schizophrenics (SCZ), the resting-state EEG source functional connections of SCZ and healthy control (HC) were investigated in this paper. Different band single-layer networks, multilayer networks, and improved multilayer networks were constructed and their topological attributes were extracted. The topological attributes of SCZ and HC were automatically distinguished using ensemble learning methods called Ensemble Learning based on Trees and Soft voting method, and the effectiveness of different network construction methods was compared based on the classification accuracy. The results showed that the classification accuracy was 89.38% for α band network, 82.5% for multilayer network, and 86.88% for improved multilayer network. Comparing patients with SCZ to those with Alzheimer’s disease (AD), the classification accuracy of improved multilayer network was the highest, which was 88.12%. The power spectrum in the α band of SCZ was significantly lower than HC, whereas there was no significant difference between SCZ and AD. This indicated that the improved multilayer network can effectively distinguish SCZ and other groups not only when their power spectrum was significantly different. The results also suggested that the improved multilayer topological attributes were regarded as biological markers in the clinical diagnosis of patients with schizophrenia and even other mental disorders.

Cognitive and neural processes underlying visual creativity have attracted substantial attention. The current research uses a critical time point analysis (CTPA) to examine how spontaneous activity in the primary visual area (PVA) is related to visual creativity. We acquired the functional magnetic resonance imaging (fMRI) data of 16 participants at the resting state and during performing a visual creative synthesis task. According to the CTPA, we then classified spontaneous activity in the PVA into critical time points (CTPs), which reflect the most useful and important functional meaning of the entire resting-state condition, and the remaining time points (RTPs). We constructed functional brain networks based on the brain activity at two different time points and then subsequently based on the brain activity at the task state in a separate manner. We explore the relationship between resting-state and task-fMRI (T-fMRI) functional brain networks. Our results found that: (1) the pattern of spontaneous activity in the PVA may associate with mental imagery, which plays an important role in visual creativity; (2) in comparison with the RTPs-based brain network, the CTP-network showed an increase in global efficiency and a decrease in local efficiency; (3) the regional integrated properties of the CTP-network could predict the integrated properties of the creative-network while the RTP-network could not. Thus, our findings indicated that spontaneous activity in the PVA at CTPs was associated with a visual creative task-evoked brain response. Our findings may provide an insight into how the visual cortex is related to visual creativity.

Cognitive and neural processes underlying visual creativity have attracted substantial attention. The current research uses a critical time point analysis (CTPA) to examine how spontaneous activity in the primary visual area (PVA) is related to visual creativity. We acquired the functional magnetic resonance imaging (fMRI) data of 16 participants at the resting state and during performing a visual creative synthesis task. According to the CTPA, we then classified spontaneous activity in the PVA into critical time points (CTPs), which reflect the most useful and important functional meaning of the entire resting-state condition, and the remaining time points (RTPs). We constructed functional brain networks based on the brain activity at two different time points and then subsequently based on the brain activity at the task state in a separate manner. We explore the relationship between resting-state and task-fMRI (T-fMRI) functional brain networks. Our results found that: (1) the pattern of spontaneous activity in the PVA may associate with mental imagery, which plays an important role in visual creativity; (2) in comparison with the RTPs-based brain network, the CTP-network showed an increase in global efficiency and a decrease in local efficiency; (3) the regional integrated properties of the CTP-network could predict the integrated properties of the creative-network while the RTP-network could not. Thus, our findings indicated that spontaneous activity in the PVA at CTPs was associated with a visual creative task-evoked brain response. Our findings may provide an insight into how the visual cortex is related to visual creativity. [ABSTRACT FROM AUTHOR]

Previous neuroimaging studies have revealed abnormal brain networks in patients with major depressive disorder (MDD) in emotional processing. While any cognitive task consists of a series of stages, little is yet known about the topology of functional brain networks in MDD for these stages during emotional face recognition. To address this problem, electroencephalography (EEG)-based functional brain networks of MDD patients at different stages of facial information processing were investigated in this study. First, EEG signals were collected from 16 patients with MDD and 18 age-, gender-, and education-matched normal subjects when performing an emotional face recognition task. Second, the global field power (GFP) method was employed to divide group-averaged event-related potentials into different stages. Third, using the phase transfer entropy (PTE) approach, the brain networks of MDD patients and normal individuals were constructed for each stage in negative and positive face processing, respectively. Finally, we compared the topological properties of brain networks of each stage between the two groups using graph theory approaches. The results showed that the analyzed three stages of emotional face processing corresponded to specific neurophysiological phases, namely, visual perception, face recognition, and emotional decision-making. It was also demonstrated that depressed patients showed abnormally decreased characteristic path length at the visual perception stage of negative face recognition and normalized characteristic path length in the stage of emotional decision-making during positive face processing compared to healthy subjects. Furthermore, while both the MDD and normal groups' brain networks were found to exhibit small-world network characteristics, the brain network of patients with depression tended to be randomized. Moreover, for patients with MDD, the centro-parietal region may lose its status as a hub in the process of facial expression identification. Together, our findings suggested that altered emotional function in MDD patients might be associated with disruptions in the topological organization of functional brain networks during emotional face recognition, which further deepened our understanding of the emotion processing dysfunction underlying MDD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Teng, Cong, Wang, Cheng, Wu, Dang, Jia, Ma, Xu and Hu.)

To study whether Tai Chi (TC) practice can improve the brain connectivity of the prefrontal lobe of college students, the positive psychological capital questionnaires and resting EEG signals were acquired from 50 college students including 25 TC practitioners and 25 demographically matched TC healthy controls. The results showed that the score of the positive psychological capital questionnaire of the TC group was significantly higher than that of the control group, and the node degree of the frontal lobe and temporal lobe of both groups was positively correlated with the score of the positive psychological capital questionnaire. In addition, the response time of the TC group under auditory stimulation was significantly shorter than that of the control group, and there was a significant positive correlation between response time and its characteristic path length, and a significant negative correlation with global efficiency. Meanwhile, during the selected range of sparsity, the difference in global network parameters between two groups is significant in the alpha band. Under all single sparsity, the clustering coefficient, global efficiency, and local efficiency of the TC group have a higher trend, while the characteristic path length tended to be shorter. In the analysis of the local characteristics of the resting brain functional network, it was found that the node degree of the frontal lobe and temporal lobe of the TC group was higher, and the difference was significant in some nodes. These results all point to the fact that TC practice has a certain impact on specific brain areas of the brain., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Li, Geng, Du, Si and Wang.)