The aim of this study is to explore the effects of continuous theta-burst transcranial magnetic stimulation (cTBS) on functional brain network in emotion processing. Before and after the intervention of cTBS over left dorsolateral prefrontal cortex (DLPFC) of ten participants who were asked to perform the emotion gender recognition task, we recorded their scalp electroencephalograms (EEG). Then we used the phase synchronization of EEG to measure the connectivity between two nodes. We then calculated the network efficiency to describe the efficiency of information transmission in brain regions. Our research showed that after the intervention of cTBS and the stimulation of the emotion face picture, there was an obvious enhancement in the event-related spectral perturbation after stimuli onset in beta band in 100–300 ms. Under the stimulation of different emotion picture, the values of global phase synchronization for negative and neutral stimuli were enhanced compared to positive ones. And the increased small-worldness was found in emotional processing. In summary, based on the effect of activity change in the left DLPFC on emotion processing brain network, the emotional processing mechanism of brain networks were preliminary explored and it provided the reference for the research of emotion processing brain network in the future.
Dementia is a neurodegenerative disease closely related to brain network dysfunction. In this study, we assessed the interdependence between brain regions in patients with early-stage dementia based on phase-lock values, and constructed a functional brain network, selecting network feature parameters for metrics based on complex network analysis methods. At the same time, the entropy information characterizing the EEG signals in time domain, frequency domain and time-frequency domain, as well as the nonlinear dynamics features such as Hjorth and Hurst indexes were extracted, respectively. Based on the statistical analysis, the feature parameters with significant differences between different conditions were screened to construct feature vectors, and finally multiple machine learning algorithms were used to realize the recognition of early categories of dementia patients. The results showed that the fusion of multiple features performed well in the categorization of Alzheimer’s disease, frontotemporal lobe dementia and healthy controls, especially in the identification of Alzheimer’s disease and healthy controls, the accuracy of β-band reached 98%, which showed its effectiveness. This study provides new ideas for the early diagnosis of dementia and computer-assisted diagnostic methods.
The aim of the present study was to investigate the alternations of brain functional networks at resting state in the schizophrenia (SCH) patients using voxel-wise degree centrality (DC) method. The resting-state functional magnetic resonance imaging (rfMRI) data were collected from 41 SCH patients and 41 matched healthy control subjects and then analyzed by voxel-wise DC method. The DC maps between the patient group and the control group were compared using by two sample t test. The correlation analysis was also performed between DC values and clinical symptom and illness duration in SCH group. Results showed that compared with the control group, SCH patients exhibited significantly decreased DC value in primary sensorimotor network, and increased DC value in executive control network. In addition, DC value of the regions with obvious differences between the two groups significantly correlated to Positive and Negative Syndrome Scale (PANSS) scores and illness duration of SCH patients. The study showed the abnormal functional integration in primary sensorimotor network and executive control network in SCH patients.
The emergence of real-time functional magnetic resonance imaging (rt-fMRI) has provided foundations for neurofeedback based on brain hemodynamics and has given the new opportunity and challenge to cognitive neuroscience research. Along with the study of advanced brain neural mechanisms, the regulation goal of rt-fMRI neurofeedback develops from the early specific brain region activity to the brain network connectivity more accordant with the brain functional activities, and the study of the latter may be a trend in the area. Firstly, this paper introduces basic principle and development of rt-fMRI neurofeedback. Then, it specifically discusses the current research status of brain connectivity neurofeedback technology, including research approaches, experimental methods, conclusions, and so on. Finally, it discusses the problems in this field in the future development.
This paper is aimed to analyze the topological properties of structural brain networks in depressive patients with and without anxiety and to explore the neuropath logical mechanisms of depression comorbid with anxiety. Diffusion tensor imaging and deterministic tractography were applied to map the white matter structural networks. We collected 20 depressive patients with anxiety (DPA), 18 depressive patients without anxiety (DP), and 28 normal controls (NC) as comparative groups. The global and nodal properties of the structural brain networks in the three groups were analyzed with graph theoretical methods.The result showed that ① the structural brain networks in three groups showed small-world properties and highly connected global hubs predominately from association cortices; ② DP group showed lower local efficiency and global efficiency compared to NC group, whereas DPA group showed higher local efficiency and global efficiency compared to NC group; ③ significant differences of network properties (clustering coefficient, characteristic path lengths, local efficiency, global efficiency) were found between DPA and DP groups; ④ DP group showed significant changes of nodal efficiency in the brain areas primarily in the temporal lobe and bilateral frontal gyrus, compared to DPA and NC groups. The analysis indicated that the DP and DPA groups showed nodal properties of the structural brain networks, compared to NC group. Moreover, the two diseased groups indicated an opposite trend in the network properties. The results of this study may provide a new imaging index for clinical diagnosis for depression comorbid with anxiety.
Selective attention promotes the perception of brain to outside world and coordinates the allocation of limited brain resources. It is a cognitive process which relies on the neural activities of attention-related brain network. As one of the important forms of brain activities, neural oscillations are closely related to selective attention. In recent years, the relationship between selective attention and neural oscillations has become a hot issue. The new method that using external rhythmic stimuli to influence neural oscillations, i.e., neural entrainment, provides a promising approach to investigate the relationship between selective attention and neural oscillations. Moreover, it provides a new method to diagnose and even to treat the attention dysfunction. This paper reviewed the research status on the relationship between selective attention and neural oscillations, and focused on the application prospects of neural entrainment in revealing this relationship and diagnosing, even treating the attention dysfunction.
The directed functional connectivity in cerebral cortical is the key to understanding the pattern of the behavioral tissue. This process was studied to explore the directed functional network of rifle shooters at cerebral cortical rhythms from electroencephalogram (EEG) data, aiming to provide neurosciences basis for the future development of accelerating rifle skill learning method. The generalized orthogonalized partial directed coherence (gOPDC) algorithm was used to calculate the effective directed functional connectivity of the experts and novices in the pre-shot period. The results showed that the frontal, frontal-central, central, parietal and occipital regions were activated. Moreover, the more directed functional connections numbers in right hemispheres were observed compared to the left hemispheres. Furthermore, as compared to experts, novices had more activated regions, the stronger strength of connections and the lower value of the global efficiency during the pre-shot period. Those indirectly supported the conclusion that the novices needed to recruit more brain resources to accomplish tasks, which was consistent with " neural efficiency” hypothesis of the functional cerebral cortical in experts.
Background music has been increasingly affecting people’s lives. The research on the influence of background music on working memory has become a hot topic in brain science. In this paper, an improved electroencephalography (EEG) experiment based on n-back paradigm was designed. Fifteen university students without musical training were randomly selected to participate in the experiment, and their behavioral data and the EEG data were collected synchronously in order to explore the influence of different types of background music on spatial positioning cognition working memory. The exact low-resolution brain tomography algorithm (eLORETA) was applied to localize the EEG sources and the cross-correlation method was used to construct the cortical brain function networks based on the EEG source signals. Then the characteristics of the networks under different conditions were analyzed and compared to study the effects of background music on people’s working memory. The results showed that the difference of peak periods after stimulated by different types of background music were mainly distributed in the signals of occipital lobe and temporal lobe (P < 0.05). The analysis results showed that the brain connectivity under the condition with background music were stronger than those under the condition without music. The connectivities in the right occipital and temporal lobes under the condition of rock music were significantly higher than those under the condition of classical music. The node degrees, the betweenness centrality and the clustering coefficients under the condition without music were lower than those under the condition with background music. The node degrees and clustering coefficients under the condition of classical music were lower than those under the condition of rock music. It indicates that music stimulation increases the brain activity and has an impact on the working memory, and the effect of rock music is more remarkable than that of classical music. The behavioral data showed that the response accuracy in the state of no music, classical music and rock music were 86.09% ± 0.090%, 80.96% ± 0.960% and 79.36% ± 0.360%, respectively. We conclude that background music has a negative impact on the working memory, for it takes up the cognitive resources and reduces the cognitive ability of spatial location.
Somatosensory vibration can stimulate somatosensory area of human body, and this stimulation is tranferred to somatosensory nerves, and influences the somatic cortex, which is on post-central gyrus and paracentral lobule posterior of cerebral cortex, so that it alters the functional status of brain. The aim of the present study was to investigate the neural mechanism of brain state induced by somatosensory vibration. Twelve subjects were involved in the 20 Hz vibration stimulation test. Linear and nonlinear methods, such as relative change of relative power (RRP), Lempel-Ziv complexity (LZC) and brain network based on cross mutual information (CMI), were applied to discuss the change of brain under somatosensory vibration stimulation. The experimental results showed the frequency following response (FFR) by RRP of spontaneous electroencephalogram (EEG) in 20 Hz vibration, and no obvious change by LZC. The information transmission among various cortical areas enhanced under 20 Hz vibration stimulation. Therefore, 20 Hz somatosensory vibration may be able to adjust the functional status of brain.
Aiming at the difference between the brain networks of children with attention deficit hyperactivity disorder (ADHD) and normal children in the task-executing state, this paper conducted a comparative study using the network features of the visual function area. Functional magnetic resonance imaging (fMRI) data of 23 children with ADHD ［age: (8.27 ± 2.77) years］ and 23 normal children ［age: (8.70 ± 2.58) years］ were obtained by the visual capture paradigm when the subjects were performing the guessing task. First, fMRI data were used to build a visual area brain function network. Then, the visual area brain function network characteristic indicators including degree distribution, average shortest path, network density, aggregation coefficient, intermediary, etc. were obtained and compared with the traditional whole brain network. Finally, support vector machines (SVM) and other classifiers in the machine learning algorithm were used to classify the feature indicators to distinguish ADHD children from normal children. In this study, visual brain function network features were used for classification, with a classification accuracy of up to 96%. Compared with the traditional method of constructing a whole brain network, the accuracy was improved by about 10%. The test results show that the use of visual area brain function network analysis can better distinguish ADHD children from normal children. This method has certain help to distinguish the brain network between ADHD children and normal children, and is helpful for the auxiliary diagnosis of ADHD children.