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.
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.
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.
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.
Repetitive transcranial magnetic stimulation (rTMS) can influence the stimulated brain regions and other distal brain regions connecting to them. The purpose of the study is to investigate the effects of low-frequency rTMS over primary motor cortex on brain by analyzing the brain functional connectivity and coordination between brain regions. 10 healthy subjects were recruited. 1 Hz rTMS was used to stimulate primary motor cortex for 20 min. 1 min resting state electroencephalography (EEG) was collected before and after the stimulation respectively. By performing phase synchronization analysis between the EEG electrodes, the brain functional network and its properties were calculated. Signed-rank test was used for statistical analysis. The result demonstrated that the global phase synchronization in alpha frequency band was decreased significantly after low-frequency rTMS (P<0.05). The phase synchronization was down-regulated between motor cortex and ipsilateral frontal/parietal cortex, and also between contralateral parietal cortex and bilateral frontal cortex. The mean degree and global efficiency of brain functional networks in alpha frequency band were significantly decreased (P<0.05), and the mean shortest path length were significantly increased (P<0.05), which suggested the information transmission of the brain networks and its efficiency was reduced after low-frequency rTMS. This study verified the inhibition function of the low-frequency rTMS to brain activities, and demonstrated that low-frequency rTMS stimulation could affect both stimulating brain regions and distal brain regions connected to them. The findings in this study could be of guidance to clinical application of low-frequency rTMS.
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 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.
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.
Cognitive impairment is one of the three primary symptoms of schizophrenic patients and shows important value in early detection and warning for high-risk individuals. To study the specifics of electroencephalogram (EEG) in patients with schizophrenia under the cognitive load, we collected EEG signals from 17 schizophrenic patients and 19 healthy controls, extracted signals of each band based on wavelet transform, calculated the characteristics of nonlinear dynamic and functional brain networks, and automatically classified the two groups of people by using a machine learning algorithm. Experimental results indicated that the correlation dimension and sample entropy showed significant differences in α, β, θ, and γ rhythm of the Fp1 and Fp2 electrodes between groups under the cognitive load. These results implied that the functional disruptions in the frontal lobe might be the important factors of cognitive impairments in schizophrenic patients. Further results of the automatic classification analysis indicated that the combination of nonlinear dynamics and functional brain network properties as the input characteristics of the classifier showed the best performance, with the accuracy of 76.77%, sensitivity of 72.09%, and specificity of 80.36%. The results of this study demonstrated that the combination of nonlinear dynamics and function brain network properties may be potential biomarkers for early screening and auxiliary diagnosis of schizophrenia.
With the wide application of virtual reality technology and the rapid popularization of virtual reality devices, the problem of brain fatigue caused by prolonged use has attracted wide attention. Sixteen healthy subjects were selected in this study. And electroencephalogram (EEG) signals were acquired synchronously while the subjects watch videos in similar types presented by traditional displayer and virtual reality separately. Two questionnaires were conducted by all subjects to evaluate the state of fatigue before and after the experiment. The mutual correlation method was selected to construct the mutual correlation brain network of EEG signals before and after watching videos in two modes. We also calculated the mutual correlation coefficient matrix and the mutual correlation binary matrix and compared the average of degree, clustering coefficient, path length, global efficiency and small world attribute during two experiments. The results showed that the subjects were easier to get fatigue by watching virtual reality video than watching video presented by traditional displayer in a certain period of time. By comparing the characteristic parameters of brain network before and after watching videos, it was found that the average degree value, the average clustering coefficient, the average global efficiency and the small world attribute decreases while the average path length value increased significantly. In addition, compared to traditional plane video, the characteristic parameters of brain network changed more greatly after watching the virtual reality video with a significant difference (P < 0.05). This study can provide theoretical basis and experimental reference for analyzing and evaluating brain fatigue induced by virtual reality visual experience.