Migraine is the most common primary headache clinically, with high disability rate and heavy burden. Functional MRI (fMRI) plays a significant role in the study of migraine. This article reviews the main advances of migraine without aura (MwoA) based on resting-state fMRI in recent years, including the exploration of the mechanism of fMRI in the occurrence and development of MwoA in terms of regional functional activities and functional network connections, as well as the research progress of the potential clinical application of fMRI in aiding diagnosis and assessing treatment effect for MwoA. At last, this article summarizes the current distresses and prospects of fMRI research on MwoA.
Although attention plays an important role in cognitive and perception, there is no simple way to measure one's attention abilities. We identified that the strength of brain functional network in sustained attention task can be used as the physiological indicator to predict behavioral performance. Behavioral and electroencephalogram (EEG) data from 14 subjects during three force control tasks were collected in this paper. The reciprocal of the product of force tolerance and variance were used to calculate the score of behavioral performance. EEG data were used to construct brain network connectivity by wavelet coherence method and then correlation analysis between each edge in connectivity matrices and behavioral score was performed. The linear regression model combined those with significantly correlated network connections into physiological indicator to predict participant's performance on three force control tasks, all of which had correlation coefficients greater than 0.7. These results indicate that brain functional network strength can provide a widely applicable biomarker for sustained attention tasks.
Transcranial direct current stimulation (tDCS) is an emerging non-invasive brain stimulation technique. However, the rehabilitation effect of tDCS on stroke disease is unclear. In this paper, based on electroencephalogram (EEG) and complex network analysis methods, the effect of tDCS on brain function network of stroke patients during rehabilitation was investigated. The resting state EEG signals of 31 stroke rehabilitation patients were collected and divided into stimulation group (16 cases) and control group (15 cases). The Pearson correlation coefficients were calculated between the channels, brain functional network of two groups were constructed before and after stimulation, and five characteristic parameters were analyzed and compared such as node degree, clustering coefficient, characteristic path length, global efficiency, and small world attribute. The results showed that node degree, clustering coefficient, global efficiency, and small world attributes of brain functional network in the tDCS group were significantly increased, characteristic path length was significantly reduced, and the difference was statistically significant (P < 0.05). It indicates that tDCS can improve the brain function network of stroke patients in rehabilitation period, and may provide theory and experimental basis for the application of tDCS in stroke rehabilitation treatment.
The measurement of network is one of the important researches in resolving neuronal population information processing mechanism using complex network theory. For the quantitative measurement problem of functional neural network, the relation between the measure indexes, i.e. the clustering coefficient, the global efficiency, the characteristic path length and the transitivity, and the network topology was analyzed. Then, the spike-based functional neural network was established and the simulation results showed that the measured network could represent the original neural connections among neurons. On the basis of the former work, the coding of functional neural network in nidopallium caudolaterale (NCL) about pigeon's motion behaviors was studied. We found that the NCL functional neural network effectively encoded the motion behaviors of the pigeon, and there were significant differences in four indexes among the left-turning, the forward and the right-turning. Overall, the establishment method of spike-based functional neural network is available and it is an effective tool to parse the brain information processing mechanism.
Objective To investigate the differences in the topology of functional brain networks between populations with good spatial navigation ability and those with poor spatial navigation ability. Methods From September 2020 to September 2021, 100 college students from PLA Army Border and Coastal Defense Academy were selected to test the spatial navigation ability. The 25 students with the highest spatial navigation ability were selected as the GN group, and the 25 with the lowest spatial navigation ability were selected as the PN group, and their resting-state functional MRI and 3D T1-weighted structural image data of the brain were collected. Graph theory analysis was applied to study the topology of the brain network, including global and local topological properties. Results The variations in the clustering coefficient, characteristic path length, and local efficiency between the GN and PN groups were not statistically significant within the threshold range (P>0.05). The brain functional connectivity networks of the GN and PN groups met the standardized clustering coefficient (γ)>1, the standardized characteristic path length (λ)≈1, and the small-world property (σ)>1, being consistent with small-world network property. The areas under curve (AUCs) for global efficiency (0.22±0.01 vs. 0.21±0.01), γ value (0.97±0.18 vs. 0.81±0.18) and σ value (0.75±0.13 vs. 0.64±0.13) of the GN group were higher than those of the PN group, and the differences were statistically significant (P<0.05); the between-group difference in AUC for λ value was not statistically significant (P>0.05). The results of the nodal level analysis showed that the AUCs for nodal clustering coefficients in the left superior frontal gyrus of orbital region (0.29±0.05 vs. 0.23±0.07), the right rectus gyrus (0.29±0.05 vs. 0.23±0.09), the middle left cingulate gyrus and its lateral surround (0.22±0.02 vs. 0.25±0.02), the left inferior occipital gyrus (0.32±0.05 vs. 0.35±0.05), the right cerebellar area 3 (0.24±0.04 vs. 0.26±0.03), and the right cerebellar area 9 (0.22±0.09 vs. 0.13±0.13) were statistically different between the two groups (P<0.05). The differences in AUCs for degree centrality and nodal efficiency between the two groups were not statistically significant (P>0.05). Conclusions Compared with people with good spatial navigation ability, the topological properties of the brains of the ones with poor spatial navigation ability still conformed to the small-world network properties, but the connectivity between brain regions reduces compared with the good spatial navigation ability group, with a tendency to convert to random networks and a reduced or increased nodal clustering coefficient in some brain regions. Differences in functional brain network connectivity exist among people with different spatial navigation abilities.
In recent years, exploring the physiological and pathological mechanisms of brain functional integration from the neural network level has become one of the focuses of neuroscience research. Due to the non-stationary and nonlinear characteristics of neural signals, its linear characteristics are not sufficient to fully explain the potential neurophysiological activity mechanism in the implementation of complex brain functions. In order to overcome the limitation that the linear algorithm cannot effectively analyze the nonlinear characteristics of signals, researchers proposed the transfer entropy (TE) algorithm. In recent years, with the introduction of the concept of brain functional network, TE has been continuously optimized as a powerful tool for nonlinear time series multivariate analysis. This paper first introduces the principle of TE algorithm and the research progress of related improved algorithms, discusses and compares their respective characteristics, and then summarizes the application of TE algorithm in the field of electrophysiological signal analysis. Finally, combined with the research progress in recent years, the existing problems of TE are discussed, and the future development direction is prospected.
This study is aimed to investigate objective indicators of mental fatigue evaluation to improve the accuracy of mental fatigue evaluation. Mental fatigue was induced by a sustained cognitive task. The brain functional networks in two states (normal state and mental fatigue state) were constructed based on electroencephalogram (EEG) data. This study used complex network theory to calculate and analyze nodal characteristics parameters (degree, betweenness centrality, clustering coefficient and average path length of node), and served them as the classification features of support vector machine (SVM). Parameters of the SVM model were optimized by gird search based on 6-fold cross validation. Then, the subjects were classified. The results show that characteristic parameters of node of brain function networks can be divided into normal state and mental fatigue state, which can be used in the objective evaluation of mental fatigue state.
Mild cognitive impairment (MCI) is a clinical transition state between age-related cognitive decline and dementia. Researchers can use neuroimaging and neurophysiological techniques to obtain structural and functional information about the human brain. Using this information researchers can construct the brain network based on complex network theory. The literature on graph theory shows that the large-scale brain network of MCI patient exhibits small-world property, which ranges intermediately between Alzheimer's disease and that in the normal control group. But brain connectivity of MCI patients presents topologically structural disorder. The disorder is significantly correlated to the cognitive functions. This article reviews the recent findings on brain connectivity of MCI patients from the perspective of multimodal data. Specifically, the article focuses on the graph theory evidences of the whole brain structural and functional and the joint covariance network disorders. At last, the article shows the limitations and future research directions in this field.
Electroconvulsive therapy (ECT) is an interventional technique capable of highly effective neuromodulation in major depressive disorder (MDD), but its antidepressant mechanism remains unclear. By recording the resting-state electroencephalogram (RS-EEG) of 19 MDD patients before and after ECT, we analyzed the modulation effect of ECT on the resting-state brain functional network of MDD patients from multiple perspectives: estimating spontaneous EEG activity power spectral density (PSD) using Welch algorithm; constructing brain functional network based on imaginary part coherence (iCoh) and calculate functional connectivity; using minimum spanning tree theory to explore the topological characteristics of brain functional network. The results show that PSD, functional connectivity, and topology in multiple frequency bands were significantly changed after ECT in MDD patients. The results of this study reveal that ECT changes the brain activity of MDD patients, which provides an important reference in the clinical treatment and mechanism analysis of MDD.