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.
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.
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.
How to extract high discriminative features that help classification from complex resting-state fMRI (rs-fMRI) data is the key to improving the accuracy of brain disease recognition such as schizophrenia. In this work, we use a weighted sparse model for brain network construction, and utilize the Kendall correlation coefficient (KCC) to extract the discriminative connectivity features for schizophrenia classification, which is conducted with the linear support vector machine. Experimental results based on the rs-fMRI of 57 schizophrenia patients and 64 healthy controls show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 81.82%) than other competing methods. Specifically, compared with the traditional network construction methods (Pearson’s correlation and sparse representation) and the commonly used feature selection methods (two-sample t-test and Least absolute shrinkage and selection operator (Lasso)), the algorithm proposed in this paper can more effectively extract the discriminative connectivity features between the schizophrenia patients and the healthy controls, and further improve the classification accuracy. At the same time, the discriminative connectivity features extracted in the work could be used as the potential clinical biomarkers to assist the identification of schizophrenia.
The construction of brain functional network based on resting-state functional magnetic resonance imaging (fMRI) is an effective method to reveal the mechanism of human brain operation, but the common brain functional network generally contains a lot of noise, which leads to wrong analysis results. In this paper, the least absolute shrinkage and selection operator (LASSO) model in compressed sensing is used to reconstruct the brain functional network. This model uses the sparsity of L1-norm penalty term to avoid over fitting problem. Then, it is solved by the fast iterative shrinkage-thresholding algorithm (FISTA), which updates the variables through a shrinkage threshold operation in each iteration to converge to the global optimal solution. The experimental results show that compared with other methods, this method can improve the accuracy of noise reduction and reconstruction of brain functional network to more than 98%, effectively suppress the noise, and help to better explore the function of human brain in noisy environment.
Analyzing the influence of mixed emotional factors on false memory through brain function network is helpful to further explore the nature of brain memory. In this study, Deese-Roediger-Mc-Dermott (DRM) paradigm electroencephalogram (EEG) experiment was designed with mixed emotional memory materials, and different kinds of music were used to induce positive, calm and negative emotions of three groups of subjects. For the obtained false memory EEG signals, standardized low resolution brain electromagnetic tomography algorithm (sLORETA) was applied in the source localization, and then the functional network of cerebral cortex was built and analyzed. The results show that the positive group has the most false memories [(83.3 ± 6.8)%], the prefrontal lobe and left temporal lobe are activated, and the degree of activation and the density of brain network are significantly larger than those of the calm group and the negative group. In the calm group, the posterior prefrontal lobe and temporal lobe are activated, and the collectivization degree and the information transmission rate of brain network are larger than those of the positive and negative groups. The negative group has the least false memories [(73.3 ± 2.2)%], and the prefrontal lobe and right temporal lobe are activated. The brain network is the sparsest in the negative group, the degree of centralization is significantly larger than that of the calm group, but the collectivization degree and the information transmission rate of brain network are smaller than the positive group. The results show that the brain is stimulated by positive emotions, so more brain resources are used to memorize and associate words, which increases false memory. The activity of the brain is inhibited by negative emotions, which hinders the brain’s memory and association of words and reduces false memory.