Effect of electroconvulsive therapy on brain functional network in major depressive disorder / 生物医学工程学杂志

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.

Research progress and application of transfer entropy algorithm / 生物医学工程学杂志

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.

The measurements of the similarity of dynamic brain functional network / 生物医学工程学杂志

Brain functional network changes over time along with the process of brain development, disease, and aging. However, most of the available measurements for evaluation of the difference (or similarity) between the individual brain functional networks are for charactering static networks, which do not work with the dynamic characteristics of the brain networks that typically involve a long-span and large-scale evolution over the time. The current study proposes an index for measuring the similarity of dynamic brain networks, named as dynamic network similarity (DNS). It measures the similarity by combining the "evolutional" and "structural" properties of the dynamic network. Four sets of simulated dynamic networks with different evolutional and structural properties (varying amplitude of changes, trend of changes, distribution of connectivity strength, range of connectivity strength) were generated to validate the performance of DNS. In addition, real world imaging datasets, acquired from 13 stroke patients who were treated by transcranial direct current stimulation (tDCS), were used to further validate the proposed method and compared with the traditional similarity measurements that were developed for static network similarity. The results showed that DNS was significantly correlated with the varying amplitude of changes, trend of changes, distribution of connectivity strength and range of connectivity strength of the dynamic networks. DNS was able to appropriately measure the significant similarity of the dynamics of network changes over the time for the patients before and after the tDCS treatments. However, the traditional methods failed, which showed significantly differences between the data before and after the tDCS treatments. The experiment results demonstrate that DNS may robustly measure the similarity of evolutional and structural properties of dynamic networks. The new method appears to be superior to the traditional methods in that the new one is capable of assessing the temporal similarity of dynamic functional imaging data.

Small-world Network Features of Brain Functional Network as Strong Void Perception for Healthy Female / 中国康复理论与实践

Objective:To identify the small-world network property of brain functional network provoked by a strong desire to void in healthy women. Methods:From 2017 to 2018, 21 healthy women were enrolled, and scanned with resting-state functional magnetic resonance imaging under the empty bladder and strong desire to void, respectively. Brain connection matrix was established with Pearson's correlation analysis, and the differences in topologic properties between the two conditions were identified with paired t-test and Bonferroni correction. The small-world parameters, named clustering coefficient (Cp), characteristic path length (Lp), global efficiency (Eglob), local efficiency (Eloc) and nodal efficiency (Enodal) were calculated. Results:There were two women dropped down because of head moving. For the other 19 women, the brain connection presented a small-world network property under the both conditions. Compared with the empty bladder, Cp, Lp, and Eloc decreased, and Eglob increased under the strong desire to void (P < 0.05); while Enodal increased in left inferior frontal gyrus and superior frontal gyrus; right cingulate gyrus, middle occipital gyrus and middle temporal gyrus; and bilateral gyrus rectus and inferior parietal lobes; and decreased in bilateral fusiform gyrus, calcarine fissure and surrounding, and lingual gyrus (P < 0.05). Conclusion:Brain functional network presents a small-world network property under both empty bladder and a strong desire to void. The regulation of lower urinary tract function involves the coordination of multiple brain regions.

Changes of brain functional network in neonates with different degrees of hypoxic-ischemic encephalopathy / 中华行为医学与脑科学杂志

Objective:To explore the changes of brain network functional connection in neonates with different degrees of hypoxic-ischemic encephalopathy(HIE), and to understand its influence on brain function.Methods:Clinical data of full-term HIE children hospitalized in Neonatology Department of Changzhou Children's Hospital from January 2017 to May 2020 were collected by convenient sampling method. A total of 44 cases were scanned by conventional and functional magnetic resonance image.Twenty-four of them met the inclusion criteria, including 11 mild patients (PT1 group) and 13 moderate and severe patients (PT2 group). The amplitude of low frequency fluctuation (ALFF) was used to compare the differences of ALFF values between PT1 group and PT2 group, and the differences of functional connectivity (FC) between PT1 group and PT2 group were compared by the method of brain network connectivity analysis.Results:In the edge analysis, compared with the PT1 group, the FC of the right supplementary motor area and the right precentral gyrus ( Z1=0.39, Z2 =-0.08), the right lingual gyrus and the right hippocampus ( Z1=0.61, Z2=0.20), the left calcarine cortex and the right amygdala ( Z1=0.30, Z2=-0.02), the right pallidus and the right posterior cingulate cortex ( Z1=0.33, Z2=0.05) were decreased in the PT2 group (all P<0.001, uncorrected). In ALFF analysis, there was no significant difference between PT1 group and PT2 group ( P>0.05, FDR adjusted). Conclusion:There are changes in functional connections in some brain regions in children with moderate and severe HIE.These functional connections are related to motor function, emotional processing, language development, cognitive function, learning and memory, etcetera.

Research on the influence of mixed emotional factors on false memory based on brain functional network / 生物医学工程学杂志

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.

Research on brain network for schizophrenia classification based on resting-state functional magnetic resonance imaging / 生物医学工程学杂志

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 ( ., 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 -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.

Brain functional network reconstruction based on compressed sensing and fast iterative shrinkage-thresholding algorithm / 生物医学工程学杂志

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

Research on classification of brain functional network features during mental fatigue / 生物医学工程学杂志

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.

Biomarker extraction of sustained attention based on brain functional network / 生物医学工程学杂志

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.

Executive Function and Motor Functional Recovery in Stroke Patients:Influence and Underlying Mechanism (review) / 中国康复理论与实践

@#Executive function is a superordinate cognitive function of the brain. Executive dysfunction post stroke plays a very impor-tant role in recovery of motor function, which is associated with motor learning, postural control, etc. Executive function training may pro-mote the recovery of motor function. The potential neurological mechanism includes the cerebral network involving a variety of areas.

Brain Functional Connectivity in Patients with Migraine Based on Complex Networks Analysis / 中国医学影像学杂志

Purpose To investigate the topological structure differences between the migraine patients group and the normal control group by using resting-state brain complex networks constructed based on graph theory. Materials and Methods Resting-state functional magnetic resonance imaging dataset were obtained from 22 migraine patients and 22 normal subjects. The functional complex networks of the two groups were constructed, and parameters including average clustering coefficient, characteristic path length, small worldness, assortativity, and betweenness of the two groups were respectively calculated. Results When compared with the parameters of normal control group, average clustering coefficient of migraine patients group was larger, small worldness and assortativity were also changed. The characteristic path length of the caudate nucleus and putamen areas presented abnormal in the migraine patients group. Betweenness centrality of the thalamus, inferior occipital gyrus and occipital gyrus increased in the migraine patients group. Conclusion The abnormal brain regions in the migraine patients group were mainly associated with pain processing, visual processing and sensory information relay. This study may contribute to better understanding and interpreting corresponding clinical condition of migraine.

Temporal Reliability and Lateralization of Resting-state Language Functional Connectivity Network Analysis Based on Order Statistics Correlation Coefifcient / 中国医学影像学杂志

Purpose To investigate the activity of language network with brain function connection network analysis method using MRI order statistics correlation coefficient, and to explore the temporal reliability and functional asymmetry, and provide the theoretical foundation for clinical researches of resting-state language network.Materials and Methods Twenty-five healthy volunteers were scanned three times in resting state. All data were processed using 32 bit Matlab 7.11.0 and DPARSF. The two main language functional areas, Broca and Wernicke, were selected as the regions of interest. The functional connectivity of language network was analyzed with order statistics correlation coefficient.Results Based on the functional connectivity diagrams using seed analysis method, the asymmetry index and intra-class correlation were obtained. The functional connectivity of resting-state language network based on order statistics correlation coefficient was similar to that using the traditional correlation coefficient methods. Conclusion The temporal reliability of resting-state language network can provide a reference value for clinical research of language disorders, as well as the clinical diagnosis and treatment of the language disorders or mental diseases caused by abnormal functional asymmetry of language network.