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Objective At present, the grading evaluation of patients with disorders of consciousness (DOC) is still a focus and difficulty in related fields. Electroencephalogram (EEG) can directly read and continuously reflect scalp electrical activity generated by brain tissue structure, with high temporal resolution. Auditory stimulation is easy to operate and has broad application prospects in clinical detection of DOC. The causal network can intuitively reflect the direction of information transmission through the causal relationship between time series, helping us better understand the information interaction between different regions of the brain of patients. This paper combines EEG and causal networks to explore the differences in brain functional connectivity between patients with unresponsive arousal syndrome (VS) and those with minimum state of consciousness (MCS) under auditory stimulation. MethodsA total of 23 DOC patients were included, including 11 MCS patients and 12 VS patients. Based on the Oddball paradigm, auditory naming stimulation was performed on DOC patients and EEG signals of DOC patients were synchronously collected. The brain functional networks were constructed using multivariate Granger causality method, and the differences in node degree, clustering coefficient, global efficiency, and causal flow of the brain networks between MCS patients and VS patients were calculated. The differences in network characteristics of patients with different levels of consciousness under auditory stimulation were compared from the perspective of cooperation between brain regions. ResultsThe causal connectivity between most brain regions in MCS patients was stronger than that in VS patients, and MCS patients had more brain network connectivity edges than VS patients. The average degree (P<0.05), average clustering coefficient, and global efficiency (P<0.05) of MCS patients under naming stimulation were higher than those of VS patients. The difference in out-degree between each node of VS patients was larger, and the difference in in-degree between each node of MCS patients was smaller. The difference in in-degree of MCS patients was more significant than that of VS patients, and the inflow and outflow of information in the brain functional network of MCS patients were stronger than those of VS patients. MCS and VS patients had differences of causal flow in the frontal and temporal lobes, the direction of information transmission in the parietal lobe and central region was not the same, and MCS patients had more electrodes as causal sources than VS patients. ConclusionThe information transmission ability of MCS patients is stronger than that of VS patients under auditory naming stimulation. Compared with VS patients, MCS patients have an increase in the number of electrode channels as the causal source, an increase in information output to other brain regions, and also an increase in the information output within brain regions, which may indicate a better state of consciousness in patients. MCS patients have more electrode channels for information output in the frontal lobe than VS patients, and the number of electrode channels for changing the direction of information transmission in the frontal lobe is the highest. The frontal lobe is closely related to the level of consciousness in patients with consciousness disorders. This study can provide a theoretical basis for the grading evaluation of consciousness levels in DOC patients.
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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.
Subject(s)
Humans , Depressive Disorder, Major/therapy , Electroconvulsive Therapy , Brain , Algorithms , ElectroencephalographyABSTRACT
Objective:To evaluate the changes in topological properties of brain functional network after induction of general anesthesia in the patients with glioma.Methods:Twenty-two patients scheduled for elective intracranial glioma resection were selected.Resting-state functional magnetic resonance imaging was performed during wakefulness and general anesthesia with endotracheal intubation in patients with glioma. Large-scale functional brain networks of each patient were constructed based on 123 regions of interest in non-surgical hemisphere. Global properties (local efficiency, clustering parameter, shortest path length, global efficiency, small world) and nodal properties (nodal degree, nodal efficiency, and between centrality) in brain functional networks were then compared between wakefulness and general anesthesia.Results:Eighteen patients were finally enrolled. Compared with the status during wakefulness, the local efficiency and clustering parameter on non-surgical side significantly decreased ( P<0.05), no significant change was found in the shortest path length and global efficiency ( P>0.05), and small world was greater than 1 throughout the entire density range; the nodal degree, nodal efficiency and between centrality of nodes located in the medial/mesal regions, such as the medial prefrontal cortex, posterior cingulate gyrus/precuneus, medial temporal lobe, anterior cingulate gyrus, thalamus, amygdala, were significantly reduced ( P<0.05); however, these node parameters increased significantly in the lateral brain regions ( P<0.05) except for the primary auditory and somatosensory cortex, which also decreased significantly after induction of general anesthesia( P<0.05). Conclusions:The functional segregation of brain functional network is widely inhibited after induction of general anesthesia, but the functional integration is still retained. The lateral brain regions show no anticorrelation with the medial brain region during general anesthesia.
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Tinnitus is an auditory perception in the absence of an auditory stimulus, and its pathogenesis is extremely complex and has not been clear so far. Tinnitus is a common disease in neurology and otorhinolaryngology. About 10% adults have experienced tinnitus. At present, there is no cure, which brings a heavy burden to society and economy. With the development of neuroimaging technology, functional magnetic resonance imaging (fMRI) has been widely used in the study of brain functional networks in neuropsychiatric diseases. This review briefly describes the pathogenesis of subjective tinnitus and summarizes the research and progress of sound therapy and neuromodulation based on brain functional network, in order to provide help for diagnosis, early treatment and clinical prognosis of tinnitus.
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Objective:To investigate the topological properties of whole-brain functional networks in first-episode drug-na?ve adolescents with major depressive disorder (MDD).Methods:Seventy-six first-episode drug-na?ve adolescents with MDD admitted to Department of Neurology, Xiangyang No.1 Hospital Affilated to Hubei University of Medicince from January 2022 to January 2023 were selected as study subjects; 66 gender- and age-matched healthy controls (HCs) were recruited via advertisement. All subjects underwent resting-state functional MRI. The whole-brain functional networks were constructed for each subject; and then, the global topological metrics (global efficiency, local efficiency, clustering coefficient, characteristic path length, normalized clustering coefficient, normalized characteristic path length, and small-worldness properties) and local topological metrics (nodal degree centrality, nodal efficiency and nodal betweenness centrality) of the functional brain networks were analyzed between the two groups using graph-theory methods. Network-based statistics were used to examine between-group differences in functional connectivity strength of whole brain networks. Results:Small-worldness properties were demonstrated in both MDD group and HC group. MDD patients showed significantly higher global efficiency (0.129[0.124, 0.132] vs. 0.131[0.128, 0.133]), significantly lower clustering coefficient and characteristic path length (0.143[0.139, 0.146] vs. 0.139[0.135, 0.144]; 0.457[0.446, 0.734] vs. 0.451[0.440, 0.463]), and significantly increased nodal centralities in the right inferior parietal lobule, bilateral caudate nucleus and bilateral thalamus of brain functional networks compared with HCs ( P<0.05, FDR-corrected). Compared with HCs, MDD patients exhibited obviously lower functional connectivity strength in the orbitofrontal-temporal and anterior cingulate-limbic-temporal circuits. Conclusion:Abnormal alterations of topological properties of the brain functional networks are found in adolescents with MDD, which may be the underlying neuropathologic basis for MDD.
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An in-depth understanding of the mechanism of lower extremity muscle coordination during walking is the key to improving the efficacy of gait rehabilitation in patients with neuromuscular dysfunction. This paper investigates the effect of changes in walking speed on lower extremity muscle synergy patterns and muscle functional networks. Eight healthy subjects were recruited to perform walking tasks on a treadmill at three different speeds, and the surface electromyographic signals (sEMG) of eight muscles of the right lower limb were collected synchronously. The non-negative matrix factorization (NNMF) method was used to extract muscle synergy patterns, the mutual information (MI) method was used to construct the alpha frequency band (8-13 Hz), beta frequency band (14-30 Hz) and gamma frequency band (31-60 Hz) muscle functional network, and complex network analysis methods were introduced to quantify the differences between different networks. Muscle synergy analysis extracted 5 muscle synergy patterns, and changes in walking speed did not change the number of muscle synergy, but resulted in changes in muscle weights. Muscle network analysis found that at the same speed, high-frequency bands have lower global efficiency and clustering coefficients. As walking speed increased, the strength of connections between local muscles also increased. The results show that there are different muscle synergy patterns and muscle function networks in different walking speeds. This study provides a new perspective for exploring the mechanism of muscle coordination at different walking speeds, and is expected to provide theoretical support for the evaluation of gait function in patients with neuromuscular dysfunction.
Subject(s)
Humans , Walking Speed , Muscle, Skeletal/physiology , Electromyography , Gait/physiology , Walking/physiologyABSTRACT
Objective:To use the resting state functional network connectivity (FNC) method based on independent component analysis (ICA) to analyze the characteristics of FNC changes in patients with basal ganglia aphasia (BGA) after stroke, and to explore its occurrence and recovery mechanism under the intervention of acupuncture combined with language rehabilitation training.Methods:Using a prospective observational research method, 16 right-handed BGA patients who were treated at Beijing Hospital of Traditional Chinese Medicine, Capital Medical University from July 2021 to December 2022, as well as 14 healthy subjects matched in age, gender, education level, and handedness, were included. The resting state functional magnetic resonance imaging, demographic information, and Western Aphasia Examination data of healthy subjects and BGA patients before and after intervention were collected. The GIFT toolbox based on MATLAB platform was applied for ICA and resting state brain network FNC analysis. The FNC differences between BGA patients and healthy subjects were compared horizontally, and the FNC changes in BGA patients before and after intervention were compared vertically.Results:Compared with healthy subjects, post-stroke BGA patients showed decreased connectivity between the basal ganglia network, default network, and visual network before intervention, while increased connectivity between the auditory network, right frontoparietal network, and anterior cuneiform network; After the intervention of acupuncture combined with language rehabilitation training, the connectivity between the basal ganglia network, visual network, and anterior cuneiform network decreased, while the connectivity between the anterior convex network and bilateral frontoparietal network decreased, while the connectivity between the default network, auditory network, right frontoparietal network, and visual network increased. The BGA patient group showed enhanced connectivity between the basal ganglia network and the left frontoparietal network before and after intervention.Conclusions:The FNC changes between the basal ganglia network and other brain networks are key to reflecting the mechanism of BGA occurrence and language function recovery. Acupuncture combined with language rehabilitation training may improve language function by enhancing the connectivity between the basal ganglia network and the left frontoparietal network, and the redistribution of attention resources may also be one of the reasons for promoting language function recovery in BGA patients.
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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.
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Algorithms , Brain/physiology , Entropy , Neural Networks, Computer , Nonlinear DynamicsABSTRACT
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.
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Humans , Aging/physiology , Brain/physiology , Brain Mapping , Magnetic Resonance Imaging/methods , Nerve Net/physiology , Transcranial Direct Current Stimulation/methodsABSTRACT
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.
Subject(s)
Humans , Electroencephalography , Emotions , Memory , Music , Prefrontal CortexABSTRACT
Objective:To investigate the topological alterations of brain functional networks in patients with chronic methamphetamine (MA) dependence.Methods:Resting-state functional magnetic resonance imaging was used to map the brain networks of 46 patients with MA-dependence (MA group) and 46 healthy controls (control group). Statistical methods were used to compare the differences of brain functional connection and topological parameters between the two groups, and the correlations between these topological parameters with significant inter-group differences and clinical measurements were analyzed.Results:(1) Brain functional connection: as compared with the control group, the MA group had significantly enhanced functional connectivity in the subnetworks consisting of several brain regions, including the inferior parietal lobule, posterior central gyrus, lateral occipital cortex, ventromedial occipital cortex, orbital gyrus, anterior central gyrus, fusiform gyrus, superior temporal gyrus and thalamus; as compared with the control group, the MA group had significantly attenuated functional connectivity in the subnetworks consisting of several brain regions, the orbit frontal cortex, precentral gyrus, paracenter lobule, inferior temporal gyrus, fusiform gyrus, parahippocampal gyrus, superior parietal lobule, postcentral gyrus, medioventral occipital cortex, lateral occipital cortex and amygdala. (2) Network topology attributes: the brain functional networks in all subjects from the MA group and control group held worldlet; but attribute of worldlet in the MA group was significantly reduced as compared with that in the control group ( P<0.05); moreover, the MA group had significantly decreased clustering coefficient, local efficiency, and modularity as compared with the control group ( P<0.05). In terms of regional topological attributes, such as betweenness centrality, the MA group presented evident reduction in the left superior frontal gyrus, right orbit frontal cortex, right middle temporal gyrus and right/left lateral occipital cortex as compared with the control group with significant differences ( P<0.05). (3)Correlation analysis: the betweenness centrality of right middle temporal gyrus exhibited a positive correlation with the age of patients using MA for the first time ( r=0.327, P=0.028); a positive correlation was found between the modularity and activating factor scores in Brief Psychiatric Rating Scale (BPRS) in MA group ( r=0.315, P=0.035). Conclusions:Part of the global/local topological attributes of the brain functional network of patients with MA addiction are damaged. The younger the patients are when they take MA for the first time, the lower the betweenness centrality of the right middle temporal gyrus; the more the local attributes are damaged; and furthermore, the deeper the network modularity, the severer the active symptoms in the psychotic symptoms.
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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.
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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.
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Objective:To estimate the predictive ability of group differences on autistic traits of social communication impairment by comparing the intrinsic functional network connectivity between default mode network and other brain networks between preschoolers with autism and typically developing control.Methods:Sixty preschoolers diagnosed autism according to DSM-5 and 60 typical developing individuals matched by age and sex were analyzed using resting-state functional magnetic resonance imaging (MRI). Establish functional network connections between the default mode network and other brain networks based on the results of the data-driven approach (independent component analysis). Subsequently, the correlation between the connectivity strength with statistical differences and the autistic traits of social communications impairments was analyzed.Results:Relative to typically developing control participants, preschoolers with autism showed increased functional connectivity between the medial prefrontal cortex and subcortical networks (basal ganglia and thalamus, Bg/Th) ( t=3.758, P<0.01, FDR-corrected). The strength of such connections was significantly associated with the severity of autistic core social communication disorders ( r=0.34, P=0.007). Furthermore, the average connection strength of DMN showed a hyper-FNC with the basal ganglion network ( t=3.455, P<0.01, FDR-corrected). Conclusion:There is an excessive functional connection between medial prefrontal cortex and subcortical nucleus in preschool autism children. The abnormal functional connection of DMN may be the key factor of core social disorder in preschool autism children.
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Modeling a protein functional network in concerned species is an efficient approach for identifying novel genesin certain biological pathways. Tea plant (Camellia sinensis) is an important commercial crop abundant innumerous characteristic secondary metabolites (e.g., polyphenols, alkaloids, alkaloids) that confer tea qualityand health benefits. Decoding novel genes responsible for tea characteristic components is an important basisfor applied genetic improvement and metabolic engineering. Herein, a high-quality protein functional networkfor tea plant (TeaPoN) was predicted using cross-species protein functional associations transferring andintegration combined with a stringent biological network criterion control. TeaPoN contained 31,273 nonredundant functional interactions among 6,634 tea proteins (or genes), with general network topologicalproperties such as scale-free and small-world. We revealed the modular organization of genes related to themajor three tea characteristic components (theanine, caffeine, catechin) in TeaPoN, which served as strongevidence for the utility of TeaPoN in novel gene mining. Importantly, several case studies regarding geneidentification for tea characteristic components were presented. To aid in the use of TeaPoN, a concise webinterface for data deposit and novel gene screening was developed (http://teapon.wchoda.com). We believe thatTeaPoN will serve as a useful platform for functional genomics studies associated with characteristic secondarymetabolites in tea plant.
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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.
Subject(s)
Humans , Algorithms , Brain , Brain Mapping , Magnetic Resonance Imaging , Schizophrenia , Diagnostic ImagingABSTRACT
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
Subject(s)
Humans , Algorithms , Brain/diagnostic imaging , Image Processing, Computer-Assisted , Magnetic Resonance ImagingABSTRACT
Exoskeleton nursing robot is a typical human-machine co-drive system. To full play the subjective control and action orientation of human, it is necessary to comprehensively analyze exoskeleton wearer's surface electromyography (EMG) in the process of moving patients, especially identifying the spatial distribution and internal relationship of the EMG information. Aiming at the location of electrodes and internal relation between EMG channels, the complex muscle system at the upper limb was abstracted as a muscle functional network. Firstly, the correlation characteristics were analyzed among EMG channels of the upper limb using the mutual information method, so that the muscle function network was established. Secondly, by calculating the characteristic index of network node, the features of muscle function network were analyzed for different movements. Finally, the node contraction method was applied to determine the key muscle group that reflected the intention of wearer's movement, and the characteristics of muscle function network were analyzed in each stage of moving patients. Experimental results showed that the location of the myoelectric collection could be determined quickly and efficiently, and also various stages of the moving process could effectively be distinguished using the muscle functional network with the key muscle groups. This study provides new ideas and methods to decode the relationship between neural controls of upper limb and physical motion.
Subject(s)
Humans , Electromyography , Exoskeleton Device , Muscle, Skeletal , Physiology , Robotics , Upper ExtremityABSTRACT
@#Objective To study the coordination mechanism of brain induced by stimulating on acupoints of different meridians by means of constructing cerebral cortex functional networks reflecting actual brain functional connections. Methods A 128-lead electroencephalogram (EEG) recording and analysis system was used to collect the EEG signals of 14 healthy subjects (eight males and six females) aged 21 to 25 in resting state and magnetic stimulation on the acupuncture points of Guangming (GB37) and Neiguan (PC6) located in different meridians from October to November, 2017. Then EEG sources were localized by group independent component analysis and standard low-resolution brain electromagnetic tomography, and the statistical relationships of EEG components were calculated. Finally, alpha-wave cerebral cortex functional networks were constructed and analyzed based on complex network theory. Results The connections of brain regions associated with movement, vision increased in the stimulation of Guangming acupoint. The brain regions associated with movement, attention and working memory in the stimulation of the Neiguan acupoint were reduced in the network. Some common brain areas were activated and some changes of functional connections were similar. Conclusion The changes in the topological structure of brain networks are basically consistent with the efficacy of the acupuncture point during magnetic stimulation on Guangming and Neiguan of different meridians, however, it causes some similar changes in functional connectivity connections of some brain regions.
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<p><b>BACKGROUND</b>Functional magnetic resonance imaging (fMRI) is a novel method for studying the changes of brain networks due to acupuncture treatment. In recent years, more and more studies have focused on the brain functional connectivity network of acupuncture stimulation.</p><p><b>OBJECTIVE</b>To offer an overview of the different influences of acupuncture on the brain functional connectivity network from studies using resting-state fMRI.</p><p><b>SEARCH STRATEGY</b>The authors performed a systematic search according to PRISMA guidelines. The database PubMed was searched from January 1, 2006 to December 31, 2016 with restriction to human studies in English language.</p><p><b>INCLUSION CRITERIA</b>Electronic searches were conducted in PubMed using the keywords "acupuncture" and "neuroimaging" or "resting-state fMRI" or "functional connectivity".</p><p><b>DATA EXTRACTION AND ANALYSIS</b>Selection of included articles, data extraction and methodological quality assessments were respectively conducted by two review authors.</p><p><b>RESULTS</b>Forty-four resting-state fMRI studies were included in this systematic review according to inclusion criteria. Thirteen studies applied manual acupuncture vs. sham, four studies applied electro-acupuncture vs. sham, two studies also compared transcutaneous electrical acupoint stimulation vs. sham, and nine applied sham acupoint as control. Nineteen studies with a total number of 574 healthy subjects selected to perform fMRI only considered healthy adult volunteers. The brain functional connectivity of the patients had varying degrees of change. Compared with sham acupuncture, verum acupuncture could increase default mode network and sensorimotor network connectivity with pain-, affective- and memory-related brain areas. It has significantly greater connectivity of genuine acupuncture between the periaqueductal gray, anterior cingulate cortex, left posterior cingulate cortex, right anterior insula, limbic/paralimbic and precuneus compared with sham acupuncture. Some research had also shown that acupuncture could adjust the limbic-paralimbic-neocortical network, brainstem, cerebellum, subcortical and hippocampus brain areas.</p><p><b>CONCLUSION</b>It can be presumed that the functional connectivity network is closely related to the mechanism of acupuncture, and central integration plays a critical role in the acupuncture mechanism.</p>