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Objective:To explore the effect of Xingnao Kaiqiao acupuncture treatment on brain network reorganization for the patients with stroke recovery, and therefore understand the neural mechanism underlying Xingnao Kaiqiao acupuncture treatment.Methods:Prospective case series study. Thirteen acute ischemia stroke patients were recruited from the Department of Neurology, Shanghai Minhang Hospital of Integrated Traditional Chinese and Western Medicine from Aug 2018 to Oct 2019. They were treated with Xingnao Kaiqiao acupuncture once a day for 10 consecutive days in addition to routine treatments, and received clinical assessments before treatment and 14 days after treatment onset. EEG signals were recorded during the first acupuncture treatment, from before inserting the needles (the baseline), during needle retention, to after removal of the needles. The brain network was constructed using phase locking index, and its clustering coefficient (CC), characteristic path length (PL) and small-worldness (S) were analyzed using one-way repeated ANOVA.Results:Compared with the baseline, the CC of delta-band network (sparsity=0.10: t=3.306, P=0.006; 0.12: t=2.909, P=0.013; 0.14: t=2.331, P=0.038) and the PL of delta-band (sparsity=0.12: t=3.236, P=0.007; 0.14: t=2.754, P=0.017, 0.18: t=2.878, P=0.014) and alpha-band (sparsity=0.10: t=2.432, P=0.032) networks were significantly decreased during the needle retention stage. Clinical assessments demonstrated a significant treatment efficacy of Xingnao Kaiqiao acupuncture, and its efficacy which was indicated by improved NIHSS score, was significantly correlated with the CC changes in the delta band network from baseline to needle retention. The correlation was strongest when the network sparsity was 0.12 ( r=0.78, P=0.002). Conclusion:Xingnao Kaiqiao acupuncture can regulate the brain network of stroke patients in real time, and this immediate regulation maybe associated with its treatment effect.
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Objective:To analyze the functional connectivity (FC) characteristics of sensory motor network (SMN) in patients with bipolar disorder type Ⅰ (BD-Ⅰ) by independent component analysis (ICA), and explore the correlation between abnormal SMN and clinical symptoms.Methods:Eighteen patients with BD-Ⅰ (BD-Ⅰ group) and 20 matched normal controls (HC group) were included.Both groups received resting state fMRI (rs-fMRI) scanning.Based on ICA-fMRI data, one-sample t-test and two-sample t-test were used to analyze the components of SMN and to explore abnormal brain regions between the two groups.Functional network analysis (FNC) was also used to explore the functional connectivity between SMN and other brain networks.Pearson correlation analysis were conducted by SPSS 17.0 to measure the potential associations between intra-and inter-network functional connectivity and age, education, score of Bech-Rafaelsen mania rating scale (BRMS), score of positive and negative syndrome scale (PANSS) and other indicators. Results:In BD-Ⅰ group, the functional connection in the right paracentral lobule (MIN: x=8, y=-32, z=68, t=4.86, P<0.001) and the right postcentral gyrus (MIN: x=41, y=-26, z=53, t=3.33, P<0.001) in SMN were higher than those in HC group.Compared with HC group, the connectivity value in patients with BD-Ⅰ increased between SMN-DAN (0.247±0.073, -0.078±0.080, t=-2.974, P<0.01, FDR adjusted), while the connectivity value decreased between SMN-DMN(-0.037±0.054, 0.272±0.067, t=3.520, P<0.01, FDR adjusted) and between SMN-rFPN(-0.034±0.055, 0.231±0.070, t=2.939, P<0.01, FDR adjusted). Conclusion:The sensorimotor network of patients with BD-Ⅰ has abnormal functional connections within and between networks, and FC values in some networks are positively correlated with manic symptoms, which may be part of the neural mechanisms of patients with BD-Ⅰ.
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Epilepsy is a neurological disease with disordered brain network connectivity. It is important to analyze the brain network mechanism of epileptic seizure from the perspective of directed functional connectivity. In this paper, causal brain networks were constructed for different sub-bands of epileptic electroencephalogram (EEG) signals in interictal, preictal and ictal phases by directional transfer function method, and the information transmission pathway and dynamic change process of brain network under different conditions were analyzed. Finally, the dynamic changes of characteristic attributes of brain networks with different rhythms were analyzed. The results show that the topology of brain network changes from stochastic network to rule network during the three stage and the node connections of the whole brain network show a trend of gradual decline. The number of pathway connections between internal nodes of frontal, temporal and occipital regions increase. There are a lot of hub nodes with information outflow in the lesion region. The global efficiency in ictal stage of α, β and γ waves are significantly higher than in the interictal and the preictal stage. The clustering coefficients in preictal stage are higher than in the ictal stage and the clustering coefficients in ictal stage are higher than in the interictal stage. The clustering coefficients of frontal, temporal and parietal lobes are significantly increased. The results of this study indicate that the topological structure and characteristic properties of epileptic causal brain network can reflect the dynamic process of epileptic seizures. In the future, this study has important research value in the localization of epileptic focus and prediction of epileptic seizure.
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Humans , Epilepsy , Brain , Seizures , Electroencephalography , Occipital LobeABSTRACT
Objective:To investigate the relationship between the changes of default network topology properties of brain function and cognitive function in patients with end-stage renal disease (ESRD).Methods:A total of 31 patients with ESRD were enrolled in the Department of Nephrology, Changzhou Second Hospital Affiliated to Nanjing Medical University from January 2019 to December 2020, and 18 healthy persons were included in the same period as the control group.The cognitive function was evaluated with the Montreal cognitive assessment (MoCA) and trail making tests, and then the subjects were examined by resting-state functional magnetic resonance imaging (rs-fMRI). After preprocessing, the brain functional network was constructed and the topology properities of the network were calculated.The SPSS 20.0 software was used for statistical analysis.Independent sample t-test, chi square test and Pearson correlation analysis were used for data statistics. Results:(1) The score of MoCA in the ESRD group(23.37±1.77) was significantly lower than that in the healthy control group(27.94±1.13)( t=9.537, P<0.001). (2) The levels of Eglobal, Elocal, Cp and Sigma in ESRD group ((0.129±0.025), (0.148±0.040), (0.188±0.046), (1.593±0.650)) were significantly lower than those in healthy control group ((0.160±0.040), (0.212±0.024), (0.276±0.049), (2.004±0.864))( t=3.591, 7.474, 7.058, 2.034, all P<0.05). The Lp value of the ESRD group (8.131±1.905) was significantly higher than that of the control group (6.777±2.150)( t=2.583, P< 0.05). The node efficiency values of bilateral dorsolateral superior frontal gyrus, left middle frontal gyrus, bilateral posterior cingulate gyrus, right hippocampus, left superior marginal gyrus, bilateral angular gyrus and bilateral cuneate anterior lobe in ESRD group ((0.133±0.071), (0.201±0.047), (0.211±0.106), (0.175±0.066), (0.276±0.113), (0.122±0.146), (0.042±0.075), (0.171±0.027), (0.154±0.078), (0.240±0.095), (0.161±0.056))were lower than those in the healthy control group((0.312±0.075), (0.289±0.091), (0.277±0.132), (0.284±0.053), (0.368±0.063), (0.231±0.227), (0.120±0.162), (0.296±0.064), (0.310±0.186), (0.318±0.066), (0.286±0.103))( t=2.107-9.436, all P<0.05). (3)Pearson correlation analysis showed that the node efficiency values of bilateral posterior cingulate gyrus and right hippocampus in ESRD group were positively correlated with the score of MoCA( r=0.36, 0.49, 0.53, all P<0.05). Conclusion:The topological structure of brain functional network is abnormal in ESRD patients, which can affect the cognitive function of patients.
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Objective:To investigate the cognitive-related networks of patients with different cognitive impairment related to white matter lesions (WMLs), and explore the correlation between the altered functional connectivity and the executive function.Methods:Patients with white matter lesions in Beijing Tiantan Hospital from January 2016 to December 2020 were selected , who were divided into WML-non-dementia vascular cognitive impairment (WML-VCIND) group( n=19)and WML-vascular dementia (VaD) group( n=19). At the same time, 24 normal control group (NC) with no cognitive impairment with age, gender and education level matched were enrolled. The Stroop B, Stroop C, trail making test(TMT-B) and verbal fluency test (VFT) were used to evaluate the executive function of all subjects. The ICA method was used to identity four cognitive-related RSNs: frontoparietal network (FPN), salience network (SN), default network (DMN), and dorsal attention network (DAN). Using SPSS 20.0 software, the functional connectivity (FC) of the regions of interest of the selected RSNs among the three groups were compared by analysis of covariance, and correlation between the altered FCs and executive function scores were explored by Spearman correlation analysis. Results:(1) The FC of the medial prefrontal cortex (MPFC)-left inferior parietal lobe (IPL.L) in the VaD group(0.267±0.320) was significantly lower than that in the NC group (0.520±0.253)( P=0.005). The FC of MPFC-posterior cingulate (PCC) was significantly lower in the VaD group(0.655±0.284) than that in the VCIND group(0.810±0.232) ( P=0.017). The FC of MPFC-left insula and MPFC-left dorsolateral prefrontal cortex (DLPFC.L)was significantly higher in the VaD group(0.411±0.277, 0.545±0.311)than that in the VCIND group(0.239±0.308, 0.353±0.270)( P=0.044, 0.028). The FC of the left superior parietal lobe (SPL.L)-right dorsolateral prefrontal cortex (DLPFC.R) in the VCIND group(0.488±0.157) was significantly higher than that in the NC group(0.301±0.257) ( P=0.010). The FC of MPFC-left insula and MPFC-DLPFC.L was higher in NC group than that in VaD group ( P=0.020, 0.037). (2) The FC of MPFC-PCC was negatively correlated with Stroop C score ( r=-0.279, P=0.036), and TMT-B score ( r=-0.313, P=0.018). The FC of MPFC-IPL.L was negatively correlated with Stroop B score ( r=-0.311, P=0.018), Stroop C score( r=-0.308, P=0.020) and TMT-B score ( r=-0.367, P=0.005), while positively correlated with VFT score ( r=0.357, P=0.006). The FC of SPL.L-DLPFC.R was positively correlated with the Stroop B score ( r=0.305, P=0.021). Conclusion:There are certain differences in the functional connectivity between brain areas within the cognitive related brain networks, and the differences in FC between different brain areas are correlated with executive function scores. The changes of the FC may indicate possibly decline in executive function, which can explain the mechanism of cognitive declines.
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@#Objective To investigate the resting brain functional connectivity in patients with post-stroke cognitive impairment (PSCI).Methods From January, 2020 to January, 2021, 24 PSCI patients were divided into mild group (n=12) and moderate group (n=12) according to Mini-Mental State Examination and Montreal Cognitive Assessment scores; and other ten healthy controls (control group) were included. All the subjects were scanned with 64-channel functional near-infrared spectroscopy in resting for six minutes. The data were analyzed with NirSpark 1.6.12 and SPSS 26.0.Results Using deoxyhemoglobin, the functional connectivity between bilateral hemispheres reduced in the moderate group compared with those in the control group (t=-2.763, P=0.024), especially for the functional connectivity between sensorimotor cortexes (F=12.674, P=0.031).Conclusion Functional connectivity between hemispheres decreases in PSCI patients, especially between sensorimotor cortexes.
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Schizophrenia and obsessive-compulsive disorder are common disorders in clinical psychiatry， accompanied by varying degrees of painful experience and social functioning impairment， and a high prevalence rate of co-morbidities. In this paper， a review of comparative study of structural and functional magnetic resonance imaging of the two diseases is presented， so as to explore the similar and specific biological markers of them and to provide a forceful imaging basis for subsequent studies.
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Presenilin (PS) is the main pathogenic gene of familial Alzheimer's disease(AD). Mutations of PS gene can promote the processing of amyloid precursor protein (APP) into a toxic form of amyloid beta protein (Aβ), which plays an important role in the pathogenesis of AD.However, the current targeted therapy for Aβ has not yet produced a good effect on AD, suggesting the existence of additional pathogenic mechanisms.In recent years, the abnormal calcium homeostasis and its pathological role in AD have attracted people's attention.The calcium signaling pathway is regulated by presenilin.And the calcium regulation of PS gene mutant neurons is impaired, resulting in reduced ability to deal with oxidative stress, which leads to cell death and promotes the occurrence of AD.In addition, damage to neuronal autophagy induced by PS gene mutations also depends on the ability to partially deplete endoplasmic reticulum calcium content.Recent studies have shown that abnormal Ca 2+ homeostasis caused by PS gene mutations can lead to impaired mitochondrial metabolism and defects in brain network activity.This review will focus on the calcium signaling pathway, and explore the pathogenesis of presenilin in AD through the regulation of calcium signals from the perspectives of impaired autophagy, endoplasmic reticulum stress, mitochondrial dysfunction, apoptosis and defects in brain network activity, so as to provide ideas for the etiology of AD and the discovery of drug targets.
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Mental fatigue is the subjective state of people after excessive consumption of information resources. Its impact on cognitive activities is mainly manifested as decreased alertness, poor memory and inattention, which is highly related to the performance after impaired working memory. In this paper, the partial directional coherence method was used to calculate the coherence coefficient of scalp electroencephalogram (EEG) of each electrode. The analysis of brain network and its attribute parameters was used to explore the changes of information resource allocation of working memory under mental fatigue. Mental fatigue was quickly induced by the experimental paradigm of adaptive N-back working memory. Twenty-five healthy college students were randomly recruited as subjects, including 14 males and 11 females, aged from 20 to 27 years old, all right-handed. The behavioral data and resting scalp EEG data were collected simultaneously. The results showed that the main information transmission pathway of the brain changed under mental fatigue, mainly in the frontal lobe and parietal lobe. The significant changes in brain network parameters indicated that the information transmission path of the brain decreased and the efficiency of information transmission decreased significantly. In the causal flow of each electrode and the information flow of each brain region, the inflow of information resources in the frontal lobe decreased under mental fatigue. Although the parietal lobe region and occipital lobe region became the main functional connection areas in the fatigue state, the inflow of information resources in these two regions was still reduced as a whole. These results indicated that mental fatigue affected the information resources allocation of working memory, especially in the frontal and parietal regions which were closely related to working memory.
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Adult , Aged , Female , Humans , Male , Young Adult , Brain , Frontal Lobe , Memory, Short-Term , Mental Fatigue , Resource AllocationABSTRACT
Objective:To explore the relationship between small-worldness of brain network and cognitive impairment in patients with white matter lesions (WMLs) based on diffusion tensor imaging (DTI). Methods:From January, 2016 to December, 2017, 46 WMLs patients and 36 controls matched genders, ages and education levels from Beijing Tiantan Hospital were screened with DTI. The patients were divided into vascular cognitive impairment non-dementia (VCIND) and vascular dementia (VaD) groups according to the results of cognitive assessments. The brain structure network was created based on DTI data, and the topological properties of the whole-brain small-world network were calculated, and the correlation between the small-worldness and the severity of cognitive impairment was analyzed. Results:The global efficiency, local efficiency, shortest path length and clustering coefficient were different between the patients and the controls (F > 3.252, P < 0.05), as well as the properties of the small-world network, λ, γ and σ (F > 7.378, P < 0.01). The λ, γ and σ were correlated with the total score of Montreal Cognitive Assessment (|r| > 0.402, P < 0.05). Conclusion:The brain structure network is small-world network for patients with WMLs, and the decrease of small-world properties may relate to the cognitive impairment.
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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.
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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 ( < 0.05). This study can provide theoretical basis and experimental reference for analyzing and evaluating brain fatigue induced by virtual reality visual experience.
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Humans , Brain , Physiology , Electroencephalography , Healthy Volunteers , Mental Fatigue , Virtual RealityABSTRACT
Electroencephalography (EEG) signals are strongly correlated with human emotions. The importance of nodes in the emotional brain network provides an effective means to analyze the emotional brain mechanism. In this paper, a new ranking method of node importance, weighted -order propagation number method, was used to design and implement a classification algorithm for emotional brain networks. Firstly, based on DEAP emotional EEG data, a cross-sample entropy brain network was constructed, and the importance of nodes in positive and negative emotional brain networks was sorted to obtain the feature matrix under multi-threshold scales. Secondly, feature extraction and support vector machine (SVM) were used to classify emotion. The classification accuracy was 83.6%. The results show that it is effective to use the weighted -order propagation number method to extract the importance characteristics of brain network nodes for emotion classification, which provides a new means for feature extraction and analysis of complex networks.
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Background music has been increasingly affecting people's lives. The research on the influence of background music on working memory has become a hot topic in brain science. In this paper, an improved electroencephalography (EEG) experiment based on n-back paradigm was designed. Fifteen university students without musical training were randomly selected to participate in the experiment, and their behavioral data and the EEG data were collected synchronously in order to explore the influence of different types of background music on spatial positioning cognition working memory. The exact low-resolution brain tomography algorithm (eLORETA) was applied to localize the EEG sources and the cross-correlation method was used to construct the cortical brain function networks based on the EEG source signals. Then the characteristics of the networks under different conditions were analyzed and compared to study the effects of background music on people's working memory. The results showed that the difference of peak periods after stimulated by different types of background music were mainly distributed in the signals of occipital lobe and temporal lobe ( < 0.05). The analysis results showed that the brain connectivity under the condition with background music were stronger than those under the condition without music. The connectivities in the right occipital and temporal lobes under the condition of rock music were significantly higher than those under the condition of classical music. The node degrees, the betweenness centrality and the clustering coefficients under the condition without music were lower than those under the condition with background music. The node degrees and clustering coefficients under the condition of classical music were lower than those under the condition of rock music. It indicates that music stimulation increases the brain activity and has an impact on the working memory, and the effect of rock music is more remarkable than that of classical music. The behavioral data showed that the response accuracy in the state of no music, classical music and rock music were 86.09% ± 0.090%, 80.96% ± 0.960% and 79.36% ± 0.360%, respectively. We conclude that background music has a negative impact on the working memory, for it takes up the cognitive resources and reduces the cognitive ability of spatial location.
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Humans , Brain , Cognition , Electroencephalography , Memory, Short-Term , MusicABSTRACT
Aiming at the difference between the brain networks of children with attention deficit hyperactivity disorder (ADHD) and normal children in the task-executing state, this paper conducted a comparative study using the network features of the visual function area. Functional magnetic resonance imaging (fMRI) data of 23 children with ADHD ［age: (8.27 ± 2.77) years］ and 23 normal children ［age: (8.70 ± 2.58) years］ were obtained by the visual capture paradigm when the subjects were performing the guessing task. First, fMRI data were used to build a visual area brain function network. Then, the visual area brain function network characteristic indicators including degree distribution, average shortest path, network density, aggregation coefficient, intermediary, etc. were obtained and compared with the traditional whole brain network. Finally, support vector machines (SVM) and other classifiers in the machine learning algorithm were used to classify the feature indicators to distinguish ADHD children from normal children. In this study, visual brain function network features were used for classification, with a classification accuracy of up to 96%. Compared with the traditional method of constructing a whole brain network, the accuracy was improved by about 10%. The test results show that the use of visual area brain function network analysis can better distinguish ADHD children from normal children. This method has certain help to distinguish the brain network between ADHD children and normal children, and is helpful for the auxiliary diagnosis of ADHD children.
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Child , Child, Preschool , Humans , Attention Deficit Disorder with Hyperactivity/diagnostic imaging , Brain/diagnostic imaging , Brain Mapping , Cognition , Magnetic Resonance ImagingABSTRACT
Objective: To investigate the impact of protracted abstinence on heroin addicts' psychological craving and brain network function under drug cue task. Methods: Totally 37 heroin addicts (protracted abstinence group) and 32 matched normal volunteers (control group) were recruited. Resting state and craving task fMRI data were acquired, and the psychological craving quantitative scoring was evaluated. Resting fMRI data were analyzed to obtain task negative correlation networks (TNN) and task positive correlation networks (TPN). After modeling task-state fMRI data and comparing with the control group, the abnormal activated brain areas in TNN and TPN in the protracted abstinence group were obtained, and the correlation with psychologic behavior was analyzed. Results: The craving scores of the protracted abstinence group were significantly higher than those of control group before and after the presentation of task cues (all P<0.01). Under the craving task, compared with control group, the right para-hippocampal gyrus was significantly activated in the protracted abstinence group in TNN, and the significantly enhanced networks in TPN included visual spatial network (bilateral anterior central gyrus and inferior frontal gyrus) and sensorimotor network (left posterior central gyrus). The activation intensity of bilateral inferior frontal gyrus in protracted abstinence group was negatively correlated with the duration of heroin use (right: r=-0.37, P=0.02; left: r=-0.41, P=0.01). Conclusion: The subjective craving of heroin addicts who are forced to quit is still increased, and the abnormal function of multiple large brain networks has not completely recovered, which may be the neuropathological basis of relapse.
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Background@#Patients with Alzheimer disease (AD) and amnesic mild cognitive impairment (aMCI) have deficits in emotion recognition. However, it has not yet been determined whether patients with AD and aMCI also experience difficulty in recognizing the emotions conveyed by music. This study was conducted to investigate whether musical emotion recognition is impaired or retained in patients with AD and aMCI.@*Methods@#All patients were recruited from the First Affiliated Hospital of Anhui Medical University between March 1, 2015 and January 31, 2017. Using the musical emotion recognition test, patients with AD (n = 16), patients with aMCI (n = 19), and healthy controls (HCs, n = 16) were required to choose one of four emotional labels (happy, sad, peaceful, and fearful) that matched each musical excerpt. Emotion recognition scores in three groups were compared using one-way analysis of variance (ANOVA) test. We also investigated the relationship between the emotion recognition scores and Mini-Mental State Examination (MMSE) using Pearson’s correlation analysis test in patients with AD and aMCI.@*Results@#Compared to the HC group, both of the patient groups showed deficits in the recognition of fearful musical emotions (HC: 7.88 ± 1.36; aMCI: 5.05 ± 2.34; AD: 3.69 ± 2.02), with results of a one-way ANOVA confirming a significant main effect of group (F(2,50) = 18.70, P < 0.001). No significant differences were present among the three groups for the happy (F(2,50)=2.57, P = 0.09), peaceful (F(2,50) = 0.38, P = 0.09), or sad (F(2,50) = 2.50, P = 0.09) musical emotions. The recognition of fearful musical emotion was positively associated with general cognition, which was evaluated by MMSE in patients with AD and aMCI (r = 0.578, P < 0.001). The correlations between the MMSE scores and recognition of the remaining emotions were not significant (happy, r = 0.228, P= 0.11; peaceful, r= 0.047, P= 0.74; sad, r= 0.207, P= 0.15).@*Conclusion@#This study showed that both patients with AD and aMCI had decreased ability to distinguish fearful emotions, which might be correlated with diminished cognitive function.
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Objective To analyze the effects of mild traumatic brain injury ( mTBI) on functional brain network parameters and to study the properties of small world of patients. Methods Ten patients with mild traumatic brain injury and 13 healthy controls matched by age and sex were collected from the Ninth People's Hospital Affiliated to Medical College of Shanghai Jiao Tong University. Resting functional magnetic resonance images of all subjects were collected. Functional networks were constructed and brain network properties were analyzed. Results The brain injury group and the healthy control group had small world at-tributes in the selected sparseness range. The global efficiency of patients with mTBI patients was significantly reduced compared to healthy control group (0. 174±0. 141 vs 0. 184±0. 062),t=2. 417,P=0. 025),and the shortest path length was increased compared to healthy control group (0. 797±0. 930 vs 0. 734±0. 488),t=-2. 083,P=0. 048). Conclusion The functional brain network of patients with mTBI patients has small world attributes,and the parameters of functional brain network of patients have changed.
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Objective To investigate the possible electroencephalograph network connectivity mech-anism of working memory disorder in patients with frontal lobe epilepsy ( FLE). Methods Seventeen adult FLE patients were enrolled as the case group and 22 normal adults were selected as the control group. The behavioral data and 34-channel electroencephalographs were recorded in the working memory behavioral par-adigm. The directed transfer function was used to construct the causal connectivity brain network of theta band of working memory delay period. Two-sample t-test or tˊ-test was used to analyze the differences in be-havioral and frontal causal connectivity between the two groups. Results (1) Compared with control group, working memory behavioral data of FLE group showed significantly lower correct rate ((92. 778±4. 399)%, (96. 258±2. 470)%,tˊ=-2. 925,P<0. 01),and significantly longer reaction time ((978. 586± 65. 161) ms,(798. 671±196. 207)ms,tˊ=4. 023,P<0. 001). (2) Compared with control group,causal connectivity of FLE group showed significantly reduced whole brain ( tˊ=-6. 008, P<0. 001), Fz channel ( tˊ=-7. 703,P<0. 001),frontal region (tˊ=-14. 667,P<0. 001),frontal-temporal interval ( t=-14. 467,P<0. 001),and frontal-central interval (t=-3. 501,P<0. 001). The causal connectivity of frontal-occipital in-terval had no significant difference( t=1. 056,P=0. 281). Conclusions Working memory disorders were found in FLE patients. The abnormal causal connectivity of frontal brain network in theta band may be one of its potential neurophysiological mechanisms.
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A single infusion of ketamine has sustained antidepressant effects and significantly de-creases the risk of suicide,and the effects can last up for 7-10 days,but the underlying mechanism is un-clear. The mechanism was reviewed underlying the antidepressant effects of ketamine,and found that ket-amine may exert its antidepressant effect by regulating sleep/wake cycle,synaptic pruning,molecular path-ways,and neural circuits for treatment-refractory depression. Further studies are needed to investigate the ge-netic,molecular mechanisms underlying the sustained antidepressant effect of ketamine,and the associated imaging findings through in vivo imaging of animals and imaging genetics techniques,explore the optimal time for administration of ketamine,and then provide accurate scientific basis for enhancing its anti-depressant effect.