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Xin H, Fu Y, Wen H, Feng M, Sui C, Gao Y, Guo L, Liang C. Cognition and motion dysfunction-associated brain functional network disruption in diabetic peripheral neuropathy. Hum Brain Mapp 2024; 45:e26563. [PMID: 38224534 PMCID: PMC10785193 DOI: 10.1002/hbm.26563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/31/2023] [Accepted: 11/28/2023] [Indexed: 01/17/2024] Open
Abstract
Neuroimaging studies have demonstrated extensive brain functional alterations in cognitive and motor functional areas in Type 2 diabetes mellitus (T2DM) with diabetic peripheral neuropathy (DPN), suggesting potential alterations in large-scale brain networks related to DPN and associated cognition and motor dysfunction. In this study, using resting-state functional connectivity (FC) and graph theory computational approaches, we investigated the topological disruptions of brain functional networks in 28 DPN, 43 T2DM without DPN (NDPN), and 32 healthy controls (HCs) and examined the correlations between altered network topological metrics and cognitive/motor function parameters in T2DM. For global topology, NDPN exhibited a significantly decreased shortest path length compared with HCs, suggesting increased efficient global integration. For regional topology, DPN and NDPN had separated topological reorganization of functional hubs compared with HCs. In addition, DPN showed significantly decreased nodal efficiency (Enodal ), mainly in the bilateral superior occipital gyrus (SOG), right cuneus, middle temporal gyrus (MTG), and left inferior parietal gyrus (IPL), compared with NDPN, whereas NDPN showed significantly increased Enodal compared with HCs. Intriguingly, in T2DM patients, the Enodal of the right SOG was significantly negatively correlated with Toronto Clinical Scoring System scores, while the Enodal of the right postcentral gyrus (PoCG) and MTG were significantly positively correlated with Montreal Cognitive Assessment scores. Conclusively, DPN and NDPN patients had segregated disruptions in the brain functional network, which were related to cognition and motion dysfunctions. Our findings provide a theoretical basis for understanding the neurophysiological mechanism of DPN and its effective prevention and treatment in T2DM.
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Affiliation(s)
- Haotian Xin
- Department of Radiology, Shandong Provincial HospitalShandong UniversityJinanChina
| | - Yajie Fu
- Department of Radiology, Shandong Provincial HospitalShandong UniversityJinanChina
- Department of Medical UltrasoundThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medical ImagingJinanChina
| | - Hongwei Wen
- Key Laboratory of Cognition and Personality (Ministry of Education), Faculty of PsychologySouthwest UniversityChongqingChina
| | - Mengmeng Feng
- Department of Radiology, Shandong Provincial HospitalShandong UniversityJinanChina
| | - Chaofan Sui
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain AgingMinistry of Education; Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityJinanChina
| | - Yian Gao
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain AgingMinistry of Education; Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityJinanChina
| | - Lingfei Guo
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain AgingMinistry of Education; Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityJinanChina
| | - Changhu Liang
- Department of Radiology, Shandong Provincial HospitalShandong UniversityJinanChina
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain AgingMinistry of Education; Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityJinanChina
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Tian Y, Xu G, Zhang J, Chen K, Liu S. Nodal properties of the resting-state brain functional network in childhood and adolescence. J Neuroimaging 2023; 33:1015-1023. [PMID: 37735776 DOI: 10.1111/jon.13155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/05/2023] [Accepted: 09/06/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND AND PURPOSE Changes in the topological properties of brain functional network nodes during childhood and adolescence can provide more detailed and intuitive information on the rules of brain development. This study aims to explore the characteristics of nodal attributes in child and adolescent brain functional networks and analyze the correlation between nodal attributes in different brain regions and age. METHODS Forty-two healthy volunteers aged 6-18 years who were right-handed primary and middle school students were recruited, and the subgroup analysis included children (6-12 years, n = 19) and adolescents (13-18 years, n = 23). Resting-state functional magnetic resonance imaging data were collected using a 3.0 Tesla MRI scanner. The topological properties of the functional brain network were analyzed using graph theory. RESULTS Compared with the children group, the degree centrality and nodal efficiency of multiple brain regions in the adolescent group were significantly increased, and the nodal shortest path was reduced (q<0.05, false discovery rate corrected). These brain regions were widely distributed in the whole brain and significantly correlated with age. Compared with the children group, reduced degree centralities were observed in the left dorsolateral fusiform gyrus, left rostral cuneus gyrus, and right medial superior occipital gyrus. CONCLUSION The transmission efficiency of the brain's core network gradually increased, and the subnetwork function gradually improved in children and adolescents with age. The functional development of each brain area in the occipital visual cortex was uneven and there was functional differentiation within the occipital visual cortex.
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Affiliation(s)
- Yu Tian
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Zunyi, China
- Department of Radiology, the Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China
| | - Gaoqiang Xu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Jing Zhang
- Department of Radiology, the Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China
| | - Kuntao Chen
- Department of Radiology, the Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China
| | - Songjiang Liu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Zunyi, China
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Hu Z, Zhou C, He L. Abnormal dynamic functional network connectivity in patients with early-onset bipolar disorder. Front Psychiatry 2023; 14:1169488. [PMID: 37448493 PMCID: PMC10338119 DOI: 10.3389/fpsyt.2023.1169488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 06/12/2023] [Indexed: 07/15/2023] Open
Abstract
Objective To explore the changes in dynamic functional brain network connectivity (dFNC) in patients with early-onset bipolar disorder (BD). Methods Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 39 patients with early-onset BD and 22 healthy controls (HCs). Four repeated and stable dFNC states were characterised by independent component analysis (ICA), sliding time windows and k-means clustering, and three dFNC temporal metrics (fraction of time, mean dwell time and number of transitions) were obtained. The dFNC temporal metrics and the differences in dFNC between the two groups in different states were evaluated, and the correlations between the differential dFNC metrics and neuropsychological scores were analysed. Results The dFNC analysis showed four connected patterns in all subjects. Compared with the HCs, the dFNC patterns of early-onset BD were significantly altered in all four states, mainly involving impaired cognitive and perceptual networks. In addition, early-onset BD patients had a decreased fraction of time and mean dwell time in state 2 and an increased mean dwell time in state 3 (p < 0.05). The mean dwell time in state 3 of BD showed a positive correlation trend with the HAMA score (r = 0.4049, p = 0.0237 × 3 > 0.05 after Bonferroni correction). Conclusion Patients with early-onset BD had abnormal dynamic properties of brain functional network connectivity, suggesting that their dFNC was unstable, mainly manifesting as impaired coordination between cognitive and perceptual networks. This study provided a new imaging basis for the neuropathological study of emotional and cognitive deficits in early-onset BD.
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Affiliation(s)
- Ziyi Hu
- Department of Radiology, First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Chun Zhou
- Department of Radiology, First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Laichang He
- Department of Radiology, First Affiliated Hospital of Nanchang University, Nanchang, China
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Du Y, Guo Y, Calhoun VD. Aging brain shows joint declines in brain within-network connectivity and between-network connectivity: a large-sample study ( N > 6,000). Front Aging Neurosci 2023; 15:1159054. [PMID: 37273655 PMCID: PMC10233064 DOI: 10.3389/fnagi.2023.1159054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 04/21/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction Numerous studies have shown that aging has important effects on specific functional networks of the brain and leads to brain functional connectivity decline. However, no studies have addressed the effect of aging at the whole-brain level by studying both brain functional networks (i.e., within-network connectivity) and their interaction (i.e., between-network connectivity) as well as their joint changes. Methods In this work, based on a large sample size of neuroimaging data including 6300 healthy adults aged between 49 and 73 years from the UK Biobank project, we first use our previously proposed priori-driven independent component analysis (ICA) method, called NeuroMark, to extract the whole-brain functional networks (FNs) and the functional network connectivity (FNC) matrix. Next, we perform a two-level statistical analysis method to identify robust aging-related changes in FNs and FNCs, respectively. Finally, we propose a combined approach to explore the synergistic and paradoxical changes between FNs and FNCs. Results Results showed that the enhanced FNCs mainly occur between different functional domains, involving the default mode and cognitive control networks, while the reduced FNCs come from not only between different domains but also within the same domain, primarily relating to the visual network, cognitive control network, and cerebellum. Aging also greatly affects the connectivity within FNs, and the increased within-network connectivity along with aging are mainly within the sensorimotor network, while the decreased within-network connectivity significantly involves the default mode network. More importantly, many significant joint changes between FNs and FNCs involve default mode and sub-cortical networks. Furthermore, most synergistic changes are present between the FNCs with reduced amplitude and their linked FNs, and most paradoxical changes are present in the FNCs with enhanced amplitude and their linked FNs. Discussion In summary, our study emphasizes the diversity of brain aging and provides new evidence via novel exploratory perspectives for non-pathological aging of the whole brain.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Yating Guo
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
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Wong KKL, Xu J, Chen C, Ghista D, Zhao H. Functional magnetic resonance imaging providing the brain effect mechanism of acupuncture and moxibustion treatment for depression. Front Neurol 2023; 14:1151421. [PMID: 37025199 PMCID: PMC10070747 DOI: 10.3389/fneur.2023.1151421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 02/22/2023] [Indexed: 04/08/2023] Open
Abstract
The efficacy of acupuncture and moxibustion in the treatment of depression has been fully recognized internationally. However, its central mechanism is still not developed into a unified standard, and it is generally believed that the central mechanism is regulation of the cortical striatum thalamic neural pathway of the limbic system. In recent years, some scholars have applied functional magnetic resonance imaging (fMRI) to study the central mechanism and the associated brain effects of acupuncture and moxibustion treatment for depression. This study reviews the acupuncture and moxibustion treatment of depression from two aspects: (1) fMRI study of the brain function related to the acupuncture treatment of depression: different acupuncture and moxibustion methods are summarized, the fMRI technique is elaborately explained, and the results of fMRI study of the effects of acupuncture are analyzed in detail, and (2) fMRI associated "brain functional network" effects of acupuncture and moxibustion on depression, including the effects on the hippocampus, the amygdala, the cingulate gyrus, the frontal lobe, the temporal lobe, and other brain regions. The study of the effects of acupuncture on brain imaging is not adequately developed and still needs further improvement and development. The brain function networks associated with the acupuncture treatment of depression have not yet been adequately developed to provide a scientific and standardized mechanism of the effects of acupuncture. For this purpose, this study analyzes in-depth the clinical studies on the treatment of anxiety and depression by acupuncture and moxibustion, by depicting how the employment of fMRI technology provides significant imaging changes in the brain regions. Therefore, the study also provides a reference for future clinical research on the treatment of anxiety and depression.
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Affiliation(s)
- Kelvin K. L. Wong
- The Research Center for Medical AI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jinping Xu
- The Research Center for Medical AI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Cang Chen
- The Research Center for Medical AI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Dhanjoo Ghista
- The Research Center for Medical AI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hong Zhao
- Acupuncture and Moxibustion Department, Luohu District Hospital of Traditional Chinese Medicine, Shenzhen, China
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Adebisi AT, Veluvolu KC. Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review. Front Aging Neurosci 2023; 15:1039496. [PMID: 36936496 PMCID: PMC10020520 DOI: 10.3389/fnagi.2023.1039496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/06/2023] [Indexed: 03/06/2023] Open
Abstract
Background Dementia-related disorders have been an age-long challenge to the research and healthcare communities as their various forms are expressed with similar clinical symptoms. These disorders are usually irreversible at their late onset, hence their lack of validated and approved cure. Since their prodromal stages usually lurk for a long period of time before the expression of noticeable clinical symptoms, a secondary prevention which has to do with treating the early onsets has been suggested as the possible solution. Connectivity analysis of electrophysiology signals has played significant roles in the diagnosis of various dementia disorders through early onset identification. Objective With the various applications of electrophysiology signals, the purpose of this study is to systematically review the step-by-step procedures of connectivity analysis frameworks for dementia disorders. This study aims at identifying the methodological issues involved in such frameworks and also suggests approaches to solve such issues. Methods In this study, ProQuest, PubMed, IEEE Xplore, Springer Link, and Science Direct databases are employed for exploring the evolution and advancement of connectivity analysis of electrophysiology signals of dementia-related disorders between January 2016 to December 2022. The quality of assessment of the studied articles was done using Cochrane guidelines for the systematic review of diagnostic test accuracy. Results Out of a total of 4,638 articles found to have been published on the review scope between January 2016 to December 2022, a total of 51 peer-review articles were identified to completely satisfy the review criteria. An increasing trend of research in this domain is identified within the considered time frame. The ratio of MEG and EEG utilization found within the reviewed articles is 1:8. Most of the reviewed articles employed graph theory metrics for their analysis with clustering coefficient (CC), global efficiency (GE), and characteristic path length (CPL) appearing more frequently compared to other metrics. Significance This study provides general insight into how to employ connectivity measures for the analysis of electrophysiology signals of dementia-related disorders in order to better understand their underlying mechanism and their differential diagnosis.
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Affiliation(s)
- Abdulyekeen T. Adebisi
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Kalyana C. Veluvolu
- School of Electronics Engineering, Kyungpook National University, Daegu, Republic of Korea
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Wang Y, Xu L, Fang H, Wang F, Gao T, Zhu Q, Jiao G, Ke X. Social Brain Network of Children with Autism Spectrum Disorder: Characterization of Functional Connectivity and Potential Association with Stereotyped Behavior. Brain Sci 2023; 13:brainsci13020280. [PMID: 36831823 PMCID: PMC9953760 DOI: 10.3390/brainsci13020280] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 01/21/2023] [Accepted: 01/26/2023] [Indexed: 02/11/2023] Open
Abstract
Objective: To identify patterns of social dysfunction in adolescents with autism spectrum disorder (ASD), study the potential linkage between social brain networks and stereotyped behavior, and further explore potential targets of non-invasive nerve stimulation to improve social disorders. Methods: Voxel-wise and ROI-wise analysis methods were adopted to explore abnormalities in the functional activity of social-related regions of the brain. Then, we analyzed the relationships between clinical variables and the statistical indicators of social-related brain regions. Results: Compared with the typically developing group, the functional connectivity strength of social-related brain regions with the precentral gyrus, postcentral gyrus, supplementary motor area, paracentral lobule, median cingulum, and paracingulum gyri was significantly weakened in the ASD group (all p < 0. 01). The functional connectivity was negatively correlated with communication, social interaction, communication + social interaction, and the total score of the ADOS scale (r = -0.38, -0.39, -0.40, and -0.3, respectively; all p < 0.01), with social awareness, social cognition, social communication, social motivation, autistic mannerisms, and the total score of the SRS scale (r = -0.32, -0.32, -0.40, -0.30, -0.28, and -0.27, respectively; all p < 0.01), and with the total score of SCQ (r = -0.27, p < 0.01). In addition, significant intergroup differences in clustering coefficients and betweenness centrality were seen across multiple brain regions in the ASD group. Conclusions: The functional connectivity between social-related brain regions and many other brain regions was significantly weakened compared to the typically developing group, and it was negatively correlated with social disorders. Social network dysfunction seems to be related to stereotyped behavior. Therefore, these social-related brain regions may be taken as potential stimulation targets of non-invasive nerve stimulation to improve social dysfunction in children with ASD in the future.
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Ni J, Jiang W, Gong X, Fan Y, Qiu H, Dou J, Zhang J, Wang H, Li C, Su M. Effect of rTMS intervention on upper limb motor function after stroke: A study based on fNIRS. Front Aging Neurosci 2023; 14:1077218. [PMID: 36711205 PMCID: PMC9880218 DOI: 10.3389/fnagi.2022.1077218] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 12/19/2022] [Indexed: 01/15/2023] Open
Abstract
Background Stroke is a disease with a high fatality rate worldwide and a major cause of long-term disability. In the rehabilitation of limb motor function after stroke, the rehabilitation of upper limb function takes a long time and the recovery progress is slow, which seriously affects the patients' self-care ability in daily life. Repeated transcranial magnetic stimulation (rTMS) has been increasingly used to improve limb dysfunction in patients with stroke. However, a standardized reference for selecting a magnetic stimulation regimen is not available. Whether to increase the inhibition of the contralateral hemispheric motor cortex remains controversial. This study has evaluated the effects of different rTMS stimulation programs on upper limb function and corresponding brain functional network characteristics of patients with stroke and sought a new objective standard based on changes in brain network parameters to guide accurate rTMS stimulation programs. Method Thirty-six patients with stroke were selected and divided into control group and treatment group by number table method, with 18 patients in each group, and 3 patients in the control group were turned out and lost due to changes in disease condition. The treatment group was divided into two groups. TMS1 group was given 1 Hz magnetic stimulation in the M1 region of the contralesional hemisphere +10 Hz magnetic stimulation in the M1 region of the affected hemisphere, and the TMS2 group was given 10 Hz magnetic stimulation in the M1 region of the affected hemisphere. The control group was given false stimulation. The treatment course was once a day for 5 days a week for 4 weeks. The Fugl-Meyer Assessment for upper extremity (FMA-UE) sand near-infrared brain function were collected before treatment, 2 weeks after treatment, and 4 weeks after treatment, and the brain function network was constructed. Changes in brain oxygenated hemoglobin concentration and brain network parameters were analyzed with the recovery of motor function (i.e., increased FMA score). Meanwhile, according to the average increment of brain network parameters, the rTMS stimulation group was divided into two groups with good efficacy and poor efficacy. Network parameters of the two groups before and after rTMS treatment were analyzed statistically. Results (1) Before treatment, there was no statistical difference in Fugl-Meyer score between the control group and the magnetic stimulation group (p = 0.178).Compared with before treatment, Fugl-Meyer scores of 2 and 4 weeks after treatment were significantly increased in both groups (p <0.001), and FMA scores of 4 weeks after treatment were significantly improved compared with 2 weeks after treatment (p < 0.001). FMA scores increased faster in the magnetic stimulation group at 2 and 4 weeks compared with the control group at the same time point (p <0.001).TMS1 and TMS2 were compared at the same time point, FMA score in TMS2 group increased more significantly after 4 weeks of treatment (p = 0.010). (2) Before treatment, HbO2 content in healthy sensory motor cortex (SMC) area of magnetic stimulation group and control group was higher than that in other region of interest (ROI) area, but there was no significant difference in ROI between the two groups. After 4 weeks of treatment, the HbO2 content in the healthy SMC area was significantly decreased (p < 0.001), while the HbO2 content in the affected SMC area was significantly increased, and the change was more significant in the magnetic stimulation group (p < 0.001). (3) In-depth study found that with the recovery of motor function (FMA upper limb score increase ≥4 points) after magnetic stimulation intervention, brain network parameters were significantly improved. The mean increment of network parameters in TMS1 group and TMS2 group was significantly different (χ 2 = 5.844, p = 0.016). TMS2 group was more advantageous than TMS1 group in improving the mean increment of brain network parameters. Conclusion (1) The rTMS treatment is beneficial to the recovery of upper limb motor function in stroke patients, and can significantly improve the intensity of brain network connection and reduce the island area. The island area refers to an isolated activated brain area that cannot transmit excitation to other related brain areas. (2) When the node degree of M1_Healthy region less than 0.52, it is suggested to perform promotion therapy only in the affected hemisphere. While the node degree greater than 0.52, and much larger than that in the M1_affected region. it is suggested that both inhibition in the contralesional hemisphere and high-frequency excitatory magnetic stimulation in the affected hemisphere can be performed. (3) In different brain functional network connection states, corresponding adjustment should be made to the treatment plan of rTMS to achieve optimal therapeutic effect and precise rehabilitation treatment.
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Affiliation(s)
- Jing Ni
- Department of Physical Medicine and Rehabilitation, Dushu Lake Hospital Affiliated of Soochow University, Suzhou, Jiangsu, China,Department of Physical Medicine and Rehabilitation, Jiangsu Rongjun Hospital, Wuxi, Jiangsu, China
| | - Wei Jiang
- Department of Physical Medicine and Rehabilitation, Dushu Lake Hospital Affiliated of Soochow University, Suzhou, Jiangsu, China,Department of Physical Medicine and Rehabilitation, Jiangsu Rongjun Hospital, Wuxi, Jiangsu, China
| | - Xueyang Gong
- Department of Physical Medicine and Rehabilitation, Dushu Lake Hospital Affiliated of Soochow University, Suzhou, Jiangsu, China,Department of Physical Medicine and Rehabilitation, Wuxi International Tongren Rehabilitation Hospital, Wuxi, Jiangsu, China
| | - Yingjie Fan
- Department of Physical Medicine and Rehabilitation, Dushu Lake Hospital Affiliated of Soochow University, Suzhou, Jiangsu, China,Institute of Rehabilitation Soochow University, Suzhou, Jiangsu, China
| | - Hao Qiu
- Department of Physical Medicine and Rehabilitation, Dushu Lake Hospital Affiliated of Soochow University, Suzhou, Jiangsu, China,Institute of Rehabilitation Soochow University, Suzhou, Jiangsu, China
| | - Jiaming Dou
- Department of Physical Medicine and Rehabilitation, Dushu Lake Hospital Affiliated of Soochow University, Suzhou, Jiangsu, China,Department of Physical Medicine and Rehabilitation, Wuxi International Tongren Rehabilitation Hospital, Wuxi, Jiangsu, China
| | - Juan Zhang
- Department of Physical Medicine and Rehabilitation, Dushu Lake Hospital Affiliated of Soochow University, Suzhou, Jiangsu, China,First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Hongxing Wang
- Department of Rehabilitation Medicine, Zhongda Hospital Southeast University, Nanjing, Jiangsu, China,*Correspondence: Hongxing Wang, ✉
| | - Chunguang Li
- The Key Laboratory of Robotics and System of Jiangsu Province, School of Mechanical and Electric Engineering, Soochow University, Suzhou, Jiangsu, China,Chunguang Li, ✉
| | - Min Su
- Department of Physical Medicine and Rehabilitation, Dushu Lake Hospital Affiliated of Soochow University, Suzhou, Jiangsu, China,Institute of Rehabilitation Soochow University, Suzhou, Jiangsu, China,First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China,Min Su, ✉
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Wen X, Yang M, Hsu L, Zhang D. Test-retest reliability of modular-relevant analysis in brain functional network. Front Neurosci 2022; 16:1000863. [PMID: 36570835 PMCID: PMC9770801 DOI: 10.3389/fnins.2022.1000863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
Introduction The human brain could be modeled as a complex network via functional magnetic resonance imaging (fMRI), and the architecture of these brain functional networks can be studied from multiple spatial scales with different graph theory tools. Detecting modules is an important mesoscale network measuring approach that has provided crucial insights for uncovering how brain organizes itself among different functional subsystems. Despite its successful application in a wide range of brain network studies, the lack of comprehensive reliability assessment prevents its potential extension to clinical trials. Methods To fill this gap, this paper, using resting-state test-retest fMRI data, systematically explored the reliabilities of five popular network metrics derived from modular structure. Considering the repeatability of network partition depends heavily on network size and module detection algorithm, we constructed three types of brain functional networks for each subject by using a set of coarse-to-fine brain atlases and adopted four methods for single-subject module detection and twelve methods for group-level module detection. Results The results reported moderate-to-good reliability in modularity, intra- and inter-modular functional connectivities, within-modular degree and participation coefficient at both individual and group levels, indicating modular-relevant network metrics can provide robust evaluation results. Further analysis identified the significant influence of module detection algorithm and node definition approach on reliabilities of network partitions and its derived network analysis results. Discussion This paper provides important guidance for choosing reliable modular-relevant network metrics and analysis strategies in future studies.
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Affiliation(s)
- Xuyun Wen
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu, China
| | - Mengting Yang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu, China
| | - Liming Hsu
- Center for Animal MRI, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu, China
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Zhong H, Wang J, Li H, Tian J, Fang J, Xu Y, Jiao W, Li G. Reorganization of Brain Functional Network during Task Switching before and after Mental Fatigue. Sensors (Basel) 2022; 22:8036. [PMID: 36298387 PMCID: PMC9611295 DOI: 10.3390/s22208036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/18/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
Mental fatigue is a widely studied topic on account of its serious negative effects. But how the neural mechanism of task switching before and after mental fatigue remains a question. To this end, this study aims to use brain functional network features to explore the answer to this question. Specifically, task-state EEG signals were recorded from 20 participants. The tasks include a 400-s 2-back-task (2-BT), followed by a 6480-s of mental arithmetic task (MAT), and then a 400-s 2-BT. Network features and functional connections were extracted and analyzed based on the selected task switching states, referred to from Pre_2-BT to Pre_MAT before mental fatigue and from Post_MAT to Post_2-BT after mental fatigue. The results showed that mental fatigue has been successfully induced by long-term MAT based on the significant changes in network characteristics and the high classification accuracy of 98% obtained with Support Vector Machines (SVM) between Pre_2-BT and Post_2-BT. when the task switched from Pre_2-BT to Pre_MAT, delta and beta rhythms exhibited significant changes among all network features and the selected functional connections showed an enhanced trend. As for the task switched from Post_MAT to Post_2-BT, the network features and selected functional connectivity of beta rhythm were opposite to the trend of task switching before mental fatigue. Our findings provide new insights to understand the neural mechanism of the brain in the process of task switching and indicate that the network features and functional connections of beta rhythm can be used as neural markers for task switching before and after mental fatigue.
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Affiliation(s)
- Hongyang Zhong
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
| | - Jie Wang
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
| | - Huayun Li
- College of Teacher Education, Zhejiang Normal University, Jinhua 321004, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China
| | - Jinghong Tian
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Jiaqi Fang
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Yanting Xu
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Weidong Jiao
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Gang Li
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China
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Zhang G, Li N, Liu H, Zheng H, Zheng W. Dynamic connectivity patterns of resting-state brain functional networks in healthy individuals after acute alcohol intake. Front Neurosci 2022; 16:974778. [PMID: 36203810 PMCID: PMC9531019 DOI: 10.3389/fnins.2022.974778] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/02/2022] [Indexed: 02/04/2023] Open
Abstract
AIMS Currently, there are only a few studies concerning brain functional alterations after acute alcohol exposure, and the majority of existing studies attach more importance to the spatial properties of brain function without considering the temporal properties. The current study adopted sliding window to investigate the resting-state brain networks in healthy volunteers after acute alcohol intake and to explore the dynamic changes in network connectivity. MATERIALS AND METHODS Twenty healthy volunteers were enrolled in this study. Blood-oxygen-level-dependent (BOLD) data prior to drinking were obtained as control, while that 0.5 and 1 h after drinking were obtained as the experimental group. Reoccurring functional connectivity patterns (states) were determined following group independent component analysis (ICA), sliding window analysis and k-means clustering. Between-group comparisons were performed with respect to the functional connectivity states fractional windows, mean dwell time, and the number of transitions. RESULTS Three optimal functional connectivity states were identified. The fractional windows and mean dwell time of 0.5 h group and 1 h group increased in state 3, while the fraction window and mean dwell time of 1 h group decreased in state 1. State 1 is characterized by strong inter-network connections between basal ganglia network (BGN) and sensorimotor network (SMN), BGN and cognitive executive network (CEN), and default mode network (DMN) and visual network (VN). However, state 3 is distinguished by relatively weak intra-network connections in SMN, VN, CEN, and DMN. State 3 was thought to be a characteristic connectivity pattern of the drunk brain. State 1 was believed to represent the brain's main connection pattern when awake. Such dynamic changes in brain network connectivity were consistent with participants' subjective feelings after drinking. CONCLUSION The current study reveals the dynamic change in resting-state brain functional network connectivity before and after acute alcohol intake. It was discovered that there might be relatively independent characteristic functional network connection patterns under intoxication, and the corresponding patterns characterize the clinical manifestations of volunteers. As a valuable imaging biomarker, dynamic functional network connectivity (dFNC) offers a new approach and basis for further explorations on brain network alterations after alcohol consumption and the alcohol-related mechanisms for neurological damage.
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Affiliation(s)
- Gengbiao Zhang
- Department of Radiology, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Ni Li
- The Family Medicine Branch, Department of Radiology, The First Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Hongkun Liu
- Department of Radiology, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Hongyi Zheng
- Department of Radiology, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Wenbin Zheng
- Department of Radiology, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
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Xu T, Dragomir A, Liu X, Yin H, Wan F, Bezerianos A, Wang H. An EEG study of human trust in autonomous vehicles based on graphic theoretical analysis. Front Neuroinform 2022; 16:907942. [PMID: 36051853 PMCID: PMC9426721 DOI: 10.3389/fninf.2022.907942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 06/16/2022] [Indexed: 11/22/2022] Open
Abstract
With the development of autonomous vehicle technology, human-centered transport research will likely shift to the interaction between humans and vehicles. This study focuses on the human trust variation in autonomous vehicles (AVs) as the technology becomes increasingly intelligent. This study uses electroencephalogram data to analyze human trust in AVs during simulated driving conditions. Two driving conditions, the semi-autonomous and the autonomous, which correspond to the two highest levels of automatic driving, are used for the simulation, accompanied by various driving and car conditions. The graph theoretical analysis (GTA) is the primary method for data analysis. In semi-autonomous driving mode, the local efficiency and cluster coefficient are lower in car-normal conditions than in car-malfunction conditions with the car approaching. This finding suggests that the human brain has a strong information processing ability while facing predictable potential hazards. However, when it comes to a traffic light with a car malfunctioning under the semi-autonomous driving mode, the characteristic path length is higher for the car malfunction manifesting a weak information processing ability while facing unpredictable potential hazards. Furthermore, in fully automatic driving conditions, participants cannot do anything and need low-level brain function to take emergency actions as lower local efficiency and small worldness for car malfunction. Our results shed light on the design of the human-machine interaction and human factor engineering on the high level of an autonomous vehicle.
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Affiliation(s)
- Tao Xu
- The Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, China
| | - Andrei Dragomir
- The N1 Institute, National University of Singapore, Singapore, Singapore
| | - Xucheng Liu
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, Macao SAR, China
| | - Haojun Yin
- The Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, China
| | - Feng Wan
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, Macao SAR, China
| | - Anastasios Bezerianos
- Hellenic Institute of Transport (HIT), Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece
| | - Hongtao Wang
- The Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, China
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Yan F, Song D, Dong Z, Zhang Y, Wang H, Huang L, Wang Y, Wang Q. Alternation of EEG Characteristics During Transcutaneous Acupoint Electrical Stimulation-Induced Sedation. Clin EEG Neurosci 2022; 53:204-214. [PMID: 33256427 DOI: 10.1177/1550059420976303] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Recent studies have shown that applying acupuncture during general anesthesia can reduce the dosage of anesthetics. Hence, it is speculated that acupuncture may have a sedative effect. However, existing studies employed acupuncture in combination with anesthetics, which makes determine acupuncture's role in producing sedation difficult. In this work, we investigated the sedative effect of acupuncture by using transcutaneous acupoint electrical stimulation (TAES) at bilateral Zusanli (ST36), Shenmen (HT7) and Sanyinjiao (SP6). Using a cross-over design, 2 separate sessions, that are, the resting and TAES sessions, were conducted for each subject. The sedative effect was quantified by using the bispectral index (BIS). The difference in brain activities between resting and TAES sessions was investigated by analyzing the simultaneously recorded EEG signals. Our results showed that a statistically significant difference in BIS values existed between resting and TAES sessions, which suggested that TAES alone was capable of inducing observable sedation. Using power spectrum analysis, we showed that TAES-induced sedation was accompanied by a reduction in alpha band power and an increment in delta band power. Permutation entropy was lower during the TAES session, which suggested that TAES reduced the complexity of the EEG signal. Moreover, a significant reduction in the global strength of brain functional connections was observed during TAES. These findings suggest that TAES alone can induce observable sedative effects, and this sedation effect is accompanied by changes in brain activities that have shown to be correlated with consciousness.
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Affiliation(s)
- Fei Yan
- Department of Anesthesiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Dawei Song
- Department of Anesthesiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Zhen Dong
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Yun Zhang
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Haidong Wang
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Liyu Huang
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Yubo Wang
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Qiang Wang
- Department of Anesthesiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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Sun H, Wang A, He S. Temporal and Spatial Analysis of Alzheimer's Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network. Int J Environ Res Public Health 2022; 19:ijerph19084508. [PMID: 35457373 PMCID: PMC9030143 DOI: 10.3390/ijerph19084508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/27/2022] [Accepted: 04/02/2022] [Indexed: 11/23/2022]
Abstract
Most current research on Alzheimer’s disease (AD) is based on transverse measurements. Given the nature of neurodegeneration in AD progression, observing longitudinal changes in the structural features of brain networks over time may improve the accuracy of the predicted transformation and provide a good measure of the progression of AD. Currently, there is no cure for patients with existing AD dementia, but patients with mild cognitive impairment (MCI) in the prodromal stage of AD dementia may be diagnosed. The study of the early diagnosis of MCI and the prediction of MCI to AD transformation is of great significance for the monitoring of the MCI to AD transformation process. Despite the high rate of MCI conversion to AD, the neuropathological cause of MCI is heterogeneous. However, many people with MCI remain stable. Treatment options are different for patients with stable MCI and those with underlying dementia. Therefore, it is of great significance for clinical practice to predict whether patients with MCI will develop AD dementia. This paper proposes an improved algorithm that is based on a convolution neural network (CNN) with residuals combined with multi-layer long short-term memory (LSTM) to diagnose AD and predict MCI. Firstly, multi-time resting-state fMRI images were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database for preprocessing, and then an AAL brain partition template was used to construct a 90 × 90 functional connectivity (FC) network matrix of a whole-brain region of interest (ROI). Secondly, the diversity of training samples was increased by generating an adversarial network (GAN). Finally, a CNN with residuals and a multi-layer LSTM model were constructed to automatically classify and predict the functional adjacency matrix. This method can not only distinguish Alzheimer’s disease from normal health conditions at multiple time points, but can also predict progressive MCI (pMCI) and stable MCI (sMCI) at multiple time points. The classification accuracies in AD vs. NC and sMCI vs.pMCI reached 93.5% and 75.5%, respectively.
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Affiliation(s)
- Haijing Sun
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (H.S.); (S.H.)
- College of Intelligent Science and Engineering, Shenyang University, Shenyang 110044, China
| | - Anna Wang
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (H.S.); (S.H.)
- Correspondence:
| | - Shanshan He
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (H.S.); (S.H.)
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Fan H, Luo Z. Functional Integration of Mirror Neuron System and Sensorimotor Cortex under Virtual Self-Actions Visual Perception. Behav Brain Res 2022; 423:113784. [PMID: 35122793 DOI: 10.1016/j.bbr.2022.113784] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 01/14/2022] [Accepted: 01/31/2022] [Indexed: 11/19/2022]
Abstract
Virtual reality (VR) technology, with the advantage of immersive visual experience, has been increasingly applied in the rehabilitation therapy of motor deficits. The functional integration of the mirror neuron system and the sensorimotor cortex under the visual perception of actions is one of the theoretical bases for the application of action observation in the neurorehabilitation of motor deficits. Whether the visual experience changes brought by VR technology can further promote this functional integration to be further confirmed. Using the exact low-resolution brain electromagnetic tomography (eLORETA) source localization method, we calculated and statistically tested the whole brain cortical voxel current density estimation under the Electroencephalogram (EEG) signals collected during action observation under the first-person and third-person perspectives in the VR scene for twenty healthy adults. Furthermore, the functional connectivity between the mirror neuron system and the sensorimotor cortex was analyzed using the lagged phase synchronization method. Under the first-person perspective in the VR scene, the current density changes of the core cortices of the mirror neuron system lead to a larger average event-related potential, more significant suppression in the α1 and α2 frequency bands of EEG signals, and a significant enhancement of functional connectivity between the core cortices of the mirror neuron system and the sensorimotor cortex. These findings indicate that compared with the traditional action observation, the visual reappearance of self-actions in the VR scene further stimulates the activity of the core cortices of the mirror neuron system, and promotes the functional integration of the core cortices of the mirror neuron system and the sensorimotor cortex.
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Affiliation(s)
- Hao Fan
- Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang, China.
| | - Zhizeng Luo
- Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang, China.
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Zhang G, Liu H, Zheng H, Li N, Kong L, Zheng W. Analysis on topological alterations of functional brain networks after acute alcohol intake using resting-state functional magnetic resonance imaging and graph theory. Front Hum Neurosci 2022; 16:985986. [PMID: 36226262 PMCID: PMC9549745 DOI: 10.3389/fnhum.2022.985986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 09/07/2022] [Indexed: 02/05/2023] Open
Abstract
AIMS Alcohol consumption could lead to a series of health problems and social issues. In the current study, we investigated the resting-state functional brain networks of healthy volunteers before and after drinking through graph-theory analysis, aiming to ascertain the effects of acute alcohol intake on topology and information processing mode of the functional brain networks. MATERIALS AND METHODS Thirty-three healthy volunteers were enrolled in this experiment. Each volunteer accepted alcohol breathalyzer tests followed by resting-state magnetic resonance imaging at three time points: before drinking, 0.5 h after drinking, and 1 h after drinking. The data obtained were grouped based on scanning time into control group, 0.5-h group and 1-h group, and post-drinking data were regrouped according to breath alcohol concentration (BrAC) into relative low BrAC group (A group; 0.5-h data, n = 17; 1-h data, n = 16) and relative high BrAC group (B group; 0.5-h data, n = 16; 1-h data, n = 17). The graph-theory approach was adopted to construct whole-brain functional networks and identify the differences of network topological properties among all the groups. RESULTS The network topology of most groups was altered after drinking, with the B group presenting the most alterations. For global network measures, B group exhibited increased global efficiency, Synchronization, and decreased local efficiency, clustering coefficient, normalized clustering coefficient, characteristic path length, normalized characteristic path length, as compared to control group. Regarding nodal network measures, nodal clustering coefficient and nodal local efficiency of some nodes were lower in B group than control group. These changes suggested that the network integration ability and synchrony improved, while the segregation ability diminished. CONCLUSION This study revealed the effects of acute alcohol intake on the topology and information processing mode of resting-state functional brain networks, providing new perceptions and insights into the effects of alcohol on the brain.
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Affiliation(s)
- Gengbiao Zhang
- Department of Radiology, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Hongkun Liu
- Department of Radiology, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Hongyi Zheng
- Department of Radiology, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Ni Li
- The Family Medicine Branch, Department of Radiology, The First Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Lingmei Kong
- Department of Radiology, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Wenbin Zheng
- Department of Radiology, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
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Han H, Li X, Gan JQ, Yu H, Wang H. Biomarkers Derived from Alterations in Overlapping Community Structure of Resting-state Brain Functional Networks for Detecting Alzheimer's Disease. Neuroscience 2021; 484:38-52. [PMID: 34973385 DOI: 10.1016/j.neuroscience.2021.12.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 12/17/2021] [Accepted: 12/23/2021] [Indexed: 11/27/2022]
Abstract
Recent studies show that overlapping community structure is an important feature of the brain functional network. However, alterations in such overlapping community structure in Alzheimer's disease (AD) patients have not been examined yet. In this study, we investigate the overlapping community structure in AD by using resting-state functional magnetic resonance imaging (rs-fMRI) data. The collective sparse symmetric non-negative matrix factorization (cssNMF) is adopted to detect the overlapping community structure. Experimental results on 28 AD patients and 32 normal controls (NCs) from the ADNI2 dataset show that the two groups have remarkable differences in terms of the optimal number of communities, the hierarchy of communities detected at different scales, network functional segregation, and nodal functional diversity. In particular, the frontal-parietal and basal ganglia networks exhibit significant differences between the two groups. A machine learning framework proposed in this paper for AD detection achieved an accuracy of 76.7% when using the detected community strengths of the frontal-parietal and basal ganglia networks only as input features. These findings provide novel insights into the understanding of pathological changes in the brain functional network organization of AD and show the potential of the community structure-related features for AD detection.
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Affiliation(s)
- Hongfang Han
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China; Institute of Artificial Intelligence of Hefei Comprehensive National Science Center, Hefei 230094, Anhui, PR China
| | - Xuan Li
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China; School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
| | - John Q Gan
- School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
| | - Hua Yu
- Department of Cardiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, Anhui, PR China
| | - Haixian Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China; Institute of Artificial Intelligence of Hefei Comprehensive National Science Center, Hefei 230094, Anhui, PR China.
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倪 召, 李 颖, 赵 营, 杨 硕, 尹 宁. [Research on the influence of mixed emotional factors on false memory based on brain functional network]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2021; 38:828-837. [PMID: 34713650 PMCID: PMC9927440 DOI: 10.7507/1001-5515.202008042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 04/13/2021] [Indexed: 11/03/2022]
Abstract
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.
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Affiliation(s)
- 召兵 倪
- 河北工业大学 电气工程学院 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130)State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China
| | - 颖 李
- 河北工业大学 电气工程学院 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130)State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China
- 河北工业大学 电气工程学院 天津市生物电工与智能健康重点实验室(天津 300130)Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China
| | - 营鸽 赵
- 河北工业大学 电气工程学院 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130)State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China
| | - 硕 杨
- 河北工业大学 电气工程学院 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130)State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China
- 河北工业大学 电气工程学院 天津市生物电工与智能健康重点实验室(天津 300130)Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China
| | - 宁 尹
- 河北工业大学 电气工程学院 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130)State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China
- 河北工业大学 电气工程学院 天津市生物电工与智能健康重点实验室(天津 300130)Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China
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Abstract
Brain functional network (BFN) has become an increasingly important tool to explore individual differences and identify neurological/mental diseases. For estimating a "good" BFN (with more discriminative information for example), researchers have developed various methods, in which the most popular and simplest is Pearson's correlation (PC). Despite its empirical effectiveness, PC only encodes the low-order (second-order) statistics between brain regions. To model high-order statistics, researchers recently proposed to estimate BFN by conducting two sequential PCs (denoted as PC 2 in this paper), and found that PC 2-based BFN can provide additional information for group difference analysis. This inspires us to think about (1) what will happen if continuing the correlation operation to construct much higher-order BFN by PC n (n>2), and (2) whether the higher-order correlation will result in stronger discriminative ability. To answer these questions, we use PC n -based BFNs to predict individual differences (Female vs. Male) as well as identify subjects with mild cognitive impairment (MCI) from healthy controls (HCs). Through experiments, we have the following findings: (1) with the increase of n, the discriminative ability of PC n -based BFNs tends to decrease; (2) fusing the PC n -based BFNs (n>1) with the PC 1-based BFN can generally improve the sensitivity for MCI identification, but fail to help the classification accuracy. In addition, we empirically find that the sequence of BFN adjacency matrices estimated by PC n (n = 1,2,3,⋯ ) will converge to a binary matrix with elements of ± 1.
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Affiliation(s)
- Tingting Guo
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Yining Zhang
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Yanfang Xue
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.,Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.,Department of Artificial Intelligence, Korea University, Seoul, South Korea
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20
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Cao Y, Zhan Y, Du M, Zhao G, Liu Z, Zhou F, He L. Disruption of human brain connectivity networks in patients with cervical spondylotic myelopathy. Quant Imaging Med Surg 2021; 11:3418-3430. [PMID: 34341720 DOI: 10.21037/qims-20-874] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 03/08/2021] [Indexed: 02/05/2023]
Abstract
Background Brain functional plasticity and reorganization in patients with cervical spondylotic myelopathy (CSM) is increasingly being explored and validated. However, specific topological alterations in functional networks and their role in CSM brain functional reorganization remain unclear. This study investigates the topological architecture of intrinsic brain functional networks in CSM patients using graph theory. Methods Functional MRI was conducted on 67 CSM patients and 60 healthy controls (HCs). The topological organization of the whole-brain functional network was then calculated using theoretical graph analysis. The difference in categorical variables between groups was compared using a chi-squared test, while that between continuous variables was evaluated using a two-sample t-test. Nonparametric permutation tests were used to compare network measures between the two groups. Results Small-world architecture in functional brain networks were identified in both CSM patients and HCs. Compared with HCs, CSM patients showed a decreased area under the curve (AUC) of the characteristic path length (FDR q=0.040), clustering coefficient (FDR q=0.037), and normalized characteristic path length (FDR q=0.038) of the network. In contrast, there was an increased AUC of normalized clustering coefficient (FDR q=0.014), small-worldness (FDR q=0.009), and global network efficiency (FDR q=0.027) of the network. In local brain regions, nodal topological properties revealed group differences which were predominantly in the default-mode network (DMN), left postcentral gyrus, bilateral putamen, lingual gyrus, and posterior cingulate gyrus. Conclusions This study reported altered functional topological organization in CSM patients. Decreased nodal centralities in the visual cortex and sensory-motor regions may indicate sensory-motor dysfunction and blurred vision. Furthermore, increased nodal centralities in the cerebellum may be compensatory for sensory-motor dysfunction in CSM, while the increased DMN may indicate increased psychological processing in CSM patients.
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Affiliation(s)
- Yuan Cao
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China.,Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Yaru Zhan
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Nanchang, China.,Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, China
| | - Miao Du
- College of Electrical Engineering of Sichuan University, Chengdu, China
| | - Guoshu Zhao
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Nanchang, China.,Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, China
| | - Zhili Liu
- Department of Orthopedic Surgery, the First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Fuqing Zhou
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Nanchang, China.,Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, China
| | - Laichang He
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Nanchang, China.,Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, China
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21
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Abstract
Spinal cord injury (SCI) destroys the sensorimotor pathway and blocks the information flow between the peripheral nerve and the brain, resulting in autonomic function loss. Numerous studies have explored the effects of obstructed information flow on brain structure and function and proved the extensive plasticity of the brain after SCI. Great progress has also been achieved in therapeutic strategies for SCI to restore the "re-innervation" of the cerebral cortex to the limbs to some extent. Although no thorough research has been conducted, the changes of brain structure and function caused by "re-domination" have been reported. This article is a review of the recent research progress on local structure, functional changes, and circuit reorganization of the cerebral cortex after SCI. Alterations of structure and electrical activity characteristics of brain neurons, features of brain functional reorganization, and regulation of brain functions by reconfigured information flow were also explored. The integration of brain function is the basis for the human body to exercise complex/fine movements and is intricately and widely regulated by information flow. Hence, its changes after SCI and treatments should be considered.
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Affiliation(s)
- Can Zhao
- Institute of Rehabilitation Engineering, China Rehabilitation Science Institute, Beijing, China
- School of Rehabilitation, Capital Medical University, Beijing, China
| | - Shu-Sheng Bao
- Beijing Key Laboratory for Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Meng Xu
- Department of Orthopedics, The First Medical Center of PLA General Hospital, Beijing, China
| | - Jia-Sheng Rao
- Beijing Key Laboratory for Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
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22
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Yang M, Cao M, Chen Y, Chen Y, Fan G, Li C, Wang J, Liu T. Large-Scale Brain Functional Network Integration for Discrimination of Autism Using a 3-D Deep Learning Model. Front Hum Neurosci 2021; 15:687288. [PMID: 34149385 PMCID: PMC8206477 DOI: 10.3389/fnhum.2021.687288] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/03/2021] [Indexed: 11/13/2022] Open
Abstract
GOAL Brain functional networks (BFNs) constructed using resting-state functional magnetic resonance imaging (fMRI) have proven to be an effective way to understand aberrant functional connectivity in autism spectrum disorder (ASD) patients. It is still challenging to utilize these features as potential biomarkers for discrimination of ASD. The purpose of this work is to classify ASD and normal controls (NCs) using BFNs derived from rs-fMRI. METHODS A deep learning framework was proposed that integrated convolutional neural network (CNN) and channel-wise attention mechanism to model both intra- and inter-BFN associations simultaneously for ASD diagnosis. We investigate the effects of each BFN on performance and performed inter-network connectivity analysis between each pair of BFNs. We compared the performance of our CNN model with some state-of-the-art algorithms using functional connectivity features. RESULTS We collected 79 ASD patients and 105 NCs from the ABIDE-I dataset. The mean accuracy of our classification algorithm was 77.74% for classification of ASD versus NCs. CONCLUSION The proposed model is able to integrate information from multiple BFNs to improve detection accuracy of ASD. SIGNIFICANCE These findings suggest that large-scale BFNs is promising to serve as reliable biomarkers for diagnosis of ASD.
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Affiliation(s)
- Ming Yang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
- The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, China
| | - Menglin Cao
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
- The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, China
| | - Yuhao Chen
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
- The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, China
| | | | - Geng Fan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
- The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, China
| | - Chenxi Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
- The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, China
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
- The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, China
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
- The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, China
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23
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Zhang Y, Luo Q, Huang CC, Lo CYZ, Langley C, Desrivières S, Quinlan EB, Banaschewski T, Millenet S, Bokde ALW, Flor H, Garavan H, Gowland P, Heinz A, Ittermann B, Martinot JL, Artiges E, Paillère-Martinot ML, Nees F, Orfanos DP, Poustka L, Fröhner JH, Smolka MN, Walter H, Whelan R, Tsai SJ, Lin CP, Bullmore E, Schumann G, Sahakian BJ, Feng J. The Human Brain Is Best Described as Being on a Female/Male Continuum: Evidence from a Neuroimaging Connectivity Study. Cereb Cortex 2021; 31:3021-3033. [PMID: 33471126 PMCID: PMC8107794 DOI: 10.1093/cercor/bhaa408] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 11/17/2020] [Accepted: 12/25/2020] [Indexed: 12/30/2022] Open
Abstract
Psychological androgyny has long been associated with greater cognitive flexibility, adaptive behavior, and better mental health, but whether a similar concept can be defined using neural features remains unknown. Using the neuroimaging data from 9620 participants, we found that global functional connectivity was stronger in the male brain before middle age but became weaker after that, when compared with the female brain, after systematic testing of potentially confounding effects. We defined a brain gender continuum by estimating the likelihood of an observed functional connectivity matrix to represent a male brain. We found that participants mapped at the center of this continuum had fewer internalizing symptoms compared with those at the 2 extreme ends. These findings suggest a novel hypothesis proposing that there exists a neuroimaging concept of androgyny using the brain gender continuum, which may be associated with better mental health in a similar way to psychological androgyny.
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Affiliation(s)
- Yi Zhang
- Shanghai Centre for Mathematical Sciences, Fudan University, Shanghai, 200433, China.,Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and Research and Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China.,Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Qiang Luo
- Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and Research and Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China.,State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200433, China
| | - Chu-Chung Huang
- Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and Research and Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China
| | - Christelle Langley
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK.,Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Sylvane Desrivières
- Medical Research Council-Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Erin Burke Quinlan
- Medical Research Council-Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, 69117, Germany
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, 69117, Germany
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, D02 PN40, Ireland
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Manheim, 69117, Germany.,Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, 68131, Germany
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT 05405, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Berlin, 10117, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Abbestraße 2, 10587 Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 ``Developmental trajectories & psychiatry''; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli; 91190 Gif-sur-Yvette, France.,Etablissement Public de Santé (EPS) Barthélemy Durand, 91700 Sainte-Geneviève-des-Bois, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 ``Developmental trajectories & psychiatry''; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli; 91190 Gif-sur-Yvette, France.,Etablissement Public de Santé (EPS) Barthélemy Durand, 91700 Sainte-Geneviève-des-Bois, France
| | - Marie-Laure Paillère-Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 ``Developmental trajectories & psychiatry''; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli; 91190 Gif-sur-Yvette, France.,Assistance Publique-Hêpitaux de Paris, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, 75006, France
| | - Frauke Nees
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, D02 PN40, Ireland.,Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Manheim, 69117, Germany.,Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, 24118, Germany
| | - Dimitri Papadopoulos Orfanos
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, 37075, Germany
| | - Luise Poustka
- Clinic for Child and Adolescent Psychiatry, Medical University of Vienna, Vienna, 1090 Wien, Austria.,Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, 01087, Germany
| | - Juliane H Fröhner
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, D02 PN40, Ireland
| | - Michael N Smolka
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, 11217, Taiwan
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Berlin, 10117, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, D02 PN40, Ireland
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, 11217, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, 11221, Taiwan
| | - Ching-Po Lin
- Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and Research and Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China.,Institute of Neuroscience, National Yang-Ming University, Taipei, 11221, Taiwan
| | - Ed Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK.,Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK.,Cambridgeshire and Peterborough National Health Service (NHS) Foundation Trust, Huntingdon, CB21 5EF, UK
| | - Gunter Schumann
- PONS Research Group, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, 10117, Germany.,PONS Centre, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Barbara J Sahakian
- Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and Research and Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China.,Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK.,Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Jianfeng Feng
- Shanghai Centre for Mathematical Sciences, Fudan University, Shanghai, 200433, China.,Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and Research and Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China.,Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK.,Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, 200433, China
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24
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Zhang Y, Wang Y, Chen N, Guo M, Wang X, Chen G, Li Y, Yang L, Li S, Yao Z, Hu B. Age-Associated Differences of Modules and Hubs in Brain Functional Networks. Front Aging Neurosci 2021; 12:607445. [PMID: 33536893 PMCID: PMC7848126 DOI: 10.3389/fnagi.2020.607445] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 12/21/2020] [Indexed: 01/07/2023] Open
Abstract
Healthy aging is usually accompanied by changes in the functional modular organization of the human brain, which may result in the decline of cognition and underlying brain dysfunction. However, the relationship between age-related brain functional modular structure differences and cognition remain debatable. In this study, we investigated the age-associated differences of modules and hubs from young, middle and old age groups, using resting-state fMRI data from a large cross-sectional adulthood sample. We first divided the subjects into three age groups and constructed an individual-level network for each subject. Subsequently, a module-guided group-level network construction method was applied to form a weighted network for each group from which functional modules were detected. The intra- and inter-modular connectivities were observed negatively correlated with age. According to the detected modules, we found the number of connector hubs in the young group was more than middle-age and old group, while the quantity of provincial hubs in middle-age group was discovered more than other two groups. Further ROI-wise analysis shows that different hubs have distinct age-associated trajectories of intra- and inter-modular connections, which suggests the different types of topological role transitions in functional networks across age groups. Our results indicated an inverse association between functional segregation/integration with age, which demonstrated age-associated differences in communication effeciency. This study provides a new perspective and useful information to better understand the normal aging of brain networks.
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Affiliation(s)
- Yinghui Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
- Guangyuan Mental Health Center, Guangyuan, China
| | - Yin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Nan Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Man Guo
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiuzhen Wang
- Guangyuan Mental Health Center, Guangyuan, China
| | | | - Yongchao Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Lin Yang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Shan Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
- Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, China
- Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Ministry of Education, Lanzhou, China
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25
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郭 庆, 滕 月, 仝 灿, 李 迪, 王 雪. [ Brain functional network reconstruction based on compressed sensing and fast iterative shrinkage-thresholding algorithm]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2020; 37:855-862. [PMID: 33140610 PMCID: PMC10320527 DOI: 10.7507/1001-5515.201908024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Indexed: 06/11/2023]
Abstract
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.
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Affiliation(s)
- 庆 郭
- 东北大学 医学与生物信息工程学院(沈阳 110169)College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, P.R.China
- 医学影像智能计算教育部重点实验室(沈阳 110169)Key Laboratory for Medical Imaging Intelligent Computing of Ministry of Education, Shenyang 110169, P.R.China
| | - 月阳 滕
- 东北大学 医学与生物信息工程学院(沈阳 110169)College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, P.R.China
- 医学影像智能计算教育部重点实验室(沈阳 110169)Key Laboratory for Medical Imaging Intelligent Computing of Ministry of Education, Shenyang 110169, P.R.China
| | - 灿 仝
- 东北大学 医学与生物信息工程学院(沈阳 110169)College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, P.R.China
- 医学影像智能计算教育部重点实验室(沈阳 110169)Key Laboratory for Medical Imaging Intelligent Computing of Ministry of Education, Shenyang 110169, P.R.China
| | - 迪森 李
- 东北大学 医学与生物信息工程学院(沈阳 110169)College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, P.R.China
| | - 雪飞 王
- 东北大学 医学与生物信息工程学院(沈阳 110169)College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, P.R.China
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26
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Xin Z, Chen X, Zhang Q, Wang J, Xi Y, Liu J, Li B, Dong X, Lin Y, Zhang W, Chen J, Luo W. Alteration in topological properties of brain functional network after 2-year high altitude exposure: A panel study. Brain Behav 2020; 10:e01656. [PMID: 32909397 PMCID: PMC7559604 DOI: 10.1002/brb3.1656] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 04/08/2020] [Accepted: 04/13/2020] [Indexed: 12/19/2022] Open
Abstract
INTRODUCTION High altitude (HA) exposure leads to cognitive impairment while the underlying mechanism is still unclear. Brain functional network is crucial for advanced functions, and its alteration is implicated in cognitive decline in multiple diseases. The aim of current study was to investigate the topological changes in HA-exposed brain functional network. METHODS Based on Shaanxi-Tibet immigrant cohort, neuropsychological tests and resting-state functional MRI were applied to evaluate the participants' cognitive function and functional connection (FC) changes, respectively. GRETNA toolbox was used to construct the brain functional network. The gray matter was parcellated into 116 anatomically defined regions according to Automated Anatomical Labeling atlas. Subsequently, the mean time series for each of the 116 regions were extracted and computed for Pearson's correlation coefficients. The relation matrix was further processed and seen as brain functional network. Correlation between functional network changes and neuropsychological results was also examined. RESULTS The cognitive performance was impaired by HA exposure as indicated by neuropsychological test. HA exposure led to alterations of degree centrality and nodal efficiency in multiple brain regions. Moreover, two subnetworks were extracted in which the FCs significantly decreased after exposure. In addition, the alterations in FCs within above two subnetworks were significantly correlated with changes of memory and reaction time. CONCLUSIONS Our results suggest that HA exposure modulates the topological property of functional network and FCs of some important regions, which may impair the attention, perception, memory, motion ignition, and modulation processes, finally decreasing cognitive performance in neuropsychological tests.
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Affiliation(s)
- Zhenlong Xin
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Xiaoming Chen
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Qian Zhang
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Jiye Wang
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Yibin Xi
- Department of Radiology, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Jian Liu
- Network Center, Air Force Medical University, Xi'an, China
| | - Baojuan Li
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Xiaoru Dong
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Yiwen Lin
- School of Basic Medical Science, Peking University, Beijing, China
| | - Wenbin Zhang
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Jingyuan Chen
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Wenjing Luo
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
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余 仁, 余 海, 万 红. [Research on brain network for schizophrenia classification based on resting-state functional magnetic resonance imaging]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2020; 37:661-669. [PMID: 32840083 PMCID: PMC10319543 DOI: 10.7507/1001-5515.201908007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Indexed: 11/03/2022]
Abstract
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.
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Affiliation(s)
- 仁萍 余
- 郑州大学 电气工程学院(郑州 450001)School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P.R.China
- 郑州大学 河南省脑科学与脑机接口重点实验室(郑州 450001)Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou 450001, P.R.China
| | - 海飞 余
- 郑州大学 电气工程学院(郑州 450001)School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P.R.China
- 郑州大学 河南省脑科学与脑机接口重点实验室(郑州 450001)Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou 450001, P.R.China
| | - 红 万
- 郑州大学 电气工程学院(郑州 450001)School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P.R.China
- 郑州大学 河南省脑科学与脑机接口重点实验室(郑州 450001)Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou 450001, P.R.China
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Li G, Jiang Y, Jiao W, Xu W, Huang S, Gao Z, Zhang J, Wang C. The Maximum Eigenvalue of the Brain Functional Network Adjacency Matrix: Meaning and Application in Mental Fatigue Evaluation. Brain Sci 2020; 10:brainsci10020092. [PMID: 32050462 PMCID: PMC7071607 DOI: 10.3390/brainsci10020092] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 02/06/2020] [Accepted: 02/08/2020] [Indexed: 02/04/2023] Open
Abstract
The maximum eigenvalue of the adjacency matrix (AM) has been supposed to contain rich information about the corresponding network. An experimental study focused on revealing the meaning and application of the maximum eigenvalue is missing. To this end, AM was constructed using mutual information (MI) to determine the functional connectivity with electroencephalogram (EEG) data recorded with a mental fatigue model, and then was converted into both binary and weighted brain functional network (BFN) and corresponding random networks (RNs). Both maximum eigenvalue and corresponding network characters in BFNs and RNs were considered to explore the changes during the formation of mental fatigue. The results indicated that large maximum eigenvalue means more edges in the corresponding network, along with a high degree and a short characteristic path length both in weighted and binary BFNs. Interestingly, the maximum eigenvalue of AM was always a little larger than that of the corresponding random matrix (RM), and had an obvious linearity with the sum of the AM elements, indicating that the maximum eigenvalue can be able to distinguish the network structures which have the same mean degree. What is more, the maximum eigenvalue, which increased with the deepening of mental fatigue, can become a good indicator for mental fatigue estimation.
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Affiliation(s)
- Gang Li
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (G.L.); (W.J.); (W.X.); (S.H.); (Z.G.); (C.W.)
| | - Yonghua Jiang
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (G.L.); (W.J.); (W.X.); (S.H.); (Z.G.); (C.W.)
- Correspondence: (Y.J.); (J.Z.)
| | - Weidong Jiao
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (G.L.); (W.J.); (W.X.); (S.H.); (Z.G.); (C.W.)
| | - Wanxiu Xu
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (G.L.); (W.J.); (W.X.); (S.H.); (Z.G.); (C.W.)
| | - Shan Huang
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (G.L.); (W.J.); (W.X.); (S.H.); (Z.G.); (C.W.)
| | - Zhao Gao
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (G.L.); (W.J.); (W.X.); (S.H.); (Z.G.); (C.W.)
| | - Jianhua Zhang
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education of China, School of Mechanical Engineering, Shandong University, Jinan 250061, China
- Correspondence: (Y.J.); (J.Z.)
| | - Chengwu Wang
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (G.L.); (W.J.); (W.X.); (S.H.); (Z.G.); (C.W.)
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Abstract
Brain functional networks (BFNs) constructed from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely applied to the analysis and diagnosis of brain diseases, such as Alzheimer's disease and its prodrome, namely mild cognitive impairment (MCI). Constructing a meaningful brain network based on, for example, sparse representation (SR) is the most essential step prior to the subsequent analysis or disease identification. However, the independent coding process of SR fails to capture the intrinsic locality and similarity characteristics in the data. To address this problem, we propose a novel weighted graph (Laplacian) regularized SR framework, based on which BFN can be optimized by considering both intrinsic correlation similarity and local manifold structure in the data, as well as sparsity prior of the brain connectivity. Additionally, the non-convergence of the graph Laplacian in the self-representation model has been solved properly. Combined with a pipeline of sparse feature selection and classification, the effectiveness of our proposed method is demonstrated by identifying MCI based on the constructed BFNs.
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Affiliation(s)
- Renping Yu
- Henan Key Laboratory of Brain Science and Brain-Computer interface Technology, Department of Biomedical Engineering, School of Electric Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
| | - Mingming Chen
- Henan Key Laboratory of Brain Science and Brain-Computer interface Technology, Department of Biomedical Engineering, School of Electric Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Seong-Whan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Xuan Fei
- College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Dinggang Shen
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
- IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
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Zhang Y, Zhang H, Chen X, Liu M, Zhu X, Lee SW, Shen D. Strength and Similarity Guided Group-level Brain Functional Network Construction for MCI Diagnosis. Pattern Recognit 2019; 88:421-430. [PMID: 31579344 PMCID: PMC6774624 DOI: 10.1016/j.patcog.2018.12.001] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Sparse representation-based brain functional network modeling often results in large inter-subject variability in the network structure. This could reduce the statistical power in group comparison, or even deteriorate the generalization capability of the individualized diagnosis of brain diseases. Although group sparse representation (GSR) can alleviate such a limitation by increasing network similarity across subjects, it could, in turn, fail in providing satisfactory separability between the subjects from different groups (e.g., patients vs. controls). In this study, we propose to integrate individual functional connectivity (FC) information into the GSR-based network construction framework to achieve higher between-group separability while maintaining the merit of within-group consistency. Our method was based on an observation that the subjects from the same group have generally more similar FC patterns than those from different groups. To this end, we propose our new method, namely "strength and similarity guided GSR (SSGSR)", which exploits both BOLD signal temporal correlation-based "low-order" FC (LOFC) and inter-subject LOFC-profile similarity-based "high-order" FC (HOFC) as two priors to jointly guide the GSR-based network modeling. Extensive experimental comparisons are carried out, with the rs-fMRI data from mild cognitive impairment (MCI) subjects and healthy controls, between the proposed algorithm and other state-of-the-art brain network modeling approaches. Individualized MCI identification results show that our method could achieve a balance between the individually consistent brain functional network construction and the adequately maintained inter-group brain functional network distinctions, thus leading to a more accurate classification result. Our method also provides a promising and generalized solution for the future connectome-based individualized diagnosis of brain disease.
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Affiliation(s)
- Yu Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Psychiatry and Behavior Sciences, Stanford University, Stanford, CA 94305, USA
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xiaobo Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xiaofeng Zhu
- Guangxi Key Lab of MIMS, Guangxi Normal University, Guilin 541004, Guangxi, P.R. China
- Institute of Natural and Mathematical Sciences, Massey University Albany Campus, Auckland 0745, New Zealand
| | - Seong-Whan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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Zhan C, Chen HJ, Gao YQ, Zou TX. Functional Network-Based Statistics Reveal Abnormal Resting-State Functional Connectivity in Minimal Hepatic Encephalopathy. Front Neurol 2019; 10:33. [PMID: 30761070 PMCID: PMC6362410 DOI: 10.3389/fneur.2019.00033] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 01/10/2019] [Indexed: 12/17/2022] Open
Abstract
Purpose: Whole-brain functional network analysis is an emerging methodology for exploring the mechanisms underlying hepatic encephalopathy (HE). This study aimed to identify the brain subnetwork that is significantly altered within the functional connectome in minimal HE (MHE), the earliest stage of HE. Materials and Methods: The study enrolled 19 cirrhotic patients with MHE and 19 controls who underwent the resting-state functional magnetic resonance imaging and cognitive assessment based on the Psychometric Hepatic Encephalopathy Score (PHES). A whole-brain functional connectivity (FC) matrix was calculated for each subject. Then, network-based statistical analyses of the functional connectome were used to perform group comparisons, and correlation analyses were conducted to identify the relationships between FC alterations and cognitive performance. Results: MHE patients showed significant reduction of positive FC within a subnetwork that predominantly involved the regions of the default-mode network, such as the bilateral posterior cingulate gyrus, bilateral medial prefrontal cortex, bilateral hippocampus and parahippocampal gyrus, bilateral angular gyrus, and left lateral temporal cortex. Meanwhile, MHE patients showed significant reduction of negative FC between default-mode network regions (such as the bilateral posterior cingulate gyrus, medial prefrontal cortex, and angular gyrus) and the regions involved in the somatosensory network (i.e., bilateral precentral and postcentral gyri) and the language network (i.e., the bilateral Rolandic operculum). The correlations of FC within the default-mode subnetwork and PHES results were noted. Conclusion: Default-mode network dysfunction may be one of the core issues in the pathophysiology of MHE. Our findings support the notion that HE is a neurological disease related to intrinsic brain network disruption.
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Affiliation(s)
- Chuanyin Zhan
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Hua-Jun Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yong-Qing Gao
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Tian-Xiu Zou
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
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Li T, Xue T, Wang B, Zhang J. Decoding Voluntary Movement of Single Hand Based on Analysis of Brain Connectivity by Using EEG Signals. Front Hum Neurosci 2018; 12:381. [PMID: 30455636 PMCID: PMC6231062 DOI: 10.3389/fnhum.2018.00381] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Accepted: 09/04/2018] [Indexed: 11/13/2022] Open
Abstract
Research about decoding neurophysiological signals mainly aims to elucidate the details of human motion control from the perspective of neural activity. We performed brain connectivity analysis with EEG to propose a brain functional network (BFN) and used a feature extraction algorithm for decoding the voluntary hand movement of a subject. By analyzing the characteristic parameters obtained from the BFN, we extracted the most important electrode nodes and frequencies for identifying the direction of movement of a hand. The results demonstrated that the most sensitive EEG components were for frequencies delta, theta, and gamma1 from electrodes F4, F8, C3, Cz, C4, CP4, T3, and T4. Finally, we proposed a model for decoding voluntary movement of the right hand by using a hierarchical linear model (HLM). Through a voluntary hand movement experiment in a spiral trajectory, the Poisson coefficient between the measurement trajectory and the decoding trajectory was used as a test standard to compare the HLM with the traditional multiple linear regression model. It was found that the decoding model based on the HLM obtained superior results. This paper contributes a feature extraction method based on brain connectivity analysis that can mine more comprehensive feature information related to a specific mental state of a subject. The decoding model based on the HLM possesses a strong structure for data manipulation that facilitates precise decoding.
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Affiliation(s)
- Ting Li
- Shaanxi Key Laboratory of Clothing Intelligence, School of Computer Science, Xi'an Polytechnic University, Xi'an, China
| | - Tao Xue
- Shaanxi Key Laboratory of Clothing Intelligence, School of Computer Science, Xi'an Polytechnic University, Xi'an, China
| | - Baozeng Wang
- State and Local Joint Engineering Research Center for Advanced Networking and Intelligent Information Services, School of Computer Science, Xi'an Polytechnic University, Xi'an, China
| | - Jinhua Zhang
- State and Local Joint Engineering Research Center for Advanced Networking and Intelligent Information Services, School of Computer Science, Xi'an Polytechnic University, Xi'an, China.,State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
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Yang S, Ai N, Wang L, Zhang Y, Xu G. [Research on classification of brain functional network features during mental fatigue]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2018; 35:171-175. [PMID: 29745520 PMCID: PMC9935096 DOI: 10.7507/1001-5515.201609032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Indexed: 11/03/2022]
Abstract
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.
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Affiliation(s)
- Shuo Yang
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130,
| | - Na Ai
- Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, P.R.China
| | - Lei Wang
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, P.R.China
| | - Ying Zhang
- Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, P.R.China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, P.R.China
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Jia W, Shan S, Zhang J. [Biomarker extraction of sustained attention based on brain functional network]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2018; 35:176-181. [PMID: 29745521 DOI: 10.7507/1001-5515.201611045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
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.
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Affiliation(s)
- Wenxiao Jia
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, P.R.China
| | - Siyuan Shan
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, P.R.China
| | - Jicong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191,
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Abstract
Motivated by the need for understanding neurological disorders, large-scale imaging genetic studies are being increasingly conducted. A salient objective in such studies is to identify important neuroimaging biomarkers such as the brain functional connectivity, as well as genetic biomarkers, which are predictive of disorders. However, typical approaches for estimating the group level brain functional connectivity do not account for potential variation, resulting from demographic and genetic factors, while usual methods for discovering genetic biomarkers do not factor in the influence of the brain network on the imaging phenotype. We propose a novel semiparametric Bayesian conditional graphical model for joint variable selection and graph estimation, which simultaneously estimates the brain network after accounting for heterogeneity, and infers significant genetic biomarkers. The proposed approach specifies priors on the regression coefficients, which clusters brain regions having similar activation patterns depending on covariates, leading to dimension reduction. A novel graphical prior is proposed, which encourages modularity in brain organization by specifying denser and sparse connections within and across clusters, respectively. The posterior computation proceeds via a Markov chain Monte Carlo. We apply the approach to data obtained from the Alzheimer's disease neuroimaging initiative and demonstrate numerical advantages via simulation studies.
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Affiliation(s)
- Suprateek Kundu
- Department of Biostatistics, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, 3651 Tower, 1415 Washington Heights, Ann Arbor, MI 48019, USA
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Yu HL, Xu GZ, Guo L, Fu LD, Yang S, Shi S, Lv H. Magnetic stimulation at Neiguan (PC6) acupoint increases connections between cerebral cortex regions. Neural Regen Res 2016; 11:1141-6. [PMID: 27630699 PMCID: PMC4994458 DOI: 10.4103/1673-5374.187053] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Stimulation at specific acupoints can activate cortical regions in human subjects. Previous studies have mainly focused on a single brain region. However, the brain is a network and many brain regions participate in the same task. The study of a single brain region alone cannot clearly explain any brain-related issues. Therefore, for the present study, magnetic stimulation was used to stimulate the Neiguan (PC6) acupoint, and 32-channel electroencephalography data were recorded before and after stimulation. Brain functional networks were constructed based on electroencephalography data to determine the relationship between magnetic stimulation at the PC6 acupoint and cortical excitability. Results indicated that magnetic stimulation at the PC6 acupoint increased connections between cerebral cortex regions.
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Affiliation(s)
- Hong-Li Yu
- Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
| | - Gui-Zhi Xu
- Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
| | - Lei Guo
- Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
| | - Ling-di Fu
- Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
| | - Shuo Yang
- Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
| | - Shuo Shi
- Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
| | - Hua Lv
- Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
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Qu X, Yan J, Li X, Zhang P, Liu X. Topography of Synchronization of Somatosensory Evoked Potentials Elicited by Stimulation of the Sciatic Nerve in Rat. Front Comput Neurosci 2016; 10:43. [PMID: 27199728 PMCID: PMC4854893 DOI: 10.3389/fncom.2016.00043] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Accepted: 04/18/2016] [Indexed: 01/14/2023] Open
Abstract
Purpose: Traditionally, the topography of somatosensory evoked potentials (SEPs) is generated based on amplitude and latency. However, this operation focuses on the physical morphology and field potential-power, so it suffers from difficulties in performing identification in an objective manner. In this study, measurement of the synchronization of SEPs is proposed as a method to explore brain functional networks as well as the plasticity after peripheral nerve injury. Method: SEPs elicited by unilateral sciatic nerve stimulation in twelve adult male Sprague-Dawley (SD) rats in the normal group were compared with SEPs evoked after unilateral sciatic nerve hemisection in four peripheral nerve injured SD rats. The characterization of synchronized networks from SEPs was conducted using equal-time correlation, correlation matrix analysis, and comparison to randomized surrogate data. Eigenvalues of the correlation matrix were used to identify the clusters of functionally synchronized neuronal activity, and the participation index (PI) was calculated to indicate the involvement of each channel in the cluster. The PI value at the knee point of the PI histogram was used as a threshold to demarcate the cortical boundary. Results: Ten out of the twelve normal rats showed only one synchronized brain network. The remaining two normal rats showed one strong and one weak network. In the peripheral nerve injured group, only one synchronized brain network was found in each rat. In the normal group, all network shapes appear regular and the network is largely contained in the posterior cortex. In the injured group, the network shapes appear irregular, the network extends anteriorly and posteriorly, and the network area is significantly larger. There are considerable individual variations in the shape and location of the network after peripheral nerve injury. Conclusion: The proposed method can detect functional brain networks. Compared to the results of the traditional SEP-morphology-based analysis method, the synchronized functional network area is much larger. Furthermore, the proposed method can also characterize the rapid cortical plasticity after a peripheral nerve is acutely injured.
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Affiliation(s)
- Xuefeng Qu
- Division of the Comprehensive Epilepsy Center and Neurofunctional Monitoring Laboratory, Department of Neurology, Peking University People's Hospital Beijing, China
| | - Jiaqing Yan
- School of Electrical and Control Engineering, North China University of Technology Beijing, China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Peixun Zhang
- Department of Trauma and Orthopaedics, Peking University People's Hospital Beijing, China
| | - Xianzeng Liu
- Division of the Comprehensive Epilepsy Center and Neurofunctional Monitoring Laboratory, Department of Neurology, Peking University People's Hospital Beijing, China
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Lei W, Li M, Deng W, Zhou Y, Ma X, Wang Q, Guo W, Li Y, Jiang L, Han Y, Huang C, Hu X, Li T. Sex-Specific Patterns of Aberrant Brain Function in First-Episode Treatment-Naive Patients with Schizophrenia. Int J Mol Sci 2015; 16:16125-43. [PMID: 26193256 DOI: 10.3390/ijms160716125] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Revised: 06/03/2015] [Accepted: 06/26/2015] [Indexed: 02/05/2023] Open
Abstract
Male and female patients with schizophrenia show significant differences in a number of important clinical features, yet the neural substrates of these differences are still poorly understood. Here we explored the sex differences in the brain functional aberrations in 124 treatment-naïve patients with first-episode schizophrenia (61 males), compared with 102 age-matched healthy controls (50 males). Maps of degree centrality (DC) and amplitude of low-frequency fluctuations (ALFF) were constructed using resting-state functional magnetic resonance imaging data and compared between groups. We found that: (1) Selective DC reduction was observed in the right putamen (Put_R) in male patients and the left middle frontal gyrus (MFG) in female patients; (2) Functional connectivity analysis (using Put_R and MFG as seeds) found that male and female patients have disturbed functional integration in two separate networks, i.e., the sensorimotor network and the default mode network; (3) Significant ALFF alterations were also observed in these two networks in both genders; (4) Sex specific brain functional alterations were associated with various symptoms in patients. These results suggested that sex-specific patterns of functional aberration existed in schizophrenia, and these patterns were associated with the clinical features both in male and female patients.
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Guo L, Wang Y, Yu H, Yin N, Li Y. Study of brain functional network based on sample entropy of EEG under magnetic stimulation at PC6 acupoint. Biomed Mater Eng 2014; 24:1063-9. [PMID: 24211997 DOI: 10.3233/bme-130904] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Acupuncture is based on the theory of traditional Chinese medicine. Its therapeutic effectiveness has been proved by clinical practice. However, its mechanism of action is still unclear. Magnetic stimulation at acupuncture point provides a new means for studying the theory of acupuncture. Based on the Graph Theory, the construction and analysis method of complex network can help to investigate the topology of brain functional network and understand the working mechanism of brain. In this study, magnetic stimulation was used to stimulate Neiguan (PC6) acupoint and the EEG (Electroencephalograph) signal was recorded. Using non-linear method (Sample Entropy) and complex network theory, brain functional network based on EEG signal under magnetic stimulation at PC6 acupoint was constructed and analyzed. In addition, the features of complex network were comparatively analyzed between the quiescent and stimulated states. Our experimental results show the topology of the network is changed, the connection of the network is enhanced, the efficiency of information transmission is improved and the small-world property is strengthened through stimulating the PC6 acupoint.
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Affiliation(s)
- Lei Guo
- Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China
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