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Wei Q, Lin S, Xu S, Zou J, Chen J, Kang M, Hu J, Liao X, Wei H, Ling Q, Shao Y, Yu Y. Graph theoretical analysis and independent component analysis of diabetic optic neuropathy: A resting-state functional magnetic resonance imaging study. CNS Neurosci Ther 2024; 30:e14579. [PMID: 38497532 PMCID: PMC10945884 DOI: 10.1111/cns.14579] [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: 03/12/2023] [Revised: 05/06/2023] [Accepted: 12/14/2023] [Indexed: 03/19/2024] Open
Abstract
AIMS This study aimed to investigate the resting-state functional connectivity and topologic characteristics of brain networks in patients with diabetic optic neuropathy (DON). METHODS Resting-state functional magnetic resonance imaging scans were performed on 23 patients and 41 healthy control (HC) subjects. We used independent component analysis and graph theoretical analysis to determine the topologic characteristics of the brain and as well as functional network connectivity (FNC) and topologic properties of brain networks. RESULTS Compared with HCs, patients with DON showed altered global characteristics. At the nodal level, the DON group had fewer nodal degrees in the thalamus and insula, and a greater number in the right rolandic operculum, right postcentral gyrus, and right superior temporal gyrus. In the internetwork comparison, DON patients showed significantly increased FNC between the left frontoparietal network (FPN-L) and ventral attention network (VAN). Additionally, in the intranetwork comparison, connectivity between the left medial superior frontal gyrus (MSFG) of the default network (DMN) and left putamen of auditory network was decreased in the DON group. CONCLUSION DON patients altered node properties and connectivity in the DMN, auditory network, FPN-L, and VAN. These results provide evidence of the involvement of specific brain networks in the pathophysiology of DON.
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Affiliation(s)
- Qian Wei
- Department of Endocrine and MetabolicThe First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Jiangxi Clinical Research Center for Endocrine and Metabolic Disease, Jiangxi Branch of National Clinical Research Center for Metabolic DiseaseNanchangJiangxiChina
- Queen Mary SchoolThe Nanchang UniversityNanchangJiangxiChina
| | - Si‐Min Lin
- Department of RadiologyXiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen UniversityXiamenFujianChina
| | - San‐Hua Xu
- Department of OphthalmologyThe First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
| | - Jie Zou
- Department of OphthalmologyThe First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
| | - Jun Chen
- Department of OphthalmologyThe First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
| | - Min Kang
- Department of OphthalmologyThe First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
| | - Jin‐Yu Hu
- Department of OphthalmologyThe First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
| | - Xu‐Lin Liao
- Department of Ophthalmology and Visual SciencesThe Chinese University of Hong KongHong KongChina
| | - Hong Wei
- Department of OphthalmologyThe First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
| | - Qian Ling
- Department of OphthalmologyThe First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
| | - Yi Shao
- Department of OphthalmologyThe First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
- Department of OphthalmologyEye & ENT Hospital of Fudan UniversityShanghaiChina
| | - Yao Yu
- Department of Endocrine and MetabolicThe First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Jiangxi Clinical Research Center for Endocrine and Metabolic Disease, Jiangxi Branch of National Clinical Research Center for Metabolic DiseaseNanchangJiangxiChina
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Newlin NR, Rheault F, Schilling KG, Landman BA. Characterizing Streamline Count Invariant Graph Measures of Structural Connectomes. J Magn Reson Imaging 2023; 58:1211-1220. [PMID: 36840398 PMCID: PMC10447626 DOI: 10.1002/jmri.28631] [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: 10/20/2022] [Revised: 01/21/2023] [Accepted: 01/23/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND While graph measures are used increasingly to characterize human connectomes, uncertainty remains in how to use these metrics in a quantitative and reproducible manner. Specifically, there is a lack of community consensus regarding the number of streamlines needed to generate connectomes. PURPOSE The purpose was to define the relationship between streamline count and graph-measure value, reproducibility, and repeatability. STUDY TYPE Retrospective analysis of previously prospective study. POPULATION Ten healthy subjects, 70% female, aged 25.3 ± 5.9 years. FIELD STRENGTH/SEQUENCE A 3-T, T1-weighted sequences and diffusion-weighted imaging (DWI) with two gradient strengths (b-values = 1200 and 3000 sec/mm2 , echo time [TE] = 68 msec, repetition time [TR] = 5.4 seconds, 120 slices, field of view = 188 mm2 ). ASSESSMENT A total of 13 graph-theory measures were derived for each subject by generating probabilistic whole-brain tractography from DWI and mapping the structural connectivity to connectomes. The streamline count invariance from changes in mean, repeatability, and reproducibility were derived. STATISTICAL TESTS Paired t-test with P value <0.05 was used to compare graph-measure means with a reference, intraclass correlation coefficient (ICC) to measure repeatability, and concordance correlation coefficient (CCC) to measure reproducibility. RESULTS Modularity and global efficiency converged to their reference mean with ICC > 0.90 and CCC > 0.99. Edge count, small-worldness, randomness, and average betweenness centrality converged to the reference mean, with ICC > 0.90 and CCC > 0.95. Assortativity and average participation coefficient converged with ICC > 0.75 and CCC > 0.90. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient did not converge, though had ICC > 0.90 and CCC > 0.99. For these measures, alternate definitions that converge a reference mean are provided. DATA CONCLUSION Modularity and global efficiency are streamline count invariant for greater than 6 million and 100,000 streamlines, respectively. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient were strongly dependent on streamline count. EVIDENCE LEVEL 1. TECHNICAL EFFICACY Stage 1.
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Affiliation(s)
- Nancy R. Newlin
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - François Rheault
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Bennett A. Landman
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
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Bao B, Sun Y, Lin J, Gao T, Shen J, Hu W, Zhu H, Zhu T, Li J, Wang Z, Wei H, Zheng X. Altered cortical thickness and structural covariance networks in upper limb amputees: A graph theoretical analysis. CNS Neurosci Ther 2023; 29:2901-2911. [PMID: 37122148 PMCID: PMC10493660 DOI: 10.1111/cns.14226] [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: 06/29/2022] [Revised: 04/03/2023] [Accepted: 04/09/2023] [Indexed: 05/02/2023] Open
Abstract
BACKGROUND The extensive functional and structural remodeling that occurs in the brain after amputation often results in phantom limb pain (PLP). These closely related phenomena are still not fully understood. METHODS Using magnetic resonance imaging (MRI) and graph theoretical analysis (GTA), we explored how alterations in brain cortical thickness (CTh) and structural covariance networks (SCNs) in upper limb amputees (ULAs) relate to PLP. In all, 45 ULAs and 45 healthy controls (HCs) underwent structural MRI. Regional network properties, including nodal degree, betweenness centrality (BC), and node efficiency, were analyzed with GTA. Similarly, global network properties, including global efficiency (Eglob), local efficiency (Eloc), clustering coefficient (Cp), characteristic path length (Lp), and the small-worldness index, were evaluated. RESULTS Compared with HCs, ULAs had reduced CThs in the postcentral and precentral gyri contralateral to the amputated limb; this decrease in CTh was negatively correlated with PLP intensity in ULAs. ULAs showed varying degrees of change in node efficiency in regional network properties compared to HCs (p < 0.005). There were no group differences in Eglob, Eloc, Cp, and Lp properties (all p > 0.05). The real-worldness SCN of ULAs showed a small-world topology ranging from 2% to 34%, and the area under the curve of the small-worldness index in ULAs was significantly different compared to HCs (p < 0.001). CONCLUSION These results suggest that the topological organization of human CNS functional networks is altered after amputation of the upper limb, providing further support for the cortical remapping theory of PLP.
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Affiliation(s)
- Bingbo Bao
- Department of Orthopedic SurgeryShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
| | - Yi Sun
- Department of Orthopedic SurgeryShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
| | - Junqing Lin
- Department of Orthopedic SurgeryShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
| | - Tao Gao
- Department of Orthopedic SurgeryShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
| | - Junjie Shen
- Department of Orthopedic SurgeryShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
| | - Wencheng Hu
- Department of Orthopedic SurgeryShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
| | - Hongyi Zhu
- Department of Orthopedic SurgeryShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
| | - Tianhao Zhu
- Department of Orthopedic SurgeryShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
| | - Jing Li
- Institute of Diagnostic and Interventional RadiologyShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
| | - Zhibin Wang
- Institute of Diagnostic and Interventional RadiologyShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
| | - Haifeng Wei
- Department of Orthopedic SurgeryShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
| | - Xianyou Zheng
- Department of Orthopedic SurgeryShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
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Yu X, Yu J, Li Y, Cong J, Wang C, Fan R, Wang W, Zhou L, Xu C, Li Y, Liu Y. Altered intrinsic functional brain architecture in patients with functional constipation: a surface-based network study. Front Neurosci 2023; 17:1241993. [PMID: 37811328 PMCID: PMC10551127 DOI: 10.3389/fnins.2023.1241993] [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] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 09/06/2023] [Indexed: 10/10/2023] Open
Abstract
Background Functional constipation (FCon) is a common functional gastrointestinal disorder (FGID). Studies have indicated a higher likelihood of psychiatric disorders, such as anxiety, depression, sleep disturbances, and impaired concentration, among patients with FCon. However, the underlying pathophysiological mechanisms responsible for these symptoms in FCon patients remain to be fully elucidated. The human brain is a complex network architecture with several fundamental organizational properties. Neurological interactions between gut symptoms and psychiatric issues may be closely associated with these complex networks. Methods In the present study, a total of 35 patients with FCon and 40 healthy controls (HC) were recruited for a series of clinical examinations and resting-state functional magnetic imaging (RS-fMRI). We employed the surface-based analysis (SBA) approach, utilizing the Schaefer cortical parcellation template and Tikhonov regularization. Graph theoretical analysis (GTA) and functional connectivity (FC) analysis of RS-fMRI were conducted to investigate the aberrant network alterations between the two groups. Additionally, correlation analyses were performed between the network indices and clinical variables in patients with FCon. Results At the global level, we found altered topological properties and networks in patients with FCon, mainly including the significantly increased clustering coefficient (CP), local efficiency (Eloc), and shortest path length (LP), whereas the decreased global efficiency (Eglob) compared to HC. At the regional level, patients with FCon exhibited increased nodal efficiency in the frontoparietal network (FPN). Furthermore, FC analysis demonstrated several functional alterations within and between the Yeo 7 networks, particularly including visual network (VN), limbic network (LN), default mode network (DMN), and somatosensory-motor network (SMN) in sub-network and large-scale network analysis. Correlation analysis revealed that there were no significant associations between the network metrics and clinical variables in the present study. Conclusion These results highlight the altered topological architecture of functional brain networks associated with visual perception abilities, emotion regulation, sensorimotor processing, and attentional control, which may contribute to effectively targeted treatment modalities for patients with FCon.
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Affiliation(s)
- Xiang Yu
- Department of Radiology, Tianjin Union Medical Center, Tianjin, China
| | - Jingjie Yu
- Department of Psychiatry and Psychology, Tianjin Union Medical Center, Tianjin, China
| | - Yuwei Li
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
| | - Jiying Cong
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
| | - Chao Wang
- Department of Radiology, Tianjin Union Medical Center, Tianjin, China
| | - Ran Fan
- Department of Radiology, Tianjin Union Medical Center, Tianjin, China
| | - Wanbing Wang
- Graduate School of Tianjin Nankai University, Tianjin, China
| | - Lige Zhou
- Graduate School of Tianjin Medical University, Tianjin, China
| | - Chen Xu
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
| | - Yiming Li
- Department of Radiology, Tianjin Union Medical Center, Tianjin, China
| | - Yawu Liu
- Department of Neurology, University of Eastern Finland, Kuopio, Finland
- Department of Clinical Radiology, Kuopio University Hospital, Kuopio, Finland
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Jung WH, Kim E. Different topological patterns in structural covariance networks between high and low delay discounters. Front Psychol 2023; 14:1210652. [PMID: 37711326 PMCID: PMC10498536 DOI: 10.3389/fpsyg.2023.1210652] [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] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 08/18/2023] [Indexed: 09/16/2023] Open
Abstract
Introduction People prefer immediate over future rewards because they discount the latter's value (a phenomenon termed "delay discounting," used as an index of impulsivity). However, little is known about how the preferences are implemented in brain in terms of the coordinated pattern of large-scale structural brain networks. Methods To examine this question, we classified high discounting group (HDG) and low discounting group (LDG) in young adults by assessing their propensity for intertemporal choice. We compared global and regional topological properties in gray matter volume-based structural covariance networks between two groups using graph theoretical analysis. Results HDG had less clustering coefficient and characteristic path length over the wide sparsity range than LDG, indicating low network segregation and high integration. In addition, the degree of small-worldness was more significant in HDG. Locally, HDG showed less betweenness centrality (BC) in the parahippocampal gyrus and amygdala than LDG. Discussion These findings suggest the involvement of structural covariance network topology on impulsive choice, measured by delay discounting, and extend our understanding of how impulsive choice is associated with brain morphological features.
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Affiliation(s)
- Wi Hoon Jung
- Department of Psychology, Gachon University, Seongnam, Republic of Korea
| | - Euitae Kim
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
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Chen VCH, Chuang W, Chen CW, Tsai YH, McIntyre RS, Weng JC. Detecting microstructural alterations of cerebral white matter associated with breast cancer and chemotherapy revealed by generalized q-sampling MRI. Front Psychiatry 2023; 14:1161246. [PMID: 37363171 PMCID: PMC10289548 DOI: 10.3389/fpsyt.2023.1161246] [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: 02/08/2023] [Accepted: 05/22/2023] [Indexed: 06/28/2023] Open
Abstract
Objective Previous studies have discussed the impact of chemotherapy on the brain microstructure. There is no evidence of the impact regarding cancer-related psychiatric comorbidity on cancer survivors. We aimed to evaluate the impact of both chemotherapy and mental health problem on brain microstructural alterations and consequent cognitive dysfunction in breast cancer survivors. Methods In this cross-sectional study conducted in a tertiary center, data from 125 female breast cancer survivors who had not received chemotherapy (BB = 65; 49.86 ± 8.23 years) and had received chemotherapy (BA = 60; 49.82 ± 7.89 years) as well as from 71 age-matched healthy controls (47.18 ± 8.08 years) was collected. Chemotherapeutic agents used were docetaxel and epirubicin. We used neuropsychological testing and questionnaire to evaluate psychiatric comorbidity, cognitive dysfunction as well as generalized sampling imaging (GQI) and graph theoretical analysis (GTA) to detect microstructural alterations in the brain. Findings Cross-comparison between groups revealed that neurotoxicity caused by chemotherapy and cancer-related psychiatric comorbidity may affect the corpus callosum and middle frontal gyrus. In addition, GQI indices were correlated with the testing scores of cognitive function, quality of life, anxiety, and depression. Furthermore, weaker connections between brain regions and lower segregated ability were found in the post-treatment group. Conclusion This study suggests that chemotherapy and cancer-related mental health problem both play an important role in the development of white matter alterations and cognitive dysfunction.
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Affiliation(s)
- Vincent Chin-Hung Chen
- School of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan
| | - Wei Chuang
- Department of Medical Imaging and Radiological Sciences, Department of Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan
| | - Chien-Wei Chen
- School of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi, Taiwan
| | - Yuan-Hsiung Tsai
- School of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi, Taiwan
| | - Roger S. McIntyre
- Mood Disorder Psychopharmacology Unit, Department of Psychiatry, University Health Network, University of Toronto, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Departments of Psychiatry and Pharmacology, University of Toronto, Toronto, ON, Canada
| | - Jun-Cheng Weng
- Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan
- Department of Medical Imaging and Radiological Sciences, Department of Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan
- Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan
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Cheng Q, Xiao H, Luo Y, Zhong L, Guo Y, Fan X, Zhang X, Liu Y, Weng A, Ou Z, Zhang W, Wu H, Hu Q, Peng K, Xu J, Liu G. Cortico-basal ganglia networks dysfunction associated with disease severity in patients with idiopathic blepharospasm. Front Neurosci 2023; 17:1159883. [PMID: 37065925 PMCID: PMC10098005 DOI: 10.3389/fnins.2023.1159883] [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: 02/06/2023] [Accepted: 03/15/2023] [Indexed: 04/18/2023] Open
Abstract
Background Structural changes occur in brain regions involved in cortico-basal ganglia networks in idiopathic blepharospasm (iBSP); whether these changes influence the function connectivity patterns of cortico-basal ganglia networks remains largely unknown. Therefore, we aimed to investigate the global integrative state and organization of functional connections of cortico-basal ganglia networks in patients with iBSP. Methods Resting-state functional magnetic resonance imaging data and clinical measurements were acquired from 62 patients with iBSP, 62 patients with hemifacial spasm (HFS), and 62 healthy controls (HCs). Topological parameters and functional connections of cortico-basal ganglia networks were evaluated and compared among the three groups. Correlation analyses were performed to explore the relationship between topological parameters and clinical measurements in patients with iBSP. Results We found significantly increased global efficiency and decreased shortest path length and clustering coefficient of cortico-basal ganglia networks in patients with iBSP compared with HCs, however, such differences were not observed between patients with HFS and HCs. Further correlation analyses revealed that these parameters were significantly correlated with the severity of iBSP. At the regional level, the functional connectivity between the left orbitofrontal area and left primary somatosensory cortex and between the right anterior part of pallidum and right anterior part of dorsal anterior cingulate cortex was significantly decreased in patients with iBSP and HFS compared with HCs. Conclusion Dysfunction of the cortico-basal ganglia networks occurs in patients with iBSP. The altered network metrics of cortico-basal ganglia networks might be served as quantitative markers for evaluation of the severity of iBSP.
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Affiliation(s)
- Qinxiu Cheng
- Chinese Academy of Sciences, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Shenzhen, China
| | - Han Xiao
- Department of Nuclear Medicine, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Yuhan Luo
- Department of Neurology, National Key Clinical Department and Key Discipline of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Linchang Zhong
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yaomin Guo
- Department of Neurology, National Key Clinical Department and Key Discipline of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xinxin Fan
- Chinese Academy of Sciences, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Shenzhen, China
| | - Xiaodong Zhang
- Chinese Academy of Sciences, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Shenzhen, China
| | - Ying Liu
- Department of Neurology, National Key Clinical Department and Key Discipline of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ai Weng
- Department of Neurology, National Key Clinical Department and Key Discipline of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zilin Ou
- Department of Neurology, National Key Clinical Department and Key Discipline of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Weixi Zhang
- Department of Neurology, National Key Clinical Department and Key Discipline of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huawang Wu
- Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qingmao Hu
- Chinese Academy of Sciences, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Shenzhen, China
| | - Kangqiang Peng
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- Kangqiang Peng,
| | - Jinping Xu
- Chinese Academy of Sciences, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Shenzhen, China
- Jinping Xu,
| | - Gang Liu
- Department of Neurology, National Key Clinical Department and Key Discipline of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Gang Liu,
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Deng L, Liu H, Liu W, Liao Y, Liang Q, Wang W. Alteration in topological organization characteristics of gray matter covariance networks in patients with prediabetes. Zhong Nan Da Xue Xue Bao Yi Xue Ban 2022; 47:1375-1384. [PMID: 36411688 PMCID: PMC10930362 DOI: 10.11817/j.issn.1672-7347.2022.220085] [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] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Indexed: 06/16/2023]
Abstract
OBJECTIVES Prediabetes is associated with an increased risk of cognitive impairment and neurodegenerative diseases. However, the exact mechanism of prediabetes-related brain diseases has not been fully elucidated. The brain structure of patients with prediabetes has been damaged to varying degrees, and these changes may affect the topological characteristics of large-scale brain networks. The structural covariance of connected gray matter has been demonstrated valuable in inferring large-scale structural brain networks. The alterations of gray matter structural covariance networks in prediabetes remain unclear. This study aims to examine the topological features and robustness of gray matter structural covariance networks in prediabetes. METHODS A total of 48 subjects were enrolled in this study, including 23 patients with prediabetes (the PD group) and 25 age-and sex-matched healthy controls (the Ctr group). All subjects' high-resolution 3D T1 images of the brain were collected by a 3.0 Tesla MR machine. Mini-mental state examination was used to evaluate the cognitive status of each subject. We calculated the gray matter volume of 116 brain regions with automated anatomical labeling (AAL) template, and constructed gray matter structural covariance networks by thresholding interregional structural correlation matrices as well as graph theoretical analysis. The area under the curve (AUC) in conjunction with permutation testing was employed for testing the differences in network measures, which included small world parameter (Sigma), normalized clustering coefficient (Gamma), normalized path length (Lambda), global efficiency, characteristic path length, local efficiency, mean clustering coefficient, and network robustness parameters. RESULTS The network in both groups followed small-world characteristics, showing that Sigma was greater than 1, the Lambda was much higher than 1, and Gamma was close to 1. Compared with the Ctr group, the network of the PD group showed increased Sigma, Lambda, and Gamma across a range of network sparsity. The Gamma of the PD group was significantly higher than that in the Ctr group in the network sparsity range of 0.12-0.16, but there was no difference between the 2 groups (all P>0.05). The grey matter network showed an increased characteristic path length and a decreased global efficiency in the PD group, but AUC analysis showed that there was no significant difference between groups (all P>0.05). For the network separation measures, the local efficiency and mean clustering coefficient of the gray matter network in the PD group were significantly increased and AUC analysis also confirmed it (P=0.001 and P=0.004, respectively). In addition, network robustness analysis showed that the grey matter network of the PD group was more vulnerable to random damage (P=0.001). CONCLUSIONS The prediabetic gray matter network shows an increased average clustering coefficient and local efficiency, and is more vulnerable to random damage than the healthy control, suggesting that the topological characteristics of the prediabetes grey matter covariant network have changed (network separation enhanced and network robustness reduced), which may provide new insights into the brain damage relevant to the disease.
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Affiliation(s)
- Lingling Deng
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
| | - Huasheng Liu
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Wen Liu
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Yunjie Liao
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Qi Liang
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
| | - Wei Wang
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China
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Salsabilian S, Bibineyshvili Y, Margolis D, Najafizadeh L. Identifying mild traumatic brain injury using measures of frequency-specified networks. J Neural Eng 2022; 19. [PMID: 36167053 DOI: 10.1088/1741-2552/ac954e] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 09/27/2022] [Indexed: 11/12/2022]
Abstract
Objective. Early diagnosis of mild traumatic brain injury (mTBI) is challenging, yet crucial for providing patients with timely treatments and minimizing the risks of developing injury-related disorders. To tackle this problem, this paper presents a framework based on measures of frequency-specified brain functional networks identifying mTBI.Approach. Cortical activity of 15 control and 15 injury Thy1-GCaMP6s mice are recorded, using widefield calcium imaging, prior to and 20 minutes after inducing injury. Power spectral distribution (PSD) of the recorded cortical activities are examined, and the frequency bands with significant difference in PSD between the injury and control groups are identified. Frequency-specified functional networks are then constructed. Employing graph theoretical analysis, various network measures from the constructed frequency-specified functional networks are extracted and used as features. Several classifiers are utilized to evaluate the performance of the computed network measures, either individually or collectively as features, to classify mTBI from control.Main results. Spectral analysis reveals the presence of two dominant frequency bands (low: <1 Hz) and high: [1-8] Hz) in the cortical activities recorded via calcium imaging. Comparison of the brain networks of control and injury groups shows significant reduction (p<0.05) in global functional connectivity following injury, specially for the high frequency band network. Interestingly, graph measures of the high frequency band network provided higher classification accuracy results, compared to those computed from the low frequency band network, suggesting that mTBI network-based features are frequency dependent. Using all network measures collectively as a multi-measure feature vector and a CNN classifier, a model for identifying mTBI is developed, offering an average classification accuracy of 97.28%.Significance. Results signifies the importance of considering frequency-specific analysis in functional networks for mTBI identification, and demonstrate the possibility of using network measures for early mTBI diagnosis.
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Affiliation(s)
- Shiva Salsabilian
- Department of Electrical and Computer Engineering, Rutgers University, 94 Brett Rd., Piscataway, New Jersey, 08854-8058, UNITED STATES
| | | | - David Margolis
- Cell Biology and Neuroscience, Rutgers The State University of New Jersey, 604 Allison Road, Piscataway, New Jersey, 08854-8082, UNITED STATES
| | - Laleh Najafizadeh
- Electrical and Computer Engineering, Rutgers University, 94 Brett Rd., Piscataway, New Jersey, 08854-8058, UNITED STATES
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10
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Tsai JD, Ho MC, Shen CY, Weng JC. Assessment of disrupted brain functional connectome in tuberous sclerosis complex using resting-state fMRI. Medicine (Baltimore) 2022; 101:e29024. [PMID: 35356911 PMCID: PMC10684191 DOI: 10.1097/md.0000000000029024] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 02/16/2022] [Indexed: 12/31/2022] Open
Abstract
ABSTRACT Tuberous sclerosis complex (TSC) is a rare genetic disorder with multisystem involvement. TSC is characterized by benign hamartomas in multiple organs, including the brain, and its clinical phenotypes may be associated with abnormal functional connections. We aimed to use resting-state functional connectivity to provide findings of disrupted functional brain networks in TSC patients using graph theoretical analysis (GTA) and network-based statistic (NBS) analysis.Forty TSC patients (age = 24.11+/-11.44 years old) and 18 age-matched (25.13+/- 10.01 years old) healthy controls were recruited; they underwent resting-state functional magnetic resonance imaging using a 3T magnetic resonance imaging scanner. After image preprocessing and removing physiological noises, GTA was used to calculate the topological parameters of the brain network. NBS analysis was then used to determine the differences in cerebrum functional connectivity between the 2 groups.In GTA, several topological parameters, including the clustering coefficient, local efficiency, transitivity, and modularity, were better in controls than in TSC patients (P < .05). In NBS analysis, the edges of the brain networks between the groups were compared. One subnetwork showed more edges in controls than in TSC patients (P < .05), including the connections from the frontal lobe to the temporal and parietal lobe.The study results provide the findings on disrupted functional connectivity and organization in TSC patients compared with controls. The findings may help better understand the underlying physiological mechanisms of brain connection in TSC.
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Affiliation(s)
| | | | | | - Jun-Cheng Weng
- Correspondence: Jun-Cheng Weng, Department of Medical Imaging and Radiological Sciences, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist.,Taoyuan City 33302, Taiwan (e-mail: mail: ).
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11
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Yan S, Zhang G, Zhou Y, Tian T, Qin Y, Wu D, Lu J, Zhang S, Liu WV, Zhu W. Abnormalities of Cortical Morphology and Structural Covariance Network in Patients with Subacute Basal Ganglia Stroke. Acad Radiol 2022; 29 Suppl 3:S157-S165. [PMID: 34556428 DOI: 10.1016/j.acra.2021.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 07/29/2021] [Accepted: 08/05/2021] [Indexed: 11/01/2022]
Abstract
RATIONALE AND OBJECTIVES The direct damage caused by ischemic stroke is relatively localized, but structural reorganization of cortical regions could occur across the brain. Changes of large-scale, cortical structural brain networks after basal ganglia stroke are less well reported. We, therefore, aim to explore the abnormalities of cortical morphology and structural network topology in patients with unilateral basal ganglia stroke during the subacute period. MATERIALS AND METHODS Thirty patients with first-ever basal ganglia stroke and thirty age- and sex-matched healthy controls were recruited for our analysis. Patients underwent structural magnetic resonance imaging examinations and clinical assessment from seven days to three months post-stroke. Alterations in cortical morphology and topological properties of the cortical structural network were measured respectively using the surface-based morphology and graph-theoretical methods. RESULTS We observed focal cortical atrophy, specifically in areas of frontal and temporal cortices. Moreover, the cortical thickness in the contralesional transverse temporal gyrus and superior temporal gyrus was positively correlated with cognitive function scores. Network analysis revealed that patients with basal ganglia stroke showed increased clustering coefficient, increased mean local efficiency as well as a reorganization of degree-based hubs. In addition, these patients also showed reduced robustness under a random attack compared to healthy controls. CONCLUSION These findings indicated a unique pattern of cortical reorganization and the abnormal topological organization of cortical thickness-based structural covariance networks in patients with basal ganglia stroke, which is beneficial to understand the pathophysiological mechanisms of functional disorders at the cortical structural network level and find potential targets for induced neuromodulation.
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12
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Liu S, Yin N, Li C, Li X, Ni J, Pan X, Ma R, Wu J, Feng J, Shen B. Topological Abnormalities of Pallido-Thalamo-Cortical Circuit in Functional Brain Network of Patients With Nonchemotherapy With Non-small Cell Lung Cancer. Front Neurol 2022; 13:821470. [PMID: 35211086 PMCID: PMC8860807 DOI: 10.3389/fneur.2022.821470] [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: 11/24/2021] [Accepted: 01/07/2022] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION Some previous studies in patients with lung cancer have mainly focused on exploring the cognitive dysfunction and deficits of brain function associated with chemotherapy. However, little is known about functional brain alterations that might occur prior to chemotherapy. Therefore, this study aimed to evaluate brain functional changes in patients with nonchemotherapy before chemotherapy with non-small cell lung cancer (NSCLC). METHODS Resting-state functional MRI data of 35 patients with NSCLC and 46 matched healthy controls (HCs) were acquired to construct functional brain networks. Graph theoretical analysis was then applied to investigate the differences of the network and nodal measures between groups. Finally, the receiver operating characteristic (ROC) curve analysis was performed to distinguish between NSCLC and HC. RESULTS Decreased nodal strength was found in the left inferior frontal gyrus (opercular part), inferior frontal gyrus (triangular part), inferior occipital gyrus, and right inferior frontal gyrus (triangular part) of patients with NSCLC while increased nodal strength was found in the right pallidum and thalamus. NSCLC also showed decreased nodal betweenness in the right superior occipital gyrus. Different hub regions distribution was found between groups, however, no hub regions showed group differences in the nodal measures. Furthermore, the ROC curve analysis showed good performance in distinguishing NSCLC from HC. CONCLUSION These results suggested that topological abnormalities of pallido-thalamo-cortical circuit in functional brain network might be related to NSCLC prior to chemotherapy, which provided new insights concerning the pathophysiological mechanisms of NSCLC and could serve as promising biological markers for the identification of patients with NSCLC.
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Affiliation(s)
- Siwen Liu
- Research Center for Clinical Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Na Yin
- Department of Radiology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Chenchen Li
- Department of Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaoyou Li
- Department of Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Jie Ni
- Department of Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Xuan Pan
- Department of Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Rong Ma
- Research Center for Clinical Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Jianzhong Wu
- Research Center for Clinical Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Jifeng Feng
- Research Center for Clinical Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.,Department of Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Bo Shen
- Department of Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
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Chen VCH, Kao CJ, Tsai YH, Cheok MT, McIntyre RS, Weng JC. Assessment of Disrupted Brain Structural Connectome in Depressive Patients With Suicidal Ideation Using Generalized Q-Sampling MRI. Front Hum Neurosci 2021; 15:711731. [PMID: 34512298 PMCID: PMC8430248 DOI: 10.3389/fnhum.2021.711731] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 05/20/2021] [Accepted: 07/30/2021] [Indexed: 11/13/2022] Open
Abstract
Suicide is one of the leading causes of mortality worldwide. Various factors could lead to suicidal ideation (SI), while depression is the predominant cause among all mental disorders. Studies have shown that alterations in brain structures and networks may be highly associated with suicidality. This study investigated both neurological structural variations and network alterations in depressed patients with suicidal ideation by using generalized q-sampling imaging (GQI) and Graph Theoretical Analysis (GTA). This study recruited 155 participants and divided them into three groups: 44 depressed patients with suicidal ideation (SI+; 20 males and 24 females with mean age = 42, SD = 12), 56 depressed patients without suicidal ideation (Depressed; 24 males and 32 females with mean age = 45, SD = 11) and 55 healthy controls (HC; nine males and 46 females with mean age = 39, SD = 11). Both the generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA) values were evaluated in a voxel-based statistical analysis by GQI. We analyzed different topological parameters in the graph theoretical analysis and the subnetwork interconnections in the Network-based Statistical (NBS) analysis. In the voxel-based statistical analysis, both the GFA and NQA values in the SI+ group were generally lower than those in the Depressed and HC groups in the corpus callosum and cingulate gyrus. Furthermore, we found that the SI+ group demonstrated higher global integration and lower local segregation among the three groups of participants. In the network-based statistical analysis, we discovered that the SI+ group had stronger connections of subnetworks in the frontal lobe than the HC group. We found significant structural differences in depressed patients with suicidal ideation compared to depressed patients without suicidal ideation and healthy controls and we also found several network alterations among these groups of participants, which indicated that white matter integrity and network alterations are associated with patients with depression as well as suicidal ideation.
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Affiliation(s)
- Vincent Chin-Hung Chen
- School of Medicine, Chang Gung University, Taoyuan, Taiwan.,Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan
| | - Chun-Ju Kao
- Department of Medical Imaging and Radiological Sciences, Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan
| | - Yuan-Hsiung Tsai
- School of Medicine, Chang Gung University, Taoyuan, Taiwan.,Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi, Taiwan
| | - Man Teng Cheok
- Department of Medical Imaging and Radiological Sciences, Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan.,Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Roger S McIntyre
- Mood Disorder Psychopharmacology Unit, University Health Network, Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada.,Departments of Psychiatry and Pharmacology, University of Toronto, Toronto, ON, Canada
| | - Jun-Cheng Weng
- Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan.,Department of Medical Imaging and Radiological Sciences, Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan.,Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
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Clarke H, Messaritaki E, Dimitriadis SI, Metzler-Baddeley C. Dementia Risk Factors Modify Hubs but Leave Other Connectivity Measures Unchanged in Asymptomatic Individuals: A Graph Theoretical Analysis. Brain Connect 2021; 12:26-40. [PMID: 34030485 PMCID: PMC8867081 DOI: 10.1089/brain.2020.0935] [Citation(s) in RCA: 6] [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] [Indexed: 01/12/2023] Open
Abstract
Background: Alzheimer's disease (AD) is the most common form of dementia with genetic and environmental risk contributing to its development. Graph theoretical analyses of brain networks constructed from structural and functional magnetic resonance imaging (MRI) measurements have identified connectivity changes in AD and individuals with mild cognitive impairment. However, brain connectivity in asymptomatic individuals at risk of AD remains poorly understood. Methods: We analyzed diffusion-weighted MRI data from 161 asymptomatic individuals (38–71 years) from the Cardiff Ageing and Risk of Dementia Study (CARDS). We calculated white matter tracts and constructed whole-brain, default mode network (DMN) and visual structural brain networks that incorporate multiple structural metrics as edge weights. We then calculated the relationship of three AD risk factors, namely Apolipoprotein-E ɛ4 (APOE4) genotype, family history of dementia (FH), and central obesity (Waist-Hip-Ratio [WHR]), on graph theoretical measures and hubs. Results: We observed no risk-related differences in clustering coefficients, characteristic path lengths, eccentricity, diameter, and radius across the whole-brain, DMN or visual system. However, a hub in the right paracentral lobule was present in all the high-risk groups (FH, APOE4, obese), but absent in low-risk groups (no FH, APOE4-ve, healthy WHR). Discussion: We identified no risk-related effects on graph theoretical metrics in the structural brain networks of cognitively healthy individuals. However, high risk was associated with a hub in the right paracentral lobule, a medial fronto-parietal cortical area with motor and sensory functions. This finding is consistent with accumulating evidence for right parietal cortex contributions in AD. If this phenotype is shown to predict symptom development in longitudinal studies, it could be used as an early biomarker of AD.
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Affiliation(s)
- Hannah Clarke
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,School of Medicine, UK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Eirini Messaritaki
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,BRAIN Biomedical Research Unit, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Stavros I Dimitriadis
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom.,School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroscience and Mental Health Research Institute, School of Medicine, Cardiff University, Cardiff, United Kingdom.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Claudia Metzler-Baddeley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroscience and Mental Health Research Institute, School of Medicine, Cardiff University, Cardiff, United Kingdom
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Zhang T, Liao Q, Zhang D, Zhang C, Yan J, Ngetich R, Zhang J, Jin Z, Li L. Predicting MCI to AD Conversation Using Integrated sMRI and rs-fMRI: Machine Learning and Graph Theory Approach. Front Aging Neurosci 2021; 13:688926. [PMID: 34421570 PMCID: PMC8375594 DOI: 10.3389/fnagi.2021.688926] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [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: 03/31/2021] [Accepted: 06/23/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Graph theory and machine learning have been shown to be effective ways of classifying different stages of Alzheimer's disease (AD). Most previous studies have only focused on inter-subject classification with single-mode neuroimaging data. However, whether this classification can truly reflect the changes in the structure and function of the brain region in disease progression remains unverified. In the current study, we aimed to evaluate the classification framework, which combines structural Magnetic Resonance Imaging (sMRI) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) metrics, to distinguish mild cognitive impairment non-converters (MCInc)/AD from MCI converters (MCIc) by using graph theory and machine learning. METHODS With the intra-subject (MCInc vs. MCIc) and inter-subject (MCIc vs. AD) design, we employed cortical thickness features, structural brain network features, and sub-frequency (full-band, slow-4, slow-5) functional brain network features for classification. Three feature selection methods [random subset feature selection algorithm (RSFS), minimal redundancy maximal relevance (mRMR), and sparse linear regression feature selection algorithm based on stationary selection (SS-LR)] were used respectively to select discriminative features in the iterative combinations of MRI and network measures. Then support vector machine (SVM) classifier with nested cross-validation was employed for classification. We also compared the performance of multiple classifiers (Random Forest, K-nearest neighbor, Adaboost, SVM) and verified the reliability of our results by upsampling. RESULTS We found that in the classifications of MCIc vs. MCInc, and MCIc vs. AD, the proposed RSFS algorithm achieved the best accuracies (84.71, 89.80%) than the other algorithms. And the high-sensitivity brain regions found with the two classification groups were inconsistent. Specifically, in MCIc vs. MCInc, the high-sensitivity brain regions associated with both structural and functional features included frontal, temporal, caudate, entorhinal, parahippocampal, and calcarine fissure and surrounding cortex. While in MCIc vs. AD, the high-sensitivity brain regions associated only with functional features included frontal, temporal, thalamus, olfactory, and angular. CONCLUSIONS These results suggest that our proposed method could effectively predict the conversion of MCI to AD, and the inconsistency of specific brain regions provides a novel insight for clinical AD diagnosis.
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Affiliation(s)
| | | | | | | | | | | | | | - Zhenlan Jin
- Key Laboratory for NeuroInformation of Ministry of Education, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ling Li
- Key Laboratory for NeuroInformation of Ministry of Education, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
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Guo Y, Peng K, Liu Y, Zhong L, Dang C, Yan Z, Wang Y, Zeng J, Zhang W, Ou Z, Liu G. Topological Alterations in White Matter Structural Networks in Blepharospasm. Mov Disord 2021; 36:2802-2810. [PMID: 34320254 DOI: 10.1002/mds.28736] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 04/12/2021] [Accepted: 07/03/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Accumulating evidence indicates regional structural changes in the white matter (WM) of brains in patients with blepharospasm (BSP); however, whether large-scale WM structural networks undergo widespread reorganization in these patients remains unclear. OBJECTIVE We investigated topology changes and global and local features of large-scale WM structural networks in BSP patients compared with hemifacial spasm (HFS) patients or healthy controls (HCs). METHODS This cross-sectional study applied graph theoretical analysis to assess deterministic diffusion tensor tractography findings in 41 BSP patients, 41 HFS patients, and 41 HCs. WM structural connectivity in 246 cortical and subcortical regions was assessed, and topological parameters of the resulting graphs were calculated. Networks were compared among BSP, HFS, and HCs groups. RESULTS Compared to HCs, both BSP and HFS patients showed alterations in network integration and segregation characterized by increased global efficiency and modularity and reduced shortest path length. Moreover, increased nodal efficiency in multiple cortical and subcortical regions was found in BSP and HFS patients compared with HCs. However, these differences were not found between BSP and HFS patients. Whereas all participants showed highly similar hub distribution patterns, BSP patients had additional hub regions not present in either HFS patients or HCs, which were located in the primary head and face motor cortex and basal ganglia. CONCLUSIONS Our findings suggest that the large-scale WM structural network undergoes an extensive reorganization in BSP, probably due to both dystonia-specific abnormalities and facial hyperkinetic movements. © 2021 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Yaomin Guo
- Department of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Kangqiang Peng
- Department of Medical Imaging, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Ying Liu
- Department of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Linchang Zhong
- Department of Medical Imaging, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Chao Dang
- Department of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhicong Yan
- Department of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ying Wang
- Department of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jinsheng Zeng
- Department of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Weixi Zhang
- Department of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zilin Ou
- Department of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Gang Liu
- Department of Neurology, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China
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Gao S, Chen J, Xu Y, Liu S, Lu C, Guan Y, Yang X. Altered Structural and Functional Connectivity Contribute to Rapid Ejaculation: Insights from a Multimodal Neuroimaging Study. Neuroscience 2021; 471:93-101. [PMID: 34216696 DOI: 10.1016/j.neuroscience.2021.06.034] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/22/2021] [Accepted: 06/23/2021] [Indexed: 01/01/2023]
Abstract
Little is known about how the aberrant structural and functional connectivity relates to the rapid ejaculation. Data of diffusion tensor imaging and resting state functional magnetic resonance imaging were acquired from 32 PE patients and 38 healthy controls (HCs). Firstly, we investigated the structural connectivity (SC) disruptions of PE patients using the method of graph theoretical analysis. Brain regions with impaired nodal strength were then defined as regions of interest (ROI). Secondly, the corresponding functional connectivity (FC) changes were explored. Finally, the correlation analyses were performed between brain areas with abnormal connectivity and clinical characteristics. Structural analysis revealed that PE patients had increased nodal strength in the right superior frontal gyrus (dorsolateral), left middle frontal gyrus, right superior frontal gyrus (medial), right superior frontal gyrus (medial orbital) and decreased nodal strength in the left amygdala. FC analysis revealed that PE patients had decreased FC values in the default mode network, visual recognition network and subcortical network, as well as increased FC values in the attention network. Moreover, correlation analysis revealed that the nodal strength of right superior frontal gyrus (dorsolateral) was negatively associated with the intra-vaginal ejaculation latency, while FC values between the left middle frontal gyrus and middle occipital gyrus were positively related to the total scores of the premature ejaculation diagnostic tool (PEDT). Our results indicated that PE might be associated with the abnormal SC of areas in the prefrontal-amygdala pathway and aberrant FC in certain functional brain networks, especially in default mode network.
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Affiliation(s)
- Songzhan Gao
- Department of Andrology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jianhuai Chen
- Department of Andrology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Yan Xu
- Department of Andrology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Shaowei Liu
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Chao Lu
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Yichun Guan
- Department of Reproductive Medicine, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Xianfeng Yang
- Department of Andrology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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Yeh CH, Jones DK, Liang X, Descoteaux M, Connelly A. Mapping Structural Connectivity Using Diffusion MRI: Challenges and Opportunities. J Magn Reson Imaging 2021; 53:1666-1682. [PMID: 32557893 PMCID: PMC7615246 DOI: 10.1002/jmri.27188] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [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: 02/03/2020] [Revised: 04/21/2020] [Accepted: 04/21/2020] [Indexed: 12/13/2022] Open
Abstract
Diffusion MRI-based tractography is the most commonly-used technique when inferring the structural brain connectome, i.e., the comprehensive map of the connections in the brain. The utility of graph theory-a powerful mathematical approach for modeling complex network systems-for analyzing tractography-based connectomes brings important opportunities to interrogate connectome data, providing novel insights into the connectivity patterns and topological characteristics of brain structural networks. When applying this framework, however, there are challenges, particularly regarding methodological and biological plausibility. This article describes the challenges surrounding quantitative tractography and potential solutions. In addition, challenges related to the calculation of global network metrics based on graph theory are discussed.Evidence Level: 5Technical Efficacy: Stage 1.
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Affiliation(s)
- Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Department of Child and Adolescent Psychiatry, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia
| | - Xiaoyun Liang
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Alan Connelly
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
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Ghaderi AH, Jahan A, Akrami F, Moghadam Salimi M. Transcranial photobiomodulation changes topology, synchronizability, and complexity of resting state brain networks. J Neural Eng 2021; 18. [PMID: 33873167 DOI: 10.1088/1741-2552/abf97c] [Citation(s) in RCA: 6] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 04/19/2021] [Indexed: 02/06/2023]
Abstract
Objective. Transcranial photobiomodulation (tPBM) is a recently proposed non-invasive brain stimulation approach with various effects on the nervous system from the cells to the whole brain networks. Specially in the neural network level, tPBM can alter the topology and synchronizability of functional brain networks. However, the functional properties of the neural networks after tPBM are still poorly clarified.Approach. Here, we employed electroencephalography and different methods (conventional and spectral) in the graph theory analysis to track the significant effects of tPBM on the resting state brain networks. The non-parametric statistical analysis showed that just one short-term tPBM session over right medial frontal pole can significantly change both topological (i.e. clustering coefficient, global efficiency, local efficiency, eigenvector centrality) and dynamical (i.e. energy, largest eigenvalue, and entropy) features of resting state brain networks.Main results. The topological results revealed that tPBM can reduce local processing, centrality, and laterality. Furthermore, the increased centrality of central electrode was observed.Significance. These results suggested that tPBM can alter topology of resting state brain network to facilitate the neural information processing. On the other hand, the dynamical results showed that tPBM reduced stability of synchronizability and increased complexity in the resting state brain networks. These effects can be considered in association with the increased complexity of connectivity patterns among brain regions and the enhanced information propagation in the resting state brain networks. Overall, both topological and dynamical features of brain networks suggest that although tPBM decreases local processing (especially in the right hemisphere) and disrupts synchronizability of network, but it can increase the level of information transferring and processing in the brain network.
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Affiliation(s)
- Amir Hossein Ghaderi
- Centre for Vision Research, York University, Toronto, Canada.,Department of psychology, University of Calgary, Calgary, Canada.,Iranian Neurowave Lab, Isfahan, Iran
| | - Ali Jahan
- Department of Speech Therapy, Faculty of Rehabilitation Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Fatemeh Akrami
- Iranian Neurowave Lab, Isfahan, Iran.,Faculty of Health Management and Information, Iran University of Medical Science, Tehran, Iran
| | - Maryam Moghadam Salimi
- Department of Physical Therapy, Faculty of Rehabilitation Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
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20
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Roos A, Fouche JP, Ipser JC, Narr KL, Woods RP, Zar HJ, Stein DJ, Donald KA. Structural and functional brain network alterations in prenatal alcohol exposed neonates. Brain Imaging Behav 2021; 15:689-99. [PMID: 32306280 DOI: 10.1007/s11682-020-00277-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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] [Indexed: 10/25/2022]
Abstract
Prenatal alcohol exposure leads to alterations in cognition, behavior and underlying brain architecture. However, prior studies have not integrated structural and functional imaging data in children with prenatal alcohol exposure. The aim of this study was to characterize disruptions in both structural and functional brain network organization after prenatal alcohol exposure in very early life. A group of 11 neonates with prenatal alcohol exposure and 14 unexposed controls were investigated using diffusion weighted structural and resting state functional magnetic resonance imaging. Covariance networks were created using graph theoretical analyses for each data set, controlling for age and sex. Group differences in global hub arrangement and regional connectivity were determined using nonparametric permutation tests. Neonates with prenatal alcohol exposure and controls exhibited similar global structural network organization. However, global functional networks of neonates with prenatal alcohol exposure comprised of temporal and limbic hubs, while hubs were more distributed in controls representing an early default mode network. On a regional level, controls showed prominent structural and functional connectivity in parietal and occipital regions. Neonates with prenatal alcohol exposure showed regionally, predominant structural and functional connectivity in several subcortical regions and occipital regions. The findings suggest early functional disruption on a global and regional level after prenatal alcohol exposure and indicate suboptimal organization of functional networks. These differences likely underlie sensory dysregulation and behavioral difficulties in prenatal alcohol exposure.
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21
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Yue Z, Wang P, Li X, Ren J, Wu B. Abnormal brain functional networks in end-stage renal disease patients with cognitive impairment. Brain Behav 2021; 11:e02076. [PMID: 33605530 PMCID: PMC8035483 DOI: 10.1002/brb3.2076] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 01/31/2021] [Accepted: 02/04/2021] [Indexed: 02/05/2023] Open
Abstract
INTRODUCTION Cognitive impairment (CI) is common in patients with end-stage renal disease (ESRD). Neuroimaging studies have demonstrated structural and functional brain alterations underlying CI in patients with ESRD. However, the patterns of change in whole-brain functional networks in ESRD patients with CI remain poorly understood. METHODS We enrolled 66 patients with ESRD (36 patients with CI and 30 patients without CI) and 48 healthy control subjects (HCs). We calculated the topological properties using a graph theoretical analysis. An analysis of covariance (ANCOVA) was used to compare network metrics among the three groups. Moreover, we analyzed the relationships between altered network measures and clinical variables in ESRD patients with CI. RESULTS Compared with HCs, both patient groups showed lower local efficiency and small-worldness. ESRD patients had decreased nodal centralities in the default mode regions and right amygdala. Comparison of the two patient groups showed significantly decreased global (small-worldness) and nodal (nodal centralities in the default mode regions) properties in the CI group. Altered nodal centralities in the bilateral medial part of the superior frontal gyrus, left posterior cingulate gyrus, and right precuneus were associated with cognitive performance in the CI group. CONCLUSION Disrupted brain functional networks were demonstrated in patients with ESRD, which were more severe in those with CI. Moreover, impaired nodal centralities in the default mode regions might underlie CI in patients with ESRD.
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Affiliation(s)
- Zheng Yue
- Department of Magnetic Resonance, The First Affiliated Hospital of Xinxiang Medical University, Weihui, China
| | - Pengming Wang
- Department of Radiology, The First Affiliated Hospital of Xinxiang Medical University, Weihui, China
| | - Xuekun Li
- Department of Magnetic Resonance, The First Affiliated Hospital of Xinxiang Medical University, Weihui, China
| | - Jipeng Ren
- Department of Magnetic Resonance, The First Affiliated Hospital of Xinxiang Medical University, Weihui, China
| | - Baolin Wu
- Department of Magnetic Resonance, The First Affiliated Hospital of Xinxiang Medical University, Weihui, China.,Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
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22
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Jouzizadeh M, Ghaderi AH, Cheraghmakani H, Baghbanian SM, Khanbabaie R. Resting-State Brain Network Deficits in Multiple Sclerosis Participants: Evidence from Electroencephalography and Graph Theoretical Analysis. Brain Connect 2021; 11:359-367. [PMID: 33780635 DOI: 10.1089/brain.2020.0857] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Multiple sclerosis (MS) is a chronic inflammatory disease leading to demyelination and axonal loss in the central nervous system that causes focal lesions of gray and white matter. However, the functional impairments of brain networks in this disease are still unspecified and need to be clearer. Materials and Methods: In the present study, we investigate the resting-state brain network impairments for MS participants in comparison to a normal group using electroencephalography (EEG) and graph theoretical analysis with a source localization method. Thirty-four age- and gender-matched participants from each MS group and normal group participated in this study. We recorded 5 min of EEG in the resting-state eyes open condition for each participant. One min (15 equal 4-sec artifact-free segments) of the EEG signals were selected for each participant, and the Low-Resolution Electromagnetic Tomography software was employed to calculate the functional connectivity among whole cortical regions in six frequency bands (delta, theta, alpha, beta1, beta2, and beta3). Graph theoretical analysis was used to calculate the clustering coefficient (CL), betweenness centrality (BC), shortest path length (SPL), and small-world propensity (SWP) for weighted connectivity matrices. Nonparametric permutation tests were utilized to compare these measures between groups. Results: Significant differences between the MS group and the normal group in the average of BC and SWP were found in the alpha band. The significant differences in the BC were spread over all lobes. Conclusion: These results suggest that the resting-state brain network for the MS group is disrupted in local and global scales, and EEG has the capability of revealing these impairments.
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Affiliation(s)
- Mojtaba Jouzizadeh
- Department of Physics, Babol Noshirvani University of Technology, Babol, Iran
| | - Amir Hossein Ghaderi
- Department of Psychology and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Hamed Cheraghmakani
- Department of Neurology, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | | | - Reza Khanbabaie
- Department of Physics, Babol Noshirvani University of Technology, Babol, Iran.,Department of Physics, I.K. Barber School of Arts and Sciences, University of British Columbia, Kelowna, British Columbia, Canada
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23
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Chen H, Geng W, Shang S, Shi M, Zhou L, Jiang L, Wang P, Yin X, Chen YC. Alterations of brain network topology and structural connectivity-functional connectivity coupling in capsular versus pontine stroke. Eur J Neurol 2021; 28:1967-1976. [PMID: 33657258 DOI: 10.1111/ene.14794] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [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: 12/04/2020] [Revised: 02/25/2021] [Accepted: 02/27/2021] [Indexed: 01/23/2023]
Abstract
BACKGROUND AND PURPOSE This study was conducted to investigate whether capsular stroke (CS) and pontine stroke (PS) have different topological alterations of structural connectivity (SC) and functional connectivity (FC), as well as correlations of SC-FC coupling with movement assessment scores. METHODS Resting-state functional magnetic resonance imaging and diffusion tensor imaging were prospectively acquired in 46 patients with CS, 36 with PS, and 29 healthy controls (HCs). Graph theoretical network analyses of SC and FC were performed. Patients with left and right lesions were analyzed separately. RESULTS With regard to FC, the PS and CS groups both showed higher local efficiency than the HCs, and the CS group also had a higher clustering coefficient (Cp) than the HCs in the right lesion analysis. With regard to SC, the PS and CS groups both showed different normalized clustering coefficient (γ), small-worldness (σ), and characteristic path length (Lp) compared with the HC group. Additionally, the CS group showed higher normalized characteristic path length (λ) and a lower Cp than the HCs and the PS group showed higher λ and lower global efficiency than the HCs in the right-lesion analysis. However, γ, σ, Cp and Lp were only significantly different in the PS and CS groups compared with the HC group in the right-lesion analysis. Importantly, the CS group was found to have a weaker SC-FC coupling than the PS group and the HC group in the right-lesion analysis. In addition, both patient groups had weaker structural-functional connectome correlation than the HCs. CONCLUSIONS The CS and PS groups both showed FC and SC disruption and the CS group had a weaker SC-FC coupling than the PS group in the right lesion analysis. This may provide useful information for individualized rehabilitative strategies.
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Affiliation(s)
- Huiyou Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Wen Geng
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Song'an Shang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Mengye Shi
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Leilei Zhou
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Liang Jiang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Peng Wang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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Hao J, Luo W, Xie Y, Feng Y, Sun W, Peng W, Zhao J, Zhang P, Ding J, Wang X. Functional Network Alterations as Markers for Predicting the Treatment Outcome of Cathodal Transcranial Direct Current Stimulation in Focal Epilepsy. Front Hum Neurosci 2021; 15:637071. [PMID: 33815082 PMCID: PMC8009991 DOI: 10.3389/fnhum.2021.637071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 12/02/2020] [Accepted: 02/23/2021] [Indexed: 11/13/2022] Open
Abstract
Background and Purpose Transcranial direct current stimulation (tDCS) is an emerging non-invasive neuromodulation technique for focal epilepsy. Because epilepsy is a disease affecting the brain network, our study was aimed to evaluate and predict the treatment outcome of cathodal tDCS (ctDCS) by analyzing the ctDCS-induced functional network alterations. Methods Either the active 5-day, -1.0 mA, 20-min ctDCS or sham ctDCS targeting at the most active interictal epileptiform discharge regions was applied to 27 subjects suffering from focal epilepsy. The functional networks before and after ctDCS were compared employing graph theoretical analysis based on the functional magnetic resonance imaging (fMRI) data. A support vector machine (SVM) prediction model was built to predict the treatment outcome of ctDCS using the graph theoretical measures as markers. Results Our results revealed that the mean clustering coefficient and the global efficiency decreased significantly, as well as the characteristic path length and the mean shortest path length at the stimulation sites in the fMRI functional networks increased significantly after ctDCS only for the patients with response to the active ctDCS (at least 20% reduction rate of seizure frequency). Our prediction model achieved the mean prediction accuracy of 68.3% (mean sensitivity: 70.0%; mean specificity: 67.5%) after the nested cross validation. The mean area under the receiver operating curve was 0.75, which showed good prediction performance. Conclusion The study demonstrated that the response to ctDCS was related to the topological alterations in the functional networks of epilepsy patients detected by fMRI. The graph theoretical measures were promising for clinical prediction of ctDCS treatment outcome.
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Affiliation(s)
- Jiaxin Hao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wenyi Luo
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuhai Xie
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yu Feng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wei Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Weifeng Peng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Puming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jing Ding
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China
| | - Xin Wang
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology, the Institutes of Brain Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
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25
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Chen VCH, Kao CJ, Tsai YH, McIntyre RS, Weng JC. Mapping Brain Microstructure and Network Alterations in Depressive Patients with Suicide Attempts Using Generalized Q-Sampling MRI. J Pers Med 2021; 11:jpm11030174. [PMID: 33802354 PMCID: PMC7998726 DOI: 10.3390/jpm11030174] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 12/24/2020] [Revised: 02/25/2021] [Accepted: 02/25/2021] [Indexed: 12/28/2022] Open
Abstract
Depressive disorder is one of the leading causes of disability worldwide, with a high prevalence and chronic course. Depressive disorder carries an increased risk of suicide. Alterations in brain structure and networks may play an important role in suicidality among depressed patients. Diffusion magnetic resonance imaging (MRI) is a noninvasive method to map white-matter fiber orientations and provide quantitative parameters. This study investigated the neurological structural differences and network alterations in depressed patients with suicide attempts by using generalized q-sampling imaging (GQI). Our study recruited 155 participants and assigned them into three groups: 44 depressed patients with a history of suicide attempts (SA), 56 depressed patients without a history of suicide attempts (D) and 55 healthy controls (HC). We used the GQI to analyze the generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA) values in voxel-based statistical analysis, topological parameters in graph theoretical analysis and subnetwork connectivity in network-based statistical analysis. GFA indicates the measurement of neural anisotropy and represents white-matter integrity; NQA indicates the amount of anisotropic spins that diffuse along fiber orientations and represents white-matter compactness. In the voxel-based statistical analysis, we found lower GFA and NQA values in the SA group than in the D and HC groups and lower GFA and NQA values in the D group than in the HC group. In the graph theoretical analysis, the SA group demonstrated higher local segregation and lower global integration among the three groups. In the network-based statistical analysis, the SA group showed stronger subnetwork connections in the frontal and parietal lobes, and the D group showed stronger subnetwork connections in the parietal lobe than the HC group. Alternations were found in the structural differences and network measurements in healthy controls and depressed patients with and without a history of suicide attempt.
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Affiliation(s)
- Vincent Chin-Hung Chen
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan; (V.C.-H.C.); (Y.-H.T.)
- Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi 61363, Taiwan
| | - Chun-Ju Kao
- Department of Medical Imaging and Radiological Sciences, Bachelor Program in Artificial Intelligence, Chang Gung University, No. 259, Wenhua 1st Rd., Taoyuan 33302, Taiwan;
| | - Yuan-Hsiung Tsai
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan; (V.C.-H.C.); (Y.-H.T.)
- Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi 61363, Taiwan
| | - Roger S. McIntyre
- Mood Disorder Psychopharmacology Unit, University Health Network, Department of Psychiatry, University of Toronto, Toronto, ON M5S, Canada;
- Institute of Medical Science, University of Toronto, Toronto, ON M5S, Canada
- Departments of Psychiatry and Pharmacology, University of Toronto, Toronto, ON M5S, Canada
| | - Jun-Cheng Weng
- Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi 61363, Taiwan
- Department of Medical Imaging and Radiological Sciences, Bachelor Program in Artificial Intelligence, Chang Gung University, No. 259, Wenhua 1st Rd., Taoyuan 33302, Taiwan;
- Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan 33302, Taiwan
- Correspondence: ; Tel.: +886-3-2118800 (ext. 5394)
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Wang E, Jia Y, Ya Y, Xu J, Mao C, Luo W, Fan G, Jiang Z. Abnormal Topological Organization of Sulcal Depth-Based Structural Covariance Networks in Parkinson's Disease. Front Aging Neurosci 2021; 12:575672. [PMID: 33519416 PMCID: PMC7843381 DOI: 10.3389/fnagi.2020.575672] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 12/14/2020] [Indexed: 11/13/2022] Open
Abstract
Recent research on Parkinson's disease (PD) has demonstrated the topological abnormalities of structural covariance networks (SCNs) using various morphometric features from structural magnetic resonance images (sMRI). However, the sulcal depth (SD)-based SCNs have not been investigated. In this study, we used SD to investigate the topological alterations of SCNs in 60 PD patients and 56 age- and gender-matched healthy controls (HC). SCNs were constructed by thresholding SD correlation matrices of 68 regions and analyzed using graph theoretical approaches. Compared with HC, PD patients showed increased normalized clustering coefficient and normalized path length, as well as a reorganization of degree-based and betweenness-based hubs (i.e., less frontal hubs). Moreover, the degree distribution analysis showed more high-degree nodes in PD patients. In addition, we also found the increased assortativity and reduced robustness under a random attack in PD patients compared to HC. Taken together, these findings indicated an abnormal topological organization of SD-based SCNs in PD patients, which may contribute in understanding the pathophysiology of PD at the network level.
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Affiliation(s)
- Erlei Wang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yujing Jia
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yang Ya
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Jin Xu
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Chengjie Mao
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Weifeng Luo
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Guohua Fan
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhen Jiang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
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Cai S, Wang X, Yang F, Chen D, Huang L. Differences in Brain Structural Covariance Network Characteristics in Children and Adults With Autism Spectrum Disorder. Autism Res 2021; 14:265-275. [PMID: 33386783 DOI: 10.1002/aur.2464] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 12/09/2020] [Accepted: 12/10/2020] [Indexed: 11/07/2022]
Abstract
Systematically describing the structural topological configuration of human brain during development is an essential task. Autism spectrum disorder (ASD) represents a powerful challenge for psychiatry and neuroscience researchers. In this study, we investigated variations in the structural covariance network properties of 441 patients with ASD ranging in age from 7 to 45 years and in 426 age-matched healthy controls (HCs) using structural magnetic resonance neuroimaging from the ABIDE database. We applied a sliding window approach to study topological variation during development using comprehensive graph theoretical analysis. The main findings are as follows: (1) Cross-sectional trajectories of the network characteristics exhibited inverted U-shapes in both HCs and participants with ASD, with the latter exhibiting a 7-year delay in reaching the maximum value, (2) network resilience to targeted attacks peaked at 18' and 19' in the HCs and at 25' in the participants with ASD, and the weakest resilience occurred at age 7', (3) the HCs and participants with ASD exhibited normalized mean degree differences in the right amygdala, and (4) significant differences in the network characteristics were observed in the 18' age group at most of the densities analyzed. We used cross-sectional analysis to infer distinct neurodevelopmental trajectories in ASD in the brain structural connectome. Our findings are consistent with the notion that adolescence is a sensitive period of brain development with strong potential for brain plasticity, offering opportunities for environmental adaptation and social integration and for increasing vulnerability. ASD may be a product of susceptibility. LAY SUMMARY: We used cross-sectional analysis to preliminarily infer distinct neurodevelopmental trajectories in ASD in the brain structural connectome. The main findings are as follows: (1) Cross-sectional trajectories of the network characteristics exhibited inverted U-shapes in both HCs and participants with ASD, with the latter exhibiting a 7-year delay in reaching the maximum value, (2) Network resilience to targeted attacks peaked at 18' and 19' in the HCs and at 25' in the participants with ASD, and the weakest resilience occurred at age 7', (3) The HCs and participants with ASD exhibited normalized mean degree differences in the right amygdala, and (4) significant differences in the network characteristics were observed in the 18' age group at most of the densities analyzed.
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Affiliation(s)
- Suping Cai
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Xuwen Wang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Fan Yang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Dihui Chen
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Liyu Huang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, China
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28
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Zheng W, Zhao Z, Zhang Z, Liu T, Zhang Y, Fan J, Wu D. Developmental pattern of the cortical topology in high-functioning individuals with autism spectrum disorder. Hum Brain Mapp 2020; 42:660-675. [PMID: 33085836 PMCID: PMC7814766 DOI: 10.1002/hbm.25251] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [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: 07/07/2020] [Revised: 09/24/2020] [Accepted: 10/07/2020] [Indexed: 12/15/2022] Open
Abstract
A number of studies have indicated alterations of brain morphology in individuals with autism spectrum disorder (ASD); however, how ASD influences the topological organization of the brain cortex at different developmental stages is not yet well characterized. In this study, we used structural images of 492 high‐functioning participants in the Autism Brain Imaging Data Exchange database acquired from 17 international imaging centers, including 75 autistic children (age 7–11 years), 91 adolescents with ASD (age 12–17 years), and 80 young adults with ASD (age 18–29 years), and 246 typically developing controls (TDCs) that were age, gender, handedness, and full‐scale IQ matched. Cortical thickness (CT) and surface area (SA) were extracted and the covariance between cortical regions across participants were treated as a network to examine developmental patterns of the cortical topological organization at different stages. A center‐paired resampling strategy was developed to control the center bias during the permutation test. Compared with the TDCs, network of SA (but not CT) of individuals with ASD showed reduced small‐worldness in childhood, and the network hubs were reorganized in the adulthood such that hubs inclined to connect with nonhub nodes and demonstrated more dispersed spatial distribution. Furthermore, the SA network of the ASD cohort exhibited increased segregation of the inferior parietal lobule and prefrontal cortex, and insular‐opercular cortex in all three age groups, resulting in the emergence of two unique modules in the autistic brain. Our findings suggested that individuals with ASD may undergo remarkable remodeling of the cortical topology from childhood to adulthood, which may be associated with the altered social and cognitive functions in ASD.
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Affiliation(s)
- Weihao Zheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Zhe Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Tingting Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Jin Fan
- Department of Psychology, Queens College, The City University of New York, New York, New York, USA
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
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Naro A, Maggio MG, Leo A, Calabrò RS. Multiplex and Multilayer Network EEG Analyses: A Novel Strategy in the Differential Diagnosis of Patients with Chronic Disorders of Consciousness. Int J Neural Syst 2020; 31:2050052. [PMID: 33034532 DOI: 10.1142/s0129065720500525] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The deterioration of specific topological network measures that quantify different features of whole-brain functional network organization can be considered a marker for awareness impairment. Such topological measures reflect the functional interactions of multiple brain structures, which support the integration of different sensorimotor information subtending awareness. However, conventional, single-layer, graph theoretical analysis (GTA)-based approaches cannot always reliably differentiate patients with Disorders of Consciousness (DoC). Using multiplex and multilayer network analyses of frequency-specific and area-specific networks, we investigated functional connectivity during resting-state EEG in 17 patients with Unresponsive Wakefulness Syndrome (UWS) and 15 with Minimally Conscious State (MCS). Multiplex and multilayer network metrics indicated the deterioration and heterogeneity of functional networks and, particularly, the frontal-parietal (FP), as the discriminant between patients with MCS and UWS. These data were not appreciable when considering each individual frequency-specific network. The distinctive properties of multiplex/multilayer network metrics and individual frequency-specific network metrics further suggest the value of integrating the networks as opposed to analyzing frequency-specific network metrics one at a time. The hub vulnerability of these regions was positively correlated with the behavioral responsiveness, thus strengthening the clinically-based differential diagnosis. Therefore, it may be beneficial to adopt both multiplex and multilayer network analyses when expanding the conventional GTA-based analyses in the differential diagnosis of patients with DoC. Multiplex analysis differentiated patients at a group level, whereas the multilayer analysis offered complementary information to differentiate patients with DoC individually. Although further studies are necessary to confirm our preliminary findings, these results contribute to the issue of DoC differential diagnosis and may help in guiding patient-tailored management.
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Affiliation(s)
- Antonino Naro
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy, Via Palermo, SS 113, Contrada Casazza, 98124 Messina, Italy
| | - Maria Grazia Maggio
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy, Via Palermo, SS 113, Contrada Casazza, 98124 Messina, Italy
| | - Antonino Leo
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy, Via Palermo, SS 113, Contrada Casazza, 98124 Messina, Italy
| | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy, Via Palermo, SS 113, Contrada Casazza, 98124 Messina, Italy
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30
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Zhuang Y, Zhang Z, Tivarus M, Qiu X, Zhong J, Schifitto G. Whole-brain computational modeling reveals disruption of microscale brain dynamics in HIV infected individuals. Hum Brain Mapp 2020; 42:95-109. [PMID: 32941693 PMCID: PMC7721235 DOI: 10.1002/hbm.25207] [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] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 08/13/2020] [Accepted: 08/30/2020] [Indexed: 01/07/2023] Open
Abstract
MRI‐based neuroimaging techniques have been used to investigate brain injury associated with HIV‐infection. Whole‐brain cortical mean‐field dynamic modeling provides a way to integrate structural and functional imaging outcomes, allowing investigation of microscale brain dynamics. In this study, we adopted the relaxed mean‐field dynamic modeling to investigate structural and functional connectivity in 42 HIV‐infected subjects before and after 12‐week of combination antiretroviral therapy (cART) and compared them with 46 age‐matched healthy subjects. Microscale brain dynamics were modeled by a set of parameters including two region‐specific microscale brain properties, recurrent connection strengths, and subcortical inputs. We also analyzed the relationship between the model parameters (i.e., the recurrent connection and subcortical inputs) and functional network topological characterizations, including smallworldness, clustering coefficient, and network efficiency. The results show that untreated HIV‐infected individuals have disrupted local brain dynamics that in part correlate with network topological measurements. Notably, after 12 weeks of cART, both the microscale brain dynamics and the network topological measurements improved and were closer to those in the healthy brain. This was also associated with improved cognitive performance, suggesting that improvement in local brain dynamics translates into clinical improvement.
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Affiliation(s)
- Yuchuan Zhuang
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA
| | - Zhengwu Zhang
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.,Department of Neuroscience, University of Rochester Medical Center, Rochester, New York, USA
| | - Madalina Tivarus
- Department of Neuroscience, University of Rochester Medical Center, Rochester, New York, USA.,Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, USA
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA
| | - Jianhui Zhong
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, USA.,Department of Biomedical Engineering, University of Rochester, Rochester, New York, USA
| | - Giovanni Schifitto
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
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31
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Wu B, Li X, Zhang M, Zhang F, Long X, Gong Q, Jia Z. Disrupted brain functional networks in patients with end-stage renal disease undergoing hemodialysis. J Neurosci Res 2020; 98:2566-2578. [PMID: 32930417 DOI: 10.1002/jnr.24725] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [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: 06/17/2020] [Revised: 08/17/2020] [Accepted: 08/17/2020] [Indexed: 02/05/2023]
Abstract
Patterns of change in whole-brain functional networks remain poorly understood in patients with end-stage renal disease (ESRD) undergoing hemodialysis (HD). We conducted a prospective research to investigate the topological properties of whole-brain functional networks in those patients using a graph-based network analysis. Resting-state functional magnetic resonance imaging was performed on 51 ESRD patients (25 HD and 26 nondialysis patients) and 36 healthy controls (HCs). We compared the topological properties of brain functional networks among the three groups, and analyzed the relationships between those significant parameters and clinical variables in ESRD patients. Progressively disrupted global topological organizations were observed from nondialysis patients to HD patients compared with HCs (all p < 0.05 after Bonferroni correction). HD patients, relative to HCs, showed significantly decreased nodal centralities in the left temporal pole: superior temporal gyrus, bilateral median cingulate and paracingulate gyri, bilateral hippocampus, bilateral parahippocampal gyrus, and bilateral amygdala, and showed increased nodal centralities in the orbital part of the bilateral middle frontal gyrus, left cuneus, and left superior occipital gyrus (all p < 0.05 after Bonferroni correction). Furthermore, nodal centralities in the bilateral hippocampus were significantly decreased in HD patients compared with nondialysis patients (p < 0.05 after Bonferroni correction). Dialysis duration negatively correlated with global efficiency in ESRD patients undergoing HD (r = -0.676, FDR q = 0.004). This study indicates that ESRD patients exhibit disruptions in brain functional networks, which are more severe in HD patients, and these alterations are correlated with cognitive performance and clinical markers.
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Affiliation(s)
- Baolin Wu
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China.,Department of MR, First Affiliated Hospital of Xinxiang Medical University, Weihui, PR China
| | - Xuekun Li
- Department of MR, First Affiliated Hospital of Xinxiang Medical University, Weihui, PR China
| | - Meng Zhang
- Department of MR, First Affiliated Hospital of Xinxiang Medical University, Weihui, PR China
| | - Feifei Zhang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China
| | - Xipeng Long
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China.,Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, PR China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China.,Psychoradiology Research Unit of Chinese Academy of Medical Sciences (2018RU011), Chengdu, PR China
| | - Zhiyun Jia
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China.,Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, PR China
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32
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Lei D, Pinaya WHL, van Amelsvoort T, Marcelis M, Donohoe G, Mothersill DO, Corvin A, Gill M, Vieira S, Huang X, Lui S, Scarpazza C, Young J, Arango C, Bullmore E, Qiyong G, McGuire P, Mechelli A. Detecting schizophrenia at the level of the individual: relative diagnostic value of whole-brain images, connectome-wide functional connectivity and graph-based metrics. Psychol Med 2020; 50:1852-1861. [PMID: 31391132 PMCID: PMC7477363 DOI: 10.1017/s0033291719001934] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 06/25/2019] [Accepted: 07/11/2019] [Indexed: 02/05/2023]
Abstract
BACKGROUND Previous studies using resting-state functional neuroimaging have revealed alterations in whole-brain images, connectome-wide functional connectivity and graph-based metrics in groups of patients with schizophrenia relative to groups of healthy controls. However, it is unclear which of these measures best captures the neural correlates of this disorder at the level of the individual patient. METHODS Here we investigated the relative diagnostic value of these measures. A total of 295 patients with schizophrenia and 452 healthy controls were investigated using resting-state functional Magnetic Resonance Imaging at five research centres. Connectome-wide functional networks were constructed by thresholding correlation matrices of 90 brain regions, and their topological properties were analyzed using graph theory-based methods. Single-subject classification was performed using three machine learning (ML) approaches associated with varying degrees of complexity and abstraction, namely logistic regression, support vector machine and deep learning technology. RESULTS Connectome-wide functional connectivity allowed single-subject classification of patients and controls with higher accuracy (average: 81%) than both whole-brain images (average: 53%) and graph-based metrics (average: 69%). Classification based on connectome-wide functional connectivity was driven by a distributed bilateral network including the thalamus and temporal regions. CONCLUSION These results were replicated across the three employed ML approaches. Connectome-wide functional connectivity permits differentiation of patients with schizophrenia from healthy controls at single-subject level with greater accuracy; this pattern of results is consistent with the 'dysconnectivity hypothesis' of schizophrenia, which states that the neural basis of the disorder is best understood in terms of system-level functional connectivity alterations.
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Affiliation(s)
- Du Lei
- Departments of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Walter H. L. Pinaya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
- Center of Mathematics, Computation, and Cognition, Universidade Federal do ABC, Santo André, Brazil
| | - Therese van Amelsvoort
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherland
| | - Machteld Marcelis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherland
- Mental Health Care Institute Eindhoven (GGzE), Eindhoven, The Netherlands
| | - Gary Donohoe
- School of Psychology & Center for neuroimaging and Cognitive genomics, NUI Galway University, Galway, Ireland
| | - David O. Mothersill
- School of Psychology & Center for neuroimaging and Cognitive genomics, NUI Galway University, Galway, Ireland
| | - Aiden Corvin
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Michael Gill
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Xiaoqi Huang
- Departments of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Su Lui
- Departments of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
- Department of General Psychology, University of Padua, Padua, Italy
| | - Jonathan Young
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
- IXICO plc, London, UK
| | - Celso Arango
- Hospital General Universitario Gregorio Marañon. School of Medicine, Universidad Complutense Madrid. IiSGM, CIBERSAM, Madrid, Spain
| | - Edward Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Gong Qiyong
- Departments of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
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Fang S, Zhou C, Fan X, Jiang T, Wang Y. Epilepsy-Related Brain Network Alterations in Patients With Temporal Lobe Glioma in the Left Hemisphere. Front Neurol 2020; 11:684. [PMID: 32765403 PMCID: PMC7380082 DOI: 10.3389/fneur.2020.00684] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [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: 03/17/2020] [Accepted: 06/08/2020] [Indexed: 12/12/2022] Open
Abstract
Background: Seizures are a common symptom in patients with temporal lobe gliomas and may result in brain network alterations. However, brain network changes caused by glioma-related epilepsy (GRE) remain poorly understood. Objective: In this study, we applied graph theory analysis to delineate topological networks with resting-state functional magnetic resonance images (rs-fMRI) and investigated characteristics of functional networks in patients with GRE. Methods: Thirty patients with low-grade gliomas in the left temporal lobe were enrolled and classified into GRE (n = 15) and non-GRE groups. Twenty healthy participants matched for age, sex, and education level were enrolled. All participants had rs-fMRI data. Sensorimotor, visual, default mode, auditory, and right executive control networks were used to construct connection matrices. Topological properties of those sub-networks were investigated. Results: Compared to that in the GRE group, four edges with higher functional connectivity were noted in the non-GRE group. Moreover, 21 edges with higher functional connectivity were identified in the non-GRE group compared to the healthy group. All significant alterations in functional edges belong to the visual network. Increased global efficiency and decreased shortest path lengths were noted in the non-GRE group compared to the GRE and healthy groups. Compared with that in the healthy group, nodal efficiency of three nodes was higher in the GRE and non-GRE groups and the degree centrality of six nodes was altered in the non-GRE group. Conclusion: Temporal lobe gliomas in the left hemisphere and GRE altered visual networks in an opposing manner. These findings provide a novel insight into brain network alterations induced by GRE.
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Affiliation(s)
- Shengyu Fang
- Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunyao Zhou
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xing Fan
- Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing, China
| | - Tao Jiang
- Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Wu B, Li X, Zhou J, Zhang M, Long Q. Altered Whole-Brain Functional Networks in Drug-Naïve, First-Episode Adolescents With Major Depression Disorder. J Magn Reson Imaging 2020; 52:1790-1798. [PMID: 32618061 DOI: 10.1002/jmri.27270] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [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: 05/04/2020] [Revised: 06/15/2020] [Accepted: 06/16/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Neuroimaging studies have demonstrated disrupted brain functional networks in major depression disorder (MDD); however, alterations to whole-brain networks specifically associated with adolescent MDD remain poorly understood. PURPOSE To investigate the topological architecture of intrinsic brain functional networks in drug-naïve, first-episode adolescent MDD patients using graph theoretical analysis. STUDY TYPE Prospective. SUBJECTS In all, 109 adolescent MDD patients and 70 healthy control subjects. FIELD STRENGTH/SEQUENCES 3.0T; gradient-echo echo-planar imaging sequence. ASSESSMENT After the construction of whole-brain functional networks by thresholding partial correlation matrices of 90 brain regions, we calculated the topological properties (eg, small-world, efficiency, and nodal centrality) using graph theoretical analysis. STATISTICAL TESTS A chi-squared test was used to compare the gender-ratio difference, and a two-sample t-test was used in the comparison of age. We compared network measures between the two groups using nonparametric permutation tests. Exploratory partial correlation analyses were used to determine the relationships between the topological metrics showing significant between-group differences and the clinical variables for adolescent MDD patients. RESULTS Small-world architecture in brain functional networks was identified for both the MDD and control groups. However, depressed adolescents exhibited lower characteristic path length, normalized characteristic path length and clustering coefficient, and higher global efficiency than controls (false discovery rate [FDR] q < 0.05). Compared with controls, depressed adolescents exhibited increased nodal centralities in the default mode regions, including the right anterior cingulate and paracingulate gyri, left posterior cingulate gyrus, right superior frontal gyrus (medial part), bilateral hippocampus, and bilateral parahippocampal gyrus, and decreased nodal centralities in the orbitofrontal, temporal, and occipital regions (FDR q < 0.05). DATA CONCLUSION This study indicated that drug-naïve, first-episode adolescent MDD patients exhibit disruptions in whole-brain functional networks. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1790-1798.
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Affiliation(s)
- Baolin Wu
- Huaxi MR Research Center (HMRRC), Functional and molecular imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Xuekun Li
- Department of MR, The First Affiliated Hospital of Xinxiang Medical University, Weihui, China
| | - Jun Zhou
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Meng Zhang
- Department of MR, The First Affiliated Hospital of Xinxiang Medical University, Weihui, China
| | - Qingyun Long
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
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35
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Moreira da Silva N, Cowie CJA, Blamire AM, Forsyth R, Taylor PN. Investigating Brain Network Changes and Their Association With Cognitive Recovery After Traumatic Brain Injury: A Longitudinal Analysis. Front Neurol 2020; 11:369. [PMID: 32581989 PMCID: PMC7296134 DOI: 10.3389/fneur.2020.00369] [Citation(s) in RCA: 4] [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: 10/22/2019] [Accepted: 04/14/2020] [Indexed: 01/25/2023] Open
Abstract
Traumatic brain injury (TBI) can result in acute cognitive deficits and diffuse axonal injury reflected in white matter brain network alterations, which may, or may not, later recover. Our objective is to first characterize the ways in which brain networks change after TBI and, second, investigate if those changes are associated with recovery of cognitive deficits. We aim to make initial progress in discerning the relationships between brain network changes, and their (dys)functional correlates. We analyze longitudinally acquired MRI from 23 TBI patients (two time points: 6 days, 12 months post-injury) and cross-sectional data from 28 controls to construct white matter brain networks. Cognitive assessment was also performed. Graph theory and regression analysis were applied to identify changed brain network metrics after injury that are associated with subsequent improvements in cognitive function. Sixteen brain network metrics were found to be discriminative of different post-injury phases. Eleven of those explain 90% (adjusted R 2) of the variability observed in cognitive recovery following TBI. Brain network metrics that had a high contribution to the explained variance were found in frontal and temporal cortex, additional to the anterior cingulate cortex. Our preliminary study suggests that network reorganization may be related to recovery of impaired cognitive function in the first year after a TBI.
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Affiliation(s)
- Nádia Moreira da Silva
- CNNP Lab, Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Christopher J. A. Cowie
- Faculty of Medical Sciences, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Neurosurgery, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Andrew M. Blamire
- Institute of Cellular Medicine, Newcastle MR Centre, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rob Forsyth
- Institute of Cellular Medicine, Newcastle MR Centre, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Peter Neal Taylor
- CNNP Lab, Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
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36
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Yang D, Huang L, Luo C, Li M, Qin R, Ma J, Shao P, Xu H, Zhang B, Xu Y, Zhang M. Impaired Structural Network Properties Caused by White Matter Hyperintensity Related to Cognitive Decline. Front Neurol 2020; 11:250. [PMID: 32373044 PMCID: PMC7186334 DOI: 10.3389/fneur.2020.00250] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [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: 10/23/2019] [Accepted: 03/16/2020] [Indexed: 12/12/2022] Open
Abstract
Purpose: There is a high correlation between white matter hyperintensity (WMH) and cognitive impairment (CI) in elderly people. However, not all WMH will develop into CI, and the potential mechanism of WMH-related CI is still unclear. This study aimed to investigate the topological properties of white matter structural network in WMH-related CI. Methods: Forty-one WMH subjects with CI (WMH-CI), 42 WMH subjects without CI (WMH-no-CI), and 52 elderly healthy controls (HC) were recruited. Diffusion tensor imaging (DTI) fiber tractography and graph theoretical analysis were applied to construct the structural network. We compared network properties and clinical features among the three groups. Multiple linear regression analysis was performed to investigate the relationships among WMH volumes, impaired network properties, and cognitive functions in the WMH-CI group. Results: Compared with the controls, both WMH groups showed decreased network strength, global efficiency, and increased characteristic path length (Lp) at the level of the whole brain. The WMH-CI group displayed more profound impairments of nodal efficiency and nodal path length (NLp) within multiple regions including precentral, cingulate, and medial temporal gyrus. The disrupted network properties were associated with CI and WMH burdens in the WMH-CI group. Furthermore, a mediation effect of NLp in the left inferior frontal gyrus was observed for the association between periventricular WMH (PWMH) and memory deficit. Conclusions: Brain structural network in WMH-CI is significantly disturbed, and this disturbance is related to the severity of WMH and CI. Increased NLp in the left opercular part of inferior frontal gyrus (IFGoperc.L) was shown to be a mediation framework between PWMH and WMH-related memory, which shed light on investigating the underlying mechanisms of CI caused by WMH.
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Affiliation(s)
- Dan Yang
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Lili Huang
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Caimei Luo
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Mengchun Li
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Junyi Ma
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Pengfei Shao
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Hengheng Xu
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Bing Zhang
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yun Xu
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Meijuan Zhang
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
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Sang L, Liu C, Wang L, Zhang J, Zhang Y, Li P, Qiao L, Li C, Qiu M. Disrupted Brain Structural Connectivity Network in Subcortical Ischemic Vascular Cognitive Impairment With No Dementia. Front Aging Neurosci 2020; 12:6. [PMID: 32063840 PMCID: PMC7000429 DOI: 10.3389/fnagi.2020.00006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [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: 09/10/2019] [Accepted: 01/10/2020] [Indexed: 11/28/2022] Open
Abstract
The alteration of the functional topological organization in subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND) patients has been illuminated by previous neuroimaging studies. However, in regard to the changes in the structural connectivity of brain networks, little has been reported. In this study, a total of 27 subjects, consisting of 13 SIVCIND patients, and 14 normal controls, were recruited. Each of the structural connectivity networks was constructed by diffusion tensor tractography. Subsequently, graph theory, and network-based statistics (NBS) were employed to analyze the whole-brain mean factional anisotropy matrix. After removing the factor of age, gender, and duration of formal education, the clustering coefficients (Cp) and global efficiency (Eglob) were significantly decreased and the mean path length (Lp) was significantly increased in SIVCIND patients compared with normal controls. Using the combination of four network topological parameters as the classification feature, a classification accuracy of 78% was obtained by leave-one-out cross-validation for all subjects with a sensitivity of 69% and a specificity of 86%. Moreover, we also found decreased structural connections in the SIVCIND patients, which mainly concerned fronto-occipital, fronto-subcortical, and tempo-occipital connections (NBS corrected, p < 0.01). Additionally, significantly altered nodal centralities were found in several brain regions of the SIVCIND patients, mainly located in the prefrontal, subcortical, and temporal cortices. These results suggest that cognitive impairment in SIVCIND patients is associated with disrupted topological organization and provide structural evidence for developing reliable biomarkers related to cognitive decline in SIVCIND.
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Affiliation(s)
- Linqiong Sang
- Department of Medical Imaging, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Li Wang
- Department of Medical Imaging, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Jingna Zhang
- Department of Medical Imaging, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Ye Zhang
- Department of Medical Imaging, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Pengyue Li
- Department of Medical Imaging, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Liang Qiao
- Department of Medical Imaging, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Chuanming Li
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingguo Qiu
- Department of Medical Imaging, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
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38
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Miyoshi T, Tanioka K, Yamamoto S, Yadohisa H, Hiroyasu T, Hiwa S. Revealing Changes in Brain Functional Networks Caused by Focused-Attention Meditation Using Tucker3 Clustering. Front Hum Neurosci 2020; 13:473. [PMID: 32038204 PMCID: PMC6990115 DOI: 10.3389/fnhum.2019.00473] [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: 05/23/2019] [Accepted: 12/30/2019] [Indexed: 01/10/2023] Open
Abstract
This study examines the effects of focused-attention meditation on functional brain states in novice meditators. There are a number of feature metrics for functional brain states, such as functional connectivity, graph theoretical metrics, and amplitude of low frequency fluctuation (ALFF). It is necessary to choose appropriate metrics and also to specify the region of interests (ROIs) from a number of brain regions. Here, we use a Tucker3 clustering method, which simultaneously selects the feature vectors (graph theoretical metrics and fractional ALFF) and the ROIs that can discriminate between resting and meditative states based on the characteristics of the given data. In this study, breath-counting meditation, one of the most popular forms of focused-attention meditation, was used and brain activities during resting and meditation states were measured by functional magnetic resonance imaging. The results indicated that the clustering coefficients of the eight brain regions, Frontal Inf Oper L, Occipital Inf R, ParaHippocampal R, Cerebellum 10 R, Cingulum Mid R, Cerebellum Crus1 L, Occipital Inf L, and Paracentral Lobule R increased through the meditation. Our study also provided the framework of data-driven brain functional analysis and confirmed its effectiveness on analyzing neural basis of focused-attention meditation.
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Affiliation(s)
- Takuma Miyoshi
- Graduate School of Life and Medical Sciences, Doshisha University, Kyoto, Japan
| | - Kensuke Tanioka
- Clinical Study Support Center, Wakayama Medical University Hospital, Wakayama, Japan
| | - Shoko Yamamoto
- Graduate School of Life and Medical Sciences, Doshisha University, Kyoto, Japan
| | - Hiroshi Yadohisa
- Department of Culture and Information Science, Doshisha University, Kyoto, Japan
| | - Tomoyuki Hiroyasu
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan
| | - Satoru Hiwa
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan
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39
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Lei D, Pinaya WHL, Young J, van Amelsvoort T, Marcelis M, Donohoe G, Mothersill DO, Corvin A, Vieira S, Huang X, Lui S, Scarpazza C, Arango C, Bullmore E, Gong Q, McGuire P, Mechelli A. Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual. Hum Brain Mapp 2019; 41:1119-1135. [PMID: 31737978 PMCID: PMC7268084 DOI: 10.1002/hbm.24863] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [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: 05/15/2019] [Revised: 10/23/2019] [Accepted: 10/31/2019] [Indexed: 02/05/2023] Open
Abstract
Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the diagnostic and prognostic assessment of this disorder. However, the vast majority of studies published so far have used either structural or functional neuroimaging data, without accounting for the multimodal nature of the disorder. Structural MRI and resting‐state functional MRI data were acquired from a total of 295 patients with schizophrenia and 452 healthy controls at five research centers. We extracted features from the data including gray matter volume, white matter volume, amplitude of low‐frequency fluctuation, regional homogeneity and two connectome‐wide based metrics: structural covariance matrices and functional connectivity matrices. A support vector machine classifier was trained on each dataset separately to distinguish the subjects at individual level using each of the single feature as well as their combination, and 10‐fold cross‐validation was used to assess the performance of the model. Functional data allow higher accuracy of classification than structural data (mean 82.75% vs. 75.84%). Within each modality, the combination of images and matrices improves performance, resulting in mean accuracies of 81.63% for structural data and 87.59% for functional data. The use of all combined structural and functional measures allows the highest accuracy of classification (90.83%). We conclude that combining multimodal measures within a single model is a promising direction for developing biologically informed diagnostic tools in schizophrenia.
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Affiliation(s)
- Du Lei
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Walter H L Pinaya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Jonathan Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Therese van Amelsvoort
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Machteld Marcelis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands.,Mental Health Care Institute Eindhoven (GGzE), Eindhoven, The Netherlands
| | - Gary Donohoe
- School of Psychology & Center for neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - David O Mothersill
- School of Psychology & Center for neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - Aiden Corvin
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK.,Department of General Psychology, University of Padua, Padua, Italy
| | - Celso Arango
- Child and Adolescent Department of Psychiatry, Hospital General Universitario Gregorio Marañon, School of Medicine, Universidad Complutense Madrid, IiSGM, CIBERSAM, Madrid, Spain
| | - Ed Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
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40
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Weng JC, Chou YS, Tsai YH, Lee CT, Hsieh MH, Chen VCH. Connectome Analysis of Brain Functional Network Alterations in Depressive Patients with Suicidal Attempt. J Clin Med 2019; 8:jcm8111966. [PMID: 31739450 PMCID: PMC6912611 DOI: 10.3390/jcm8111966] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [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: 09/25/2019] [Revised: 11/07/2019] [Accepted: 11/12/2019] [Indexed: 11/16/2022] Open
Abstract
Our study aimed to clarify the neuroimaging correlates of suicide attempt by comparing differences in functional magnetic resonance imaging (fMRI) among depressed suicide attempters, depressed patients without suicide attempt history, and healthy controls through comprehensive and novel fMRI analyses and methods in the same study population. The association between depression severity and aspects of the brain imaging was also discussed. Our study recruited 109 participants who were assigned to three groups: 33 depressed patients with suicide attempt (SA), 32 depressed patients without suicide attempt (NS), and 44 healthy controls (HC). All participants were scanned using a 3 T MRI imaging system to obtain resting-state functional images. In seed-based correlation analysis, we found altered functional connectivity in some brain regions of the SA compared with the NS or HC, especially in the hippocampus and thalamus. In the voxel-based analysis, our results showed differential activation and regional homogeneity of the temporal lobe and several brain regions in the SA compared with the NS and HC. We also found that some brain areas correlated with the Hamilton Depression Rating Scale (HAM-D), anxiety, and depression scores, especially in the frontal and temporal lobes. Graph theoretical analysis (GTA) and network-based statistical (NBS) analyses revealed different topological organization as well as slightly better global integration and worse local segregation of the brain network (i.e., more like a random network) in depressed participants compared with healthy participants. We concluded that the brain function of major depressive disorders with and without suicide attempts changed compared with healthy participants.
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Affiliation(s)
- Jun-Cheng Weng
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 33302, Taiwan;
- Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan 33302, Taiwan
- Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi 61363, Taiwan
| | - Yu-Syuan Chou
- Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung 40201, Taiwan;
| | - Yuan-Hsiung Tsai
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan;
- Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi 61363, Taiwan
| | - Chun-Te Lee
- Department of Psychiatry, School of Medicine, Chung Shan Medical University and Hospital, Taichung 40201, Taiwan; (C.-T.L.); (M.-H.H.)
| | - Ming-Hong Hsieh
- Department of Psychiatry, School of Medicine, Chung Shan Medical University and Hospital, Taichung 40201, Taiwan; (C.-T.L.); (M.-H.H.)
| | - Vincent Chin-Hung Chen
- Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi 61363, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan;
- Correspondence: ; Tel.: +886-5-3621000 (ext. 2315)
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41
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Qin C, Liang Y, Tan X, Leng X, Lin H, Zeng H, Zhang C, Yang J, Li Y, Zheng Y, Qiu S. Altered Whole-Brain Functional Topological Organization and Cognitive Function in Type 2 Diabetes Mellitus Patients. Front Neurol 2019; 10:599. [PMID: 31275222 PMCID: PMC6593281 DOI: 10.3389/fneur.2019.00599] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [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: 02/02/2019] [Accepted: 05/21/2019] [Indexed: 12/18/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) is associated with cognitive dysfunction and may even progress to dementia. However, the underlying mechanism of altered functional topological organization and cognitive impairments remains unclear. This study explored the topological properties of functional whole brain networks in T2DM patients with graph theoretical analysis using a resting-state functional magnetic resonance imaging (rs-fMRI) technique. Thirty T2DM patients (aged 51.77 ± 1.42 years) and 30 sex-, age-, and education-matched healthy controls (HCs) (aged 48.87 ± 0.98 years) underwent resting-state functional imaging in a 3.0 T MR scanner in addition to detailed neuropsychological and laboratory tests. Then, graph theoretical network analysis was performed to explore the global and nodal topological alterations in the functional whole brain networks of the T2DM patients. Finally, correlation analyses were performed to investigate the relationship between the altered topological parameters, cognitive performances and clinical variables. Compared to HCs, we found that T2DM patients displayed worse performances in general cognitive function and several cognitive domains, including episodic memory, attention and executive function. In addition, T2DM patients showed a higher small-worldness (σ), a higher normalized clustering coefficient (γ) and a higher local efficiency (Eloc). Moreover, decreased nodal topological properties were mainly distributed in the occipital lobes, frontal lobes, left median cingulate and paracingulate gyri, and left amygdala, while increased nodal topological properties were mainly distributed in the right gyrus rectus, right anterior cingulate and paracingulate gyri, right posterior cingulate gyrus, bilateral caudate nucleus, bilateral cerebellum 3, bilateral cerebellum crus 1, vermis (1, 2) and vermis 3. Some disrupted nodal topological properties were correlated with cognitive performance and HbA1c levels in T2DM patients. This study shows altered functional topological organization in T2DM patients, mainly suggesting a compensation mechanism of the functional whole brain network in the relatively early stage to counteract cognitive impairments.
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Affiliation(s)
- Chunhong Qin
- Department of Radiology, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yi Liang
- Department of Radiology, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xin Tan
- Department of Radiology, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xi Leng
- Department of Radiology, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Huan Lin
- Department of Radiology, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Hui Zeng
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Chi Zhang
- Department of Radiology, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jinquan Yang
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yifan Li
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yanting Zheng
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Shijun Qiu
- Department of Radiology, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Guangzhou University of Chinese Medicine, Guangzhou, China
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42
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Wu YT, Huang SR, Jao CW, Soong BW, Lirng JF, Wu HM, Wang PS. Impaired Efficiency and Resilience of Structural Network in Spinocerebellar Ataxia Type 3. Front Neurosci 2019; 12:935. [PMID: 30618564 PMCID: PMC6304428 DOI: 10.3389/fnins.2018.00935] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [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: 09/13/2018] [Accepted: 11/27/2018] [Indexed: 12/25/2022] Open
Abstract
Background: Recent studies have shown that the patients with spinocerebellar ataxia type 3 (SCA3) may not only have disease involvement in the cerebellum and brainstem but also in the cerebral regions. However, the relations between the widespread degenerated brain regions remains incompletely explored. Methods: In the present study, we investigate the topological properties of the brain networks of SCA3 patients (n = 40) constructed based on the correlation of three-dimensional fractal dimension values. Random and targeted attacks were applied to measure the network resilience of normal and SCA3 groups. Results: The SCA3 networks had significantly smaller clustering coefficients (P < 0.05) and global efficiency (P < 0.05) but larger characteristic path length (P < 0.05) than the normal controls networks, implying loss of small-world features. Furthermore, the SCA3 patients were associated with reduced nodal betweenness (P < 0.001) in the left supplementary motor area, bilateral paracentral lobules, and right thalamus, indicating that the motor control circuit might be compromised. Conclusions: The SCA3 networks were more vulnerable to targeted attacks than the normal controls networks because of the effects of pathological topological organization. The SCA3 revealed a more sparsity and disrupted structural network with decreased values in the largest component size, mean degree, mean density, clustering coefficient, and global efficiency and increased value in characteristic path length. The cortico-cerebral circuits in SCA3 were disrupted and segregated into occipital-parietal (visual-spatial cognition) and frontal-pre-frontal (motor control) clusters. The cerebellum of SCA3 were segregated from cerebellum-temporal-frontal circuits and clustered into a frontal-temporal cluster (cognitive control). Therefore, the disrupted structural network presented in this study might reflect the clinical characteristics of SCA3.
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Affiliation(s)
- Yu-Te Wu
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.,Institute of Biophotonics and Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Shang-Ran Huang
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Chi-Wen Jao
- Institute of Biophotonics and Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Bing-Wen Soong
- Department of Neurology, Shuang Ho Hospital and Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan.,Department of Neurology, Taipei Veterans General Hospital and Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Jiing-Feng Lirng
- Department of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Hsiu-Mei Wu
- Department of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Po-Shan Wang
- Institute of Biophotonics and Brain Research Center, National Yang-Ming University, Taipei, Taiwan.,Department of Neurology, Taipei Municipal Gan-Dau Hospital, Taipei, Taiwan
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43
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Hiwa S, Katayama T, Hiroyasu T. Functional near-infrared spectroscopy study of the neural correlates between auditory environments and intellectual work performance. Brain Behav 2018; 8:e01104. [PMID: 30183142 PMCID: PMC6192398 DOI: 10.1002/brb3.1104] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 06/14/2018] [Accepted: 07/29/2018] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION Many people spend a considerable amount of time performing intellectual activities within auditory environments that affect work efficiency. To investigate auditory environments that improve working efficiency, we investigated the relationship between brain activity and performance of the number memory task in environments with and without white noise using functional near-infrared spectroscopy (fNIRS). METHODS Twenty-nine healthy subjects (aged 21.9 ± 1.4 years) performed the number memory task in both the white noise and silent environments. Cerebral blood flow changes during the task were measured using an ETG-7100 fNIRS system (Hitachi, Ltd., Tokyo, Japan). The psychological states of the subjects were also estimated by subjective ratings of the pleasantness of the auditory environment. Then, they were divided into three groups based on their task scores. The differences in the cerebral blood flow (CBF) changes, functional connection strength, and the subjects' feelings of pleasantness to the noise between the subject groups were analyzed and discussed. RESULTS The first group felt that the white noise was pleasant, which strengthened the bilateral functional connections between the brain regions related to the memory task. Therefore, the subjects' task performance improved in the white noise environment. Although the second group felt that the white noise was uncomfortable, the frontal regions related to attention control were more activated in the white noise environment to sustain the task performance in the noisy environment. The third group felt that the white noise was unpleasant, and their CBF decreased in that environment, which was associated with deteriorated task performance. CONCLUSIONS Task performance was closely related to the subjects' feelings of pleasantness to the noise. The results of the analysis of the CBF changes and functional connectivity suggested that the effects of the white noise on brain activity differed among the three groups.
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44
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Kaminski M, Blinowska KJ. Is Graph Theoretical Analysis a Useful Tool for Quantification of Connectivity Obtained by Means of EEG/MEG Techniques? Front Neural Circuits 2018; 12:76. [PMID: 30319364 PMCID: PMC6168619 DOI: 10.3389/fncir.2018.00076] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 09/04/2018] [Indexed: 11/13/2022] Open
Affiliation(s)
- Maciej Kaminski
- Department of Biomedical Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
| | - Katarzyna J Blinowska
- Department of Biomedical Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland.,Institute of Biocybernetics and Biomedical Engineering of Polish Academy of Sciences, Warsaw, Poland
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45
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Guo D, Guo F, Zhang Y, Li F, Xia Y, Xu P, Yao D. Periodic Visual Stimulation Induces Resting-State Brain Network Reconfiguration. Front Comput Neurosci 2018; 12:21. [PMID: 29643772 PMCID: PMC5883080 DOI: 10.3389/fncom.2018.00021] [Citation(s) in RCA: 9] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 03/12/2018] [Indexed: 11/13/2022] Open
Abstract
Periodic visual stimulation can evoke the steady-state visual potential (SSVEP) in the brain. Owing to its superior characteristics, the SSVEP has been widely used in neural engineering and cognitive neuroscience studies. However, the underlying mechanisms of the SSVEP are not well understood. In this study, we introduced a brain reconfiguration methodology to explore the possible mechanisms of the SSVEP. The EEG data from five periodic stimuli consistently indicated that the periodic visual stimulation could induce resting-state brain network reconfiguration and that the responses evoked by the stimuli were correlated to the network reconfiguration indexes. For each stimulus frequency, larger response amplitudes corresponded to higher reconfiguration indexes from the resting-state network to a stimulus-evoked network. These findings demonstrate that an external periodic visual stimulation can induce the modification of intrinsic oscillatory activities by reconfiguring resting-state activity at a network level, which could facilitate the responses evoked by the stimulus. These findings provide new insights into the response mechanisms of periodic visual stimulation.
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Affiliation(s)
- Daqing Guo
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Fengru Guo
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Yangsong Zhang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China.,School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China
| | - Fali Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Yang Xia
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Xu
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
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Sang L, Chen L, Wang L, Zhang J, Zhang Y, Li P, Li C, Qiu M. Progressively Disrupted Brain Functional Connectivity Network in Subcortical Ischemic Vascular Cognitive Impairment Patients. Front Neurol 2018. [PMID: 29535678 PMCID: PMC5834750 DOI: 10.3389/fneur.2018.00094] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [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] [Indexed: 11/24/2022] Open
Abstract
Cognitive impairment caused by subcortical ischemic vascular disease (SIVD) has been elucidated by many neuroimaging studies. However, little is known regarding the changes in brain functional connectivity networks in relation to the severity of cognitive impairment in SIVD. In the present study, 20 subcortical ischemic vascular cognitive impairment no dementia patients (SIVCIND) and 20 dementia patients (SIVaD) were enrolled; additionally, 19 normal controls were recruited. Each participant underwent a resting-state functional MRI scan. Whole-brain functional networks were analyzed with graph theory and network-based statistics (NBS) to study the functional organization of networks and find alterations in functional connectivity among brain regions. After adjustments for age, gender, and duration of formal education, there were significant group differences for two network functional organization indices, global efficiency and local efficiency, which decreased (NC > SIVCIND > SIVaD) as cognitive impairment worsened. Between-group differences in functional connectivity (NBS corrected, p < 0.01) mainly involved the orbitofrontal, parietal, and temporal cortices, as well as the basal ganglia. The brain connectivity network was progressively disrupted as cognitive impairment worsened, with an increased number of decreased connections between brain regions. We also observed more reductions in nodal efficiency in the prefrontal and temporal cortices for SIVaD than for SIVCIND. These findings indicated a progressively disrupted pattern of the brain functional connectivity network with increased cognitive impairment and showed promise for the development of reliable biomarkers of network metric changes related to cognitive impairment caused by SIVD.
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Affiliation(s)
- Linqiong Sang
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Lin Chen
- Department of Psychology, Third Military Medical University, Chongqing, China
| | - Li Wang
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Jingna Zhang
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Ye Zhang
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Pengyue Li
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Chuanming Li
- Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Mingguo Qiu
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
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Hosseini SMH, Mazaika P, Mauras N, Buckingham B, Weinzimer SA, Tsalikian E, White NH, Reiss AL. Altered Integration of Structural Covariance Networks in Young Children With Type 1 Diabetes. Hum Brain Mapp 2018; 37:4034-4046. [PMID: 27339089 DOI: 10.1002/hbm.23293] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 05/24/2016] [Accepted: 06/12/2016] [Indexed: 02/05/2023] Open
Abstract
Type 1 diabetes mellitus (T1D), one of the most frequent chronic diseases in children, is associated with glucose dysregulation that contributes to an increased risk for neurocognitive deficits. While there is a bulk of evidence regarding neurocognitive deficits in adults with T1D, little is known about how early-onset T1D affects neural networks in young children. Recent data demonstrated widespread alterations in regional gray matter and white matter associated with T1D in young children. These widespread neuroanatomical changes might impact the organization of large-scale brain networks. In the present study, we applied graph-theoretical analysis to test whether the organization of structural covariance networks in the brain for a cohort of young children with T1D (N = 141) is altered compared to healthy controls (HC; N = 69). While the networks in both groups followed a small world organization-an architecture that is simultaneously highly segregated and integrated-the T1D network showed significantly longer path length compared with HC, suggesting reduced global integration of brain networks in young children with T1D. In addition, network robustness analysis revealed that the T1D network model showed more vulnerability to neural insult compared with HC. These results suggest that early-onset T1D negatively impacts the global organization of structural covariance networks and influences the trajectory of brain development in childhood. This is the first study to examine structural covariance networks in young children with T1D. Improving glycemic control for young children with T1D might help prevent alterations in brain networks in this population. Hum Brain Mapp 37:4034-4046, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- S M Hadi Hosseini
- Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University, Stanford, California.
| | - Paul Mazaika
- Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University, Stanford, California
| | - Nelly Mauras
- Division of Endocrinology, Nemours Children's Health System, Jacksonville, Florida
| | - Bruce Buckingham
- Division of Pediatric Endocrinology, Stanford University, Stanford, California
| | - Stuart A Weinzimer
- Division of Pediatric Endocrinology, Yale University, New Haven, Connecticut
| | - Eva Tsalikian
- Division of Pediatric Endocrinology, University of Iowa, Iowa City, Iowa
| | - Neil H White
- Department of Pediatrics, Washington University, St. Louis, Missouri
| | - Allan L Reiss
- Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University, Stanford, California
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48
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Chen VCH, Liu YC, Chao SH, McIntyre RS, Cha DS, Lee Y, Weng JC. Brain structural networks and connectomes: the brain-obesity interface and its impact on mental health. Neuropsychiatr Dis Treat 2018; 14:3199-3208. [PMID: 30538478 PMCID: PMC6263220 DOI: 10.2147/ndt.s180569] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [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] [Indexed: 12/15/2022] Open
Abstract
PURPOSE Obesity is a complex and multifactorial disease identified as a global epidemic. Convergent evidence indicates that obesity differentially influences patients with neuropsychiatric disorders providing a basis for hypothesizing that obesity alters brain structure and function associated with the brain's propensity toward disturbances in mood and cognition. Herein, we characterize alterations in brain structures and networks among obese subjects (ie, body mass index [BMI] ≥30 kg/m2) when compared with non-obese controls. PATIENTS AND METHODS We obtained noninvasive diffusion tensor imaging and generalized q-sampling imaging scans of 20 obese subjects (BMI=37.9±5.2 SD) and 30 non-obese controls (BMI=22.6±3.4 SD). Graph theoretical analysis and network-based statistical analysis were performed to assess structural and functional differences between groups. We additionally assessed for correlations between diffusion indices, BMI, and anxiety and depressive symptom severity (ie, Hospital Anxiety and Depression Scale total score). RESULTS The diffusion indices of the posterior limb of the internal capsule, corona radiata, and superior longitudinal fasciculus were significantly lower among obese subjects when compared with controls. Moreover, obese subjects were more likely to report anxiety and depressive symptoms. There were fewer structural network connections observed in obese subjects compared with non-obese controls. Topological measures of clustering coefficient (C), local efficiency (Elocal), global efficiency (Eglobal), and transitivity were significantly lower among obese subjects. Similarly, three sub-networks were identified to have decreased structural connectivity among frontal-temporal regions in obese subjects compared with non-obese controls. CONCLUSION We extend knowledge further by delineating structural interconnectivity alterations within and across brain regions that are adversely affected in individuals who are obese.
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Affiliation(s)
- Vincent Chin-Hung Chen
- School of Medicine, Chang Gung University, Taoyuan, Taiwan.,Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan,
| | - Yi-Chun Liu
- Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan
| | - Seh-Huang Chao
- Center of Metabolic and Bariatric Surgery, Jen-Ai Hospital, Taichung, Taiwan
| | - Roger S McIntyre
- Mood Disorder Psychopharmacology Unit, University Health Network, Department of Psychiatry, University of Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada.,Departments of Psychiatry and Pharmacology, University of Toronto, Toronto, ON, Canada
| | - Danielle S Cha
- Mood Disorder Psychopharmacology Unit, University Health Network, Department of Psychiatry, University of Toronto, ON, Canada.,School of Medicine, University of Queensland, Queensland, Brisbane, Australia
| | - Yena Lee
- Mood Disorder Psychopharmacology Unit, University Health Network, Department of Psychiatry, University of Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Jun-Cheng Weng
- Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan, .,Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan,
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Lu FM, Dai J, Couto TA, Liu CH, Chen H, Lu SL, Tang LR, Tie CL, Chen HF, He MX, Xiang YT, Yuan Z. Diffusion Tensor Imaging Tractography Reveals Disrupted White Matter Structural Connectivity Network in Healthy Adults with Insomnia Symptoms. Front Hum Neurosci 2017; 11:583. [PMID: 29249951 PMCID: PMC5715269 DOI: 10.3389/fnhum.2017.00583] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [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: 09/06/2017] [Accepted: 11/17/2017] [Indexed: 12/17/2022] Open
Abstract
Neuroimaging studies have revealed that insomnia is characterized by aberrant neuronal connectivity in specific brain regions, but the topological disruptions in the white matter (WM) structural connectivity networks remain largely unknown in insomnia. The current study uses diffusion tensor imaging (DTI) tractography to construct the WM structural networks and graph theory analysis to detect alterations of the brain structural networks. The study participants comprised 30 healthy subjects with insomnia symptoms (IS) and 62 healthy subjects without IS. Both the two groups showed small-world properties regarding their WM structural connectivity networks. By contrast, increased local efficiency and decreased global efficiency were identified in the IS group, indicating an insomnia-related shift in topology away from regular networks. In addition, the IS group exhibited disrupted nodal topological characteristics in regions involving the fronto-limbic and the default-mode systems. To our knowledge, this is the first study to explore the topological organization of WM structural network connectivity in insomnia. More importantly, the dysfunctions of large-scale brain systems including the fronto-limbic pathways, salience network and default-mode network in insomnia were identified, which provides new insights into the insomnia connectome. Topology-based brain network analysis thus could be a potential biomarker for IS.
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Affiliation(s)
- Feng-Mei Lu
- Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau, China
| | - Jing Dai
- Chengdu Mental Health Center, Chengdu, China.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Tania A Couto
- Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau, China
| | - Chun-Hong Liu
- Beijing Institute of Traditional Chinese Medicine, Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University, Beijing, China.,Department of Radiology, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Heng Chen
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shun-Li Lu
- Department of Radiology, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Li-Rong Tang
- Department of Radiology, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Chang-Le Tie
- Department of Radiology, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Hua-Fu Chen
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Man-Xi He
- Chengdu Mental Health Center, Chengdu, China.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu-Tao Xiang
- Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau, China
| | - Zhen Yuan
- Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau, China
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50
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Lu FM, Zhou JS, Zhang J, Wang XP, Yuan Z. Disrupted small-world brain network topology in pure conduct disorder. Oncotarget 2017; 8:65506-65524. [PMID: 29029449 PMCID: PMC5630349 DOI: 10.18632/oncotarget.19098] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [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: 03/28/2017] [Accepted: 05/06/2017] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVES Conduct disorder (CD) is characterized by the violation of the rights of others or basic social rules and a repetitive, persistent pattern of antisocial and aggressive behaviors. A large number of functional and structural neuroimaging studies have identified widely abnormalities in specific brain regions in CD, but the alterations in the topological organization of functional networks among them remain largely unknown. METHODS Resting-state functional magnetic resonance imaging was applied to investigate the intrinsic functional connectivity in 18 pure CD patients and eighteen typically developing healthy controls. We first constructed the functional networks and then examined the CD-related alteration in topology properties using graph theoretical analysis. RESULTS Both the CD group and healthy controls exhibited small-world topology. However, the CD group showed decreased global and local efficiency. Changes in the nodal characteristics in CD group were found predominantly in the default-mode network, visual, and striatum regions. In addition, altered fronto-limbic-striatum network topology was found to have a relationship with clinical scores. CONCLUSIONS Our findings indicate the altered nodal topology of brain functional connectivity networks in CD. SIGNIFICANCE The results provide unequivocal evidence of a topological disruption in the brain networks that suggest some possible pathophysiological mechanisms underlying CD.
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Affiliation(s)
- Feng-Mei Lu
- Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Jian-Song Zhou
- Mental Health Institute, Second Xiangya Hospital, Central South University, Hunan Province Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Changsha, China
| | - Jiang Zhang
- School of Electrical Engineering and Information, Sichuan University, Chengdu, China
| | - Xiao-Ping Wang
- Mental Health Institute, Second Xiangya Hospital, Central South University, Hunan Province Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Changsha, China
| | - Zhen Yuan
- Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau SAR, China
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