Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder

Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Dongmei Zhi [verfasserIn] Vince D. Calhoun [verfasserIn] Luxian Lv [verfasserIn] Xiaohong Ma [verfasserIn] Qing Ke [verfasserIn] Zening Fu [verfasserIn] Yuhui Du [verfasserIn] Yongfeng Yang [verfasserIn] Xiao Yang [verfasserIn] Miao Pan [verfasserIn] Shile Qi [verfasserIn] Rongtao Jiang [verfasserIn] Qingbao Yu [verfasserIn] Jing Sui [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	major depressive disorder independent component analysis dynamic functional network connectivity graph theory resting-state functional magnetic resonance imaging

Übergeordnetes Werk:	In: Frontiers in Psychiatry - Frontiers Media S.A., 2010, 9(2018)
Übergeordnetes Werk:	volume:9 ; year:2018

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fpsyt.2018.00339

Katalog-ID:	DOAJ018831893

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520			\|a Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder.
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allfields	10.3389/fpsyt.2018.00339 doi (DE-627)DOAJ018831893 (DE-599)DOAJb0303cb73e284960b4004d0aa3c9fb59 DE-627 ger DE-627 rakwb eng RC435-571 Dongmei Zhi verfasserin aut Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder. major depressive disorder independent component analysis dynamic functional network connectivity graph theory resting-state functional magnetic resonance imaging Psychiatry Dongmei Zhi verfasserin aut Vince D. Calhoun verfasserin aut Vince D. Calhoun verfasserin aut Luxian Lv verfasserin aut Luxian Lv verfasserin aut Xiaohong Ma verfasserin aut Xiaohong Ma verfasserin aut Qing Ke verfasserin aut Zening Fu verfasserin aut Yuhui Du verfasserin aut Yuhui Du verfasserin aut Yongfeng Yang verfasserin aut Yongfeng Yang verfasserin aut Xiao Yang verfasserin aut Xiao Yang verfasserin aut Miao Pan verfasserin aut Miao Pan verfasserin aut Shile Qi verfasserin aut Shile Qi verfasserin aut Rongtao Jiang verfasserin aut Rongtao Jiang verfasserin aut Qingbao Yu verfasserin aut Jing Sui verfasserin aut Jing Sui verfasserin aut Jing Sui verfasserin aut Jing Sui verfasserin aut In Frontiers in Psychiatry Frontiers Media S.A., 2010 9(2018) (DE-627)631498796 (DE-600)2564218-2 16640640 nnns volume:9 year:2018 https://doi.org/10.3389/fpsyt.2018.00339 kostenfrei https://doaj.org/article/b0303cb73e284960b4004d0aa3c9fb59 kostenfrei https://www.frontiersin.org/article/10.3389/fpsyt.2018.00339/full kostenfrei https://doaj.org/toc/1664-0640 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2018
spelling	10.3389/fpsyt.2018.00339 doi (DE-627)DOAJ018831893 (DE-599)DOAJb0303cb73e284960b4004d0aa3c9fb59 DE-627 ger DE-627 rakwb eng RC435-571 Dongmei Zhi verfasserin aut Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder. major depressive disorder independent component analysis dynamic functional network connectivity graph theory resting-state functional magnetic resonance imaging Psychiatry Dongmei Zhi verfasserin aut Vince D. Calhoun verfasserin aut Vince D. Calhoun verfasserin aut Luxian Lv verfasserin aut Luxian Lv verfasserin aut Xiaohong Ma verfasserin aut Xiaohong Ma verfasserin aut Qing Ke verfasserin aut Zening Fu verfasserin aut Yuhui Du verfasserin aut Yuhui Du verfasserin aut Yongfeng Yang verfasserin aut Yongfeng Yang verfasserin aut Xiao Yang verfasserin aut Xiao Yang verfasserin aut Miao Pan verfasserin aut Miao Pan verfasserin aut Shile Qi verfasserin aut Shile Qi verfasserin aut Rongtao Jiang verfasserin aut Rongtao Jiang verfasserin aut Qingbao Yu verfasserin aut Jing Sui verfasserin aut Jing Sui verfasserin aut Jing Sui verfasserin aut Jing Sui verfasserin aut In Frontiers in Psychiatry Frontiers Media S.A., 2010 9(2018) (DE-627)631498796 (DE-600)2564218-2 16640640 nnns volume:9 year:2018 https://doi.org/10.3389/fpsyt.2018.00339 kostenfrei https://doaj.org/article/b0303cb73e284960b4004d0aa3c9fb59 kostenfrei https://www.frontiersin.org/article/10.3389/fpsyt.2018.00339/full kostenfrei https://doaj.org/toc/1664-0640 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2018
allfields_unstemmed	10.3389/fpsyt.2018.00339 doi (DE-627)DOAJ018831893 (DE-599)DOAJb0303cb73e284960b4004d0aa3c9fb59 DE-627 ger DE-627 rakwb eng RC435-571 Dongmei Zhi verfasserin aut Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder. major depressive disorder independent component analysis dynamic functional network connectivity graph theory resting-state functional magnetic resonance imaging Psychiatry Dongmei Zhi verfasserin aut Vince D. Calhoun verfasserin aut Vince D. Calhoun verfasserin aut Luxian Lv verfasserin aut Luxian Lv verfasserin aut Xiaohong Ma verfasserin aut Xiaohong Ma verfasserin aut Qing Ke verfasserin aut Zening Fu verfasserin aut Yuhui Du verfasserin aut Yuhui Du verfasserin aut Yongfeng Yang verfasserin aut Yongfeng Yang verfasserin aut Xiao Yang verfasserin aut Xiao Yang verfasserin aut Miao Pan verfasserin aut Miao Pan verfasserin aut Shile Qi verfasserin aut Shile Qi verfasserin aut Rongtao Jiang verfasserin aut Rongtao Jiang verfasserin aut Qingbao Yu verfasserin aut Jing Sui verfasserin aut Jing Sui verfasserin aut Jing Sui verfasserin aut Jing Sui verfasserin aut In Frontiers in Psychiatry Frontiers Media S.A., 2010 9(2018) (DE-627)631498796 (DE-600)2564218-2 16640640 nnns volume:9 year:2018 https://doi.org/10.3389/fpsyt.2018.00339 kostenfrei https://doaj.org/article/b0303cb73e284960b4004d0aa3c9fb59 kostenfrei https://www.frontiersin.org/article/10.3389/fpsyt.2018.00339/full kostenfrei https://doaj.org/toc/1664-0640 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2018
allfieldsGer	10.3389/fpsyt.2018.00339 doi (DE-627)DOAJ018831893 (DE-599)DOAJb0303cb73e284960b4004d0aa3c9fb59 DE-627 ger DE-627 rakwb eng RC435-571 Dongmei Zhi verfasserin aut Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder. major depressive disorder independent component analysis dynamic functional network connectivity graph theory resting-state functional magnetic resonance imaging Psychiatry Dongmei Zhi verfasserin aut Vince D. Calhoun verfasserin aut Vince D. Calhoun verfasserin aut Luxian Lv verfasserin aut Luxian Lv verfasserin aut Xiaohong Ma verfasserin aut Xiaohong Ma verfasserin aut Qing Ke verfasserin aut Zening Fu verfasserin aut Yuhui Du verfasserin aut Yuhui Du verfasserin aut Yongfeng Yang verfasserin aut Yongfeng Yang verfasserin aut Xiao Yang verfasserin aut Xiao Yang verfasserin aut Miao Pan verfasserin aut Miao Pan verfasserin aut Shile Qi verfasserin aut Shile Qi verfasserin aut Rongtao Jiang verfasserin aut Rongtao Jiang verfasserin aut Qingbao Yu verfasserin aut Jing Sui verfasserin aut Jing Sui verfasserin aut Jing Sui verfasserin aut Jing Sui verfasserin aut In Frontiers in Psychiatry Frontiers Media S.A., 2010 9(2018) (DE-627)631498796 (DE-600)2564218-2 16640640 nnns volume:9 year:2018 https://doi.org/10.3389/fpsyt.2018.00339 kostenfrei https://doaj.org/article/b0303cb73e284960b4004d0aa3c9fb59 kostenfrei https://www.frontiersin.org/article/10.3389/fpsyt.2018.00339/full kostenfrei https://doaj.org/toc/1664-0640 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2018
allfieldsSound	10.3389/fpsyt.2018.00339 doi (DE-627)DOAJ018831893 (DE-599)DOAJb0303cb73e284960b4004d0aa3c9fb59 DE-627 ger DE-627 rakwb eng RC435-571 Dongmei Zhi verfasserin aut Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder. major depressive disorder independent component analysis dynamic functional network connectivity graph theory resting-state functional magnetic resonance imaging Psychiatry Dongmei Zhi verfasserin aut Vince D. Calhoun verfasserin aut Vince D. Calhoun verfasserin aut Luxian Lv verfasserin aut Luxian Lv verfasserin aut Xiaohong Ma verfasserin aut Xiaohong Ma verfasserin aut Qing Ke verfasserin aut Zening Fu verfasserin aut Yuhui Du verfasserin aut Yuhui Du verfasserin aut Yongfeng Yang verfasserin aut Yongfeng Yang verfasserin aut Xiao Yang verfasserin aut Xiao Yang verfasserin aut Miao Pan verfasserin aut Miao Pan verfasserin aut Shile Qi verfasserin aut Shile Qi verfasserin aut Rongtao Jiang verfasserin aut Rongtao Jiang verfasserin aut Qingbao Yu verfasserin aut Jing Sui verfasserin aut Jing Sui verfasserin aut Jing Sui verfasserin aut Jing Sui verfasserin aut In Frontiers in Psychiatry Frontiers Media S.A., 2010 9(2018) (DE-627)631498796 (DE-600)2564218-2 16640640 nnns volume:9 year:2018 https://doi.org/10.3389/fpsyt.2018.00339 kostenfrei https://doaj.org/article/b0303cb73e284960b4004d0aa3c9fb59 kostenfrei https://www.frontiersin.org/article/10.3389/fpsyt.2018.00339/full kostenfrei https://doaj.org/toc/1664-0640 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2018
language	English
source	In Frontiers in Psychiatry 9(2018) volume:9 year:2018
sourceStr	In Frontiers in Psychiatry 9(2018) volume:9 year:2018
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	major depressive disorder independent component analysis dynamic functional network connectivity graph theory resting-state functional magnetic resonance imaging Psychiatry
isfreeaccess_bool	true
container_title	Frontiers in Psychiatry
authorswithroles_txt_mv	Dongmei Zhi @@aut@@ Vince D. Calhoun @@aut@@ Luxian Lv @@aut@@ Xiaohong Ma @@aut@@ Qing Ke @@aut@@ Zening Fu @@aut@@ Yuhui Du @@aut@@ Yongfeng Yang @@aut@@ Xiao Yang @@aut@@ Miao Pan @@aut@@ Shile Qi @@aut@@ Rongtao Jiang @@aut@@ Qingbao Yu @@aut@@ Jing Sui @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	631498796
id	DOAJ018831893
language_de	englisch
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callnumber-first	R - Medicine
author	Dongmei Zhi
spellingShingle	Dongmei Zhi misc RC435-571 misc major depressive disorder misc independent component analysis misc dynamic functional network connectivity misc graph theory misc resting-state functional magnetic resonance imaging misc Psychiatry Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder
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topic_title	RC435-571 Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder major depressive disorder independent component analysis dynamic functional network connectivity graph theory resting-state functional magnetic resonance imaging
topic	misc RC435-571 misc major depressive disorder misc independent component analysis misc dynamic functional network connectivity misc graph theory misc resting-state functional magnetic resonance imaging misc Psychiatry
topic_unstemmed	misc RC435-571 misc major depressive disorder misc independent component analysis misc dynamic functional network connectivity misc graph theory misc resting-state functional magnetic resonance imaging misc Psychiatry
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title	Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder
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title_full	Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder
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author_browse	Dongmei Zhi Vince D. Calhoun Luxian Lv Xiaohong Ma Qing Ke Zening Fu Yuhui Du Yongfeng Yang Xiao Yang Miao Pan Shile Qi Rongtao Jiang Qingbao Yu Jing Sui
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title_sort	aberrant dynamic functional network connectivity and graph properties in major depressive disorder
callnumber	RC435-571
title_auth	Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder
abstract	Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder.
abstractGer	Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder.
abstract_unstemmed	Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder.
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title_short	Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder
url	https://doi.org/10.3389/fpsyt.2018.00339 https://doaj.org/article/b0303cb73e284960b4004d0aa3c9fb59 https://www.frontiersin.org/article/10.3389/fpsyt.2018.00339/full https://doaj.org/toc/1664-0640
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up_date	2024-07-03T20:15:25.007Z
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