Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia

Background: Dynamic spatial functional network connectivity (dsFNC) has shown advantages in detecting functional alterations impacted by mental disorders using magnitude-only fMRI data. However, complete fMRI data are complex-valued with unique and useful phase information.Methods: We propose dsFNC...
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Autor*in:	Li, Wei-Xing [verfasserIn] Lin, Qiu-Hua [verfasserIn] Zhao, Bin-Hua [verfasserIn] Kuang, Li-Dan [verfasserIn] Zhang, Chao-Ying [verfasserIn] Han, Yue [verfasserIn] Calhoun, Vince D. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Complex-valued fMRI data Spatial source phase Dynamic functional network connectivity (dFNC) Markov chains Schizophrenia

Übergeordnetes Werk:	Enthalten in: Journal of neuroscience methods - Amsterdam [u.a.] : Elsevier Science, 1979, 403
Übergeordnetes Werk:	volume:403

DOI / URN:	10.1016/j.jneumeth.2023.110049

Katalog-ID:	ELV066831245

Internformat


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245	1	0	\|a Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia
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520			\|a Background: Dynamic spatial functional network connectivity (dsFNC) has shown advantages in detecting functional alterations impacted by mental disorders using magnitude-only fMRI data. However, complete fMRI data are complex-valued with unique and useful phase information.Methods: We propose dsFNC of spatial source phase (SSP) maps, derived from complex-valued fMRI data (named SSP-dsFNC), to capture the dynamics elicited by the phase. We compute mutual information for connectivity quantification, employ statistical analysis and Markov chains to assess dynamics, ultimately classifying schizophrenia patients (SZs) and healthy controls (HCs) based on connectivity variance and Markov chain state transitions across windows.Results: SSP-dsFNC yielded greater dynamics and more significant HC-SZ differences, due to the use of complete brain information from complex-valued fMRI data.Comparison with Existing Methods: Compared with magnitude-dsFNC, SSP-dsFNC detected additional and meaningful connections across windows (e.g., for right frontal parietal) and achieved 14.6% higher accuracy for classifying HCs and SZs.Conclusions: This work provides new evidence about how SSP-dsFNC could be impacted by schizophrenia, and this information could be used to identify potential imaging biomarkers for psychotic diagnosis.
650		4	\|a Complex-valued fMRI data
650		4	\|a Spatial source phase
650		4	\|a Dynamic functional network connectivity (dFNC)
650		4	\|a Markov chains
650		4	\|a Schizophrenia
700	1		\|a Lin, Qiu-Hua \|e verfasserin \|4 aut
700	1		\|a Zhao, Bin-Hua \|e verfasserin \|4 aut
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700	1		\|a Zhang, Chao-Ying \|e verfasserin \|4 aut
700	1		\|a Han, Yue \|e verfasserin \|4 aut
700	1		\|a Calhoun, Vince D. \|e verfasserin \|4 aut
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publishDate	2023
allfields	10.1016/j.jneumeth.2023.110049 doi (DE-627)ELV066831245 (ELSEVIER)S0165-0270(23)00268-6 DE-627 ger DE-627 rda eng 610 VZ 44.90 bkl Li, Wei-Xing verfasserin aut Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Dynamic spatial functional network connectivity (dsFNC) has shown advantages in detecting functional alterations impacted by mental disorders using magnitude-only fMRI data. However, complete fMRI data are complex-valued with unique and useful phase information.Methods: We propose dsFNC of spatial source phase (SSP) maps, derived from complex-valued fMRI data (named SSP-dsFNC), to capture the dynamics elicited by the phase. We compute mutual information for connectivity quantification, employ statistical analysis and Markov chains to assess dynamics, ultimately classifying schizophrenia patients (SZs) and healthy controls (HCs) based on connectivity variance and Markov chain state transitions across windows.Results: SSP-dsFNC yielded greater dynamics and more significant HC-SZ differences, due to the use of complete brain information from complex-valued fMRI data.Comparison with Existing Methods: Compared with magnitude-dsFNC, SSP-dsFNC detected additional and meaningful connections across windows (e.g., for right frontal parietal) and achieved 14.6% higher accuracy for classifying HCs and SZs.Conclusions: This work provides new evidence about how SSP-dsFNC could be impacted by schizophrenia, and this information could be used to identify potential imaging biomarkers for psychotic diagnosis. Complex-valued fMRI data Spatial source phase Dynamic functional network connectivity (dFNC) Markov chains Schizophrenia Lin, Qiu-Hua verfasserin aut Zhao, Bin-Hua verfasserin aut Kuang, Li-Dan verfasserin aut Zhang, Chao-Ying verfasserin aut Han, Yue verfasserin aut Calhoun, Vince D. verfasserin aut Enthalten in Journal of neuroscience methods Amsterdam [u.a.] : Elsevier Science, 1979 403 Online-Ressource (DE-627)306659786 (DE-600)1500499-5 (DE-576)081986416 1872-678X nnns volume:403 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.90 Neurologie VZ AR 403
spelling	10.1016/j.jneumeth.2023.110049 doi (DE-627)ELV066831245 (ELSEVIER)S0165-0270(23)00268-6 DE-627 ger DE-627 rda eng 610 VZ 44.90 bkl Li, Wei-Xing verfasserin aut Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Dynamic spatial functional network connectivity (dsFNC) has shown advantages in detecting functional alterations impacted by mental disorders using magnitude-only fMRI data. However, complete fMRI data are complex-valued with unique and useful phase information.Methods: We propose dsFNC of spatial source phase (SSP) maps, derived from complex-valued fMRI data (named SSP-dsFNC), to capture the dynamics elicited by the phase. We compute mutual information for connectivity quantification, employ statistical analysis and Markov chains to assess dynamics, ultimately classifying schizophrenia patients (SZs) and healthy controls (HCs) based on connectivity variance and Markov chain state transitions across windows.Results: SSP-dsFNC yielded greater dynamics and more significant HC-SZ differences, due to the use of complete brain information from complex-valued fMRI data.Comparison with Existing Methods: Compared with magnitude-dsFNC, SSP-dsFNC detected additional and meaningful connections across windows (e.g., for right frontal parietal) and achieved 14.6% higher accuracy for classifying HCs and SZs.Conclusions: This work provides new evidence about how SSP-dsFNC could be impacted by schizophrenia, and this information could be used to identify potential imaging biomarkers for psychotic diagnosis. Complex-valued fMRI data Spatial source phase Dynamic functional network connectivity (dFNC) Markov chains Schizophrenia Lin, Qiu-Hua verfasserin aut Zhao, Bin-Hua verfasserin aut Kuang, Li-Dan verfasserin aut Zhang, Chao-Ying verfasserin aut Han, Yue verfasserin aut Calhoun, Vince D. verfasserin aut Enthalten in Journal of neuroscience methods Amsterdam [u.a.] : Elsevier Science, 1979 403 Online-Ressource (DE-627)306659786 (DE-600)1500499-5 (DE-576)081986416 1872-678X nnns volume:403 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.90 Neurologie VZ AR 403
allfields_unstemmed	10.1016/j.jneumeth.2023.110049 doi (DE-627)ELV066831245 (ELSEVIER)S0165-0270(23)00268-6 DE-627 ger DE-627 rda eng 610 VZ 44.90 bkl Li, Wei-Xing verfasserin aut Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Dynamic spatial functional network connectivity (dsFNC) has shown advantages in detecting functional alterations impacted by mental disorders using magnitude-only fMRI data. However, complete fMRI data are complex-valued with unique and useful phase information.Methods: We propose dsFNC of spatial source phase (SSP) maps, derived from complex-valued fMRI data (named SSP-dsFNC), to capture the dynamics elicited by the phase. We compute mutual information for connectivity quantification, employ statistical analysis and Markov chains to assess dynamics, ultimately classifying schizophrenia patients (SZs) and healthy controls (HCs) based on connectivity variance and Markov chain state transitions across windows.Results: SSP-dsFNC yielded greater dynamics and more significant HC-SZ differences, due to the use of complete brain information from complex-valued fMRI data.Comparison with Existing Methods: Compared with magnitude-dsFNC, SSP-dsFNC detected additional and meaningful connections across windows (e.g., for right frontal parietal) and achieved 14.6% higher accuracy for classifying HCs and SZs.Conclusions: This work provides new evidence about how SSP-dsFNC could be impacted by schizophrenia, and this information could be used to identify potential imaging biomarkers for psychotic diagnosis. Complex-valued fMRI data Spatial source phase Dynamic functional network connectivity (dFNC) Markov chains Schizophrenia Lin, Qiu-Hua verfasserin aut Zhao, Bin-Hua verfasserin aut Kuang, Li-Dan verfasserin aut Zhang, Chao-Ying verfasserin aut Han, Yue verfasserin aut Calhoun, Vince D. verfasserin aut Enthalten in Journal of neuroscience methods Amsterdam [u.a.] : Elsevier Science, 1979 403 Online-Ressource (DE-627)306659786 (DE-600)1500499-5 (DE-576)081986416 1872-678X nnns volume:403 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.90 Neurologie VZ AR 403
allfieldsGer	10.1016/j.jneumeth.2023.110049 doi (DE-627)ELV066831245 (ELSEVIER)S0165-0270(23)00268-6 DE-627 ger DE-627 rda eng 610 VZ 44.90 bkl Li, Wei-Xing verfasserin aut Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Dynamic spatial functional network connectivity (dsFNC) has shown advantages in detecting functional alterations impacted by mental disorders using magnitude-only fMRI data. However, complete fMRI data are complex-valued with unique and useful phase information.Methods: We propose dsFNC of spatial source phase (SSP) maps, derived from complex-valued fMRI data (named SSP-dsFNC), to capture the dynamics elicited by the phase. We compute mutual information for connectivity quantification, employ statistical analysis and Markov chains to assess dynamics, ultimately classifying schizophrenia patients (SZs) and healthy controls (HCs) based on connectivity variance and Markov chain state transitions across windows.Results: SSP-dsFNC yielded greater dynamics and more significant HC-SZ differences, due to the use of complete brain information from complex-valued fMRI data.Comparison with Existing Methods: Compared with magnitude-dsFNC, SSP-dsFNC detected additional and meaningful connections across windows (e.g., for right frontal parietal) and achieved 14.6% higher accuracy for classifying HCs and SZs.Conclusions: This work provides new evidence about how SSP-dsFNC could be impacted by schizophrenia, and this information could be used to identify potential imaging biomarkers for psychotic diagnosis. Complex-valued fMRI data Spatial source phase Dynamic functional network connectivity (dFNC) Markov chains Schizophrenia Lin, Qiu-Hua verfasserin aut Zhao, Bin-Hua verfasserin aut Kuang, Li-Dan verfasserin aut Zhang, Chao-Ying verfasserin aut Han, Yue verfasserin aut Calhoun, Vince D. verfasserin aut Enthalten in Journal of neuroscience methods Amsterdam [u.a.] : Elsevier Science, 1979 403 Online-Ressource (DE-627)306659786 (DE-600)1500499-5 (DE-576)081986416 1872-678X nnns volume:403 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.90 Neurologie VZ AR 403
allfieldsSound	10.1016/j.jneumeth.2023.110049 doi (DE-627)ELV066831245 (ELSEVIER)S0165-0270(23)00268-6 DE-627 ger DE-627 rda eng 610 VZ 44.90 bkl Li, Wei-Xing verfasserin aut Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Dynamic spatial functional network connectivity (dsFNC) has shown advantages in detecting functional alterations impacted by mental disorders using magnitude-only fMRI data. However, complete fMRI data are complex-valued with unique and useful phase information.Methods: We propose dsFNC of spatial source phase (SSP) maps, derived from complex-valued fMRI data (named SSP-dsFNC), to capture the dynamics elicited by the phase. We compute mutual information for connectivity quantification, employ statistical analysis and Markov chains to assess dynamics, ultimately classifying schizophrenia patients (SZs) and healthy controls (HCs) based on connectivity variance and Markov chain state transitions across windows.Results: SSP-dsFNC yielded greater dynamics and more significant HC-SZ differences, due to the use of complete brain information from complex-valued fMRI data.Comparison with Existing Methods: Compared with magnitude-dsFNC, SSP-dsFNC detected additional and meaningful connections across windows (e.g., for right frontal parietal) and achieved 14.6% higher accuracy for classifying HCs and SZs.Conclusions: This work provides new evidence about how SSP-dsFNC could be impacted by schizophrenia, and this information could be used to identify potential imaging biomarkers for psychotic diagnosis. Complex-valued fMRI data Spatial source phase Dynamic functional network connectivity (dFNC) Markov chains Schizophrenia Lin, Qiu-Hua verfasserin aut Zhao, Bin-Hua verfasserin aut Kuang, Li-Dan verfasserin aut Zhang, Chao-Ying verfasserin aut Han, Yue verfasserin aut Calhoun, Vince D. verfasserin aut Enthalten in Journal of neuroscience methods Amsterdam [u.a.] : Elsevier Science, 1979 403 Online-Ressource (DE-627)306659786 (DE-600)1500499-5 (DE-576)081986416 1872-678X nnns volume:403 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.90 Neurologie VZ AR 403
language	English
source	Enthalten in Journal of neuroscience methods 403 volume:403
sourceStr	Enthalten in Journal of neuroscience methods 403 volume:403
format_phy_str_mv	Article
bklname	Neurologie
institution	findex.gbv.de
topic_facet	Complex-valued fMRI data Spatial source phase Dynamic functional network connectivity (dFNC) Markov chains Schizophrenia
dewey-raw	610
isfreeaccess_bool	false
container_title	Journal of neuroscience methods
authorswithroles_txt_mv	Li, Wei-Xing @@aut@@ Lin, Qiu-Hua @@aut@@ Zhao, Bin-Hua @@aut@@ Kuang, Li-Dan @@aut@@ Zhang, Chao-Ying @@aut@@ Han, Yue @@aut@@ Calhoun, Vince D. @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	306659786
dewey-sort	3610
id	ELV066831245
language_de	englisch
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author	Li, Wei-Xing
spellingShingle	Li, Wei-Xing ddc 610 bkl 44.90 misc Complex-valued fMRI data misc Spatial source phase misc Dynamic functional network connectivity (dFNC) misc Markov chains misc Schizophrenia Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia
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topic_title	610 VZ 44.90 bkl Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia Complex-valued fMRI data Spatial source phase Dynamic functional network connectivity (dFNC) Markov chains Schizophrenia
topic	ddc 610 bkl 44.90 misc Complex-valued fMRI data misc Spatial source phase misc Dynamic functional network connectivity (dFNC) misc Markov chains misc Schizophrenia
topic_unstemmed	ddc 610 bkl 44.90 misc Complex-valued fMRI data misc Spatial source phase misc Dynamic functional network connectivity (dFNC) misc Markov chains misc Schizophrenia
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title	Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia
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title_full	Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia
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title_sort	dynamic functional network connectivity based on spatial source phase maps of complex-valued fmri data: application to schizophrenia
title_auth	Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia
abstract	Background: Dynamic spatial functional network connectivity (dsFNC) has shown advantages in detecting functional alterations impacted by mental disorders using magnitude-only fMRI data. However, complete fMRI data are complex-valued with unique and useful phase information.Methods: We propose dsFNC of spatial source phase (SSP) maps, derived from complex-valued fMRI data (named SSP-dsFNC), to capture the dynamics elicited by the phase. We compute mutual information for connectivity quantification, employ statistical analysis and Markov chains to assess dynamics, ultimately classifying schizophrenia patients (SZs) and healthy controls (HCs) based on connectivity variance and Markov chain state transitions across windows.Results: SSP-dsFNC yielded greater dynamics and more significant HC-SZ differences, due to the use of complete brain information from complex-valued fMRI data.Comparison with Existing Methods: Compared with magnitude-dsFNC, SSP-dsFNC detected additional and meaningful connections across windows (e.g., for right frontal parietal) and achieved 14.6% higher accuracy for classifying HCs and SZs.Conclusions: This work provides new evidence about how SSP-dsFNC could be impacted by schizophrenia, and this information could be used to identify potential imaging biomarkers for psychotic diagnosis.
abstractGer	Background: Dynamic spatial functional network connectivity (dsFNC) has shown advantages in detecting functional alterations impacted by mental disorders using magnitude-only fMRI data. However, complete fMRI data are complex-valued with unique and useful phase information.Methods: We propose dsFNC of spatial source phase (SSP) maps, derived from complex-valued fMRI data (named SSP-dsFNC), to capture the dynamics elicited by the phase. We compute mutual information for connectivity quantification, employ statistical analysis and Markov chains to assess dynamics, ultimately classifying schizophrenia patients (SZs) and healthy controls (HCs) based on connectivity variance and Markov chain state transitions across windows.Results: SSP-dsFNC yielded greater dynamics and more significant HC-SZ differences, due to the use of complete brain information from complex-valued fMRI data.Comparison with Existing Methods: Compared with magnitude-dsFNC, SSP-dsFNC detected additional and meaningful connections across windows (e.g., for right frontal parietal) and achieved 14.6% higher accuracy for classifying HCs and SZs.Conclusions: This work provides new evidence about how SSP-dsFNC could be impacted by schizophrenia, and this information could be used to identify potential imaging biomarkers for psychotic diagnosis.
abstract_unstemmed	Background: Dynamic spatial functional network connectivity (dsFNC) has shown advantages in detecting functional alterations impacted by mental disorders using magnitude-only fMRI data. However, complete fMRI data are complex-valued with unique and useful phase information.Methods: We propose dsFNC of spatial source phase (SSP) maps, derived from complex-valued fMRI data (named SSP-dsFNC), to capture the dynamics elicited by the phase. We compute mutual information for connectivity quantification, employ statistical analysis and Markov chains to assess dynamics, ultimately classifying schizophrenia patients (SZs) and healthy controls (HCs) based on connectivity variance and Markov chain state transitions across windows.Results: SSP-dsFNC yielded greater dynamics and more significant HC-SZ differences, due to the use of complete brain information from complex-valued fMRI data.Comparison with Existing Methods: Compared with magnitude-dsFNC, SSP-dsFNC detected additional and meaningful connections across windows (e.g., for right frontal parietal) and achieved 14.6% higher accuracy for classifying HCs and SZs.Conclusions: This work provides new evidence about how SSP-dsFNC could be impacted by schizophrenia, and this information could be used to identify potential imaging biomarkers for psychotic diagnosis.
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title_short	Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia
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author2	Lin, Qiu-Hua Zhao, Bin-Hua Kuang, Li-Dan Zhang, Chao-Ying Han, Yue Calhoun, Vince D.
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up_date	2024-07-06T19:09:12.052Z
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Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia

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