Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study
Spatial covariance mapping of brain activity has been used increasingly with metabolic imaging to detect and quantify abnormal disease patterns in patient populations. Metabolic topographies such as the Parkinson's disease-related pattern (PDRP), while extensively validated, require access to p...
Ausführliche Beschreibung
Autor*in: |
Andrea Rommal [verfasserIn] An Vo [verfasserIn] Katharina A. Schindlbeck [verfasserIn] Andrea Greuel [verfasserIn] Marina C. Ruppert [verfasserIn] Carsten Eggers [verfasserIn] David Eidelberg [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Übergeordnetes Werk: |
In: Neuroimage: Reports - Elsevier, 2021, 1(2021), 3, Seite 100026- |
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Übergeordnetes Werk: |
volume:1 ; year:2021 ; number:3 ; pages:100026- |
Links: |
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DOI / URN: |
10.1016/j.ynirp.2021.100026 |
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Katalog-ID: |
DOAJ00515815X |
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520 | |a Spatial covariance mapping of brain activity has been used increasingly with metabolic imaging to detect and quantify abnormal disease patterns in patient populations. Metabolic topographies such as the Parkinson's disease-related pattern (PDRP), while extensively validated, require access to positron emission tomography (PET) and radiation exposure. Recently, we developed a fully non-invasive approach to identify analogous disease networks with resting-state functional MRI (rs-fMRI) using independent component analysis (ICA) and bootstrap resampling. We designated the original rs-fMRI PD topography as fPDRPNS after its site of identification at North Shore University Hospital (Manhasset, New York).In this study, we validated fPDRPNS in rs-fMRI scans of PD patients (n = 51; 25 training and 26 testing) and age-matched healthy control subjects (n = 25) acquired in Cologne, Germany. These scans were also used to identify an independent rs-fMRI PD pattern termed fPDRPCOL. The resulting topography and expression levels (subject scores) were then compared to corresponding fPDRPNS values computed in the two populations.We found that fPDRPNS and fPDRPCOL were topographically similar. Prominent contributions arose from the putamen, globus pallidus, pons, cerebellum, and thalamus, which have been linked to the core zone of the PDRP in prior studies. Indeed, a significant correlation was noted between core region weights on the two fPDRP topographies (r = 0.62, p < 0.005). Expression levels for fPDRPCOL and fPDRPNS were significantly correlated in the patients scanned at each site (Cologne: r = 0.39, p < 0.01; North Shore: r = 0.65, p < 0.005). Abnormal elevations in fPDRPCOL core expression were observed for both patient groups (Cologne: p = 0.01; North Shore: p = 0.05) compared to healthy controls. Correlations of fPDRP subject scores with clinical motor disability ratings were significant in each of the derivation samples (fPDRPCOL p < 0.005 for Cologne patients; fPDRPNS p < 0.05 for North Shore patients); clinical correlations were less robust on out-of-sample testing. Of note, significant clinical correlations were observed (p < 0.05) when expression values were computed for the fPDRP core in isolation as opposed to the whole network.The findings demonstrate the reproducibility of fPDRP networks across patient populations, sites, and scanning platforms. Rs-fMRI may provide a non-invasive alternative to metabolic PET for the quantitative assessment of disease networks in the clinical setting. | ||
650 | 4 | |a Parkinson's disease | |
650 | 4 | |a Brain network | |
650 | 4 | |a Resting-state functional MRI | |
650 | 4 | |a Independent component analysis | |
653 | 0 | |a Neurosciences. Biological psychiatry. Neuropsychiatry | |
700 | 0 | |a An Vo |e verfasserin |4 aut | |
700 | 0 | |a Katharina A. Schindlbeck |e verfasserin |4 aut | |
700 | 0 | |a Andrea Greuel |e verfasserin |4 aut | |
700 | 0 | |a Marina C. Ruppert |e verfasserin |4 aut | |
700 | 0 | |a Carsten Eggers |e verfasserin |4 aut | |
700 | 0 | |a David Eidelberg |e verfasserin |4 aut | |
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10.1016/j.ynirp.2021.100026 doi (DE-627)DOAJ00515815X (DE-599)DOAJa089a2c1662445f5a80962bc458618c5 DE-627 ger DE-627 rakwb eng RC321-571 Andrea Rommal verfasserin aut Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Spatial covariance mapping of brain activity has been used increasingly with metabolic imaging to detect and quantify abnormal disease patterns in patient populations. Metabolic topographies such as the Parkinson's disease-related pattern (PDRP), while extensively validated, require access to positron emission tomography (PET) and radiation exposure. Recently, we developed a fully non-invasive approach to identify analogous disease networks with resting-state functional MRI (rs-fMRI) using independent component analysis (ICA) and bootstrap resampling. We designated the original rs-fMRI PD topography as fPDRPNS after its site of identification at North Shore University Hospital (Manhasset, New York).In this study, we validated fPDRPNS in rs-fMRI scans of PD patients (n = 51; 25 training and 26 testing) and age-matched healthy control subjects (n = 25) acquired in Cologne, Germany. These scans were also used to identify an independent rs-fMRI PD pattern termed fPDRPCOL. The resulting topography and expression levels (subject scores) were then compared to corresponding fPDRPNS values computed in the two populations.We found that fPDRPNS and fPDRPCOL were topographically similar. Prominent contributions arose from the putamen, globus pallidus, pons, cerebellum, and thalamus, which have been linked to the core zone of the PDRP in prior studies. Indeed, a significant correlation was noted between core region weights on the two fPDRP topographies (r = 0.62, p < 0.005). Expression levels for fPDRPCOL and fPDRPNS were significantly correlated in the patients scanned at each site (Cologne: r = 0.39, p < 0.01; North Shore: r = 0.65, p < 0.005). Abnormal elevations in fPDRPCOL core expression were observed for both patient groups (Cologne: p = 0.01; North Shore: p = 0.05) compared to healthy controls. Correlations of fPDRP subject scores with clinical motor disability ratings were significant in each of the derivation samples (fPDRPCOL p < 0.005 for Cologne patients; fPDRPNS p < 0.05 for North Shore patients); clinical correlations were less robust on out-of-sample testing. Of note, significant clinical correlations were observed (p < 0.05) when expression values were computed for the fPDRP core in isolation as opposed to the whole network.The findings demonstrate the reproducibility of fPDRP networks across patient populations, sites, and scanning platforms. Rs-fMRI may provide a non-invasive alternative to metabolic PET for the quantitative assessment of disease networks in the clinical setting. Parkinson's disease Brain network Resting-state functional MRI Independent component analysis Neurosciences. Biological psychiatry. Neuropsychiatry An Vo verfasserin aut Katharina A. Schindlbeck verfasserin aut Andrea Greuel verfasserin aut Marina C. Ruppert verfasserin aut Carsten Eggers verfasserin aut David Eidelberg verfasserin aut In Neuroimage: Reports Elsevier, 2021 1(2021), 3, Seite 100026- (DE-627)1761087703 26669560 nnns volume:1 year:2021 number:3 pages:100026- https://doi.org/10.1016/j.ynirp.2021.100026 kostenfrei https://doaj.org/article/a089a2c1662445f5a80962bc458618c5 kostenfrei http://www.sciencedirect.com/science/article/pii/S2666956021000246 kostenfrei https://doaj.org/toc/2666-9560 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2038 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_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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2021 3 100026- |
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10.1016/j.ynirp.2021.100026 doi (DE-627)DOAJ00515815X (DE-599)DOAJa089a2c1662445f5a80962bc458618c5 DE-627 ger DE-627 rakwb eng RC321-571 Andrea Rommal verfasserin aut Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Spatial covariance mapping of brain activity has been used increasingly with metabolic imaging to detect and quantify abnormal disease patterns in patient populations. Metabolic topographies such as the Parkinson's disease-related pattern (PDRP), while extensively validated, require access to positron emission tomography (PET) and radiation exposure. Recently, we developed a fully non-invasive approach to identify analogous disease networks with resting-state functional MRI (rs-fMRI) using independent component analysis (ICA) and bootstrap resampling. We designated the original rs-fMRI PD topography as fPDRPNS after its site of identification at North Shore University Hospital (Manhasset, New York).In this study, we validated fPDRPNS in rs-fMRI scans of PD patients (n = 51; 25 training and 26 testing) and age-matched healthy control subjects (n = 25) acquired in Cologne, Germany. These scans were also used to identify an independent rs-fMRI PD pattern termed fPDRPCOL. The resulting topography and expression levels (subject scores) were then compared to corresponding fPDRPNS values computed in the two populations.We found that fPDRPNS and fPDRPCOL were topographically similar. Prominent contributions arose from the putamen, globus pallidus, pons, cerebellum, and thalamus, which have been linked to the core zone of the PDRP in prior studies. Indeed, a significant correlation was noted between core region weights on the two fPDRP topographies (r = 0.62, p < 0.005). Expression levels for fPDRPCOL and fPDRPNS were significantly correlated in the patients scanned at each site (Cologne: r = 0.39, p < 0.01; North Shore: r = 0.65, p < 0.005). Abnormal elevations in fPDRPCOL core expression were observed for both patient groups (Cologne: p = 0.01; North Shore: p = 0.05) compared to healthy controls. Correlations of fPDRP subject scores with clinical motor disability ratings were significant in each of the derivation samples (fPDRPCOL p < 0.005 for Cologne patients; fPDRPNS p < 0.05 for North Shore patients); clinical correlations were less robust on out-of-sample testing. Of note, significant clinical correlations were observed (p < 0.05) when expression values were computed for the fPDRP core in isolation as opposed to the whole network.The findings demonstrate the reproducibility of fPDRP networks across patient populations, sites, and scanning platforms. Rs-fMRI may provide a non-invasive alternative to metabolic PET for the quantitative assessment of disease networks in the clinical setting. Parkinson's disease Brain network Resting-state functional MRI Independent component analysis Neurosciences. Biological psychiatry. Neuropsychiatry An Vo verfasserin aut Katharina A. Schindlbeck verfasserin aut Andrea Greuel verfasserin aut Marina C. Ruppert verfasserin aut Carsten Eggers verfasserin aut David Eidelberg verfasserin aut In Neuroimage: Reports Elsevier, 2021 1(2021), 3, Seite 100026- (DE-627)1761087703 26669560 nnns volume:1 year:2021 number:3 pages:100026- https://doi.org/10.1016/j.ynirp.2021.100026 kostenfrei https://doaj.org/article/a089a2c1662445f5a80962bc458618c5 kostenfrei http://www.sciencedirect.com/science/article/pii/S2666956021000246 kostenfrei https://doaj.org/toc/2666-9560 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2038 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_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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2021 3 100026- |
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10.1016/j.ynirp.2021.100026 doi (DE-627)DOAJ00515815X (DE-599)DOAJa089a2c1662445f5a80962bc458618c5 DE-627 ger DE-627 rakwb eng RC321-571 Andrea Rommal verfasserin aut Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Spatial covariance mapping of brain activity has been used increasingly with metabolic imaging to detect and quantify abnormal disease patterns in patient populations. Metabolic topographies such as the Parkinson's disease-related pattern (PDRP), while extensively validated, require access to positron emission tomography (PET) and radiation exposure. Recently, we developed a fully non-invasive approach to identify analogous disease networks with resting-state functional MRI (rs-fMRI) using independent component analysis (ICA) and bootstrap resampling. We designated the original rs-fMRI PD topography as fPDRPNS after its site of identification at North Shore University Hospital (Manhasset, New York).In this study, we validated fPDRPNS in rs-fMRI scans of PD patients (n = 51; 25 training and 26 testing) and age-matched healthy control subjects (n = 25) acquired in Cologne, Germany. These scans were also used to identify an independent rs-fMRI PD pattern termed fPDRPCOL. The resulting topography and expression levels (subject scores) were then compared to corresponding fPDRPNS values computed in the two populations.We found that fPDRPNS and fPDRPCOL were topographically similar. Prominent contributions arose from the putamen, globus pallidus, pons, cerebellum, and thalamus, which have been linked to the core zone of the PDRP in prior studies. Indeed, a significant correlation was noted between core region weights on the two fPDRP topographies (r = 0.62, p < 0.005). Expression levels for fPDRPCOL and fPDRPNS were significantly correlated in the patients scanned at each site (Cologne: r = 0.39, p < 0.01; North Shore: r = 0.65, p < 0.005). Abnormal elevations in fPDRPCOL core expression were observed for both patient groups (Cologne: p = 0.01; North Shore: p = 0.05) compared to healthy controls. Correlations of fPDRP subject scores with clinical motor disability ratings were significant in each of the derivation samples (fPDRPCOL p < 0.005 for Cologne patients; fPDRPNS p < 0.05 for North Shore patients); clinical correlations were less robust on out-of-sample testing. Of note, significant clinical correlations were observed (p < 0.05) when expression values were computed for the fPDRP core in isolation as opposed to the whole network.The findings demonstrate the reproducibility of fPDRP networks across patient populations, sites, and scanning platforms. Rs-fMRI may provide a non-invasive alternative to metabolic PET for the quantitative assessment of disease networks in the clinical setting. Parkinson's disease Brain network Resting-state functional MRI Independent component analysis Neurosciences. Biological psychiatry. Neuropsychiatry An Vo verfasserin aut Katharina A. Schindlbeck verfasserin aut Andrea Greuel verfasserin aut Marina C. Ruppert verfasserin aut Carsten Eggers verfasserin aut David Eidelberg verfasserin aut In Neuroimage: Reports Elsevier, 2021 1(2021), 3, Seite 100026- (DE-627)1761087703 26669560 nnns volume:1 year:2021 number:3 pages:100026- https://doi.org/10.1016/j.ynirp.2021.100026 kostenfrei https://doaj.org/article/a089a2c1662445f5a80962bc458618c5 kostenfrei http://www.sciencedirect.com/science/article/pii/S2666956021000246 kostenfrei https://doaj.org/toc/2666-9560 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2038 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_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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2021 3 100026- |
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10.1016/j.ynirp.2021.100026 doi (DE-627)DOAJ00515815X (DE-599)DOAJa089a2c1662445f5a80962bc458618c5 DE-627 ger DE-627 rakwb eng RC321-571 Andrea Rommal verfasserin aut Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Spatial covariance mapping of brain activity has been used increasingly with metabolic imaging to detect and quantify abnormal disease patterns in patient populations. Metabolic topographies such as the Parkinson's disease-related pattern (PDRP), while extensively validated, require access to positron emission tomography (PET) and radiation exposure. Recently, we developed a fully non-invasive approach to identify analogous disease networks with resting-state functional MRI (rs-fMRI) using independent component analysis (ICA) and bootstrap resampling. We designated the original rs-fMRI PD topography as fPDRPNS after its site of identification at North Shore University Hospital (Manhasset, New York).In this study, we validated fPDRPNS in rs-fMRI scans of PD patients (n = 51; 25 training and 26 testing) and age-matched healthy control subjects (n = 25) acquired in Cologne, Germany. These scans were also used to identify an independent rs-fMRI PD pattern termed fPDRPCOL. The resulting topography and expression levels (subject scores) were then compared to corresponding fPDRPNS values computed in the two populations.We found that fPDRPNS and fPDRPCOL were topographically similar. Prominent contributions arose from the putamen, globus pallidus, pons, cerebellum, and thalamus, which have been linked to the core zone of the PDRP in prior studies. Indeed, a significant correlation was noted between core region weights on the two fPDRP topographies (r = 0.62, p < 0.005). Expression levels for fPDRPCOL and fPDRPNS were significantly correlated in the patients scanned at each site (Cologne: r = 0.39, p < 0.01; North Shore: r = 0.65, p < 0.005). Abnormal elevations in fPDRPCOL core expression were observed for both patient groups (Cologne: p = 0.01; North Shore: p = 0.05) compared to healthy controls. Correlations of fPDRP subject scores with clinical motor disability ratings were significant in each of the derivation samples (fPDRPCOL p < 0.005 for Cologne patients; fPDRPNS p < 0.05 for North Shore patients); clinical correlations were less robust on out-of-sample testing. Of note, significant clinical correlations were observed (p < 0.05) when expression values were computed for the fPDRP core in isolation as opposed to the whole network.The findings demonstrate the reproducibility of fPDRP networks across patient populations, sites, and scanning platforms. Rs-fMRI may provide a non-invasive alternative to metabolic PET for the quantitative assessment of disease networks in the clinical setting. Parkinson's disease Brain network Resting-state functional MRI Independent component analysis Neurosciences. Biological psychiatry. Neuropsychiatry An Vo verfasserin aut Katharina A. Schindlbeck verfasserin aut Andrea Greuel verfasserin aut Marina C. Ruppert verfasserin aut Carsten Eggers verfasserin aut David Eidelberg verfasserin aut In Neuroimage: Reports Elsevier, 2021 1(2021), 3, Seite 100026- (DE-627)1761087703 26669560 nnns volume:1 year:2021 number:3 pages:100026- https://doi.org/10.1016/j.ynirp.2021.100026 kostenfrei https://doaj.org/article/a089a2c1662445f5a80962bc458618c5 kostenfrei http://www.sciencedirect.com/science/article/pii/S2666956021000246 kostenfrei https://doaj.org/toc/2666-9560 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2038 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_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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2021 3 100026- |
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10.1016/j.ynirp.2021.100026 doi (DE-627)DOAJ00515815X (DE-599)DOAJa089a2c1662445f5a80962bc458618c5 DE-627 ger DE-627 rakwb eng RC321-571 Andrea Rommal verfasserin aut Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Spatial covariance mapping of brain activity has been used increasingly with metabolic imaging to detect and quantify abnormal disease patterns in patient populations. Metabolic topographies such as the Parkinson's disease-related pattern (PDRP), while extensively validated, require access to positron emission tomography (PET) and radiation exposure. Recently, we developed a fully non-invasive approach to identify analogous disease networks with resting-state functional MRI (rs-fMRI) using independent component analysis (ICA) and bootstrap resampling. We designated the original rs-fMRI PD topography as fPDRPNS after its site of identification at North Shore University Hospital (Manhasset, New York).In this study, we validated fPDRPNS in rs-fMRI scans of PD patients (n = 51; 25 training and 26 testing) and age-matched healthy control subjects (n = 25) acquired in Cologne, Germany. These scans were also used to identify an independent rs-fMRI PD pattern termed fPDRPCOL. The resulting topography and expression levels (subject scores) were then compared to corresponding fPDRPNS values computed in the two populations.We found that fPDRPNS and fPDRPCOL were topographically similar. Prominent contributions arose from the putamen, globus pallidus, pons, cerebellum, and thalamus, which have been linked to the core zone of the PDRP in prior studies. Indeed, a significant correlation was noted between core region weights on the two fPDRP topographies (r = 0.62, p < 0.005). Expression levels for fPDRPCOL and fPDRPNS were significantly correlated in the patients scanned at each site (Cologne: r = 0.39, p < 0.01; North Shore: r = 0.65, p < 0.005). Abnormal elevations in fPDRPCOL core expression were observed for both patient groups (Cologne: p = 0.01; North Shore: p = 0.05) compared to healthy controls. Correlations of fPDRP subject scores with clinical motor disability ratings were significant in each of the derivation samples (fPDRPCOL p < 0.005 for Cologne patients; fPDRPNS p < 0.05 for North Shore patients); clinical correlations were less robust on out-of-sample testing. Of note, significant clinical correlations were observed (p < 0.05) when expression values were computed for the fPDRP core in isolation as opposed to the whole network.The findings demonstrate the reproducibility of fPDRP networks across patient populations, sites, and scanning platforms. Rs-fMRI may provide a non-invasive alternative to metabolic PET for the quantitative assessment of disease networks in the clinical setting. Parkinson's disease Brain network Resting-state functional MRI Independent component analysis Neurosciences. Biological psychiatry. Neuropsychiatry An Vo verfasserin aut Katharina A. Schindlbeck verfasserin aut Andrea Greuel verfasserin aut Marina C. Ruppert verfasserin aut Carsten Eggers verfasserin aut David Eidelberg verfasserin aut In Neuroimage: Reports Elsevier, 2021 1(2021), 3, Seite 100026- (DE-627)1761087703 26669560 nnns volume:1 year:2021 number:3 pages:100026- https://doi.org/10.1016/j.ynirp.2021.100026 kostenfrei https://doaj.org/article/a089a2c1662445f5a80962bc458618c5 kostenfrei http://www.sciencedirect.com/science/article/pii/S2666956021000246 kostenfrei https://doaj.org/toc/2666-9560 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2038 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_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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2021 3 100026- |
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Metabolic topographies such as the Parkinson's disease-related pattern (PDRP), while extensively validated, require access to positron emission tomography (PET) and radiation exposure. Recently, we developed a fully non-invasive approach to identify analogous disease networks with resting-state functional MRI (rs-fMRI) using independent component analysis (ICA) and bootstrap resampling. We designated the original rs-fMRI PD topography as fPDRPNS after its site of identification at North Shore University Hospital (Manhasset, New York).In this study, we validated fPDRPNS in rs-fMRI scans of PD patients (n = 51; 25 training and 26 testing) and age-matched healthy control subjects (n = 25) acquired in Cologne, Germany. These scans were also used to identify an independent rs-fMRI PD pattern termed fPDRPCOL. The resulting topography and expression levels (subject scores) were then compared to corresponding fPDRPNS values computed in the two populations.We found that fPDRPNS and fPDRPCOL were topographically similar. Prominent contributions arose from the putamen, globus pallidus, pons, cerebellum, and thalamus, which have been linked to the core zone of the PDRP in prior studies. Indeed, a significant correlation was noted between core region weights on the two fPDRP topographies (r = 0.62, p < 0.005). Expression levels for fPDRPCOL and fPDRPNS were significantly correlated in the patients scanned at each site (Cologne: r = 0.39, p < 0.01; North Shore: r = 0.65, p < 0.005). Abnormal elevations in fPDRPCOL core expression were observed for both patient groups (Cologne: p = 0.01; North Shore: p = 0.05) compared to healthy controls. Correlations of fPDRP subject scores with clinical motor disability ratings were significant in each of the derivation samples (fPDRPCOL p < 0.005 for Cologne patients; fPDRPNS p < 0.05 for North Shore patients); clinical correlations were less robust on out-of-sample testing. Of note, significant clinical correlations were observed (p < 0.05) when expression values were computed for the fPDRP core in isolation as opposed to the whole network.The findings demonstrate the reproducibility of fPDRP networks across patient populations, sites, and scanning platforms. 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R - Medicine |
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Andrea Rommal |
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Andrea Rommal misc RC321-571 misc Parkinson's disease misc Brain network misc Resting-state functional MRI misc Independent component analysis misc Neurosciences. Biological psychiatry. Neuropsychiatry Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study |
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RC321-571 Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study Parkinson's disease Brain network Resting-state functional MRI Independent component analysis |
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misc RC321-571 misc Parkinson's disease misc Brain network misc Resting-state functional MRI misc Independent component analysis misc Neurosciences. Biological psychiatry. Neuropsychiatry |
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Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study |
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Andrea Rommal An Vo Katharina A. Schindlbeck Andrea Greuel Marina C. Ruppert Carsten Eggers David Eidelberg |
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parkinson's disease-related pattern (pdrp) identified using resting-state functional mri: validation study |
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Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study |
abstract |
Spatial covariance mapping of brain activity has been used increasingly with metabolic imaging to detect and quantify abnormal disease patterns in patient populations. Metabolic topographies such as the Parkinson's disease-related pattern (PDRP), while extensively validated, require access to positron emission tomography (PET) and radiation exposure. Recently, we developed a fully non-invasive approach to identify analogous disease networks with resting-state functional MRI (rs-fMRI) using independent component analysis (ICA) and bootstrap resampling. We designated the original rs-fMRI PD topography as fPDRPNS after its site of identification at North Shore University Hospital (Manhasset, New York).In this study, we validated fPDRPNS in rs-fMRI scans of PD patients (n = 51; 25 training and 26 testing) and age-matched healthy control subjects (n = 25) acquired in Cologne, Germany. These scans were also used to identify an independent rs-fMRI PD pattern termed fPDRPCOL. The resulting topography and expression levels (subject scores) were then compared to corresponding fPDRPNS values computed in the two populations.We found that fPDRPNS and fPDRPCOL were topographically similar. Prominent contributions arose from the putamen, globus pallidus, pons, cerebellum, and thalamus, which have been linked to the core zone of the PDRP in prior studies. Indeed, a significant correlation was noted between core region weights on the two fPDRP topographies (r = 0.62, p < 0.005). Expression levels for fPDRPCOL and fPDRPNS were significantly correlated in the patients scanned at each site (Cologne: r = 0.39, p < 0.01; North Shore: r = 0.65, p < 0.005). Abnormal elevations in fPDRPCOL core expression were observed for both patient groups (Cologne: p = 0.01; North Shore: p = 0.05) compared to healthy controls. Correlations of fPDRP subject scores with clinical motor disability ratings were significant in each of the derivation samples (fPDRPCOL p < 0.005 for Cologne patients; fPDRPNS p < 0.05 for North Shore patients); clinical correlations were less robust on out-of-sample testing. Of note, significant clinical correlations were observed (p < 0.05) when expression values were computed for the fPDRP core in isolation as opposed to the whole network.The findings demonstrate the reproducibility of fPDRP networks across patient populations, sites, and scanning platforms. Rs-fMRI may provide a non-invasive alternative to metabolic PET for the quantitative assessment of disease networks in the clinical setting. |
abstractGer |
Spatial covariance mapping of brain activity has been used increasingly with metabolic imaging to detect and quantify abnormal disease patterns in patient populations. Metabolic topographies such as the Parkinson's disease-related pattern (PDRP), while extensively validated, require access to positron emission tomography (PET) and radiation exposure. Recently, we developed a fully non-invasive approach to identify analogous disease networks with resting-state functional MRI (rs-fMRI) using independent component analysis (ICA) and bootstrap resampling. We designated the original rs-fMRI PD topography as fPDRPNS after its site of identification at North Shore University Hospital (Manhasset, New York).In this study, we validated fPDRPNS in rs-fMRI scans of PD patients (n = 51; 25 training and 26 testing) and age-matched healthy control subjects (n = 25) acquired in Cologne, Germany. These scans were also used to identify an independent rs-fMRI PD pattern termed fPDRPCOL. The resulting topography and expression levels (subject scores) were then compared to corresponding fPDRPNS values computed in the two populations.We found that fPDRPNS and fPDRPCOL were topographically similar. Prominent contributions arose from the putamen, globus pallidus, pons, cerebellum, and thalamus, which have been linked to the core zone of the PDRP in prior studies. Indeed, a significant correlation was noted between core region weights on the two fPDRP topographies (r = 0.62, p < 0.005). Expression levels for fPDRPCOL and fPDRPNS were significantly correlated in the patients scanned at each site (Cologne: r = 0.39, p < 0.01; North Shore: r = 0.65, p < 0.005). Abnormal elevations in fPDRPCOL core expression were observed for both patient groups (Cologne: p = 0.01; North Shore: p = 0.05) compared to healthy controls. Correlations of fPDRP subject scores with clinical motor disability ratings were significant in each of the derivation samples (fPDRPCOL p < 0.005 for Cologne patients; fPDRPNS p < 0.05 for North Shore patients); clinical correlations were less robust on out-of-sample testing. Of note, significant clinical correlations were observed (p < 0.05) when expression values were computed for the fPDRP core in isolation as opposed to the whole network.The findings demonstrate the reproducibility of fPDRP networks across patient populations, sites, and scanning platforms. Rs-fMRI may provide a non-invasive alternative to metabolic PET for the quantitative assessment of disease networks in the clinical setting. |
abstract_unstemmed |
Spatial covariance mapping of brain activity has been used increasingly with metabolic imaging to detect and quantify abnormal disease patterns in patient populations. Metabolic topographies such as the Parkinson's disease-related pattern (PDRP), while extensively validated, require access to positron emission tomography (PET) and radiation exposure. Recently, we developed a fully non-invasive approach to identify analogous disease networks with resting-state functional MRI (rs-fMRI) using independent component analysis (ICA) and bootstrap resampling. We designated the original rs-fMRI PD topography as fPDRPNS after its site of identification at North Shore University Hospital (Manhasset, New York).In this study, we validated fPDRPNS in rs-fMRI scans of PD patients (n = 51; 25 training and 26 testing) and age-matched healthy control subjects (n = 25) acquired in Cologne, Germany. These scans were also used to identify an independent rs-fMRI PD pattern termed fPDRPCOL. The resulting topography and expression levels (subject scores) were then compared to corresponding fPDRPNS values computed in the two populations.We found that fPDRPNS and fPDRPCOL were topographically similar. Prominent contributions arose from the putamen, globus pallidus, pons, cerebellum, and thalamus, which have been linked to the core zone of the PDRP in prior studies. Indeed, a significant correlation was noted between core region weights on the two fPDRP topographies (r = 0.62, p < 0.005). Expression levels for fPDRPCOL and fPDRPNS were significantly correlated in the patients scanned at each site (Cologne: r = 0.39, p < 0.01; North Shore: r = 0.65, p < 0.005). Abnormal elevations in fPDRPCOL core expression were observed for both patient groups (Cologne: p = 0.01; North Shore: p = 0.05) compared to healthy controls. Correlations of fPDRP subject scores with clinical motor disability ratings were significant in each of the derivation samples (fPDRPCOL p < 0.005 for Cologne patients; fPDRPNS p < 0.05 for North Shore patients); clinical correlations were less robust on out-of-sample testing. Of note, significant clinical correlations were observed (p < 0.05) when expression values were computed for the fPDRP core in isolation as opposed to the whole network.The findings demonstrate the reproducibility of fPDRP networks across patient populations, sites, and scanning platforms. Rs-fMRI may provide a non-invasive alternative to metabolic PET for the quantitative assessment of disease networks in the clinical setting. |
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Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study |
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https://doi.org/10.1016/j.ynirp.2021.100026 https://doaj.org/article/a089a2c1662445f5a80962bc458618c5 http://www.sciencedirect.com/science/article/pii/S2666956021000246 https://doaj.org/toc/2666-9560 |
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An Vo Katharina A. Schindlbeck Andrea Greuel Marina C. Ruppert Carsten Eggers David Eidelberg |
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An Vo Katharina A. Schindlbeck Andrea Greuel Marina C. Ruppert Carsten Eggers David Eidelberg |
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2024-07-03T13:18:52.214Z |
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