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...
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| Published in | Neuroimage. Reports Vol. 1; no. 3; p. 100026 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier Inc
01.09.2021
Elsevier |
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| Online Access | Get full text |
| ISSN | 2666-9560 2666-9560 |
| DOI | 10.1016/j.ynirp.2021.100026 |
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| 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.
•Similar rs-fMRI network topographies are expressed in independent PD populations.•A new rs-fMRI-based PD pattern (fPDRP) was derived from an independent cohort.•New and original fPDRPs show good region weight and expression level agreement.•Expression of both fPDRPs is elevated in PD patients in testing cohorts.•Pattern expression correlates with clinical ratings of motor disability. |
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| AbstractList | 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. 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. •Similar rs-fMRI network topographies are expressed in independent PD populations.•A new rs-fMRI-based PD pattern (fPDRP) was derived from an independent cohort.•New and original fPDRPs show good region weight and expression level agreement.•Expression of both fPDRPs is elevated in PD patients in testing cohorts.•Pattern expression correlates with clinical ratings of motor disability. 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 fPDRP after its site of identification at North Shore University Hospital (Manhasset, New York). In this study, we validated fPDRP 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 fPDRP . The resulting topography and expression levels (subject scores) were then compared to corresponding fPDRP values computed in the two populations. We found that fPDRP and fPDRP 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 fPDRP and fPDRP 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 fPDRP 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 (fPDRP p < 0.005 for Cologne patients; fPDRP 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. 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.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. 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. •Similar rs-fMRI network topographies are expressed in independent PD populations.•A new rs-fMRI-based PD pattern (fPDRP) was derived from an independent cohort.•New and original fPDRPs show good region weight and expression level agreement.•Expression of both fPDRPs is elevated in PD patients in testing cohorts.•Pattern expression correlates with clinical ratings of motor disability. |
| ArticleNumber | 100026 |
| Author | Ruppert, Marina C. Eggers, Carsten Eidelberg, David Rommal, Andrea Schindlbeck, Katharina A. Vo, An Greuel, Andrea |
| Author_xml | – sequence: 1 givenname: Andrea surname: Rommal fullname: Rommal, Andrea organization: Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA – sequence: 2 givenname: An surname: Vo fullname: Vo, An organization: Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA – sequence: 3 givenname: Katharina A. orcidid: 0000-0002-5643-556X surname: Schindlbeck fullname: Schindlbeck, Katharina A. organization: Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA – sequence: 4 givenname: Andrea orcidid: 0000-0002-0001-9225 surname: Greuel fullname: Greuel, Andrea organization: Department of Neurology, University Hospital Giessen and Marburg, Marburg, Germany – sequence: 5 givenname: Marina C. orcidid: 0000-0002-9025-7058 surname: Ruppert fullname: Ruppert, Marina C. organization: Department of Neurology, University Hospital Giessen and Marburg, Marburg, Germany – sequence: 6 givenname: Carsten surname: Eggers fullname: Eggers, Carsten organization: Department of Neurology, University Hospital Giessen and Marburg, Marburg, Germany – sequence: 7 givenname: David surname: Eidelberg fullname: Eidelberg, David email: deidelbe@northwell.edu organization: Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40567285$$D View this record in MEDLINE/PubMed |
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| Keywords | Parkinson's disease DRS MRI ICA SMA Independent component analysis rs-fMRI ROI fMRI PD UPDRS-III MMSE PDRP Resting-state functional MRI HC PANDA Brain network PET |
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