Xiangliang Chen1,2,3, Özgür Onur1,4, Nils Richter5,6, Ronja Fassbender7, Hannes Gramespacher8, Qumars Behfar5,9, Boris Reutern5,10, Kim Dillen11, Heidi Jacobs12,13, Juraj Kukolja14,15, Gereon R Fink5,16, Julian Dronse5,17. 1. Research Centre Jülich, 28334, Institute of Neuroscience and Medicine, Julich, Nordrhein-Westfalen, Germany. 2. University Hospital Cologne, 27182, Department of Neurology, Koln, Nordrhein-Westfalen, Germany. 3. Nanjing First Hospital, 385685, Neurology, Nangjing, China; chenxl@njmu.edu.cn. 4. University Hospital Cologne, 27182, Department of Neurology, Koln, Nordrhein-Westfalen, Germany; o.onur@fz-juelich.de. 5. Research Centre Jülich, 28334, Julich, Nordrhein-Westfalen, Germany. 6. University Hospital Cologne, 27182, Department of Neurology, Koln, Nordrhein-Westfalen, Germany; nils.richter@uk-koeln.de. 7. University Hospital Cologne, 27182, Department of Neurology, Koln, Nordrhein-Westfalen, Germany; ronja.fassbender@uk-koeln.de. 8. University Hospital Cologne, 27182, Department of Neurology, Koln, Nordrhein-Westfalen, Germany; hannes.gramespacher@uk-koeln.de. 9. University Hospital Cologne, 27182, Department of Neurology, Koln, Nordrhein-Westfalen, Germany; qumars.behfar@uk-koeln.de. 10. University Hospital Cologne, 27182, Department of Neurology, Koln, Nordrhein-Westfalen, Germany; boris.reutern@gmail.com. 11. Research Centre Jülich, 28334, Julich, Nordrhein-Westfalen, Germany; kim.dillen@uk-koeln.de. 12. Massachusetts General Hospital, 2348, Radiology, Boston, Massachusetts, United States. 13. Maastricht University, 5211, Alzheimer Centre, Maastricht, Limburg, Netherlands; hjacobs@mgh.harvard.edu. 14. HELIOS University Hospital Wuppertal, 60865, Department of Neurology and clinical Neurophysiology, Wuppertal, Nordrhein-Westfalen, Germany. 15. University of Witten/Herdecke, 12263, Faculty of Health, Witten, Nordrhein-Westfalen, Germany; juraj.kukolja@helios-gesundheit.de. 16. University Hospital Cologne, 27182, Department of Neurology, Koln, Nordrhein-Westfalen, Germany; gereon.fink@uk-koeln.de. 17. University Hospital Cologne, 27182, Department of Neurology, Koln, Nordrhein-Westfalen, Germany; julian.dronse@uk-koeln.de.
Abstract
BACKGROUND: Recently, a new resting-state functional magnetic resonance imaging (rs-fMRI) measure to evaluate the concordance between different rs-fMRI metrics has been proposed and has not been investigated in Alzheimer's disease (AD). METHODS: 3T rs-fMRI data were obtained from healthy young controls (YC, n=26), senior controls (SC, n=29), and AD patients (n=35). The fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were analyzed, followed by the calculation of their concordance using Kendall's W for each brain voxel across time. Group differences in the concordance were compared globally, within seven intrinsic brain networks, and on a voxel-by-voxel basis with covariates of age, sex, head motion, and gray matter volume. RESULTS: The global concordance was lowest in AD among the three groups, with similar differences for the single metrics. When comparing AD to SC, reductions of concordance were detected in each of the investigated networks apart from the limbic network. For SC in comparison to YC, lower global concordance without any network-level difference was observed. Voxel-wise analyses revealed lower concordance in the right middle temporal gyrus in AD compared to SC, and lower concordance in the left middle frontal gyrus in SC compared to YC. Lower fALFF was observed in the right angular gyrus in AD in comparison to SC, but ReHo and DC showed no group differences. CONCLUSIONS: The concordance of resting-state measures differentiates AD from healthy aging and may represent a novel imaging marker in AD.
BACKGROUND: Recently, a new resting-state functional magnetic resonance imaging (rs-fMRI) measure to evaluate the concordance between different rs-fMRI metrics has been proposed and has not been investigated in Alzheimer's disease (AD). METHODS: 3T rs-fMRI data were obtained from healthy young controls (YC, n=26), senior controls (SC, n=29), and ADpatients (n=35). The fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were analyzed, followed by the calculation of their concordance using Kendall's W for each brain voxel across time. Group differences in the concordance were compared globally, within seven intrinsic brain networks, and on a voxel-by-voxel basis with covariates of age, sex, head motion, and gray matter volume. RESULTS: The global concordance was lowest in AD among the three groups, with similar differences for the single metrics. When comparing AD to SC, reductions of concordance were detected in each of the investigated networks apart from the limbic network. For SC in comparison to YC, lower global concordance without any network-level difference was observed. Voxel-wise analyses revealed lower concordance in the right middle temporal gyrus in AD compared to SC, and lower concordance in the left middle frontal gyrus in SC compared to YC. Lower fALFF was observed in the right angular gyrus in AD in comparison to SC, but ReHo and DC showed no group differences. CONCLUSIONS: The concordance of resting-state measures differentiates AD from healthy aging and may represent a novel imaging marker in AD.