Alexandre Savio1, Sarah Fünger1, Masoud Tahmasian2,3,4, Srinivas Rachakonda5, Andrei Manoliu2,6, Christian Sorg2,7,8, Timo Grimmer8, Vince Calhoun5,9, Alexander Drzezga1,3, Valentin Riedl1,2,7, Igor Yakushev10,7. 1. Department of Nuclear Medicine, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany. 2. Department of Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany. 3. Department of Nuclear Medicine, Universität zu Köln, Cologne, Germany. 4. Institute of Medical Science and Technology at Shahid Beheshti University and School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran. 5. The Mind Research Network and LBERI, Albuquerque, New Mexico. 6. Department of Psychiatry, University of Zürich, Zurich, Switzerland. 7. Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, Technische Universität München, Munich, Germany. 8. Department of Psychiatry and Psychotherapy, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany; and. 9. Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico. 10. Department of Nuclear Medicine, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany igor.yakushev@tum.de.
Abstract
Functional MRI (fMRI) studies reported disruption of resting-state networks (RSNs) in several neuropsychiatric disorders. PET with 18F-FDG captures neuronal activity that is in steady state at a longer time span and is less dependent on neurovascular coupling. Methods: In the present study, we aimed to identify RSNs in 18F-FDG PET data and compare their spatial pattern with those obtained from simultaneously acquired resting-state fMRI data in 22 middle-aged healthy subjects. Results: Thirteen and 17 meaningful RSNs could be identified in PET and fMRI data, respectively. Spatial overlap was fair to moderate for the default mode, left central executive, primary and secondary visual, sensorimotor, cerebellar, and auditory networks. Despite recording different aspects of neural activity, similar RSNs were detected by both imaging modalities. Conclusion: The results argue for the common neural substrate of RSNs and encourage testing of the clinical utility of resting-state connectivity in PET data.
Functional MRI (fMRI) studies reported disruption of resting-state networks (RSNs) in several neuropsychiatric disorders. PET with 18F-FDG captures neuronal activity that is in steady state at a longer time span and is less dependent on neurovascular coupling. Methods: In the present study, we aimed to identify RSNs in 18F-FDG PET data and compare their spatial pattern with those obtained from simultaneously acquired resting-state fMRI data in 22 middle-aged healthy subjects. Results: Thirteen and 17 meaningful RSNs could be identified in PET and fMRI data, respectively. Spatial overlap was fair to moderate for the default mode, left central executive, primary and secondary visual, sensorimotor, cerebellar, and auditory networks. Despite recording different aspects of neural activity, similar RSNs were detected by both imaging modalities. Conclusion: The results argue for the common neural substrate of RSNs and encourage testing of the clinical utility of resting-state connectivity in PET data.
Authors: Koene R A Van Dijk; Trey Hedden; Archana Venkataraman; Karleyton C Evans; Sara W Lazar; Randy L Buckner Journal: J Neurophysiol Date: 2009-11-04 Impact factor: 2.714
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Authors: Elena A Allen; Erik B Erhardt; Eswar Damaraju; William Gruner; Judith M Segall; Rogers F Silva; Martin Havlicek; Srinivas Rachakonda; Jill Fries; Ravi Kalyanam; Andrew M Michael; Arvind Caprihan; Jessica A Turner; Tom Eichele; Steven Adelsheim; Angela D Bryan; Juan Bustillo; Vincent P Clark; Sarah W Feldstein Ewing; Francesca Filbey; Corey C Ford; Kent Hutchison; Rex E Jung; Kent A Kiehl; Piyadasa Kodituwakku; Yuko M Komesu; Andrew R Mayer; Godfrey D Pearlson; John P Phillips; Joseph R Sadek; Michael Stevens; Ursina Teuscher; Robert J Thoma; Vince D Calhoun Journal: Front Syst Neurosci Date: 2011-02-04
Authors: Zhaolin Chen; Sharna D Jamadar; Shenpeng Li; Francesco Sforazzini; Jakub Baran; Nicholas Ferris; Nadim Jon Shah; Gary F Egan Journal: Hum Brain Mapp Date: 2018-08-04 Impact factor: 5.038