001     904373
005     20230123101900.0
024 7 _ |a 10.1002/hbm.25393
|2 doi
024 7 _ |a 1065-9471
|2 ISSN
024 7 _ |a 1097-0193
|2 ISSN
024 7 _ |a 2128/32248
|2 Handle
024 7 _ |a 33638213
|2 pmid
024 7 _ |a WOS:000622320900001
|2 WOS
037 _ _ |a FZJ-2021-05943
082 _ _ |a 610
100 1 _ |a Ruppert, Marina C.
|0 P:(DE-HGF)0
|b 0
|e Corresponding author
245 _ _ |a The default mode network and cognition in Parkinson's disease: A multimodal resting‐state network approach
260 _ _ |a New York, NY
|c 2021
|b Wiley-Liss
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1667372348_24053
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Involvement of the default mode network (DMN) in cognitive symptoms of Parkinson's disease (PD) has been reported by resting-state functional MRI (rsfMRI) studies. However, the relation to metabolic measures obtained by [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) is largely unknown. We applied multimodal resting-state network analysis to clarify the association between intrinsic metabolic and functional connectivity abnormalities within the DMN and their significance for cognitive symptoms in PD. PD patients were classified into normal cognition (n = 36) and mild cognitive impairment (MCI; n = 12). The DMN was identified by applying an independent component analysis to FDG-PET and rsfMRI data of a matched subset (16 controls and 16 PD patients) of the total cohort. Besides metabolic activity, metabolic and functional connectivity within the DMN were compared between the patients' groups and healthy controls (n = 16). Glucose metabolism was significantly reduced in all DMN nodes in both patient groups compared to controls, with the lowest uptake in PD-MCI (p < .05). Increased metabolic and functional connectivity along fronto-parietal connections was identified in PD-MCI patients compared to controls and unimpaired patients. Functional connectivity negatively correlated with cognitive composite z-scores in patients (r = -.43, p = .005). The current study clarifies the commonalities of metabolic and hemodynamic measures of brain network activity and their individual significance for cognitive symptoms in PD, highlighting the added value of multimodal resting-state network approaches for identifying prospective biomarkers.Keywords: Parkinson's disease; [18F]-FDG-PET; default mode network; metabolic covariance; mild cognitive impairment; resting-state fMRI.
536 _ _ |a 5253 - Neuroimaging (POF4-525)
|0 G:(DE-HGF)POF4-5253
|c POF4-525
|f POF IV
|x 0
536 _ _ |a 5252 - Brain Dysfunction and Plasticity (POF4-525)
|0 G:(DE-HGF)POF4-5252
|c POF4-525
|f POF IV
|x 1
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Greuel, Andrea
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Freigang, Julia
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Tahmasian, Masoud
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Maier, Franziska
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Hammes, Jochen
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Eimeren, Thilo
|0 P:(DE-HGF)0
|b 6
700 1 _ |a Timmermann, Lars
|0 P:(DE-HGF)0
|b 7
700 1 _ |a Tittgemeyer, Marc
|0 P:(DE-HGF)0
|b 8
700 1 _ |a Drzezga, Alexander
|0 P:(DE-Juel1)177611
|b 9
700 1 _ |a Eggers, Carsten
|0 P:(DE-HGF)0
|b 10
|e Corresponding author
773 _ _ |a 10.1002/hbm.25393
|g Vol. 42, no. 8, p. 2623 - 2641
|0 PERI:(DE-600)1492703-2
|n 8
|p 2623 - 2641
|t Human brain mapping
|v 42
|y 2021
|x 1065-9471
856 4 _ |u https://juser.fz-juelich.de/record/904373/files/The%20default%20mode%20network_publisher_version.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:904373
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Department of Neurology, University Hospital of Marburg, Marburg, Germany
|0 I:(DE-HGF)0
|b 0
|6 P:(DE-HGF)0
910 1 _ |a Center for Mind, Brain, and Behavior-CMBB, Universities of Marburg and Gießen, Marburg, Germany
|0 I:(DE-HGF)0
|b 0
|6 P:(DE-HGF)0
910 1 _ |a Department of Neurology, University Hospital of Marburg, Marburg, Germany
|0 I:(DE-HGF)0
|b 1
|6 P:(DE-HGF)0
910 1 _ |a Department of Neurology, University Hospital of Marburg, Marburg, Germany
|0 I:(DE-HGF)0
|b 2
|6 P:(DE-HGF)0
910 1 _ |a Center for Mind, Brain, and Behavior-CMBB, Universities of Marburg and Gießen, Marburg, Germany
|0 I:(DE-HGF)0
|b 2
|6 P:(DE-HGF)0
910 1 _ |a Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
|0 I:(DE-HGF)0
|b 3
|6 P:(DE-HGF)0
910 1 _ |a Medical Faculty, Department of Psychiatry, University Hospital Cologne, Cologne, Germany
|0 I:(DE-HGF)0
|b 4
|6 P:(DE-HGF)0
910 1 _ |a Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty and University Hospital Cologne, University Hospital Cologne, Cologne, Germany
|0 I:(DE-HGF)0
|b 5
|6 P:(DE-HGF)0
910 1 _ |a Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty and University Hospital Cologne, University Hospital Cologne, Cologne, Germany
|0 I:(DE-HGF)0
|b 6
|6 P:(DE-HGF)0
910 1 _ |a Department of Neurology, Medical Faculty and University Hospital Cologne, University Hospital Cologne, Cologne, Germany
|0 I:(DE-HGF)0
|b 6
|6 P:(DE-HGF)0
910 1 _ |a German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
|0 I:(DE-HGF)0
|b 6
|6 P:(DE-HGF)0
910 1 _ |a Department of Neurology, University Hospital of Marburg, Marburg, Germany
|0 I:(DE-HGF)0
|b 7
|6 P:(DE-HGF)0
910 1 _ |a Center for Mind, Brain, and Behavior-CMBB, Universities of Marburg and Gießen, Marburg, Germany
|0 I:(DE-HGF)0
|b 7
|6 P:(DE-HGF)0
910 1 _ |a Max Planck Institute for Metabolism Research, Cologne, Germany
|0 I:(DE-HGF)0
|b 8
|6 P:(DE-HGF)0
910 1 _ |a Cluster of Excellence in Cellular Stress and Aging Associated Disease (CECAD), Cologne, Germany
|0 I:(DE-HGF)0
|b 8
|6 P:(DE-HGF)0
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 9
|6 P:(DE-Juel1)177611
910 1 _ |a Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty and University Hospital Cologne, University Hospital Cologne, Cologne, Germany
|0 I:(DE-HGF)0
|b 9
|6 P:(DE-Juel1)177611
910 1 _ |a German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
|0 I:(DE-HGF)0
|b 9
|6 P:(DE-Juel1)177611
910 1 _ |a Department of Neurology, University Hospital of Marburg, Marburg, Germany
|0 I:(DE-HGF)0
|b 10
|6 P:(DE-HGF)0
910 1 _ |a Center for Mind, Brain, and Behavior-CMBB, Universities of Marburg and Gießen, Marburg, Germany
|0 I:(DE-HGF)0
|b 10
|6 P:(DE-HGF)0
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5253
|x 0
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5252
|x 1
914 1 _ |y 2022
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2021-01-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-01-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2021-01-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2021-01-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2021-01-27
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b HUM BRAIN MAPP : 2019
|d 2021-01-27
915 _ _ |a Creative Commons Attribution-NonCommercial CC BY-NC 4.0
|0 LIC:(DE-HGF)CCBYNC4
|2 HGFVOC
915 _ _ |a DEAL Wiley
|0 StatID:(DE-HGF)3001
|2 StatID
|d 2021-01-27
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2021-01-27
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-01-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2021-01-27
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2021-01-27
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2021-01-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2021-01-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2021-01-27
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2021-01-27
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2021-01-27
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-2-20090406
|k INM-2
|l Molekulare Organisation des Gehirns
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)INM-2-20090406
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21