000908569 001__ 908569
000908569 005__ 20220708190359.0
000908569 0247_ $$2doi$$a10.1016/j.jalz.2017.06.1017
000908569 0247_ $$2ISSN$$a1552-5260
000908569 0247_ $$2ISSN$$a1552-5279
000908569 037__ $$aFZJ-2022-02693
000908569 082__ $$a610
000908569 1001_ $$0P:(DE-Juel1)162382$$aDronse, Julian$$b0
000908569 1112_ $$aThe Alzheimer’s Association International Conference (AAIC)$$cLondon$$d2017-07-15 - 2017-07-20$$wUK
000908569 245__ $$a[P2–362]: DIFFERENTIAL EFFECT OF GLUCOSE METABOLISM AND INTRINSIC FUNCTIONAL CONNECTIVITY ON MEMORY PERFORMANCE OVER THE SPECTRUM OF ALZHEIMER'S DISEASE
000908569 260__ $$c2017
000908569 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1657288772_31954
000908569 3367_ $$033$$2EndNote$$aConference Paper
000908569 3367_ $$2BibTeX$$aINPROCEEDINGS
000908569 3367_ $$2DRIVER$$aconferenceObject
000908569 3367_ $$2DataCite$$aOutput Types/Conference Abstract
000908569 3367_ $$2ORCID$$aOTHER
000908569 520__ $$aBackgroundWhile alterations in glucose metabolism are a well-established feature of Alzheimer's disease and linked to cognitive decline, aberrant patterns of spontaneous neural activity at rest are increasingly recognized as a characteristic of the disorder and are also evident in preclinical stages. The regional interrelationship of glucose consumption and resting-state activity and the differential contributions of these measures to memory function are still not well understood. The aim of the present study was to characterize this relationship and to assess the individual effects of the two modalities on memory function.MethodsPatients with subjective memory complaints (n=11), mild cognitive impairment (MCI) due to Alzheimer's disease (n=9), and early stage Alzheimer's dementia (n=10) were included in the analysis. We simultaneously acquired resting-state functional MRI (rs-fMRI) and [18F]fluorodeoxyglucose (FDG) PET data using a hybrid PET-MRI scanner. Independent component analysis was used to decompose rs-fMRI data into 75 spatially independent components of temporally synchronized neural activity. Using a combination of automated methods, we selected two default mode network components for the subsequent analysis. We performed voxel-wise regression analysis of intrinsic network connectivity and [18F]FDG uptake in the selected networks, correcting for voxelwise effects of gray matter volume. Mean values for both modalities were extracted from brain regions showing a significant effect of glucose consumption on intrinsic functional connectivity (p < 0.05 FWE-corrected) and entered into multiple regression models to estimate their effect on verbal memory performance (delayed recall of Logical Memory).ResultsWithin the whole group, glucose uptake was significantly positively correlated with intrinsic connectivity in the ventral default mode network. Crucially, intrinsic connectivity but not glucose uptake predicted memory performance in patients with Alzheimer's disease (in the combined group of MCI and early stage dementia patients, as well in the early stage dementia group only).ConclusionsWhile glucose metabolism and intrinsic functional connectivity of resting state networks are closely interrelated, the disruption of functional connectivity in the default mode network better predicts memory performance. These results contribute to the development of rs-fMRI changes as a diagnostic marker and potential therapeutic target for Alzheimer's disease.
000908569 536__ $$0G:(DE-HGF)POF4-5253$$a5253 - Neuroimaging (POF4-525)$$cPOF4-525$$fPOF IV$$x0
000908569 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
000908569 7001_ $$0P:(DE-Juel1)136676$$aDillen, Kim N. H.$$b1
000908569 7001_ $$0P:(DE-Juel1)144971$$aJacobs, Heidi I. L.$$b2
000908569 7001_ $$0P:(DE-Juel1)156372$$aReutern, Boris$$b3
000908569 7001_ $$aRichter, Nils$$b4
000908569 7001_ $$0P:(DE-HGF)0$$aOnur, Oezguer A.$$b5
000908569 7001_ $$0P:(DE-Juel1)131627$$aStoffels, Gabriele$$b6$$ufzj
000908569 7001_ $$0P:(DE-HGF)0$$aKops, Elena Rota$$b7
000908569 7001_ $$0P:(DE-Juel1)131797$$aTellmann, Lutz$$b8$$ufzj
000908569 7001_ $$0P:(DE-Juel1)131794$$aShah, N. Jon$$b9$$ufzj
000908569 7001_ $$0P:(DE-Juel1)131777$$aLangen, Karl-Josef$$b10$$ufzj
000908569 7001_ $$0P:(DE-Juel1)131720$$aFink, Gereon R.$$b11$$ufzj
000908569 7001_ $$0P:(DE-Juel1)131730$$aKukolja, Juraj$$b12
000908569 773__ $$0PERI:(DE-600)2201940-6$$a10.1016/j.jalz.2017.06.1017$$gVol. 13, no. 7S_Part_15$$x1552-5279$$y2017
000908569 909CO $$ooai:juser.fz-juelich.de:908569$$pVDB
000908569 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131627$$aForschungszentrum Jülich$$b6$$kFZJ
000908569 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131797$$aForschungszentrum Jülich$$b8$$kFZJ
000908569 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131794$$aForschungszentrum Jülich$$b9$$kFZJ
000908569 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131777$$aForschungszentrum Jülich$$b10$$kFZJ
000908569 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131720$$aForschungszentrum Jülich$$b11$$kFZJ
000908569 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5253$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
000908569 915__ $$0StatID:(DE-HGF)3001$$2StatID$$aDEAL Wiley$$d2021-01-28$$wger
000908569 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2021-01-28
000908569 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2021-01-28
000908569 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2021-01-28
000908569 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-01-28
000908569 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine$$d2021-01-28
000908569 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-01-28
000908569 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2021-01-28
000908569 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bALZHEIMERS DEMENT : 2019$$d2021-01-28
000908569 915__ $$0StatID:(DE-HGF)9915$$2StatID$$aIF >= 15$$bALZHEIMERS DEMENT : 2019$$d2021-01-28
000908569 9201_ $$0I:(DE-Juel1)INM-4-20090406$$kINM-4$$lPhysik der Medizinischen Bildgebung$$x0
000908569 9201_ $$0I:(DE-Juel1)INM-11-20170113$$kINM-11$$lJara-Institut Quantum Information$$x1
000908569 9201_ $$0I:(DE-Juel1)INM-3-20090406$$kINM-3$$lKognitive Neurowissenschaften$$x2
000908569 9201_ $$0I:(DE-Juel1)VDB1046$$kJARA-BRAIN$$lJülich-Aachen Research Alliance - Translational Brain Medicine$$x3
000908569 980__ $$aabstract
000908569 980__ $$aVDB
000908569 980__ $$aI:(DE-Juel1)INM-4-20090406
000908569 980__ $$aI:(DE-Juel1)INM-11-20170113
000908569 980__ $$aI:(DE-Juel1)INM-3-20090406
000908569 980__ $$aI:(DE-Juel1)VDB1046
000908569 980__ $$aUNRESTRICTED