000272081 001__ 272081
000272081 005__ 20240929004848.0
000272081 0247_ $$2doi$$a10.1002/gps.6138
000272081 0247_ $$2pmid$$apmid:39261275
000272081 0247_ $$2ISSN$$a0885-6230
000272081 0247_ $$2ISSN$$a1099-1166
000272081 0247_ $$2altmetric$$aaltmetric:167289347
000272081 037__ $$aDZNE-2024-01124
000272081 041__ $$aEnglish
000272081 082__ $$a610
000272081 1001_ $$00000-0002-0989-1175$$aHasoon, Jahfer$$b0
000272081 245__ $$aEEG Functional Connectivity Differences Predict Future Conversion to Dementia in Mild Cognitive Impairment With Lewy Body or Alzheimer Disease.
000272081 260__ $$aChichester [u.a.]$$bWiley$$c2024
000272081 3367_ $$2DRIVER$$aarticle
000272081 3367_ $$2DataCite$$aOutput Types/Journal article
000272081 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1727080927_8193
000272081 3367_ $$2BibTeX$$aARTICLE
000272081 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000272081 3367_ $$00$$2EndNote$$aJournal Article
000272081 520__ $$aPredicting which individuals may convert to dementia from mild cognitive impairment (MCI) remains difficult in clinical practice. Electroencephalography (EEG) is a widely available investigation but there is limited research exploring EEG connectivity differences in patients with MCI who convert to dementia.Participants with a diagnosis of MCI due to Alzheimer's disease (MCI-AD) or Lewy body disease (MCI-LB) underwent resting state EEG recording. They were followed up annually with a review of the clinical diagnosis (n = 66). Participants with a diagnosis of dementia at year 1 or year 2 follow up were classed as converters (n = 23) and those with a diagnosis of MCI at year 2 were classed as stable (n = 43). We used phase lag index (PLI) to estimate functional connectivity as well as analysing dominant frequency (DF) and relative band power. The Network-based statistic (NBS) toolbox was used to assess differences in network topology.The converting group had reduced DF (U = 285.5, p = 0.005) and increased relative pre-alpha power (U = 702, p = 0.005) consistent with previous findings. PLI showed reduced average beta band synchrony in the converting group (U = 311, p = 0.014) as well as significant differences in alpha and beta network topology. Logistic regression models using regional beta PLI values revealed that right central to right lateral (Sens = 56.5%, Spec = 86.0%, -2LL = 72.48, p = 0.017) and left central to right lateral (Sens = 47.8%, Spec = 81.4%, -2LL = 71.37, p = 0.012) had the best classification accuracy and fit when adjusted for age and MMSE score.Patients with MCI who convert to dementia have significant differences in EEG frequency, average connectivity and network topology prior to the onset of dementia. The MCI group is clinically heterogeneous and have underlying physiological differences that may be driving the progression of cognitive symptoms. EEG connectivity could be useful to predict which patients with MCI-AD and MCI-LB convert to dementia, regardless of the neurodegenerative aetiology.
000272081 536__ $$0G:(DE-HGF)POF4-353$$a353 - Clinical and Health Care Research (POF4-353)$$cPOF4-353$$fPOF IV$$x0
000272081 588__ $$aDataset connected to CrossRef, PubMed, , Journals: pub.dzne.de
000272081 650_7 $$2Other$$aLewy body disease
000272081 650_7 $$2Other$$acognitive dysfunction
000272081 650_7 $$2Other$$adementia
000272081 650_7 $$2Other$$adisease progression
000272081 650_7 $$2Other$$aelectroencephalography
000272081 650_7 $$2Other$$afunctional connectivity
000272081 650_7 $$2Other$$aprediction
000272081 650_2 $$2MeSH$$aHumans
000272081 650_2 $$2MeSH$$aCognitive Dysfunction: physiopathology
000272081 650_2 $$2MeSH$$aCognitive Dysfunction: etiology
000272081 650_2 $$2MeSH$$aLewy Body Disease: physiopathology
000272081 650_2 $$2MeSH$$aFemale
000272081 650_2 $$2MeSH$$aAlzheimer Disease: physiopathology
000272081 650_2 $$2MeSH$$aElectroencephalography: methods
000272081 650_2 $$2MeSH$$aMale
000272081 650_2 $$2MeSH$$aAged
000272081 650_2 $$2MeSH$$aDisease Progression
000272081 650_2 $$2MeSH$$aAged, 80 and over
000272081 7001_ $$00000-0002-9812-3150$$aHamilton, Calum A$$b1
000272081 7001_ $$0P:(DE-2719)9002248$$aSchumacher, Julia$$b2$$udzne
000272081 7001_ $$aColloby, Sean$$b3
000272081 7001_ $$aDonaghy, Paul C$$b4
000272081 7001_ $$aThomas, Alan J$$b5
000272081 7001_ $$aTaylor, John-Paul$$b6
000272081 773__ $$0PERI:(DE-600)1500455-7$$a10.1002/gps.6138$$gVol. 39, no. 9, p. e6138$$n9$$pe6138$$tInternational journal of geriatric psychiatry$$v39$$x0885-6230$$y2024
000272081 8564_ $$uhttps://pub.dzne.de/record/272081/files/DZNE-2024-01124.pdf$$yOpenAccess
000272081 8564_ $$uhttps://pub.dzne.de/record/272081/files/DZNE-2024-01124.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000272081 909CO $$ooai:pub.dzne.de:272081$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000272081 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)9002248$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b2$$kDZNE
000272081 9131_ $$0G:(DE-HGF)POF4-353$$1G:(DE-HGF)POF4-350$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lNeurodegenerative Diseases$$vClinical and Health Care Research$$x0
000272081 9141_ $$y2024
000272081 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000272081 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-08-26
000272081 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2023-08-26
000272081 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2023-08-26
000272081 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2023-08-26
000272081 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2023-08-26
000272081 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bINT J GERIATR PSYCH : 2022$$d2023-08-26
000272081 915__ $$0StatID:(DE-HGF)1180$$2StatID$$aDBCoverage$$bCurrent Contents - Social and Behavioral Sciences$$d2023-08-26
000272081 915__ $$0StatID:(DE-HGF)3001$$2StatID$$aDEAL Wiley$$d2023-08-26$$wger
000272081 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2023-08-26
000272081 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-08-26
000272081 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2023-08-26
000272081 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000272081 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2023-08-26
000272081 915__ $$0StatID:(DE-HGF)0130$$2StatID$$aDBCoverage$$bSocial Sciences Citation Index$$d2023-08-26
000272081 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-08-26
000272081 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine$$d2023-08-26
000272081 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2023-08-26$$wger
000272081 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-08-26
000272081 9201_ $$0I:(DE-2719)5000014$$kAG Storch$$lNon-Motor Symptoms in Parkinson's disease$$x0
000272081 980__ $$ajournal
000272081 980__ $$aVDB
000272081 980__ $$aUNRESTRICTED
000272081 980__ $$aI:(DE-2719)5000014
000272081 9801_ $$aFullTexts