Journal Article

DZNE-2024-01124

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png EEG Functional Connectivity Differences Predict Future Conversion to Dementia in Mild Cognitive Impairment With Lewy Body or Alzheimer Disease.

Hasoon, J. ; Hamilton, C. A. ; Schumacher, J.DZNE* ; Colloby, S. ; Donaghy, P. C. ; Thomas, A. J. ; Taylor, J.-P.

2024
Wiley Chichester [u.a.]

This record in other databases:    

Please use a persistent id in citations: doi:10.1002/gps.6138

Abstract: Predicting 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.

Keyword(s): Humans (MeSH) ; Cognitive Dysfunction: physiopathology (MeSH) ; Cognitive Dysfunction: etiology (MeSH) ; Lewy Body Disease: physiopathology (MeSH) ; Female (MeSH) ; Alzheimer Disease: physiopathology (MeSH) ; Electroencephalography: methods (MeSH) ; Male (MeSH) ; Aged (MeSH) ; Disease Progression (MeSH) ; Aged, 80 and over (MeSH) ; Lewy body disease ; cognitive dysfunction ; dementia ; disease progression ; electroencephalography ; functional connectivity ; prediction

---

Contributing Institute(s):

1. Non-Motor Symptoms in Parkinson's disease (AG Storch)

Research Program(s):

1. 353 - Clinical and Health Care Research (POF4-353) (POF4-353)

Appears in the scientific report 2024

---

Database coverage:
; ; ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; Current Contents - Social and Behavioral Sciences ; DEAL Wiley ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Social Sciences Citation Index ; Web of Science Core Collection

---