EEG-Based Functional Brain Network Analyses in Patients with Atrial Fibrillation

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Yılmaz Dere Z. B., Toplutaş E., Uzer G., Turhal E., Olgun F. E., Kılıçaslan F., ...Daha Fazla

17th International Symposium of Clinical and Applied Anatomy, Vienna, Avusturya, 3 - 06 Eylül 2026, cilt.267, sa.152887, ss.24-25, (Özet Bildiri)

Özet

Objective: Atrial fibrillation (AF) has been associated with

cognitive decline and brain structural changes even in the absence

ofclinicalstroke,yettheunderlyingneurophysiologicalmechanisms

remain incompletely understood. This study aimed to investigate

resting-state brain functional network dynamics in stroke-free AF

patientsusingEEGmicrostateanalysis.

MethodsandMaterials:This prospective study enrolled 10 AF

patients without prior stroke and 11 age-, sex-, education- and

cognitive-matched healthy controls. Cognitive status was screened

with the Mini-Mental State Examination (MMSE); all participants

scored 24 or above, confirming the absence of clinically manifest

cognitiveimpairment.Leftatrialdiameterwasassessedbyechocar-

diography. Eyes-closed resting-state EEG was recorded using a 64-

channelhigh-densitysystem.Fourcanonicalmicrostates(A,B,C,D)

were identified, and microstate parameters (duration, occurrence,

coverage, explained variance) and transition probabilities were

analyzed.

Results: MMSE scores did not differ significantly between

groups. AF patients showed significant decreases in occurrence,

coverage, and explained variance of Microstate B, associated with

the visual processing network, while Microstate C, associated with

thedefaultmodeandsaliencenetworks,demonstratedsignificantly

prolonged duration. Transition dynamics revealed increased D-to-C

transitions in the AF group, whereas C-to-B and D-to-A transitions

decreased. Left atrial diameter strongly correlated with reduced

MicrostateBcoverageanddecreasedC-to-Btransitionprobability.

Conclusion:These findings indicate that subclinical alterations

in brain functional network dynamics occur in AF patients before

overt cognitive impairment emerges,and areassociated with struc-

tural cardiac remodeling. EEG microstate analysis may serve as a

potentialtoolforevaluatingneurocognitiveinvolvementofAF.

Keywords:Atrialfibrillation,EEGmicrostateanalysis,functional

brainnetworks.

Fundings sources: No financial support was received for this

study.

Disclosurestatement:Theauthorshavenoconflictofinterestto

declare.

References:

1. Da Silva RMFL, Miranda CM, Liu T, Tse G, Roever L. Atrial

fibrillation and risk of dementia: Epidemiology, mechanisms,

andeffectofanticoagulation.FrontNeurosci.2019;13(JAN).

2. Madhavan M, Graff-Radford J, Piccini JP, Gersh BJ. Cognitive

dysfunction in atrial fibrillation. Vol. 15, Nature Reviews

Cardiology.2018.

3. Santangeli P, Di Biase L, Bai R, Mohanty S, Pump A, Cereceda

Brantes M, et al. Atrial fibrillation and the risk of incident

dementia: A meta-analysis. Heart Rhythm. 2012;9(11).

doi:10.1016/j.hrthm.2012.07.026

4. De Bruijn RFAG, Heeringa J, Wolters FJ, Franco OH, Stricker

BHC, Hofman A, et al. Association between atrial fibrillation

anddementiainthegeneralpopulation.JAMANeurol.2015;72

(11).

5. Silva DS, Avelar WM, De Campos BM, Lino APBL, Balthazar

MLF,FigueiredoMJO,etal.DefaultModeNetworkDisruption

inStroke-FreePatientswithAtrialFibrillation.Cerebrovascular

Diseases.2018;45(1–2).

6. Silva DS, Caseli BG, de Campos BM, Avelar WM, Lino APBL,

BalthazarMLF,etal.CerebralStructureandFunctioninStroke-

free Patients with Atrial Fibrillation. Journal of Stroke and

CerebrovascularDiseases.2021;30(8).

7. BaoMQ,CaoL,ChenYN,GaoGL,LuZY,WangJ,etal.Resting-

stateEEGalterationsandcognitiveimpairmentinatrialfibrilla-

tion:insightsintoneuralbiomarkersandfunctionalconnectiv-

ity.FrontNeurol.2025;16.doi:10.3389/fneur.2025.1583715

8. Custo A, Van De Ville D, Wells WM, Tomescu MI, Brunet D,

Michel CM. Electroencephalographic Resting-State Networks:

SourceLocalizationofMicrostates.BrainConnect.2017;7(10).

doi:10.1089/brain.2016.0476

9. Nagabhushan Kalburgi, S., Kleinert, T., Aryan, D., Nash, K.,

Schiller, B., & Koenig, T. (2024). MICROSTATELAB: the

EEGLAB Toolbox for resting-state Microstate Analysis. Brain

topography,37(4),621-645.

10. Britz J, Van De Ville D, Michel CM. BOLD correlates of EEG

topography reveal rapid resting-state network dynamics.