RT info:eu-repo/semantics/article
T1 Effect of MAPT gene variations on the brain electrical activity: A multiplex network study
A1 Maturana Candelas, Aaron
A1 Hornero Sánchez, Roberto
A1 Poza Crespo, Jesús
A1 Rodríguez González, Víctor
A1 Gutierrez De Pablo, Victor
A1 Pinto, Nádia
A1 Rebelo, Miguel Ângelo
A1 Gómez Peña, Carlos
K1 Alzheimer’s disease
K1 Electroencephalogram
K1 Genetics
K1 Tau
K1 Brain connectivity
K1 Microtubule-associated protein tau (MAPT )
K1 32 Ciencias Médicas
K1 33 Ciencias Tecnológicas
AB The aim of this study is to examine how variations in the microtubule-associated protein tau (MAPT ) geneaffect the brain functional network. For this purpose, resting-state electroencephalogram (EEG) data from155 participants were acquired. This database included healthy controls and Alzheimer’s disease patientscarrying seven MAPT alleles associated with risk or protective effects against neuropathologies or abnormaltau levels. To assess the impact of each genotype on brain function, a multiplex network analysis quantified theconnectivity contribution of each brain region across multiple EEG frequency bands (delta, theta, alpha, andbeta). To this end, brain functional connectivity was first calculated for each brain region and frequency bandusing the phase lag index (PLI) parameter. The PLI adjacency matrices in each frequency band corresponded tothe layers conforming the multiplex network. Subsequently, the participation coefficient (P) was computed ineach brain region to reflect node degree diversification among frequency bands. Carriers of risk and protectivealleles exhibited distinct values of P, especially in the left default mode network in healthy controls. In addition,carriers of the risk alleles generally presented higher network disruptions. Finally, significant differences innode degree values were observed across SNPs in the theta and beta frequency bands. These results suggestthat different MAPT variants may lead to diverse tau species that influence brain function, particularly in brainregions involved in information flow management in preclinical states. These insights may help understandingnetwork disturbances caused by molecular factors.
PB Elsevier
SN 1746-8094
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/78211
UL https://uvadoc.uva.es/handle/10324/78211
LA eng
NO Biomedical Signal Processing and Control, 2025, vol. 110, p. 108129
NO Producción Científica
DS UVaDOC
RD 02-oct-2025