Fernando Maestú

This study tracks how electrophysiological functional connectivity changes during adolescence following alcohol initiation, finding divergent trajectories based on drinking severity.

The paper examines the relationship between dynamic functional connectivity measured by magnetoencephalography and plasma p-tau231 levels in cognitively intact participants, finding a positive association between frontotemporal connectivity and p-tau231.

This study uses hyperbolic neural networks to analyze MEG brain networks from the Cam-CAN dataset, revealing age-related hierarchical changes in brain subnetworks.

This study examines how Alzheimer's disease plasma markers affect multilayer centrality in healthy individuals at risk, finding increased centrality in posterior regions and hub vulnerability to p-tau231.

This study examines dynamic alpha power fluctuations, their relationship with functional connectivity, and the ability of a neural mass model to replicate these patterns using MRI and MEG data.

The paper uses unsupervised machine learning to cluster electrophysiological data from healthy teenagers and identifies patterns linked to future binge drinking behavior.

This study shows that individualized alpha frequency tACS reduces functional connectivity in the default mode network, particularly between hubs, suggesting complex interactions with cortical folding.

This study uses regression models to analyze MEG power changes across age and brain regions, revealing non-linear trajectories and associations with cognitive performance and gray matter volume.

This study uses hyperbolic graph embedding to analyze MEG brain networks in individuals with subjective cognitive decline, identifying hierarchical differences linked to cognitive impairment.

The paper predicts adolescent alcohol consumption using neuroanatomical and functional connectivity variables from MEG data in a cohort of 14-16 year olds.

This paper develops a multiscale closed-loop model of Alzheimer's disease that links proteinopathy to electrophysiological changes, revealing causal relationships between Aβ and hp-tau depositions and neural activity alterations.

This study examines dynamic functional connectivity changes across Alzheimer's disease stages using MEG, finding reduced connectivity in specific brain regions and its correlation with cognitive decline and structural brain changes.

This study investigates the role of the thalamus in resting-state functional connectivity using computational models, finding that its noisy inputs, not structural connectivity, drive cortical activity and enhance synchrony.

This study uses MEG data to show that aberrant brain criticality, measured by long-range temporal correlations, characterizes early stages of Alzheimer's disease and can classify individuals with subjective cognitive decline and mild cognitive impairment.

This paper introduces a cluster-permutation statistical method for high-dimensional MEG-based functional connectivity analysis, identifying hypersynchronization in MCI patients.

The paper develops a method using MEG data to identify mild cognitive impairment by analyzing dynamic functional connectivity microstates and achieves 85% classification accuracy.

The paper investigates how heart activity modulates brain networks in healthy individuals and Alzheimer's disease patients, finding that heart-brain interaction predicts disease progression.