Xenia Kobeleva

The paper uses EEG and time-resolved pattern analysis to show theta-rhythmic neural patterns predict STM error during encoding and maintenance.

This paper introduces a Bayesian nonparametric method to identify frequency-selective neural oscillatory states without predefining frequency bands or fixing the number of states.

This study models the differential effects of Amyloid-Beta and Tau on neuronal activity in Alzheimer's Disease, finding A{beta} dominance in early stages and Tau dominance in later stages.

This paper reveals the structural connectivity architecture of the human brain, showing that subcortical regions are central hubs critical for global signal propagation.

The paper evaluates a structured diagnostic framework for ultra-rare diseases, showing improved genetic diagnoses and novel gene-disease associations using exome sequencing and AI tools.

This study identifies optimal spatiotemporal scales for whole-brain dynamics by analyzing fMRI data and quantifying network activity entropy across different spatial and temporal resolutions.

The paper compares brain network efficiency in Lewy body dementia, Alzheimer’s disease, and healthy controls using EEG data, finding reduced connectivity and compensatory responses in certain frequency bands.