Daniel J. Blackburn

This study uses cross-frequency multilayer network analysis to show increased low-frequency coupling and decreased high-frequency coupling in Alzheimer's disease compared to healthy controls, improving classification performance.

This study shows that AD astrocytes have reduced mitochondrial and glycolytic function, which can be improved by increasing hexokinase 1 expression.

This study evaluates graph neural networks for Alzheimer's disease classification using EEG-based functional connectivity measures, finding that GNNs outperform baselines but no single FC measure consistently dominates.

This paper introduces a kernel-based nonlinear manifold learning method for EEG channel selection to distinguish Alzheimer's disease patients from healthy controls.