Publications

This publication examines how multimodal computational biomarkers can improve the early detection of neurodegenerative change before clear clinical symptoms appear. The work synthesizes findings from neuroimaging, cognitive assessment, and molecular profiling to describe how machine-learning models identify subtle patterns associated with disease progression. Emphasis is placed on reproducibility, transparent feature selection, and the clinical limits of predictive algorithms in longitudinal patient cohorts.

This publication explores the use of artificial intelligence for high-precision segmentation in biomedical imaging. It reviews convolutional and transformer-based architectures applied to magnetic resonance, computed tomography, and microscopy datasets, with attention to annotation quality, model validation, and domain shift. The article argues that reliable clinical adoption requires interpretable outputs, standardized benchmarks, and prospective evaluation across diverse patient populations.

This publication analyzes current evidence linking neuroimmune signaling to cognitive resilience in aging and neurological disease. It discusses microglial activation, cytokine dynamics, blood-brain barrier integrity, and their relationship to synaptic maintenance. The review highlights how controlled inflammatory responses may support repair mechanisms, while chronic dysregulation can accelerate cognitive decline and complicate therapeutic intervention.

This publication presents graph-theoretical approaches for studying brain network plasticity across development, learning, injury, and rehabilitation. It explains how measures such as modularity, centrality, efficiency, and hub disruption can reveal large-scale changes in neural organization. The work emphasizes the importance of longitudinal data, multimodal validation, and cautious interpretation when translating network metrics into biological mechanisms.