Computational approaches to neurodegenerative diseases

Neurodegenerative diseases—such as Alzheimer’s, Parkinson’s, Huntington’s, and ALS—are progressive disorders characterized by the loss of structure and function in neurons. Despite extensive research, the underlying mechanisms driving these diseases remain poorly understood, and effective treatments are limited. Computational neuroscience offers powerful tools to model disease progression, simulate neuronal dysfunction, and integrate complex datasets across molecular, cellular, and systems levels. By combining mathematical modeling, machine learning, and large-scale brain simulations, researchers can uncover patterns in disease biomarkers, predict progression, and explore potential therapeutic strategies. Recent advances in neuroimaging, genomics, and high-throughput data collection have further enhanced the potential for computational models to inform early diagnosis and targeted interventions.

Neurodegenerative diseases pose a growing global health challenge, yet their underlying mechanisms and progression remain poorly understood. The complexity of these disorders—marked by multifactorial causes, variable symptoms, and progressive neural deterioration—makes early diagnosis and effective treatment particularly difficult. Traditional experimental approaches, while valuable, often fall short in capturing the full scope of disease dynamics across scales and time. Computational modeling offers a promising path forward by enabling the simulation of disease mechanisms, integration of diverse datasets, and prediction of progression patterns. Recent advances in neuroimaging, machine learning, network analysis, and high-throughput genomics have expanded the ability to build detailed, data-driven models of neurodegenerative processes. These tools can help identify biomarkers, simulate therapeutic interventions, and personalize treatment strategies.

This Research Topic seeks to address these challenges by highlighting computational approaches that model, analyze, or predict aspects of neurodegenerative diseases. We welcome studies that integrate experimental data with simulations, develop diagnostic tools, explore mechanistic hypotheses, or propose computational frameworks for therapeutic discovery. The goal is to accelerate progress toward understanding and managing neurodegenerative conditions through innovative computational solutions.

This Research Topic welcomes papers focused on computational approaches to neurodegenerative diseases. Key themes include modeling disease progression, identifying biomarkers, simulating therapies, and analyzing brain network changes. We encourage studies using machine learning, mathematical modeling, or data integration from neuroimaging, genomics, and clinical sources. Interdisciplinary work linking experimental data with computational tools or supporting personalized medicine is especially welcome. The goal is to highlight innovative methods that advance understanding, diagnosis, and treatment of neurodegenerative disorders.