Advances in Computational Modelling of Neurological Diseases

Computational modelling has driven significant advances in our understanding of the complexity of the human nervous system, enabling researchers to capture and explore interactions from the single-cell level to large-scale neuronal networks and behavior. These developments have provided mechanistic insights into nervous system function. In parallel, the advent of modern machine learning and data-driven methods promises to extract subtle relationships from vast neuro-behavioral datasets. However, there remains a scarcity of computational models specifically addressing neurological diseases, limiting our understanding of the underlying pathophysiology and the development of effective, individualized interventions.This Research Topic seeks to spotlight recent advances in computational modeling of the nervous system under pathological conditions. By integrating mechanistic and data-driven methods, we aim to deepen understanding of neurological disease mechanisms and translate this knowledge into improved diagnostic and therapeutic strategies. The focus is on models that investigate dysfunction across nervous system levels, reveal latent pathophysiological mechanisms from patient data, and simulate the effects of interventions.To further this field, we welcome high-quality contributions on topics including, but not limited to:- Biophysical and neuronal network models of neurological disease mechanisms.- Simulation studies of disease progression or intervention outcomes.- Machine learning for the discovery of latent patterns and/or pathophysiological mechanisms in clinical or neuro-behavioral data.- Predictive models for clinical prognosis or treatment response.- Personalised and patient-specific models (e.g., 'digital twins') for capturing individual disease progression and/or targeted therapy.- Integration of computational models with molecular, neuroimaging, electrophysiological, and behavioral data.- Methodological advances and novel computational neuroscience/physiological approaches.Given the complex nature of modelling neurological disease, we strongly encourage contributions from interdisciplinary teams. We believe that fostering collaborative work between computational neuroscientists, clinicians, systems biologists, data scientists, and engineers is essential for translating foundational research into meaningful clinical applications. Topic editors: Leonardo Abdala Elias, Francesco Negro, Renan Cipriano Moioli, Andre Fabio Kohn Source: https://www.frontiersin.org/research-topics/71934/advances-in-computational-modelling-of-neurological-diseases