Artificial Intelligence in Neuroscience: Comparative and Developmental Approaches in Brain Disorders, Neuro-Oncology, and Drug Exposure Studies

Artificial Intelligence (AI) is rapidly transforming biomedical research and clinical neuroscience by enabling the analysis of increasingly complex and multidimensional datasets. In the field of central nervous system (CNS) disorders, AI-based approaches such as machine learning, deep learning, and computational modeling are expanding our ability to interpret neuroimaging, molecular, electrophysiological, and behavioral data that often exceed the limits of traditional analytical methods. These technologies are being applied across a wide spectrum of conditions, including neurodevelopmental, neurodegenerative, neuroinflammatory, neuro-oncological, and neurotoxicological disorders. By integrating heterogeneous datasets across biological scales, AI is fostering a more comprehensive understanding of brain development, structure, and function in both physiological and pathological contexts, ultimately supporting more accurate diagnosis, prognosis, and therapeutic innovation.The main goal of this Research Topic is to address the growing need for integrative and AI-driven approaches capable of improving the understanding of CNS disorders across developmental and disease-related contexts. Despite significant advances in neuroscience, major challenges remain in deciphering the complexity of brain organization, disease heterogeneity, and the dynamic processes underlying neurodevelopment and neuropathology. AI offers a powerful opportunity to overcome these limitations by enabling the integration of multimodal datasets, improving pattern recognition, and supporting predictive modeling of disease onset, progression, and therapeutic response. This Research Topic specifically aims to bridge gaps between experimental neuroscience, clinical research, and computational sciences, promoting comparative approaches that include both developmental and pathological perspectives. The ultimate objective is to foster translational applications that can improve diagnostic accuracy, enable earlier intervention strategies, and support the development of personalized therapies for CNS disorders.This Research Topic welcomes contributions focused on the application of Artificial Intelligence in central nervous system research, with a particular emphasis on comparative neuroscience and neurodevelopmental processes, alongside neurological, psychiatric, neuro-oncological, and neurotoxicological conditions. We encourage studies integrating AI with multimodal data, including neuroimaging, genomics, transcriptomics, proteomics, electrophysiology, and behavioral analyses, as well as work addressing developmental trajectories and disease-related alterations. Submissions exploring predictive modeling of brain disorders, biomarker discovery, and AI-driven therapeutic strategies are especially encouraged. We also welcome methodological papers, computational pipelines, and translational studies bridging preclinical and clinical research. Accepted article types include Original Research, Methods, Reviews, Mini Reviews, Systematic Reviews, Perspectives, Opinion Articles, and Brief Reports. Interdisciplinary and comparative approaches are strongly encouraged to highlight both developmental and disease-related CNS mechanisms. Topic editors: Fabrizio De Luca, Elisa Roda, Maria Grazia Bottone Source: https://www.frontiersin.org/research-topics/80951/artificial-intelligence-in-neuroscience-comparative-and-developmental-approaches-in-brain-disorders-neuro-oncology-and-drug-exposure-studies