Ivan Maximov

Professor Ivan Maximov serves in the Department of health and functioning at Western Norway University of Applied Sciences, where he holds the position of Professor with research focus on advanced neuroimaging techniques. His work bridges clinical applications and computational methodology development, particularly in diffusion MRI and brain aging research.

His primary research interests include Diffusion MRI modeling, Machine Learning applications in neuroimaging, and Mathematical modeling of brain microstructure. He investigates ultra high field MRI techniques, optimal control methods for imaging, and imaging genetics approaches to understand brain-behavior relationships. His work spans multiple neurological and psychiatric conditions including multiple sclerosis, depression, psychosis, and cognitive aging.

Analysis of his recent publications reveals a strong emphasis on longitudinal brain microstructure changes, with particular attention to white matter integrity across the lifespan. His work frequently integrates polygenic risk scores with neuroimaging data, and he has made significant contributions to methodological improvements in diffusion MRI analysis, including version control considerations for brain age prediction algorithms.

Professor Maximov actively contributes to the NORMENT OUS research group and teaches advanced courses including RAD210 (Magnetic Resonance Physical Principles), RAD230 (Medical Image Processing and AI), and RAD390 (Bachelor's Thesis supervision). His teaching emphasizes the intersection of radiography, artificial intelligence, and medical innovation.

His research involves collaborations with major international consortia including UK Biobank and ENIGMA, with particular focus on large-scale diffusion MRI analysis. Current projects examine sex-specific brain changes, obesity-brain relationships, and transdiagnostic white matter alterations in psychiatric disorders. He leads methodological developments in diffusion kurtosis imaging and optimal control for MRI pulse sequence design.