A group of diseases caused by disruptions in early brain maturation is collectively known as neurodevelopmental disorders (NDDs). These are characterized by persistent deficits in cognition, behavior, social or motor functioning. The heightened neuroplasticity could be modulated by appropriate intervention during early childhood. Therefore, early detection of NDDs is critical to improve long term developmental outcomes. However, conventional and behavioral studies are insufficient to detect the subtle early alterations, causing diagnostic delays. So, for NDDs, magnetic resonance imaging (MRI) serves as a critical tool for elucidating neurochemical, microstructural, and functional abnormalities. It has the potential to detect the alterations associated with different NDDs including autism spectrum disorder, attention deficit/hyperactivity disorder, genetic/metabolic syndromes, cerebral palsy, and developmental delay. Multiple modalities of MRI such as diffusion imaging, quantitative MRI, resting state functional MRI, and spectroscopy are applied for these disorders. Collectively, these MRI modalities, machine learning and integrative genomic approaches offer promising biomarkers for early detection and risk stratification of NDDs. This review highlights the current evidence on the bases of pediatric MRI approaches, early predictive biomarkers, disease specific findings, and translational applications.
Hua et al. (Wed,) studied this question.