Neurodevelopmental disorders in children: the role of MRI in early detection and intervention planning
PMC13013303
· 10.3389/fnins.2026.1758568
Gap Declaration
Future directions Looking forward, several emerging trends are expected to significantly advance the application of MRI in early detection and personalized intervention for pediatric NDDs. Particularly, artificial intelligence (AI) and deep learning are anticipated to markedly enhance the sensitivity and scalability of neurodevelopmental MRI. Recent investigation demonstrates that convolutional neural networks, auto encoders, and generative adversarial networks applied to pediatric structural and functional MRI are already achieving high accuracy. [...] This enables precision medicine in a way that aligns neurobiological phenotypes with therapy. Emerging studies of neuroimaging biomarkers in ASD and ADHD suggest that this stratification is within scope, particularly with advances in trans diagnostic and dimensionally informed models. Future directions in MRI driven for NDDs are shown in Figure 2. Finally, the convergence of multimodal biomarkers, combining genomics, imaging, and behavioral data, represents a major frontier. Multimodal MRI studies of ASD already integrate diffusion, structural, and perfusion measures, but coupling these with genetic information and longitudinal behavioral profiles could dramatically improve early risk models.
Abstract
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 s…
Conclusions / Discussion
Future directions Looking forward, several emerging trends are expected to significantly advance the application of MRI in early detection and personalized intervention for pediatric NDDs. Particularly, artificial intelligence (AI) and deep learning are anticipated to markedly enhance the sensitivity and scalability of neurodevelopmental MRI. Recent investigation demonstrates that convolutional neural networks, auto encoders, and generative adversarial networks applied to pediatric structural and functional MRI are already achieving high accuracy. Hu and coworkers demonstrated how deep learning architectures are being used to automate feature extraction, enabling end-to-end learning on multimodal pediatric MRI data. Song and colleagues emphasized that DL models can integrate complex imaging features for early diagnosis of autism and ADHD. It overcomes the limitations of handcrafted feature extraction. Additionally, CNN based models applied to resting-state fMRI in young children have produced near perfect classification of ASD vs. controls, illustrating the power of AI to detect subtle functional connectivity patterns. Development of portable and fast MRI techniques has significant…
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Structural Hole
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Technique originates in neuroscience; functional analogues in psychology, criminal justice literature are absent.
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