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This review synthesizes recent deep learning methodologies applied to infant functional neuroimaging, analyzing how neural architectures address domain-specific challenges like severe motion artifacts, short scan durations, and rapid non-linear brain maturation. Mapping the nascent functional connectome is critical for detecting early neurodevelopmental divergence, yet conventional fMRI pipelines fail to reliably model such high-dimensional, non-stationary temporal data. The authors show that modern deep representation learning is driving a paradigm shift away from descriptive group-level averages toward predictive, individualized developmental trajectory forecasting and biologically grounded biomarker discovery.
Infant fMRI has historically been crippled by severe motion artifacts and non-stationary maturation, but modern deep representation learning is turning these chaotic, short-duration scans into predictive, individual-level developmental trajectories.
Infancy is a critical developmental window characterized by rapid functional brain reorganization, during which large-scale networks emerge, individualized connectome signatures continue to form, and early deviations may shape long-term cognitive and clinical outcomes. Functional MRI (fMRI) offers an opportunity to study these processes in vivo, yet extracting developmentally meaningful information from it remains challenging due to comparatively short scan duration, structured motion artifacts, variable scan states, and rapid brain maturation. Amid these challenges, deep learning has expanded the capacity of computational neuroimaging by learning robust representations from noisy, high-dimensional data, integrating complex spatial and temporal information, and capturing the nonlinear and rapidly evolving organization of the developing brain. Here, we review recent advances in deep learning for infant functional neuroimaging, synthesizing progress across input representation formatting, population and individualized brain mapping, longitudinal trajectory forecasting, robust and explainable model evaluation, and biological translation. Collectively, these methodological advances mark a paradigm shift in infant functional neuroimaging from descriptive, group-level analyses toward reliable, individualized, and developmentally grounded models. Future progress will depend on larger and more diverse longitudinal datasets, developmentally appropriate model designs, rigorous and standardized evaluation, and integration of computational predictions with biological mechanisms towards clinically meaningful outcomes. Addressing these priorities will help establish deep learning as a robust framework for understanding early functional brain development, identifying developmental variation at the individual level, and ultimately supporting earlier and precise assessment of neurodevelopmental risk.