婴儿功能神经影像中的深度学习：挑战、进展与未来方向
Deep Learning in Infant Functional Neuroimaging: Challenges, Advances, and Future Directions
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中文总结 AI 辅助
本文综述深度学习在婴儿功能神经影像中的应用，涵盖方法进展与挑战，旨在推动从群体描述向个体化、发展导向的模型转变，以支持早期神经发育风险评估。
中文摘要 AI 辅助
婴儿期是一个关键的发展窗口，其特征是功能性大脑的快速重组，在此期间，大规模网络逐渐形成，个体化的连接组特征持续成形，早期的偏差可能塑造长期的认知和临床结果。功能磁共振成像（fMRI）为在体内研究这些过程提供了机会，然而，由于扫描时间相对较短、结构化的运动伪影、可变的扫描状态以及快速的大脑成熟，从中提取具有发展意义的信息仍然具有挑战性。在这些挑战中，深度学习通过从嘈杂的高维数据中学习稳健的表征、整合复杂的空间和时间信息，以及捕捉发育中大脑的非线性和快速演变的组织，扩展了计算神经影像的能力。在此，我们回顾了深度学习在婴儿功能神经影像中的最新进展，综合了在输入表征格式化、群体和个体化大脑映射、纵向轨迹预测、稳健且可解释的模型评估以及生物学转化方面的进展。总的来说，这些方法论的进步标志着婴儿功能神经影像从描述性的、群体水平的分析向可靠的、个体化的和发展基础扎实的模型的范式转变。未来的进展将依赖于更大和更多样化的纵向数据集、适合发展阶段的模型设计、严格且标准化的评估，以及将计算预测与生物学机制整合以实现具有临床意义的结果。解决这些优先事项将有助于将深度学习确立为一个稳健的框架，用于理解早期功能性大脑发展，在个体水平上识别发展变异，并最终支持对神经发育风险的更早和更精确的评估。
英文摘要
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.
发表机构
- University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校生物医学成像研究中心）
- Lampe Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University（北卡罗来纳大学教堂山分校与北卡罗来纳州立大学兰珀联合生物医学工程系）
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