Leveraging artificial intelligence to develop novel tools for studying infant brain development

项目摘要。人类生命的前24个月是动态的，以快速增长为特征， 越来越多的人认识到，这对建立持续一生的认知能力和行为至关重要。然而，在这方面， 在这一敏感时期，人们对大脑结构和功能发育的轨迹知之甚少。 通常是发育中的婴儿，甚至更少有人知道这些轨迹的偏差与新生儿的发育有关。 认知和行为或预测以后的发展结果。这部分是由于目前的技术 通过磁共振成像（MRI）对婴儿大脑结构和功能进行定量的局限性-一项 重要的，非侵入性的方法来研究发育神经科学。目前，不足 方法来分析婴儿MRI扫描在前24个月的生活，特别是大脑分割- 第一步也是关键的一步，几乎是所有MRI模式定量分析的关键步骤。如果没有准确和自动化的 分割，婴儿MRI分析容易出现系统性错误，并且是劳动密集型的，限制了严格性， 婴儿MRI研究的可重复性。这一限制限制了大规模婴儿MRI的实用性 在可预见的未来，解决这些研究差距将大大推动努力， 早期识别发育迟缓和/或障碍。我建议开发基于人工智能的婴儿神经成像 通过两个大规模数据集研究早期人类大脑发育的分析工具：NIH资助的婴儿 来自环境对儿童健康的影响的Connectome项目和集中式MRI数据库 结果。在我的试点研究中，我已经显示了与地面实况标签的良好到优秀的一致性， 两个不同的源，和上级性能相比，其他常用的分割方法。我 第一个目标是开发一个具有3D卷积神经网络的自动化和可推广的大脑分割管道。 网络-AI方法。这种分割工具可以容纳和处理婴儿大脑扫描， 在生命的头两年每个月。最终的基于AI的管道将在内部进行严格的验证， 外部测试我们将发布管道作为一个用户友好的，基于Web的界面，供研究人员使用， 科学界。在目标2中，我将描绘局部脑形态测量学的生长轨迹，主要是 功能网络，并在前24个月内测量其与神经心理功能的关系 通过BCP的数据。在目标3中，我将利用两种不同的方法（AI和LPCA）来预测 评估3岁以下的发育结果。第一年纵向多模式MRI扫描， BCP。该奖项的跨学科培训阶段，在乔纳森·波斯纳博士的实验室进行， 哥伦比亚大学，包括一个全面的计划，为获取技术和专业技能， 能让我过渡到独立研究该项目的成功完成将产生可靠的 研究早期大脑发育的工具和新的数据驱动方法，填补了早期大脑发育的关键知识空白。 发展，并推动努力早期识别发育迟缓和障碍。

PROJECT SUMMARY. The first 24-months of human life are dynamic, characterized by rapid growth, and increasingly recognized as crucial for establishing cognitive abilities and behaviors that last a lifetime. However, little is known about trajectories of structural and functional brain development during this sensitive period in typically developing infants, and even less is known about how deviations in these trajectories relate to emerging cognition and behavior or predict later developmental outcomes. This is partially due to current technical limitations on quantification of brain structure and function in infants via magnetic resonance imaging (MRI) – an important, non-invasive approach to the study of developmental neuroscience. Currently there are insufficient methods to analyze infant MRI scans across the first 24 months of life, especially for brain segmentation – the first and critical step for virtually all quantitative analyses across MRI modalities. Without accurate and automated segmentation, infant MRI analysis is prone to systematic errors and is labor-intensive, limiting the rigor and reproducibility of infant MRI research. This limitation curtails and delays the utility of large-scale infant MRI datasets in the foreseeable future. Addressing these research gaps would significantly advance efforts toward early identification of developmental delays and/or disorders. I propose developing AI-based infant neuroimaging analysis tools for studying the early human brain development via two large-scale datasets: the NIH funded Baby Connectome Project and a centralized MRI data repository from Environmental Influence on Child Health Outcomes. In my pilot studies, I have shown the show good-to-excellent agreement with ground-truth labels from two different sources, and superior performance compared to other commonly used segmentation methods. My first aim is to develop an automated and generalizable brain segmentation pipeline with 3D convolutional neural networks – an AI approach. This segmentation tool can accommodate and process infant brain scans spanning each month over the first 2 years of life. The final AI-based pipeline will be rigorously validated internally, and tested externally. We will release the pipeline as a user-friendly, web-based interface for researchers to use in scientific community. In Aim 2, I will delineate the growth trajectories of regional brain morphometrics, major functional networks, and measure their relationships to neuropsychological functions during the first 24months of life via data from BCP. In Aim 3, I will leverage two different approaches (AI and LPCA) to predict the developmental outcomes assessed up to 3 years old. with the first-year longitudinal multimodal MRI scans from BCP. The interdisciplinary training phase of the award, conducted in the laboratory of Dr. Jonathan Posner at Columbia University, includes a comprehensive plan for the acquisition of technical and professional skills that will enable my transition to research independence. The successful completion of this project will yield reliable tools and novel data-driven methods for studying early brain developmental, fill critical knowledge gaps of early development, and advance efforts toward early identification of developmental delays and disorders.