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

项目总结。人类生命的头24个月是充满活力的，以快速增长为特征，并且 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， 对大脑结构和功能发育在这一敏感期的轨迹知之甚少。 通常是发育中的婴儿，而对于这些轨迹中的偏差与出现的关系更是知之甚少 认知和行为或预测以后的发展结果。这在一定程度上是由于目前的技术 磁共振成像(MRI)定量测定婴儿脑结构和功能的局限性 发展神经科学研究的重要、非侵入性方法。目前，有足够的 方法分析婴儿出生后24个月的MRI扫描，特别是脑部分割- 第一步，也是关键的一步，用于几乎所有MRI模式的定量分析。如果没有准确和自动化的 分割，婴儿MRI分析容易出现系统误差，而且是劳动密集型的，限制了严谨和 婴儿核磁共振研究的重复性。这一限制限制并推迟了大规模婴儿核磁共振的应用 在可预见的未来的数据集。解决这些研究差距将极大地推动以下努力 及早识别发育迟缓和/或障碍并监测干预措施的效果。我 建议开发基于人工智能的婴儿神经成像分析工具来研究人类早期大脑发育 通过从NIH资助的婴儿连接项目(BCP)收集的数据。在我的试点研究中，我已经展示了 基于人工智能的方法在新生儿和6个月婴儿脑分割中的卓越表现。我的第一个目标 是开发一种使用卷积神经网络的自动化和准确的脑分割管道-一 AI进场。这种分割工具可以容纳和处理每个月的婴儿脑部扫描 在生命的头两年，将以用户友好的、基于Web的界面的形式发布，供研究人员使用 在科学界(补充目标4)。在目标2中，我将描绘区域脑的生长轨迹 体积、主要功能网络，并测量它们与神经心理功能的关系 生命的前24个月来自BCP的数据。在目标3中，我将使用第一年纵向多模式MRI扫描 BCP来预测2岁时的发育结果。该奖项的跨学科培训阶段， 在哥伦比亚大学乔纳森·波斯纳博士的实验室进行的，包括一项全面的计划 获得技术和专业技能，这将使我能够过渡到研究独立。这个 该项目的成功完成将为研究发育神经科学和 提高我们有效测量和识别相关婴儿大脑结构和连接性及其 在长期发展中的作用。这项提议的目的与NICHD的目标#10“列车调查员”是一致的 在人工智能方面，主题#1“理解发展的结构基础”和#4“改进 儿童和青少年健康。“

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 and monitoring the effects of interventions. I propose developing AI-based infant neuroimaging analysis tools for studying the early human brain development via collected data from NIH funded Baby Connectome Project (BCP). In my pilot studies, I have shown the superior performance of AI-based approaches in neonatal and 6-month infant brain segmentation. My first aim is to develop an automated and accurate brain segmentation pipeline with 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, and will be released as a user-friendly, web-based interface for researchers to use in scientific community (complementary Aim 4). In Aim 2, I will delineate the growth trajectories of regional brain volumes, 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 use the first-year longitudinal multimodal MRI scans from BCP to predict the developmental outcomes at age 2. 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 a crucial new tool for studying developmental neuroscience and improve our capacity to efficiently measure and identify relevant infant brain structures and connectivity and their role in long-term development. Aims of this proposal are consistent with NICHD's goal #10 “Train Investigators in Artificial Intelligence," theme #1 “Understanding the Structural Basis of Development," and # 4 “Improving Child and Adolescent Health."