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

利用人工智能开发研究婴儿大脑发育的新工具

• 批准号: 10554951
• 负责人: YUN WANG
• 金额: $ 11.14万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-25 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10554951
• 关键词: Leveraging; artificial; intelligence; develop; novel

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."

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模式定量分析的第一步。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，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，用于研究早期人类大脑发育 通过NIH资助的婴儿连接组项目（BCP）收集的数据。在我的试点研究中，我已经证明了 基于AI的方法在新生儿和6个月婴儿脑分割中的上级性能。我的第一个目标 是开发一个自动化和准确的大脑分割管道与卷积神经网络-一个 人工智能方法。该分割工具可以适应和处理每个月的婴儿脑部扫描 在生命的前2年，并将作为一个用户友好的，基于Web的界面发布，供研究人员使用 在科学界（补充目标4）。在目标2中，我将描绘局部脑的生长轨迹， 体积，主要功能网络，并测量它们与神经心理功能的关系， 出生后前24个月的数据。在目标3中，我将使用第一年纵向多模态MRI扫描， BCP预测2岁时的发育结果。该奖项的跨学科培训阶段， 在哥伦比亚大学的乔纳森·波斯纳博士的实验室进行的，包括一个全面的计划， 获得技术和专业技能，使我能够过渡到独立研究。的 该项目的成功完成将为研究发育神经科学提供重要的新工具， 提高我们的能力，有效地测量和识别相关的婴儿大脑结构和连接， 在长期发展中的作用。本提案的目的与NICHD的目标#10“培训调查员”一致。 主题1“理解发展的结构性基础”和主题4“提高 儿童和青少年健康。"