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

基本信息

批准号：
10302034
负责人：
YUN WANG
金额：
$ 1.33万
依托单位：
NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2021-10-15
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10302034
关键词：
3 year old 3-Dimensional Address Adopted Age Agreement Amygdaloid structure Anxiety Artificial Intelligence Attention deficit hyperactivity disorder Automobile Driving Award Back Base of the Brain Behavior Birth Brain Brain imaging Brain scan Child Health Clinical Code Cognition Communities Computer software Data Data Set Development Developmental Delay Disorders Disease Early identification Ethnic Origin Functional Magnetic Resonance Imaging Funding Funding Mechanisms Future Gestational Age Growth Hippocampus (Brain)Human Individual Infant Knowledge Label Laboratories Language Learning Life MRI Scans Magnetic Resonance Imaging Manuals Measures Mental Depression Mental disorders Methodology Methods Modeling Neuropsychology Neurosciences Outcome Participant Performance Phase Pilot Projects Principal Component Analysis Problem behavior Process Psychological Transfer Psychology Race Reproducibility Research Research Personnel Rest Sample Size Sampling Scanning Shapes Source Standardization Structure Symptoms Techniques Testing Time Tissues Toddler Training United States National Institutes of Health Universities artificial neural network automated segmentation base career career development cognitive ability cognitive development cohort connectome convolutional neural network data repository early detection biomarkers emotional functioning executive function functional MRI scan gray matter imaging modality improved infancy large scale data long short term memory network multimodality neuroimaging novel rapid growth sex skills social tool user-friendly virtual web based interface web interface white matter

项目摘要

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.

项目摘要。人类生命的前 24 个月是动态的，其特点是快速生长，并且越来越多的人认识到对于建立持续一生的认知能力和行为至关重要。人们对这一敏感时期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，而关于这些轨迹的偏差与新兴婴儿之间的关系则知之甚少。认知和行为或预测以后的发展结果部分归因于当前的技术。通过磁共振成像 (MRI) 量化婴儿大脑结构和功能的局限性重要的、非侵入性的方法来研究发育神经科学目前还不够。分析婴儿出生后 24 个月内 MRI 扫描的方法，尤其是大脑分割 - 在没有准确和自动化的情况下，几乎所有 MRI 模式定量分析的第一步也是关键的一步。分割，婴儿 MRI 分析容易出现系统错误，并且是劳动密集型的，限制了严谨性和婴儿 MRI 研究的可重复性这一限制限制并延迟了大规模婴儿 MRI 的实用性。在可预见的未来，解决这些研究差距将极大地推进努力。我建议开发基于人工智能的婴儿神经影像学。通过两个大型数据集研究人类早期大脑发育的分析工具：NIH 资助的 Baby 连接组项目和环境对儿童健康影响的集中式 MRI 数据存储库在我的试点研究中，我已经证明该节目与真实标签的一致性非常好。两个不同的来源，并且与其他常用的分割方法相比具有优越的性能。第一个目标是开发具有 3D 卷积神经网络的自动化且可泛化的大脑分割流程网络——一种人工智能方法，可以适应和处理婴儿的大脑扫描。在生命的前两年中，每个月都会对最终的基于人工智能的管道进行严格的内部验证，并且我们将把该管道作为用户友好的、基于网络的界面发布，供研究人员使用。在目标 2 中，我将描绘主要区域大脑形态测量的生长轨迹。功能网络，并测量其在前 24 个月内与神经心理功能的关系在目标 3 中，我将利用两种不同的方法（AI 和 LPCA）来预测生命。通过第一年的纵向多模态 MRI 扫描评估 3 岁以下的发育结果。 BCP。该奖项的跨学科培训阶段在 Jonathan Posner 博士的实验室进行。哥伦比亚大学，包括一个获取技术和专业技能的综合计划，将使我能够过渡到独立研究。该项目的成功完成将产生可靠的成果。用于研究早期大脑发育的工具和新颖的数据驱动方法，填补了早期大脑发育的关键知识空白发育，并努力尽早发现发育迟缓和障碍。