Infant Functional Connectome Fingerprinting based on Deep Learning

基于深度学习的婴儿功能连接组指纹图谱

基本信息

批准号：
10288361
负责人：
Gang Li
金额：
$ 15.55万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
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 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10288361
关键词：
Address Adolescent Adult Adverse effects Age Appearance Atlases Brain Child Code Cognition Complex Computer Models Data Set Development Early Intervention Effectiveness Ensure Exhibits Fingerprint Functional Magnetic Resonance Imaging Goals Image Individual Infant Investigation Knowledge Learning MRI Scans Magnetic Resonance Imaging Maps Measures Methodology Methods Modeling Morphology Neural Network Simulation Noise Pattern Performance Pharmaceutical Preparations Populations at Risk Positioning Attribute Research Resolution Surface Techniques Triplet Multiple Birth Validation Work age related association cortex autoencoder base behavioral phenotyping cognitive performance computerized tools connectome deep learning deep neural network design effective intervention experience fetal drug exposure high dimensionality infancy innovation learning strategy multimodality neurodevelopment neuroimaging novel personalized intervention postnatal predictive modeling prenatal prevent random forest reconstruction tool

项目摘要

Project Abstract Functional connectome fingerprinting is to discover the reliable and robust individualized functional connectivity patterns that are capable of accurately distinguishing one individual from others, like the “fingerprint”. To date, the fingerprinting capability of functional connectome has been widely observed from older children to adolescents to adults. Meanwhile, the most contributive functional connections for fingerprinting are consistently identified as the most predictive ones for cognitive performance. However, functional connectome fingerprinting during infancy featuring the most dynamic postnatal brain development remains uninvestigated, which is essential for understanding the early individual-level intrinsic patterns of functional organization, the relationship of inter-individual distinguishability with distinct behavioral phenotypes, as well as aberrant patterns associated with prenatal drug exposure. Two major obstacles prevent from investigation of infant functional connectome fingerprint: 1) there exist significant challenges in precisely processing infant neuroimages, which typically exhibit extremely low contrast, dynamic imaging appearance, morphological and functional changes; 2) conventional methods for functional connectome fingerprinting simply use the linearly-transformed, low-order functional connectivity features and are thus unable to separate the intrinsically-entangled identity-related individualized information and age-related developmental information in infant brains. To fill critical gaps in both methodology and knowledge, this project aims to develop an innovative dedicated deep learning model for infant functional connectome fingerprinting, thus addressing three fundamental questions in neurodevelopment: 1) whether the individualized functional connectome fingerprint exists during early brain development; 2) which functional connections contribute more to fingerprinting during infancy; 3) what is the association of infant functional connectome fingerprint with cognitive performance and adverse prenatal drug exposure. Our team is well positioned to conduct this project, as we have extensive experiences in developing infant-dedicated computational tools and deep learning techniques and have acquired multiple longitudinal infant datasets involving both typically developing infants and infants with prenatal drug exposure. Two specific aims are proposed. In Aim 1, we will develop a deep neural network model for infant functional connectome fingerprinting. Specifically, to boost the discriminative capability of the functional connectivity features, we will develop a triplet autoencoder model to map these features into a new feature space with high-order discriminative information. To restrain the interference from the developmental information, we will disentangle the latent variables from the triple autoencoder into identity-code, age-code, and noise-code, and meanwhile design multiple specific losses to enforce the disentanglement. In Aim 2, we will explore the key contributive connections for fingerprinting and their association with cognition performance and adverse prenatal drug exposure. Our computational models, codes and discoveries will be released to public to greatly advance baby brain connectome studies.

项目摘要功能连接组指纹识别是为了发现可靠且稳健的个性化功能连接能够准确区分一个人与其他人的模式，例如“指纹”。从年龄较大的儿童到同时，对指纹识别最有贡献的一致功能连接是。然而，功能连接组指纹识别被认为是对认知表现最具预测性的。婴儿期是产后大脑发育最具活力的阶段，这一点尚未得到研究，对于理解功能组织的早期个体层面的内在模式、关系至关重要具有不同行为表型的个体间可区分性，以及相关的异常模式产前药物暴露阻碍了婴儿功能连接组的研究。指纹：1）在精确处理婴儿神经图像方面存在重大挑战，这通常表现出 2）常规功能连接组指纹识别方法只需使用线性变换的低阶函数连通性特征，因此无法分离内在纠缠的身份相关个性化婴儿大脑中的信息和与年龄相关的发育信息填补这两种方法的关键空白。和知识，该项目旨在开发一种创新的婴儿功能性专用深度学习模型连接组指纹识别，从而解决神经发育中的三个基本问题：1）是否个体化的功能连接组指纹存在于早期大脑发育过程中；2) 婴儿期的连接更有助于指纹识别；3）婴儿功能的关联是什么？连接组指纹与认知表现和不良产前药物暴露状况良好。我们有能力开展这个项目，因为我们在开发婴儿专用产品方面拥有丰富的经验计算工具和深度学习技术，并获得了多个纵向婴儿数据集两个具体目标是培养典型婴儿和产前药物暴露婴儿。在目标 1 中，我们将开发一种用于婴儿功能连接组指纹识别的深度神经网络模型。具体来说，为了提高功能连接特征的辨别能力，我们将开发一个三元组自动编码器模型将这些特征映射到具有高阶判别信息的新特征空间中。为了抑制发展信息的干扰，我们将潜在变量从三重自动编码器为身份代码、年龄代码和噪声代码，同时设计多个特定损失在目标 2 中，我们将探索指纹识别和识别的关键贡献联系。它们与认知表现和不良产前药物暴露的关系。代码和发现将向公众发布，以极大地推进婴儿大脑连接组研究。