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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.