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Reliable prediction of high-risk infants of autism with cortical microstructural biomarker

利用皮质微结构生物标志物可靠预测自闭症高危婴儿

基本信息

批准号：
10218522
负责人：
Minhui Ouyang
金额：
$ 21.9万
依托单位：
CHILDREN'S HOSP OF PHILADELPHIA
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：
2021-04-01 至 2023-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10218522
关键词：
2 year old 3 year old Affect Age Anisotropy Area Attention deficit hyperactivity disorder Behavior Behavior assessment Behavioral Biological Markers Birth Brain Brain Diseases Brain region Broca&apos s area Child Chronology Clinical Cognitive Complex Cost Sharing Data Diagnosis Diagnostic Diffusion Diffusion Magnetic Resonance Imaging Early Diagnosis Early Intervention Family Foundations Funding Future General Population Goals Image Individual Infant Infant Behavior Language Letters MRI Scans Machine Learning Magnetic Resonance Imaging Measurement Measures Methods Modeling Molecular Nature Neurobiology Neurodevelopmental Disorder Neurons Neuropsychological Tests Outcome Pattern Pediatric Hospitals Performance Philadelphia Play Predictive Value Process Protocols documentation Publishing Radial Research Resolution Risk Role Schizophrenia Severities Surface Symptoms Techniques Testing Thick Time Training Translating United States National Institutes of Health autism diagnostic observation schedule autism spectrum disorder base behavioral outcome clinical application clinical predictors cognitive ability cohort density diffusion anisotropy early detection biomarkers high risk high risk behavior high risk infant high risk population imaging biomarker improved infancy kernel methods machine learning algorithm multimodality neonate neuronal circuitry neuropsychiatric disorder novel potential biomarker prediction algorithm predictive marker predictive modeling recruit regression algorithm relating to nervous system scaffold serial imaging synaptogenesis

项目摘要

Abstract The diagnosis of neuropsychiatric disorders such as autism spectrum disorder (ASD) is based on behavioral assessments that cannot be confirmed until 2-3 years of age. ASD affects 1 in 59 children in the U.S., and infants from high-risk families have 20 times of risk to develop ASD compared to the general population. Current paradigm misses the precious time window for potential early intervention from birth to 2-3 years of age. Therefore, the focus in ASD research is shifting toward developing early biomarkers which can predict the risk of an infant developing future behavioral abnormalities, while the infant is still in pre- symptomatic stage. Imaging markers play major roles in understanding of both ASD and typical developing (TD) brains. Neural MRI at early infancy may improve the current paradigm for diagnosis for ASD in high-risk infants. Macrostructural measurements such as cortical thickness and surface area from conventional T1 weighted MRI (T1w) have been the primary measurements for characterizing the maturation of infant cortex. However, T1w MRI cannot reveal information about the complex microstructural changes inside the cortical mantle. Cortical microstructure, associated with the underlying cellular and molecular processes, plays a vital role in neuronal circuit formation and emergence of brain functions. We have recently developed a novel protocol to quantify the cortical microstructure using advanced diffusion MRI (dMRI), including diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). These advanced dMRI-based cortical microstructural measurements are sensitive imaging markers for the microstructural differentiation that characterizes the infant cortex maturation. We hypothesize that dMRI-based cortical microstructure measurements in early infancy could be potential biomarkers for early detection of ASD in high-risk infants. The goal of this study is to develop and test a novel protocol, using dMRI-based cortical microstructure feature, to reliably predict clinical score of infants at high risk for ASD and other brain disorders in general. We aim: 1) to develop a technique that incorporates dMRI-based cortical microstructural measures at early infancy with cutting-edge multi-kernel machine learning algorithms to reliably predict infants’ future neurodevelopmental outcomes at 2 years of age; 2) to demonstrate the initial clinical utility of the technique in infants at high-risk for ASD. To accomplish these goals, we leverage a large cohort of infant study that consists 100 TD infants who longitudinally undergo multi- modal MRI scans at early infancy and neuropsychological testing at 2 years for developing the prediction techniques, and a high-risk ASD study that allows us to longitudinally image the infants during infancy for testing our developed techniques. Finally, it should be emphasized that, although the present project focuses on its clinical applications in infants at high risk for ASD, the method developed also has important utility in detecting behavioral abnormalities in high risk subjects for other brain disorders (e.g. schizophrenia, ADHD), at a time prior to the age of diagnosis. Thus, this technique is expected to have a broad clinical impact.

摘要