The Development of Individual Differences in Adolescent Brain Structure and Risk

青少年大脑结构和风险的个体差异的发展

• 批准号: 10412438
• 负责人: JOHN Horace GILMORE
• 金额: $ 28.67万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10412438
• 关键词: 10 year old; 16 year old; Adolescence; Adolescent; Age; Anxiety; Area; Attention; Behavior; Behavior assessment; Birth; Brain; Child; Cognition; Cognitive; Data; Data Set; Databases; Development; Enrollment; Failure; Fiber; Generations; Image; Individual Differences; Life; Longitudinal Studies; Machine Learning; Magnetic Resonance Imaging; Measures; Mental disorders; Methods; Modeling; National Institute of Mental Health; Network-based; Participant; Phenotype; Process; Property; Puberty; Readiness; Research Domain Criteria; Research Personnel; Resources; Risk; Sample Size; Structure; Study Subject; Surface; Thick; Time; Training; Visit; base; behavioral outcome; cognitive testing; cohort; convolutional neural network; data archive; deep learning; detection method; disorder risk; early adolescence; executive function; image processing; image reconstruction; imaging modality; improved; innovation; machine learning algorithm; machine learning method; multimodality; postnatal period; predictive modeling; predictive test; prenatal; repository; visual processing; white matter

Rescuing Missed Longitudinal MRI visits in the UNC Early Brain Development Studies Database PROJECT ABSTRACT In our ongoing R01 (MH123747-01A1) “The Development of Individual Differences in Adolescent Brain Structure and Risk”) project, we aim to characterize the portion of individual differences in brain structure in the early adolescent brain is already present in the earlier years of life. Early adolescence and puberty is a major period of postnatal brain development, characterized by dynamic structural and functional brain maturation and reorganization, and emerging risk for psychiatric disorders, though it is not known how this period of development contributes to individual differences in brain structure and risk. The UNC Early Brain Development Study (EBDS) is a unique and innovative longitudinal study that has followed children, enrolled prenatally, with imaging and cognitive/behavioral assessments at birth, 1, 2, 4, 6, 8, and 10 years. 482 children from this cohort are now reaching adolescence, and we are following these children at 12, 14, and 16 years of age via MRI, cognitive and behavioral assessments, with a focus on the phenotypes of executive function, attention, and anxiety, consistent with RDoC constructs important for psychiatric disorder risk. One particular aim is to investigate the use of machine learning (ML) for the predictive analysis of early brain development to cognitive and behavioral outcomes in adolescence and to risk for subsequent psychiatric disorders. Yet, most machine learning (ML) algorithms applied to longitudinal data do not perform well (or at all) when data points are missing, as ML methods need both complete data and large sample sizes. As longitudinal studies suffer commonly from significant missing data at different time points due to acquisition failure as well as participant attrition, even a rich database like the UNC EBDS is reduced to a significantly lower sample size by selecting only complete datasets to apply predictive ML (less than a third of the datasets of EBDS data from age 1 – 10 years is complete). Here, we propose to rescue missing EBDS timepoints (at ages 1 - 10 yrs) of structural MR image data via multi-modal, multi-timepoints image predictions. This image data imputation includes cross-modality image generation (generating missing MRI data from existing MRI data at the same time), where available, as well as multi-timepoints imputation of longitudinal data (generating missing MRI data from existing MRI data at different time points). We will then apply our out-of-distribution model to provide additional information on the appropriateness of the imputed data. Subsequently, the same image processing that was applied to the original EBDS MRI data will be applied to the imputed/generated MRI data to compute missing information of morphometric measures (regional volumes, cortical thickness, surface area, and white matter fiber tract properties). This imputed data will be a highly significant resource for longitudinal ML/AI studies of brain development performed on the EBDS dataset, as it would allow for an increase in training data of over 200%. The original MR images, the imputed MR images, and the morphometric measures will all be shared via NDA, alongside the trained imputation network for use by others.

在早期脑发育中挽救错过的纵向MRI访视 研究数据库 项目摘要 在我们正在进行的R 01（MH 123747 - 01 A1）“青少年大脑个体差异的发展”中， 结构与风险”）项目，我们的目标是表征大脑结构中个体差异的部分， 青春期早期的大脑在生命的早期就已经存在。青春期早期和青春期是一个主要 脑发育期：出生后大脑发育的一段时期，以大脑结构和功能的动态成熟为特征， 重组，并出现精神疾病的风险，虽然目前还不知道如何在这一时期的发展 导致大脑结构和风险的个体差异。早期大脑发育研究（EBDS） 是一项独特而创新的纵向研究，对产前登记的儿童进行了随访， 出生时、1岁、2岁、4岁、6岁、8岁和10岁时的认知/行为评估。482名儿童现在 进入青春期，我们通过核磁共振成像，认知和 行为评估，重点是执行功能，注意力和焦虑的表型，一致 RDoC结构对精神疾病风险很重要。一个特别的目的是调查使用 机器学习（ML）用于早期大脑发育到认知和行为的预测分析 结果在青春期和随后的精神疾病的风险。大多数机器学习（ML） 当数据点缺失时，应用于纵向数据的算法表现不佳（或根本不佳），因为ML 方法需要完整的数据和大的样本量。由于纵向研究通常受到 由于采集失败以及参与者流失，在不同时间点存在显著的数据缺失， 通过只选择完整的数据库， 数据集应用预测ML（1 - 10岁EBDS数据集的不到三分之一是完整的）。 在这里，我们建议通过以下方法挽救结构MR图像数据缺失的EBDS时间点（1 - 10岁）： 多模态、多时间点图像预测。该图像数据插补包括跨模态图像 生成（同时从现有MRI数据生成缺失的MRI数据），以及 纵向数据的多时间点插补（根据不同时间点的现有MRI数据生成缺失的MRI数据） 时间点）。然后，我们将应用我们的分发外模型，以提供有关 插补数据的适当性。随后，应用于原始图像的相同图像处理 EBDS MRI数据将应用于插补/生成的MRI数据，以计算以下缺失信息 形态测量（区域体积、皮质厚度、表面积和白色纤维束 属性）。这些估算的数据将是大脑纵向ML/AI研究的重要资源 在EBDS数据集上进行的开发，因为它将允许训练数据增加200%以上。 原始MR图像、插补MR图像和形态测量指标均将通过NDA共享， 与经过训练的估算网络一起供其他人使用。