The Development of Individual Differences in Adolescent Brain Structure and Risk

在UNC早期脑发育中抢救错过的纵向MRI检查

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.