Longitudinal Mapping of Human Brain Development in the First Years of Life Abstract This proposal requests continued funding support for research at the University of North Carolina at Chapel Hill to develop computational tools for quantifying longitudinal structural changes in the human brain. The previous project period has been extremely successful in advancing robust tools for longitudinal brain analysis of the aging brain. In this renewal, we seek to further advance robust computational tools for comprehensive longitudinal characterization of changes in the early developing brain. This is in line with our long-term goal of creating computational tools for longitudinal charting of brain evolution across the entire human lifespan. The tools to be developed in this project will allow unified and concurrent analysis of longitudinal volumetric data and cortical surfaces, facilitating the mapping of dynamic and spatially heterogeneous structural changes during a critical period of brain development. The tools developed in this project will be tailored to studying the human brain in the first few years of life, which undergoes dynamic development in both structure and function. We will utilize the MRI data made available via the Baby Connectome Project (BCP), involving 500 pediatric subjects scanned from birth to five years of age. The outcome of BCP will inform neuroscientists what normal healthy growth looks like and facilitate discovery of the earliest manifestations of brain disorders. To fully benefit from this unique dataset, dedicated computational tools are needed for accurate processing and analysis of baby MR images, which typically exhibit dynamic heteroge- neous changes across time. However, most computational tools developed to date have been mostly focused on adult subjects and are unreliable when applied to baby MRI. We propose to address this gap with three aims: In Aim 1, we will develop computational tools to allow multifaceted analysis of MRI data, including volumes and white-matter/pial surfaces, to be carried out in common spaces for a more holistic understanding of the early developing brain. Our tools will explicitly consider the rapid changes in MR image appearances that are typical in the first year of life. Unlike conventional methods that are designed for either image volumes or cortical surfaces, resulting in inconsistencies and loss of sensitivity to subtle changes, our tools will allow joint volume-surface analysis in consistent longitudinal spaces. Improving registration accuracy by drawing information from both entities is critical for detecting subtle changes in the developing brain, which is significantly smaller with a thinner cerebral cortex. In Aim 2, we will generate longitudinal, multimodal, and whole-brain parcellation maps for the early developing brain. Subdivision of the brain into coherent regions is an essential step in the macroscopic mapping of spa- tially heterogeneous changes and in the examination of spatial and topological organization. Our approach will allow the characterization of the evolution of parcellation across time and at the same time maintain temporal consistency and inter-subject correspondences of the parcels. In Aim 3, we will develop techniques that will allow prediction of missing MRI data to increase the usability of incomplete data for improving statistical power. Missing data is a common and inevitable problem in longitudinal studies due to subject dropouts or failed scans, especially in studies involving infants. To address this problem, we will develop deep learning techniques for longitudinal prediction of missing imaging data. Successful completion of this project will empower the neuroscience community with computational tools for more precise charting of the normative early development of the human brain using MRI. As part of this project, we will deliver the first set of temporally-dense surface-volumetric atlases that will capture key developmental traits and are therefore critical for quantification of possible deviation from normal brain development.

Project Narrative Unlike the vast majority of computational tools that are developed for adult brain MRI with good image con- trast, this project will empower neuroscience researchers with robust tools needed to quantify temporal structural changes in the early developing brain, including tools for (i) Unified longitudinal analysis of volumetric and surface data of infant MRI; (ii) Multimodal parcellation with longitudinal and intersubject consistency; (iii) Deep learning prediction of missing data. The tools developed will be disseminated in the form of a software package complete with documentation and tutorials. As part of this project, we will deliver the first set of temporally-dense surface- volumetric atlases that will capture key developmental traits, critical for quantification of possible deviation from normal brain development.