Congratulations to Li Wang, PhD, and Gang Li, PhD Associate Professors, for receiving an R01 grant from the NIH!
PI: Li Wang (Contact); Gang Li
Title: Continued Development of Infant Neuroimaging Analysis Tools
Sponsor: NIH
Grant #: 1R01EB037388-01A1
Project Period: 03/01/2025-12/31/2028
Estimated Award Amount: $2,116,071 ($530,511/Year 1)
Project Goals:
The first two years are an exceptionally dynamic and critical period of brain development, featuring significant growth in both cerebrum and cerebellum. The availability of large-scale, multi-site, infant MRI datasets affords unprecedented opportunities for precise charting the dynamic early brain development, providing important insights into the origins and aberrant growth trajectories of neurodevelopmental disorders, such as autism. However, existing neuroimage analysis tools designed for adults are not suitable for infant neuroimages, due to
unique challenges of extremely low, spatiotemporally variable tissue contrast and dynamic brain characteristics. In 2020, our team has developed and released iBEAT V2.0 (infant Brain Extraction and Analysis Toolbox) with advanced deep learning techniques, which has successfully processed 18,000+ infant scans from 150+ institutions with various imaging protocols and scanners and directly contributed to 50+ journal publications.
However, iBEAT still has two major limitations. 1) It focused on the infant cerebrum MRIs and thus is inapplicable for the more challenging cerebellum MRIs, which exhibit much thinner and more tightly folded cortex than the cerebrum, extremely low and dynamic tissue contrast, and suffer from large domain-shift issue across imaging sites. 2) Some important functionalities for cerebrums are still missing, e.g., motion correction, subcortical segmentation, volumetric parcellation, and surface registration, or have degraded performance in certain scenarios. To address these issues, this project aims to significantly enrich iBEAT by 1) creating deep learning-based computational tools for cerebellar tissue segmentation, atlas building, surface
reconstruction and parcellation, and 2) adding new cerebrum-related functionalities and improving existing functionalities with our developed and new techniques, to enable comprehensive and precise analysis of cerebrum and cerebellum and their interplay during infancy. Accordingly, we propose five aims. Specifically, we will develop a novel prior-guided cerebellum tissue segmentation with self-verification (Aim 1). We will then construct the first 4D infant cerebellum atlases with longitudinally consistent, temporally continuous, and spatially detailed patterns, by developing a novel unsupervised learning-based anatomy-guided atlas construction framework (Aim 2). We will reconstruct topologically correct and geometrically accurate cerebellar cortical surfaces and further develop a novel Spherical Surface Transformer based on the deformable self-attention mechanism to precisely parcellate cerebellar cortical surfaces into distinct regions (Aim 3). We will add new
cerebrum-related modules for motion correction, subcortical segmentation, volumetric parcellation, and surface registration and further improve existing modules in terms of robustness, accuracy, and efficiency with our developed and new techniques (Aim 4). Finally, we will undertake a comprehensive upgrade for iBEAT, improving usability, robustness, compatibility, code structure, documentation, and training materials (Aim 5).