Classifications and Cognitive Implications of Infant Brain Structural Development
Poster No:
1032
Submission Type:
Abstract Submission
Authors:
Tengfei Li1, Weiyan Yin2, Zhengwang Wu1, Gang Li1, Li Wang3, Hongtu Zhu4, Weili Lin2
Institutions:
1UNC-CH, CHAPEL HILL, NC, 2UNC Chapel Hill, Chapel Hill, NC, 3UNIV OF NORTH CAROLINA CHAPEL HILL, Chapel Hill, NC, 4University of North Carolina at Chapel Hill, Chapel Hill, NC
First Author:
Co-Author(s):
Introduction:
The infant brain undergoes highly heterogeneous and nonlinear development, spanning multiple phases and reflecting the interplay between structural and functional maturation. Meanwhile, cognitive development progresses in diverse patterns across various subdomains, influenced by various genetic and environmental factors during early infancy. A substantial body of literature has examined associations between brain structure and cognitive development, often focusing on regional or local changes, such as cortical thickness in Broca's area (Barnes-Davis et al., 2020), and their roles in language-related cognitive outcomes. However, there has been limited investigation considering brain-wide structural topology as a whole. In this study, we aimed to consider the whole brain structural topologies (cortical thickness and surface area) and identify developmental subtypes in relation to cognitive performance across multiple cognitive subdomains during early infancy.
Methods:
Raw T1- and T2-weighted images from 818 brains of 367 infants aged 0-36 months were preprocessed to generate surface and cortical thickness maps on the standard space with 10,242 vertices, using iBEAT v2.0 (Wang, et. al 2023). Confounding covariates age, sex, site, and education were regressed out using generalized additive models (GAM) considering nonlinear trajectories. Centile-based normalized surface area and thickness maps were obtained using centiles/rankings among population at each vertex. Spectral clustering was applied to centile maps to classify the 818 brains into 10 distinct structural developmental subtypes/patterns. Cognitive performance was assessed at each visit of the infants using the Mullen Scales of Early Learning (MSEL), a standardized tool evaluating five subdomains: fine motor, gross motor, visual reception, receptive language, and expressive language. The mean surface area and thickness maps of the "optimal" or "worst" brains (top ranked brains with highest or lowest cognitive scores) in each subdomain were examined.
Results:
We identified 10 distinct whole-brain structural developmental clusters, with differences in MSEL composite and subdomain scores. The brain topologies for clusters 1, 2, 4, 6, 7, 8, and 10 are shown in Figure 1a: reduced surface area in the superior and inferior parietal and temporal regions for Cluster 1, in visual cortex for Custer 7, and middle and inferior frontal regions for Cluster 4, and reduced cortical thickness in superior frontal regions for Cluster 8 and lateral orbitofrontal regions for Cluster 10. From Figures 2a and 2d, Clusters 3, 5, and 9 represented normal developmental patterns, while Clusters 6, 4, and 2 exhibited language and slight visual deficiencies, language and gross motor deficiencies, and slight receptive language deficiencies, respectively; Clusters 8, 7, 10, and 1 showed patterns related to gross motor and visual function abnormalities. Specifically, Clusters 2 and 6 had significantly lower composite, receptive, and expressive language scores compared to Cluster 3 (FDR-adjusted p < 0.05). For the longitudinal changes, 59 infants remained in the same cluster across visits, while 54, 41 and 25 infants transitioned between Clusters 3 and 9, Clusters 3 and 2, and between Clusters 3 and 7, respectively. The mean surface area and cortical thickness maps of top 5% brains with highest composite scores are shown in Figure 1b. We observed (Figures 2b and 2c) that 70.6% of infants whose surface area was > 0.20 correlated with top 5% performers were from Cluster 3. Conversely, infants whose surface area was > 0.20 correlated with bottom 20% performers were 40%, 19%, and 23% from Clusters 2, 7, and 3, respectively.
Conclusions:
The proposed approaches and results provided insights into the global patterns of brain development and their associations with cognitive domains, potentially aiding in early detection and prognosis of abnormal infants' brain development such as psychiatric disorders.
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 1
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Image Registration and Computational Anatomy
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Normal Development
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Cognition
Computational Neuroscience
Data analysis
1|2Indicates the priority used for review
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2. Wang, L., Wu, Z., Chen, L., Sun, Y., Lin, W. and Li, G., 2023. iBEAT V2. 0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction. Nature protocols, 18(5), pp.1488-1509.