Unfolding Spatial Architecture of Infant Brain Networks in the First Six Months of Life
Poster No:
958
Submission Type:
Abstract Submission
Authors:
Masoud Seraji1, Sarah Shultz2, Zening Fu1, Qiang Li1, Vince Calhoun1, Armin Iraji1
Institutions:
1Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, GA, 2Emory University, Atlanta, GA
First Author:
Masoud Seraji
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science
Atlanta, GA
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science
Atlanta, GA
Co-Author(s):
Zening Fu
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science
Atlanta, GA
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science
Atlanta, GA
Qiang Li
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science
Atlanta, GA
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science
Atlanta, GA
Vince Calhoun
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science
Atlanta, GA
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science
Atlanta, GA
Armin Iraji
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science
Atlanta, GA
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science
Atlanta, GA
Introduction:
The first months after birth represent a critical period for establishing the brain's foundational functional and structural framework (Nielsen et al., 2023; Shonkoff & Phillips, 2000). Resting-state functional connectivity provides a window into functional brain organization in early infancy. While most adult functional brain networks are observable at birth and undergo developmental changes during this early postnatal period, the spatial properties of these networks remain largely unexamined. Spatial dynamics, which capture changes in the spatial configuration of functional regions over time, have revealed critical insights into brain architecture and its associations with behavior (Iraji et al., 2020). However, these properties are often overlooked in favor of temporal coupling measures. This study is the first to investigate developmental changes in the spatial organization of functional brain networks during the first six postnatal months.
Methods:
The study involved 74 neurotypical infants (43 males, 31 females). Scans were scheduled up to three pseudorandom time points between birth and 6 months, resulting in dense longitudinal coverage with 137 scans. After applying standard preprocessing procedure, quality control was meticulously applied to ensure data accuracy and minimize biases. Functional networks were analyzed with group ICA (Calhoun et al., 2009), using GIG-ICA to reconstruct subject-specific maps and timecourses (Du et al., 2016). Five spatial metrics were analyzed: network-averaged spatial similarity (NASS), network engagement range (NER), network strength, network size, and network center of mass (NCM), Together, these metrics offer a detailed view of how functional networks spatially evolve during this highly dynamic developmental period. NASS reflecting alignment between individual and group-level network maps. Concurrently, NER, representing voxel intensity fluctuation within networks suggesting a consolidation process where voxel contributions became more uniform. Network strength, calculated as the average of all the significant voxel intensities in the network, indicating the degree of involvement in the specific functional network. Network size and NCM (illustrating spatial distribution alterations of brain networks). To explore the relationships between spatial metrics and key variables, a generalized additive model (GAM) was employed, providing robust analysis of both linear and nonlinear effects.
Results:
The rs-fMRI data revealed 13 distinct functional networks(Figure 1) out of 20 estimated components, including primary and secondary visual, subcortical, cerebellar, primary and secondary motor, attention, default mode, temporal, auditory, and multiple frontal networks (mPFC, dlPFC, vlPFC). Across infancy, these networks showed notable spatial changes. NASS generally increased, indicating closer alignment with the group pattern, except in the subcortical network. NER decreased in the visual, motor, and attention networks, suggesting they became more internally consistent, while it remained steady in secondary visual, subcortical, and auditory networks. Network strength rose in the visual, motor, temporal, and frontal networks, but stayed stable in the cerebellum, attention, and subcortical regions. Network size grew in cerebellar, attentional, temporal, and frontal networks without any observed reductions. Additionally, NCM changed significantly with age in several networks, including the secondary motor, temporal, and default mode networks. Figure 2 illustrates how spatial characteristics changed in a subset of seven networks. All results underwent false discovery rate multiple-comparison correction.
·Figure 1. Sagittal, coronal, and axial views illustrate the z-scored voxel intensity distributions of spatial maps for 13 functional brain networks in infants.
·Figure 2. Age-related changes in the spatial characteristics of various networks, including network-averaged spatial similarity (NASS), network engagement range (NER), and network center of mass (NCM)
Conclusions:
This study shows that during an infant's first six months, large-scale brain networks rapidly change, laying the groundwork for later cognitive and behavioral development. It highlights the need to understand these early patterns, especially in at-risk groups, to guide early interventions for neurodevelopmental disorders.
Lifespan Development:
Early life, Adolescence, Aging 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling
Other Methods 2
Keywords:
Development
FUNCTIONAL MRI
NORMAL HUMAN
Other - Infant, ICA
1|2Indicates the priority used for review
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Provide references using APA citation style.
Du, Y., Allen, E. A., He, H., Sui, J., Wu, L., & Calhoun, V. D. (2016). Artifact removal in the context of group ICA: A comparison of single-subject and group approaches. Human Brain Mapping, 37(3), 1005–1025. https://doi.org/10.1002/HBM.23086
Iraji, A., Miller, R., Adali, T., & Calhoun, V. D. (2020). Space: A Missing Piece of the Dynamic Puzzle. Trends in Cognitive Sciences, 24(2), 135–149. https://doi.org/10.1016/J.TICS.2019.12.004/ASSET/F55BE1A5-E396-4385-90AC-B596E66C9274/MAIN.ASSETS/GR4.JPG
Nielsen, A. N., Kaplan, S., Meyer, D., Alexopoulos, D., Kenley, J. K., Smyser, T. A., Wakschlag, L. S., Norton, E. S., Raghuraman, N., Warner, B. B., Shimony, J. S., Luby, J. L., Neil, J. J., Petersen, S. E., Barch, D. M., Rogers, C. E., Sylvester, C. M., & Smyser, C. D. (2023). Maturation of large-scale brain systems over the first month of life. Cerebral Cortex, 33, 2788–2803. https://doi.org/10.1093/cercor/bhac242
Shonkoff, J. P., & Phillips, D. A. (2000). The Developing Brain. https://www.ncbi.nlm.nih.gov/books/NBK225562/