自闭症
神经科学
神经影像学
默认模式网络
大脑发育
大脑定位
计算机科学
功能连接
认知
心理学
一致性(知识库)
体素
功能集成
空间组织
网络动力学
连接体
弹道
人工神经网络
人工智能
规范性
发展认知神经科学
人脑
神经网络
干预(咨询)
循环神经网络
网络分析
网络体系结构
自闭症谱系障碍
发育阶段
相似性(几何)
机器学习
功能专门化
功能磁共振成像
动态网络分析
中心性
生物神经网络
作者
Masoud Seraji,Sarah Shultz,Qiang Li,Zening Fu,Armin Iraji,Vince D. Calhoun
标识
DOI:10.1109/embc58623.2025.11253059
摘要
Early infancy is a crucial period for brain development, during which fundamental functional and structural frameworks are established. Understanding the maturation of large-scale brain networks during this stage is essential for characterizing normative neurodevelopment and identifying potential deviations linked to neurodevelopmental disorders. In this study, we investigated developmental changes in the spatial organization of functional brain networks in infants using a longitudinal resting-state fMRI dataset comprising 137 scans from 74 low-likelihood developing infants aged 0-6 months. We applied independent component analysis to extract large-scale brain networks and utilized advanced spatial metrics, including network-averaged spatial similarity (NASS) to assess alignment with group-level patterns, network strength to quantify neural engagement based on voxel intensities, and network size to examine spatial distribution. Our findings reveal significant age-related increases in NASS across multiple networks, indicating greater consistency in functional organization over time. Additionally, most networks demonstrated increased network strength, reflecting heightened neural involvement, while network size exhibited distinct developmental trajectories, with some networks expanding and others remaining stable. These results highlight the dynamic evolution of functional brain architecture during early infancy, providing critical insights into neurodevelopmental processes.Clinical Relevance- This study provides critical insights into early brain network development, which is essential for identifying biomarkers of neurodevelopmental disorders such as autism and schizophrenia. By mapping typical maturation patterns using advanced spatial metrics, our findings offer a foundation for early detection of atypical development. Deviations in network organization and strength could serve as early indicators, supporting neuroimaging-based screening and intervention strategies to optimize neurodevelopmental outcomes.
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