Network-specific selectivity of functional connections in the neonatal brain

• Published in: Cerebral cortex (New York, N.Y. 1991) Vol. 33; no. 5; pp. 2200 - 2214
• Main Authors: Sylvester, Chad M, Kaplan, Sydney, Myers, Michael J, Gordon, Evan M, Schwarzlose, Rebecca F, Alexopoulos, Dimitrios, Nielsen, Ashley N, Kenley, Jeanette K, Meyer, Dominique, Yu, Qiongru, Graham, Alice M, Fair, Damien A, Warner, Barbara B, Barch, Deanna M, Rogers, Cynthia E, Luby, Joan L, Petersen, Steven E, Smyser, Christopher D
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 20.02.2023
• Subjects: Adult; Brain; Brain Mapping - methods; Humans; Image Processing, Computer-Assisted; Infant, Newborn; Magnetic Resonance Imaging - methods; Nerve Net; Neural Pathways; Original; fMRI; neonate; functional connectivity; infant; networks
• ISSN: 1047-3211; 1460-2199; 1460-2199
• DOI: 10.1093/cercor/bhac202

Abstract The adult human brain is organized into functional brain networks, groups of functionally connected segregated brain regions. A key feature of adult functional networks is long-range selectivity, the property that spatially distant regions from the same network have higher functional connectivity than spatially distant regions from different networks. Although it is critical to establish the status of functional networks and long-range selectivity during the neonatal period as a foundation for typical and atypical brain development, prior work in this area has been mixed. Although some studies report distributed adult-like networks, other studies suggest that neonatal networks are immature and consist primarily of spatially isolated regions. Using a large sample of neonates (n = 262), we demonstrate that neonates have long-range selective functional connections for the default mode, fronto-parietal, and dorsal attention networks. An adult-like pattern of functional brain networks is evident in neonates when network-detection algorithms are tuned to these long-range connections, when using surface-based registration (versus volume-based registration), and as per-subject data quantity increases. These results help clarify factors that have led to prior mixed results, establish that key adult-like functional network features are evident in neonates, and provide a foundation for studies of typical and atypical brain development.