Spatial development of brain networks during the first six postnatal months
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| Udgivet i: | Communications Biology vol. 8, no. 1 (2025), p. 1514-1532 |
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| Andre forfattere: | , , , , |
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Nature Publishing Group
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| Online adgang: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1038/s42003-025-08913-z |2 doi | |
| 035 | |a 3267545610 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Seraji, Masoud |u Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, USA (ROR: https://ror.org/01zkghx44) (GRID: grid.213917.f) (ISNI: 0000 0001 2097 4943); School of Psychology, University of Texas at Austin, Austin, TX, USA (ROR: https://ror.org/00hj54h04) (GRID: grid.89336.37) (ISNI: 0000 0004 1936 9924) | |
| 245 | 1 | |a Spatial development of brain networks during the first six postnatal months | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The early postnatal period is crucial for brain development and understanding neurodevelopmental disorders. This study examines spatial brain network development in early infancy, a less-explored area. Using independent component analysis on longitudinal resting-state functional magnetic resonance imaging data from 74 neurotypical infants, we examined how the spatial organization of brain networks evolves from birth to 6 months. Our findings show significant age-related changes in spatial characteristics. Network-averaged spatial similarity, reflecting alignment between individual and group-level network maps, increased with age. Concurrently, network engagement range, representing voxel intensity fluctuation within networks, decreased, 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, increased across age in networks such as the frontal-medial prefrontal cortex and visual networks. We found that network size and network center of mass (illustrating spatial distribution alterations of brain networks) increased in the temporal network. These findings fill a gap in infant neuroimaging by spatially characterizing early functional network development. Quantifying changes in topology, size, and similarity offers a framework for understanding early brain maturation and identifying atypical trajectories.Longitudinal rs-fMRI (n = 74) charts rapid spatial maturation of infant brain networks over the first 6 months: greater spatial similarity, tighter voxel engagement, and network-specific gains in strength, size, and centroids. | |
| 610 | 4 | |a Emory University | |
| 653 | |a Spatial distribution | ||
| 653 | |a Magnetic resonance imaging | ||
| 653 | |a Functional magnetic resonance imaging | ||
| 653 | |a Scanners | ||
| 653 | |a Maturation | ||
| 653 | |a Headphones | ||
| 653 | |a Infants | ||
| 653 | |a Brain research | ||
| 653 | |a Temporal lobe | ||
| 653 | |a Autism | ||
| 653 | |a Neurodevelopmental disorders | ||
| 653 | |a Brain architecture | ||
| 653 | |a Age | ||
| 653 | |a Modularity | ||
| 653 | |a Cortex (frontal) | ||
| 653 | |a Neuroimaging | ||
| 653 | |a Pediatrics | ||
| 653 | |a Longitudinal studies | ||
| 653 | |a Postpartum period | ||
| 653 | |a Gestational age | ||
| 653 | |a Babies | ||
| 653 | |a Prefrontal cortex | ||
| 700 | 1 | |a Shultz, Sarah |u Marcus Autism Center, Children’s Healthcare of Atlanta, Atlanta, GA, USA (ROR: https://ror.org/050fhx250) (GRID: grid.428158.2) (ISNI: 0000 0004 0371 6071); Division of Autism & Related Disabilities, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA (ROR: https://ror.org/03czfpz43) (GRID: grid.189967.8) (ISNI: 0000 0001 0941 6502) | |
| 700 | 1 | |a Li, Qiang |u Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, USA (ROR: https://ror.org/01zkghx44) (GRID: grid.213917.f) (ISNI: 0000 0001 2097 4943) | |
| 700 | 1 | |a Fu, Zening |u Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, USA (ROR: https://ror.org/01zkghx44) (GRID: grid.213917.f) (ISNI: 0000 0001 2097 4943) | |
| 700 | 1 | |a Calhoun, Vince D. |u Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, USA (ROR: https://ror.org/01zkghx44) (GRID: grid.213917.f) (ISNI: 0000 0001 2097 4943) | |
| 700 | 1 | |a Iraji, Armin |u Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, USA (ROR: https://ror.org/01zkghx44) (GRID: grid.213917.f) (ISNI: 0000 0001 2097 4943); Department of Computer Science, Georgia State University, Atlanta, GA, USA (ROR: https://ror.org/03qt6ba18) (GRID: grid.256304.6) (ISNI: 0000 0004 1936 7400) | |
| 773 | 0 | |t Communications Biology |g vol. 8, no. 1 (2025), p. 1514-1532 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3267545610/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3267545610/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3267545610/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |