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Abstract
White matter connectivity supports diverse cognitive demands by efficiently constraining dynamic brain activity. This efficiency can be inferred from network controllability, which represents the ease with which the brain moves between distinct mental states based on white matter connectivity. However, it remains unclear how brain networks support diverse functions at birth, a time of rapid changes in connectivity. Here, we investigate the development of network controllability during the perinatal period and the effect of preterm birth in 521 neonates. We provide evidence that elements of controllability are exhibited in the infant’s brain as early as the third trimester and develop rapidly across the perinatal period. Preterm birth disrupts the development of brain networks and altered the energy required to drive state transitions at different levels. In addition, controllability at birth is associated with cognitive ability at 18 months. Our results suggest network controllability develops rapidly during the perinatal period to support cognitive demands but could be altered by environmental impacts like preterm birth.
Network controllability represents the ease with which the brain switches between mental states and can be inferred from white matter connectivity. Here, the authors show network controllability emerges in infants as early as the third trimester, and that preterm birth disrupts the energy required to drive state transitions.
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Details
; Jiang, Rongtao 2
; Dai, Wei 3 ; Dufford, Alexander J. 4 ; Noble, Stephanie 5
; Spann, Marisa N. 6 ; Gu, Shi 7 ; Scheinost, Dustin 8
1 Yale University, Department of Biomedical Engineering, New Haven, USA (GRID:grid.47100.32) (ISNI:0000 0004 1936 8710)
2 Yale School of Medicine, Department of Radiology & Biomedical Imaging, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710)
3 Yale School of Public Health, Department of Biostatistics, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710)
4 Oregon Health & Science University, Department of Psychiatry and Center for Mental Health Innovation, Portland, USA (GRID:grid.5288.7) (ISNI:0000 0000 9758 5690)
5 Northeastern University, Department of Psychology, Boston, USA (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359); Northeastern University, Department of Bioengineering, Boston, USA (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359); Northeastern University, Center for Cognitive and Brain Health, Boston, USA (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359)
6 Vagelos College of Physicians and Surgeons, Columbia University, Department of Psychiatry, New York, USA (GRID:grid.21729.3f) (ISNI:0000 0004 1936 8729); New York State Psychiatric Institute, New York, USA (GRID:grid.413734.6) (ISNI:0000 0000 8499 1112)
7 University of Electronic Science and Technology of China, School of Computer Science and Engineering, Chengdu, China (GRID:grid.54549.39) (ISNI:0000 0004 0369 4060); University of Electronic Science and Technology of China, Shenzhen Institute for Advanced Study, Shenzhen, China (GRID:grid.54549.39) (ISNI:0000 0004 0369 4060)
8 Yale University, Department of Biomedical Engineering, New Haven, USA (GRID:grid.47100.32) (ISNI:0000 0004 1936 8710); Yale School of Medicine, Department of Radiology & Biomedical Imaging, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, Department of Statistics & Data Science, New Haven, USA (GRID:grid.47100.32) (ISNI:0000 0004 1936 8710); Yale School of Medicine, Child Study Center, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Wu Tsai Institute, Yale University, New Haven, USA (GRID:grid.47100.32) (ISNI:0000 0004 1936 8710)




