Developmental Cognitive NeuroscienceNeurosciencePeer-reviewed journal article
Neonatal multi-modal cortical profiles predict 18-month developmental outcomes
Daphna FencheliD, Ralica DimitrovaiD, Emma C. RobinsoniD, Dafnis BatalléiD, Andrew T. M. ChewiD, Shona FalconeriD, Vanessa KyriakopoulouiD, Chiara NosartiiD, Jana Maria HutteriD, Daan ChristiaensiD, Maximilian PietschiD, Jakki Brandon, Emer HughesiD, Joanna M. Allsop, Camilla O’Keeffe, Anthony N. PriceiD, Lucilio Cordero‐GrandeiD, Andreas SchuhiD, Antonios Makropoulos, Jonathan Passerat‐PalmbachiD, Jelena BožekiD, Daniel RueckertiD, Joseph V. HajnaliD, Grainne Mary McAlonaniD, Anthony David EdwardsiD, Jonathan O’MuircheartaighiD
Abstract
Developmental delays in infanthood often persist, turning into life-long difficulties, and coming at great cost for the individual and community. By examining the developing brain and its relation to developmental outcomes we can start to elucidate how the emergence of brain circuits is manifested in variability of infant motor, cognitive and behavioural capacities. In this study, we examined if cortical structural covariance at birth, indexing coordinated development, is related to later infant behaviour. We included 193 healthy term-born infants from the Developing Human Connectome Project (dHCP). An individual cortical connectivity matrix derived from morphological and microstructural features was computed for each subject (morphometric similarity networks, MSNs) and was used as input for the prediction of behavioural scores at 18 months using Connectome-Based Predictive Modeling (CPM). Neonatal MSNs successfully predicted social-emotional performance. Predictive edges were distributed between and within known functional cortical divisions with a specific important role for primary and posterior cortical regions. These results reveal that multi-modal neonatal cortical profiles showing coordinated maturation are related to developmental outcomes and that network organization at birth provides an early infrastructure for future functional skills.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 22
- FWCI
- 2.45
- Field percentile
- top 11%
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