Journal article
Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity
Scientific reports, Vol.14(1), pp.9331-13
04/23/2024
DOI: 10.1038/s41598-024-58682-8
PMCID: PMC11039622
PMID: 38653988
Abstract
The neurodevelopmental outcomes of preterm infants can be stratified based on the level of prematurity. We explored brain structural networks in extremely preterm (EP; < 28 weeks of gestation) and very-to-late (V-LP; ≥ 28 and < 37 weeks of gestation) preterm infants at term-equivalent age to predict 2-year neurodevelopmental outcomes. Using MRI and diffusion MRI on 62 EP and 131 V-LP infants, we built a multimodal feature set for volumetric and structural network analysis. We employed linear and nonlinear machine learning models to predict the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) scores, assessing predictive accuracy and feature importance. Our findings revealed that models incorporating local connectivity features demonstrated high predictive performance for BSID-III subsets in preterm infants. Specifically, for cognitive scores in preterm (variance explained, 17%) and V-LP infants (variance explained, 17%), and for motor scores in EP infants (variance explained, 15%), models with local connectivity features outperformed others. Additionally, a model using only local connectivity features effectively predicted language scores in preterm infants (variance explained, 15%). This study underscores the value of multimodal feature sets, particularly local connectivity, in predicting neurodevelopmental outcomes, highlighting the utility of machine learning in understanding microstructural changes and their implications for early intervention.
Details
- Title: Subtitle
- Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity
- Creators
- Yong Hun Jang - Hanyang UniversityJusung Ham - University of IowaPayam Hosseinzadeh Kasani - Anyang UniversityHyuna Kim - Hanyang UniversityJoo Young Lee - Hanyang UniversityGang Yi Lee - Hanyang UniversityTae Hwan Han - Hanyang University Seoul HospitalBung-Nyun Kim - Seoul National University HospitalHyun Ju Lee - Hanyang University
- Resource Type
- Journal article
- Publication Details
- Scientific reports, Vol.14(1), pp.9331-13
- DOI
- 10.1038/s41598-024-58682-8
- PMID
- 38653988
- PMCID
- PMC11039622
- NLM abbreviation
- Sci Rep
- ISSN
- 2045-2322
- eISSN
- 2045-2322
- Publisher
- Nature Publishing Group UK
- Grant note
- NRF-2020-M3E5D9080787 / Korean Government MSIT
- Language
- English
- Date published
- 04/23/2024
- Academic Unit
- Communication Sciences and Disorders; Otolaryngology
- Record Identifier
- 9985113006102771
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