title: Normalization of similarity-based individual brain networks from gray matter MRI and its association with neurodevelopment in infants with intrauterine growth restriction.
abstract: Obtaining individual biomarkers for the prediction of altered neurological outcome is a challenge of modern medicine and neuroscience. Connectomics based on magnetic resonance imaging (MRI) stands as a good candidate to exhaustively extract information from MRI by integrating the information obtained in a few network features that can be used as individual biomarkers of neurological outcome. However, this approach typically requires the use of diffusion and/or functional MRI to extract individual brain networks, which require high acquisition times and present an extreme sensitivity to motion artifacts, critical problems when scanning fetuses and infants. Extraction of individual networks based on morphological similarity from gray matter is a new approach that benefits from the power of graph theory analysis to describe gray matter morphology as a large-scale morphological network from a typical clinical anatomic acquisition such as T1-weighted MRI. In the present paper we propose a methodology to normalize these large-scale morphological networks to a brain network with standardized size based on a parcellation scheme. The proposed methodology was applied to reconstruct individual brain networks of 63 one-year-old infants, 41 infants with intrauterine growth restriction (IUGR) and 22 controls, showing altered network features in the IUGR group, and their association with neurodevelopmental outcome at two years of age by means of ordinal regression analysis of the network features obtained with Bayley Scale for Infant and Toddler Development, third edition. Although it must be more widely assessed, this methodology stands as a good candidate for the development of biomarkers for altered neurodevelopment in the pediatric population.
authors.affiliation: Fetal and Perinatal Medicine Research Group, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. Electronic address: dbatalle@clinic.ub.es.
authors.forename: Dafnis
authors.initials: D
authors.lastname: Batalle
authors.forename: Emma
authors.initials: E
authors.lastname: Muñoz-Moreno
authors.forename: Francesc
authors.initials: F
authors.lastname: Figueras
authors.forename: Nuria
authors.initials: N
authors.lastname: Bargallo
authors.forename: Elisenda
authors.initials: E
authors.lastname: Eixarch
authors.forename: Eduard
authors.initials: E
authors.lastname: Gratacos
mesh_terms: D001921:Brain / Q000254:growth & development* / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D005317:Fetal Growth Retardation / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007223:Infant; D008279:Magnetic Resonance Imaging; D008297:Male
keywords: AAL; BSID-III; Bayley Scale for Infant and Toddler Development; Bayley scale for infant development, third edition; Brain morphology; CSF; Children; Connectome; FDR; False Discovery Rate; GA; GM; Graph theory; IUGR; MRI; Neurodevelopment; WM; automated anatomical labeling; cerebrospinal fluid; gestational age; gray matter; intrauterine growth restriction; magnetic resonance imaging; white matter