title: Altered Integration of Structural Covariance Networks in Young Children With Type 1 Diabetes.
abstract: Type 1 diabetes mellitus (T1D), one of the most frequent chronic diseases in children, is associated with glucose dysregulation that contributes to an increased risk for neurocognitive deficits. While there is a bulk of evidence regarding neurocognitive deficits in adults with T1D, little is known about how early-onset T1D affects neural networks in young children. Recent data demonstrated widespread alterations in regional gray matter and white matter associated with T1D in young children. These widespread neuroanatomical changes might impact the organization of large-scale brain networks. In the present study, we applied graph-theoretical analysis to test whether the organization of structural covariance networks in the brain for a cohort of young children with T1D (N = 141) is altered compared to healthy controls (HC; N = 69). While the networks in both groups followed a small world organization-an architecture that is simultaneously highly segregated and integrated-the T1D network showed significantly longer path length compared with HC, suggesting reduced global integration of brain networks in young children with T1D. In addition, network robustness analysis revealed that the T1D network model showed more vulnerability to neural insult compared with HC. These results suggest that early-onset T1D negatively impacts the global organization of structural covariance networks and influences the trajectory of brain development in childhood. This is the first study to examine structural covariance networks in young children with T1D. Improving glycemic control for young children with T1D might help prevent alterations in brain networks in this population. Hum Brain Mapp 37:4034-4046, 2016. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University, Stanford, California. hosseiny@stanford.edu.
authors.forename: S M Hadi
authors.initials: SM
authors.lastname: Hosseini
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University, Stanford, California.
authors.forename: Paul
authors.initials: P
authors.lastname: Mazaika
authors.affiliation: Division of Endocrinology, Nemours Children's Health System, Jacksonville, Florida.
authors.forename: Nelly
authors.initials: N
authors.lastname: Mauras
authors.affiliation: Division of Pediatric Endocrinology, Stanford University, Stanford, California.
authors.forename: Bruce
authors.initials: B
authors.lastname: Buckingham
authors.affiliation: Division of Pediatric Endocrinology, Yale University, New Haven, Connecticut.
authors.forename: Stuart A
authors.initials: SA
authors.lastname: Weinzimer
authors.affiliation: Division of Pediatric Endocrinology, University of Iowa, Iowa City, Iowa.
authors.forename: Eva
authors.initials: E
authors.lastname: Tsalikian
authors.affiliation: Department of Pediatrics, Washington University, St. Louis, Missouri.
authors.forename: Neil H
authors.initials: NH
authors.lastname: White
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University, Stanford, California.
authors.forename: Allan L
authors.initials: AL
authors.lastname: Reiss
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging*; D002648:Child; D002675:Child, Preschool; D015331:Cohort Studies; D003922:Diabetes Mellitus, Type 1 / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D009434:Neural Pathways / Q000000981:diagnostic imaging
keywords: brain networks; diabetes; graph theoretical analysis; gray matter volume; network efficiency; network resilience