title: Structural covariance networks across healthy young adults and their consistency.
abstract: OBJECTIVE
  To investigate structural covariance networks (SCNs) as measured by regional gray matter volumes with structural magnetic resonance imaging (MRI) from healthy young adults, and to examine their consistency and stability.
  
  
  METHODS
  Two independent cohorts were included in this study: Group 1 (82 healthy subjects aged 18-28 years) and Group 2 (109 healthy subjects aged 20-28 years). Structural MRI data were acquired at 3.0T and 1.5T using a magnetization prepared rapid-acquisition gradient echo sequence for these two groups, respectively. We applied independent component analysis (ICA) to construct SCNs and further applied the spatial overlap ratio and correlation coefficient to evaluate the spatial consistency of the SCNs between these two datasets.
  
  
  RESULTS
  Seven and six independent components were identified for Group 1 and Group 2, respectively. Moreover, six SCNs including the posterior default mode network, the visual and auditory networks consistently existed across the two datasets. The overlap ratios and correlation coefficients of the visual network reached the maximums of 72% and 0.71.
  
  
  CONCLUSIONS
  This study demonstrates the existence of consistent SCNs corresponding to general functional networks. These structural covariance findings may provide insight into the underlying organizational principles of brain anatomy.
authors.affiliation: College of Information Science and Technology, Beijing Normal University, Beijing, China.|State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
authors.forename: Xiaojuan
authors.initials: X
authors.lastname: Guo
authors.affiliation: College of Information Science and Technology, Beijing Normal University, Beijing, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
authors.forename: Taomei
authors.initials: T
authors.lastname: Guo
authors.affiliation: Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, Arizona, USA.
authors.forename: Kewei
authors.initials: K
authors.lastname: Chen
authors.affiliation: College of Information Science and Technology, Beijing Normal University, Beijing, China.
authors.forename: Jiacai
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Laboratory of Magnetic Resonance Imaging, Beijing 306 Hospital, Beijing, China.
authors.forename: Ke
authors.initials: K
authors.lastname: Li
authors.affiliation: Laboratory of Magnetic Resonance Imaging, Beijing 306 Hospital, Beijing, China.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Jin
authors.affiliation: College of Information Science and Technology, Beijing Normal University, Beijing, China.|State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
authors.forename: Li
authors.initials: L
authors.lastname: Yao
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D001921:Brain / Q000033:anatomy & histology*; D063132:Connectome / Q000379:methods*; D005260:Female; D066128:Gray Matter / Q000033:anatomy & histology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009415:Nerve Net / Q000033:anatomy & histology*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult
keywords: consistency; gray matter volume; healthy adults; independent component analysis; magnetic resonance imaging; structural networks