ORIGINAL RESEARCH Structural Covariance Networks Across Healthy Young Adults and Their Consistency Xiaojuan Guo, PhD, 1,2 Yan Wang, BS, 1 Taomei Guo, PhD, 2 Kewei Chen, PhD, 3 Jiacai Zhang, PhD, 1 Ke Li, BS, 4 Zhen Jin, PhD, 4 and Li Yao, PhD 1,2 * Purpose: To investigate structural covariance networks (SCNs) as measured by regional gray matter volumes with struc- tural magnetic resonance imaging (MRI) from healthy young adults, and to examine their consistency and stability. Materials and 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 cor- relation 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. Conclusion: 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. J. MAGN. RESON. IMAGING 2014;00:000–000 B uilding on advances in noninvasive imaging techniques and multivariate analysis methods, magnetic resonance imaging (MRI) studies have demonstrated that the human brain is organized into complex structural and functional networks. 1–3 More specifically, several structural MRI studies have shown correlations in cortical thickness and gray mat- ter density or volume in anatomically connected or func- tionally related brain regions. 4–6 Moreover, these human anatomical networks have small-world properties and are formed together into a modular architecture according to certain topographical principles. 7,8 However, in the last decade attention has been directed primarily to brain functional resting-state networks (RSNs) such as the default mode network (DMN), visual, auditory, and motor networks. 9–11 Brain functional activities are based on the structure of the neuroanatomical substrates. Thus, functional RSNs not only reflect inherent neuronal connectivity in the human brain but also provide indirect insights into the structural networks of the brain. 3,12 Using both functional MRI (fMRI) and diffusion tensor imaging (DTI) technologies, scientists reported that functional con- nectivity within RSNs is associated with the direct or View this article online at wileyonlinelibrary.com. DOI: 10.1002/jmri.24780 Received Jul 12, 2014, Accepted for publication Sep 29, 2014. *Address reprint requests to: L.Y., No. 19, XinJieKouWai St., HaiDian District, Beijing, China. E-mail: yaoli@bnu.edu.cn From the 1 College of Information Science and Technology, Beijing Normal University, Beijing, China; 2 State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; 3 Banner Alzheimer’s Institute and Banner Good Samaritan PET Center, Phoenix, Arizona, USA; and 4 Laboratory of Magnetic Resonance Imaging, Beijing 306 Hospital, Beijing, China. Contract grant sponsor: National Key Basic Research Program (973 Program), China; Contract grant number: 2012CB720704; Contract grant sponsor: National Natural Science Foundation (NNSF), China; Contract grant numbers: 81000603, 31170970; Contract grant sponsor: Funds for International Cooperation and Exchange of NNSF, China; Contract grant number: 61210001; Contract grant sponsor: Key Program of NNSF, China; Contract grant number: 91320201; Contract grant sponsor: National Institute of Mental Health, US; Contract grant number: RO1 MH57899; Contract grant sponsor: National Institute on Aging, US; Contract grant numbers: 9R01AG031581-10, P30 AG19610; k23 AG24062; Contract grant sponsor: State of Arizona. In addition, we also used data from the Open Access Series of Imaging Studies (OASIS). The OASIS project was funded by grants P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24RR021382, and R01 MH56584 V C 2014 Wiley Periodicals, Inc. 1