Xiaojuan Guo1,2, Yan Wang1, Taomei Guo2, Kewei Chen3, Jiacai Zhang1, Ke Li4, Zhen Jin4, Li Yao1,2. 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. 4. Laboratory of Magnetic Resonance Imaging, Beijing 306 Hospital, Beijing, China.
Abstract
PURPOSE: 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. 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 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. 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.
PURPOSE: 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. 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 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. 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.
Authors: Ruiyang Ge; Paul Kot; Xiang Liu; Donna J Lang; Jane Z Wang; William G Honer; Fidel Vila-Rodriguez Journal: Hum Brain Mapp Date: 2019-05-22 Impact factor: 5.038