AI Article Synopsis
- The study investigates structural covariance networks (SCNs) in healthy young adults by analyzing gray matter volumes using MRI.
- Two cohorts of healthy subjects (82 and 109 individuals, respectively) underwent MRI scans, and independent component analysis was applied to identify and compare SCNs.
- Results showed 6-7 consistent SCNs across both groups, including key networks like the visual and auditory networks, indicating stable organizational principles in brain anatomy.
Article 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.
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| http://dx.doi.org/10.1002/jmri.24780 | DOI Listing |
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