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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
Comparative analysis of Voxel-based morphometry using T1 and T2-weighted magnetic resonance imaging to explore the
Maziar Jalalvandi1, Seyed Amir Hossein Batouli2,3,4
1Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Abstract:
Voxel-based morphometry (VBM) of T1-weighted (T1-w) magnetic resonance imaging (MRI) is primarily used to study the association of brain structure with cognitive functions. However, in theory, T2-weighted (T2-w) MRI could also be used in VBM studies because of its sensitivity to pathology and tissue changes. We aimed to compare the T1-w and T2-w images to study brain structures in association with cognitive abilities. VBM analysis was applied to T1-w and T2-w MRI data of 120 healthy participants aged 20 to 40. The MRI data was collected using a 3T machine, and it was analyzed with CAT12 to extract maps of Gray matter(GM). We used six cognitive tasks to assess cognitive abilities, including the balloon analog risk task (BART), block design, forward and backward digit span (FDST and BDST), and trail-making tasks A and B. Compared to T2-w, T1-w data showed more brain voxels in the BART, block design, FDST, TMT-A, and TMT-B tasks. However, T2-w imaging identified a greater number of voxels in the BDST. T1-w images identified more correlated brain regions with cognitive scores in the FDST, TMT-A, and B tasks than T2-w. In BART and Block design tasks, both methods revealed the same number of correlated regions, and T2-w just showed more regions than T1-w in the BDST. Findings revealed distinct patterns of sensitivity between T1-w and T2-w imaging in detecting brain regions associated with cognition. The two approaches demonstrated different strengths in identifying areas correlated with cognitive abilities. This study provides valuable guidance for selecting appropriate methods for identifying the optimal approach for detecting brain regions that exhibit the strongest correlations with cognitive abilities.
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