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Neuroimage. 2014 Oct 15;100:444-59. doi: 10.1016/j.neuroimage.2014.06.029. Epub 2014 Jun 18.

An automatic framework for quantitative validation of voxel based morphometry measures of anatomical brain asymmetry.

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Department of Signal Processing, Tampere University of Technology, P.O. Box 553, FIN-33101 Tampere, Finland. Electronic address:
Laboratory of Neuro Imaging (LONI), The Institute of Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, 2001 North Soto Street, SSB1-102, Los Angeles, CA 90032, USA; University of Michigan School of Nursing (UMSN), 400 North Ingalls, Ann Arbor, MI 48109-5482, USA.
Department of Signal Processing, Tampere University of Technology, P.O. Box 553, FIN-33101 Tampere, Finland.


The study of anatomical brain asymmetries has been a topic of great interest in the neuroimaging community in the past decades. However, the accuracy of brain asymmetry measurements has been rarely investigated. In this study, we propose a fully automatic methodology for the quantitative validation of brain tissue asymmetries as measured by Voxel Based Morphometry (VBM) from structural magnetic resonance (MR) images. Starting from a real MR image, the methodology generates simulated 3D MR images with a known and realistic pattern of inter-hemispheric asymmetry that models the left-occipital right-frontal petalia of a normal brain and the related rightward bending of the inter-hemispheric fissure. As an example, we generated a dataset of 64 simulated MR images and applied this dataset for the quantitative validation of optimized VBM measures of asymmetries in brain tissue composition. Our results suggested that VBM analysis strongly depended on the spatial normalization of the individual brain images, the selected template space, and the amount of spatial smoothing applied. The most accurate asymmetry detections were achieved by 9-degrees of freedom registration to the symmetrical template space with 4 to 8mm spatial smoothing.


Asymmetry; Gray matter; Magnetic resonance imaging; Quantitative validation; Spatial normalization; Spatial smoothing; Template space; VBM

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