Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and application for automated abnormality detection

The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
NeuroImage (Impact Factor: 6.36). 08/2010; 52(2):415-28. DOI: 10.1016/j.neuroimage.2010.04.238
Source: PubMed


Quantification of normal brain maturation is a crucial step in understanding developmental abnormalities in brain anatomy and function. The aim of this study was to develop atlas-based tools for time-dependent quantitative image analysis, and to characterize the anatomical changes that occur from 2years of age to adulthood. We used large deformation diffeomorphic metric mapping to register diffusion tensor images of normal participants into the common coordinates and used a pre-segmented atlas to segment the entire brain into 176 structures. Both voxel- and atlas-based analyses reported a structure that showed distinctive changes in terms of its volume and diffusivity measures. In the white matter, fractional anisotropy (FA) linearly increased with age in logarithmic scale, while diffusivity indices, such as apparent diffusion coefficient (ADC), and axial and radial diffusivity, decreased at a different rate in several regions. The average, variability, and the time course of each measured parameter are incorporated into the atlas, which can be used for automated detection of developmental abnormalities. As a demonstration of future application studies, the brainstem anatomy of cerebral palsy patients was evaluated and the altered anatomy was delineated.

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Available from: Kenichi Oishi, Oct 04, 2015
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    • "A whole brain, ROI, atlas-based analysis of diffusion-weighted data was performed using Diffeomap (Faria et al., 2010)( i m p l e m e n t e di First, affine (linear) normalization of the " JHU_MNI_single-subject " atlas (Mori et al., 2008)i s warped to individual native space. "
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    • "To perform the group analysis of datasets collected from several sites, datasets were matched into Neuroimaging Informatics Technology Initiative (NIFTI) format using the transversal view and radiology convention, and were registered into the standard Montreal Neurological Institute (MNI) [Burgund et al., 2002; Faria et al, 2010; Muzik et al., 2000] brain with 3  3  3 (mm 3 ) in voxel size and 61  73  61 (axial  coronal  sagittal) in resolution. The fMRIB software Library (FSL) was used to perform the pre-processing required for obtaining the 3D activation maps [Jenkinson et al., 2002; Jenkinson and Smith, 2001; Woolrich et al., 2001]. "
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