Article

A study of diffusion tensor imaging by tissue-specific, smoothing-compensated voxel-based analysis

Waisman Laboratory for Brain Imaging and Behavior, Waisman Center, Madison, WI 53705, USA.
NeuroImage (Impact Factor: 6.36). 11/2008; 44(3):870-83. DOI: 10.1016/j.neuroimage.2008.09.041
Source: PubMed

ABSTRACT

Voxel-based analysis (VBA) is commonly used for statistical analysis of image data, including the detection of significant signal differences between groups. Typically, images are co-registered and then smoothed with an isotropic Gaussian kernel to compensate for image misregistration, to improve the signal-to-noise ratio (SNR), to reduce the number of multiple comparisons, and to apply random field theory. Problems with typical implementations of VBA include poor tissue specificity from image misregistration and smoothing. In this study, we developed a new tissue-specific, smoothing-compensated (T-SPOON) method for the VBA of diffusion tensor imaging (DTI) data with improved tissue specificity and compensation for image misregistration and smoothing. When compared with conventional VBA methods, the T-SPOON method introduced substantially less errors in the normalized and smoothed DTI maps. Another confound of the conventional DTI-VBA is that it is difficult to differentiate between differences in morphometry and DTI measures that describe tissue microstructure. T-SPOON VBA decreased the effects of differential morphometry in the DTI VBA studies. T-SPOON and conventional VBA were applied to a DTI study of white matter in autism. T-SPOON VBA results were found to be more consistent with region of interest (ROI) measurements in the corpus callosum and temporal lobe regions. The T-SPOON method may be also applicable to other quantitative imaging maps such as T1 or T2 relaxometry, magnetization transfer, or PET tracer maps.

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    • "Both approaches are mainly hypothesis driven and have therefore usually been limited to single regions or specific tracts (Aoki et al., 2013), such as the midsagittal portion of the corpus callosum (Frazier and Hardan, 2009), or the core of the arcuate and uncinate fasciculus (Hardan et al., 2000;Fletcher et al., 2010), but sample sizes have tended to be small (Billeci et al., 2012;Travers et al., 2012;Mueller et al., 2013). Other studies based on meta-analytical approaches (Radua et al., 2011;Aoki et al., 2013;Cauda et al., 2014), voxel-based morphometry (Barnea-Goraly et al., 2004;Keller et al., 2007;Cheung et al., 2009;Ke et al., 2009;Lee et al., 2009;Bloemen et al., 2010;Noriuchi et al., 2010;Groen et al., 2011;Jou et al., 2011), and tract-based spatial statistics (TBSS) (Barnea-Goraly et al., 2010;Cheng et al., 2010;Sahyoun et al., 2010;Jou et al., 2011;Shukla et al., 2010;Walker et al., 2012) have attempted to overcome some of the limitations attributable to small sample size and/or operator-dependent biases. However, it remains unclear whether the findings, if replicated in larger studies, are indicative of tract-specific anomalies in ASD or part of a more generalized brain abnormality (Wolff et al., 2012). "
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    • "As a result, neuroimaging studies are increasingly focusing on distributed brain networks in ASD (Noonan et al. 2009; Just et al. 2012). Diffusion-weighted imaging studies have demonstrated local as well as global differences in white matter microstructure in individuals with ASD, who are often reported to have decreased fractional anisotropy (Barnea-Goraly et al. 2004; Alexander et al. 2007; Keller et al. 2007; Sundaram et al. 2008; Thakkar et al. 2008; Lee et al. 2009) and increased mean diffusivity (Alexander et al. 2007; Barnea-Goraly et al. 2010; Fletcher et al. 2010; Sivaswamy et al. 2010), indicating aberrant organization and reduced coherence within white matter tracts. Although specific findings are variable in the literature (Mak-Fan et al. 2012; Travers et al. 2012), the abnormalities are typically widespread and encompass various fiber tracts, including corpus callosum, internal capsule, arcuate fasciculus, uncinate fasciculus, as well as projections to numerous locations in orbitofrontal and medial prefrontal cortex, cingulate cortex, and temporal lobes. "
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    • "Correlation analyses between WM tissue properties and cognitive alterations were performed on a voxel-by-voxel basis. Diffusion-derived maps used for regression analyses were created using a novel data processing technique that increases tissue specificity and compensates for the effect of the spatial smoothing (Lee et al., 2009). "
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