Impact of scale space search on age-and gender-related changes in MRIbased cortical morphometry

McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Montréal, Québec, Canada.
Human Brain Mapping (Impact Factor: 5.97). 03/2012; 34.
Source: PubMed


In magnetic resonance imaging based brain morphometry, Gaussian smoothing is often applied to increase the signal-to-noise ratio and to increase the detection power of statistical parametric maps. However, most existing studies used a single smoothing filter without adequately justifying their choices. In this article, we want to determine the extent for which performing a morphometry analysis using multiple smoothing filters, namely conducting a scale space search, improves or decreases the detection power. We first compared scale space search with single-filter analysis through a simulated population study. The multiple comparisons in our four-dimensional scale space searches were corrected for using a unified P-value approach. Our results illustrate that, compared with a single-filter analysis, a scale space search analysis can properly capture the variations in analysis results caused by variations in smoothing, and more importantly, it can obviously increase the sensitivity for detecting brain morphometric changes. We also show that the cost of an increased critical threshold for conducting a scale space search is very small. In the second experiment, we investigated age and gender effects on cortical volume, thickness, and surface area in 104 normal subjects using scale space search. The obtained results provide a perspective of scale space theory on the morphological changes with age and gender. These results suggest that, in exploratory studies of aging, gender, and disease, conducting a scale space search is essential, if we are to produce a complete description of the structural changes or abnormalities associated with these dimensions. Hum Brain Mapp, 2012. © 2012 Wiley Periodicals, Inc.

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    • "To address this issue, we used the scale space search based brain morphometric analysis algorithm [Zhao et al., 2013] to obtain a relatively complete description of the structural changes at different scale levels (different smoothing filter sizes). Compared with conventional single-filter analyses, the scale space search method can properly capture the variations in analysis results caused by variations in smoothing, and it can obviously increase the detection sensitivity (for more details, please read [Zhao et al., 2013]). The scale space search analysis was implemented using our Scale Space Cortical Morphometry toolbox (http:// and the SurfStat toolbox ( "
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