Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S et al. Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. NeuroImage 32: 180-194

Boston University, Boston, Massachusetts, United States
NeuroImage (Impact Factor: 6.36). 09/2006; 32(1):180-94. DOI: 10.1016/j.neuroimage.2006.02.051
Source: PubMed


In vivo MRI-derived measurements of human cerebral cortex thickness are providing novel insights into normal and abnormal neuroanatomy, but little is known about their reliability. We investigated how the reliability of cortical thickness measurements is affected by MRI instrument-related factors, including scanner field strength, manufacturer, upgrade and pulse sequence. Several data processing factors were also studied. Two test-retest data sets were analyzed: 1) 15 healthy older subjects scanned four times at 2-week intervals on three scanners; 2) 5 subjects scanned before and after a major scanner upgrade. Within-scanner variability of global cortical thickness measurements was <0.03 mm, and the point-wise standard deviation of measurement error was approximately 0.12 mm. Variability was 0.15 mm and 0.17 mm in average, respectively, for cross-scanner (Siemens/GE) and cross-field strength (1.5 T/3 T) comparisons. Scanner upgrade did not increase variability nor introduce bias. Measurements across field strength, however, were slightly biased (thicker at 3 T). The number of (single vs. multiple averaged) acquisitions had a negligible effect on reliability, but the use of a different pulse sequence had a larger impact, as did different parameters employed in data processing. Sample size estimates indicate that regional cortical thickness difference of 0.2 mm between two different groups could be identified with as few as 7 subjects per group, and a difference of 0.1 mm could be detected with 26 subjects per group. These results demonstrate that MRI-derived cortical thickness measures are highly reliable when MRI instrument and data processing factors are controlled but that it is important to consider these factors in the design of multi-site or longitudinal studies, such as clinical drug trials.

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Available from: Silvester Czanner, Oct 07, 2015
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    • "Complete descriptions of these procedures are provided in previous publications (Dale et al. 1999, Fischl and Dale 2000, Fischl et al. 2001, Fischl et al. 2002, Fischl et al. 2004, Ségonne et al. 2004, Han et al. 2006, Jovicich et al. 2006). In short, brightness and contrast normalisation were performed on the images, followed by removal of all non-brain tissues with a hybrid watershed/surface deformation procedure (Ségonne et al. 2004). "
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    NeuroImage 09/2015; DOI:10.1016/j.neuroimage.2015.08.073 · 6.36 Impact Factor
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    • "In the brain morphometry analyses, we applied a scale space search analysis method (Zhao, et al., 2013) that can produce a multi-scale description of brain structural changes to address the Gaussian kernel smoothing issue, i.e. in exploratory studies, in general, no prior information is available to determine the optimal smoothing filter size, and a single-filter analysis could not completely detect all signals with different sizes contained in the image data (Han, et al., 2006; Jones, et al., 2005; Lerch and Evans, 2005). The brain connectivity networks were assessed with structural covariance network (SCN) analysis (Evans, 2013). "
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    • "After assessing the variability in approved and disapproved data, we performed a power analysis to estimate the required number of subjects needed for group comparisons of cortical thickness data, based on the measured standard errors [Han et al., 2006]. This type of calculation is important because it reveals the increased number of subjects required to compensate for the increased variability that results from including disapproved data in the analyses. "
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