Comments and ControversiesAccurate measurement of brain changes in longitudinal MRI scans using tensor-based morphometry
Introduction
This paper responds to a recent commentary in the journal NeuroImage (Thompson and Holland, 2011), regarding the accurate estimation of changes in serial brain MRI scans. Thompson and Holland (2011)3 pointed out an important issue about potential image registration bias when computing changes in brain images, which they noticed in a re-analysis of the data we previously published in NeuroImage (Hua et al., 2010). We carefully studied and agreed with the main argument in Thompson and Holland's letter and have developed a solution to the problem by using inverse-consistent registration. The resulting updated measures from tensor-based morphometry are informative and powerful for use in drug trials to assess factors that affect brain change; sample size estimates remain competitive. Measures from our inverse-consistent algorithm show very good power, and are superior to the adjustments that showed poor statistical power in the Thompson and Holland re-analysis. We would like to thank Thompson and Holland for noting surprising aspects of our prior data and helping us identify and correct them.
Section snippets
What is tensor-based morphometry?
Tensor-based morphometry (TBM) produces 3D maps of volumetric brain change found by deforming one brain to match another. Individual maps of brain changes (also called Jacobian maps) are aligned to an average group template, and group-wise comparisons can be made using voxel-based statistics. We note, for clarity, that although this general type of analysis is called TBM, many nonlinear image registration methods have been developed to compute brain changes analyzed in this way (e.g.,
Methods
As in our prior work (Hua et al., 2010), we used tensor-based morphometry (TBM) to map the 3D profile of progressive atrophy in 91 subjects with probable AD (age: 75.4 ± 7.5 years), and 188 with amnestic mild cognitive impairment (MCI; 74.6 ± 7.1 years), scanned at 0, 6, 12, 18 and 24 months (in ADNI, only the MCI subjects were scanned at 18 month intervals). In the current analysis, we added 152 healthy controls (age: 76.0 ± 4.8 years), scanned at 0, 6, 12, and 24 months. To avoid sampling different
Numerical inverse consistency
To show that our inverse-consistent registration algorithm ic-sKL-MI indeed created maps that are inverse-consistent, we made a map of the inverse consistency error, ICE = ||x − h ⁎ h− 1(x)|| where h is the mapping from one time point to another, and h− 1 is the mapping computed in reverse (i.e., by the algorithm applied to the same scans, but with the order of the scans switched). A typical map is shown in Fig. 2(a), showing that the ICE is around 0.005 mm or lower, throughout the brain, with higher
Discussion
First, we are grateful to Thompson and Holland (2011) for pointing out the nonlinear offset of 1.2–1.4% in our previously reported atrophy rate measures. Although some of this offset may result from biological sources, we showed that the intercept from all sources (including biological departures from linearity) is only 0.28% when using inverse-consistent registration to estimate the brain changes. Inverse-consistency errors in our new measures of change were effectively zero throughout the
Summary
In addition to offering high power to assess factors influencing brain change, TBM provides 3D anatomical maps showing the region and rate of brain changes, which are not necessarily provided by other numeric summary methods. As noted by Scahill et al. (2002) in their early work on AD with fluid registration, having maps of changes is advisable for treatment trials, in case treatments show region-specific effects, or beneficial effects in regions not surveyed or anticipated when focusing on a
Acknowledgments and author contributions
We thank Wes Thompson and Dominic Holland for noticing surprising aspects of our prior data that we address here. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG
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2017, NeuroImage: ClinicalCitation Excerpt :To address this, the low-dimensional basis-function approach used in early work has here been replaced with a high-dimensional diffeomorphic approach (Ashburner, 2007) that has been shown to perform very well (Klein et al., 2009). Methods other than longitudinal VBM exist to study atrophy progression, such as Tensor Based Morphometry (Hua et al., 2011) or methods based on FreeSurfer (Dale et al., 1999; Fischl et al., 1999; Reuter et al., 2012) (see for instance (Caverzasi et al., 2014a, 2014b; Landin-Romero et al., 2016; Holland et al., 2012)). Longitudinal TBM and VBM are very closely related, since both rely on the Jacobian determinants from spatial transformations to characterise volume change.
- 1
These authors contributed equally.
- 2
Data used in preparing this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. ADNI investigators include (complete listing available at: http://www.loni.ucla.edu/ADNI/Collaboration/ADNI_Manuscript_Citations.pdf)