3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry
Introduction
Alzheimer's disease (AD) is the commonest form of dementia worldwide, afflicting over 5 million people in the United States alone. In early AD, memory is typically among the first functions to be impaired, followed by a progressive decline in executive function, language, affect, and other cognitive and behavioral domains. It would be beneficial to prevent AD progression before widespread neurodegeneration has occurred, so recent therapeutic efforts have also focused on individuals with mild cognitive impairment (MCI), a transitional state between normal aging and dementia that carries a 4ā6-fold increased risk, relative to the general population, of future diagnosis of dementia (Petersen et al., 1999, Petersen, 2000, Petersen et al., 2001). Early detection requires innovations in tracking disease burden in vivo (Fleisher et al., 2007). Magnetic resonance imaging (MRI) and MRI-based image analysis methods have the potential to track brain atrophy automatically at multiple time-points. MRI has revealed fine-scale anatomical changes which are associated with cognitive decline and which occur in a spreading pattern that mirrors the advance of pathology (Thompson and Apostolova, in press). MRI-based maps of brain degeneration are beginning to reveal the distribution and evolution of cerebral volume losses, how brain changes in AD and other dementias relate to behavior, and which brain changes predict imminent decline (Scahill et al., 2003, Apostolova et al., 2006, Apostolova and Thompson, 2007).
Tensor-based morphometry (TBM) is a relatively new image analysis technique that identifies regional structural differences from the gradients of the deformation fields that align, or āwarpā, images to a common anatomical template (reviewed in (Ashburner and Friston, 2003)). Highly automated methods such as TBM are being tested to examine their utility in large-scale clinical trials, and in studies to identify factors that influence disease onset, progression (Leow et al., 2005b, Cardenas et al., 2007), or normal development (Thompson et al., 2000a, Chung et al., 2001, Hua et al., in press). In TBM, a nonlinear registration algorithm reshapes each 3D structural image to match a target brain image ā either based on an individual subject, or specially constructed to reflect the mean anatomy of a population (Kochunov et al., 2001, Kochunov et al., 2002, Lepore et al., 2007). Color-coded Jacobian maps ā which show the local expansion or compression factor at each point in the image ā indicate local volume loss or gain relative to a reference image (Freeborough and Fox, 1998, Chung et al., 2001, Fox et al., 2001, Ashburner and Friston, 2003, Riddle et al., 2004). TBM may also be used to map systematic anatomic differences between different patient groups using cross-sectional data (Davatzikos et al., 2003, Shen and Davatzikos, 2003, Studholme et al., 2004, Dubb et al., 2005, Brun et al., 2007, Chiang et al., 2007a, Chiang et al., 2007b, Lee et al., 2007, Lepore et al., 2008).
The traditional TBM design (Ashburner, 2007, Chiang et al., 2007a, Chiang et al., 2007b) computes individual Jacobian maps, i.e. āexpansion factor mapsā, from the non-linear registrations that align each subject's MRI image to a reference brain. Distinguishing features of group morphometry emerge after the maps of individual anatomical differences from the template are compared statistically across groups, or correlated with relevant clinical measures. This scheme may be called āaveraging individual differencesā in the sense that the signal analyzed is based on maps of anatomical differences computed for every individual separately (Rohlfing et al., 2005). We use this term to distinguish it from an approach that directly aligns mean anatomical templates representing each group (Rohlfing et al., 2005, Aljabar et al., 2008). By contrast, when a Jacobian map is created for each subject ā which is the standard TBM approach that we use to report findings in this paper ā correlations may be assessed between the detected individual differences and individual factors such as age, sex and clinical scores. We compare the standard and direct approaches later in this paper.
3D maps that define the level of atrophy (relative to appropriate controls) at a certain disease stage (Jack et al., 2005), may have value in staging the degenerative process, predicting outcomes, and understanding atrophic patterns characteristic of different dementia subtypes or stages, e.g. when individuals transition from MCI and AD. In this study, we examined the level of atrophy in AD and MCI relative to controls; we studied how specific methodological choices (e.g., sample size, initial linear registration) affected the statistical power to detect these differences; and we also investigated, at a voxelwise level, how brain atrophy correlated with clinical measures such as MMSE, and global Clinical Dementia Rating (CDR). Finally, we compared our results using the traditional TBM design with ones from directly aligning group average images ā a relatively new concept in deformation-based group morphometry, which has been advocated recently in the literature (Rohlfing et al., 2005, Aljabar et al., 2006, Aljabar et al., 2008).
Section snippets
Subjects
The Alzheimer's Disease Neuroimaging Initiative (ADNI) (Mueller et al., 2005a, Mueller et al., 2005b) is a large multi-site longitudinal MRI and FDG-PET (fluorodeoxyglucose positron emission tomography) study of 800 adults, ages 55 to 90, including 200 elderly controls, 400 subjects with mild cognitive impairment, and 200 patients with AD. The ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and
3D maps of brain atrophy in MCI and AD
We first examined the level of brain atrophy using the method of averaging individual differences. The resulting statistical maps (Fig. 2) detected the known characteristic patterns of atrophy in AD, revealing profound tissue loss in the temporal lobes bilaterally, the hippocampus, thalamus, widening of the bodies of the lateral ventricles and expansion of the circular sulcus of the insula.
Permutation tests were conducted to assess the overall significance of the maps, corrected for multiple
Discussion
This study had four main findings. First, a TBM method based on directly aligning group averaged images was found to be problematic, as it did not correctly control for false positives. This problem was solved by aligning each subject to a single template, and analyzing individual maps. Second, we showed a CDF-based method that can help to decide which methodological choices affect power in TBM; linear (9 parameter) initial registration and larger samples were found to give higher effect sizes,
Acknowledgments
Data used in preparing this article were obtained from the Alzheimerās Disease Neuroimaging Initiative database (www.loni. ucla.edu/ADNI). Many ADNI investigators therefore contributed to the design and implementation of ADNI or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators is available at www.loni.ucla.edu/ADNI/ Collaboration/ADNI_Citation.shtml. This work was primarily funded by the ADNI (Principal Investigator:
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