Elsevier

NeuroImage

Volume 41, Issue 1, 15 May 2008, Pages 19-34
NeuroImage

3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry

https://doi.org/10.1016/j.neuroimage.2008.02.010Get rights and content

Abstract

Tensor-based morphometry (TBM) creates three-dimensional maps of disease-related differences in brain structure, based on nonlinearly registering brain MRI scans to a common image template. Using two different TBM designs (averaging individual differences versus aligning group average templates), we compared the anatomical distribution of brain atrophy in 40 patients with Alzheimer's disease (AD), 40 healthy elderly controls, and 40 individuals with amnestic mild cognitive impairment (aMCI), a condition conferring increased risk for AD. We created an unbiased geometrical average image template for each of the three groups, which were matched for sex and age (mean age: 76.1Ā years+/-Ā 7.7 SD). We warped each individual brain image (NĀ =Ā 120) to the control group average template to create Jacobian maps, which show the local expansion or compression factor at each point in the image, reflecting individual volumetric differences. Statistical maps of group differences revealed widespread medial temporal and limbic atrophy in AD, with a lesser, more restricted distribution in MCI. Atrophy and CSF space expansion both correlated strongly with Mini-Mental State Exam (MMSE) scores and Clinical Dementia Rating (CDR). Using cumulative p-value plots, we investigated how detection sensitivity was influenced by the sample size, the choice of search region (whole brain, temporal lobe, hippocampus), the initial linear registration method (9- versus 12-parameter), and the type of TBM design. In the future, TBM may help to (1) identify factors that resist or accelerate the disease process, and (2) measure disease burden in treatment trials.

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 4ā€“6-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 ā€˜warpā€™, 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. ā€œexpansion 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 ā€˜averaging 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 Alzheimerā€™s 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:

References (107)

  • ChristensenG.E. et al.

    Synthesizing average 3D anatomical shapes

    Neuroimage

    (2006)
  • ChungM.K. et al.

    A unified statistical approach to deformation-based morphometry

    Neuroimage

    (2001)
  • DavatzikosC. et al.

    Voxel-based morphometry using the RAVENS maps: methods and validation using simulated longitudinal atrophy

    Neuroimage

    (2001)
  • DavatzikosC. et al.

    Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

    Neurobiol. Aging

    (2008)
  • DubbA. et al.

    Characterization of brain plasticity in schizophrenia using template deformation

    Acad. Radiol.

    (2005)
  • FanY. et al.

    Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

    Neuroimage

    (2008)
  • FleisherA. et al.

    Baseline structural MRI correlates of clinical measures in the Alzheimer's disease neuroimaging initiative

  • FolsteinM.F. et al.

    Mini-mental state. A practical method for grading the cognitive state of patients for the clinician

    J. Psychiatr. Res.

    (1975)
  • FoxN.C. et al.

    Imaging of onset and progression of Alzheimer's disease with voxel-compression mapping of serial magnetic resonance images

    Lancet

    (2001)
  • FreeboroughP.A. et al.

    Interactive algorithms for the segmentation and quantitation of 3-D MRI brain scans

    Comput. Methods Programs Biomed.

    (1997)
  • GeeJ. et al.

    Alzheimer's disease and frontotemporal dementia exhibit distinct atrophy-behavior correlates

    Acad. Radiol.

    (2003)
  • GenoveseC.R. et al.

    Thresholding of statistical maps in functional neuroimaging using the false discovery rate

    Neuroimage

    (2002)
  • GoodC.D. et al.

    A voxel-based morphometric study of ageing in 465 normal adult human brains

    Neuroimage

    (2001)
  • JoshiS. et al.

    Unbiased diffeomorphic atlas construction for computational anatomy

    Neuroimage

    (2004)
  • JovicichJ. et al.

    Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data

    Neuroimage

    (2006)
  • KarasG.B. et al.

    Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease

    Neuroimage

    (2004)
  • KochunovP. et al.

    An optimized individual target brain in the Talairach coordinate system

    Neuroimage

    (2002)
  • LangersD.R. et al.

    Enhanced signal detection in neuroimaging by means of regional control of the global false discovery rate

    Neuroimage

    (2007)
  • LeeA.D. et al.

    3D pattern of brain abnormalities in Fragile X syndrome visualized using tensor-based morphometry

    Neuroimage

    (2007)
  • LeowA.D. et al.

    Longitudinal stability of MRI for mapping brain change using tensor-based morphometry

    Neuroimage

    (2006)
  • LorenzenP. et al.

    Multi-modal image set registration and atlas formation

    Med. Image Anal.

    (2006)
  • MuellerS.G. et al.

    The Alzheimer's disease neuroimaging initiative

    Neuroimaging Clin. North Am.

    (2005)
  • MuellerS.G. et al.

    Ways toward an early diagnosis in Alzheimer's disease: The Alzheimer's Disease Neuroimaging Initiative (ADNI)

    Alzheimers. Dement.

    (2005)
  • RiddleW.R. et al.

    Characterizing changes in MR images with color-coded Jacobians

    Magn. Reson. Imaging

    (2004)
  • SalmondC.H. et al.

    Distributional assumptions in voxel-based morphometry

    Neuroimage

    (2002)
  • ShattuckD.W. et al.

    BrainSuite: an automated cortical surface identification tool

    Med. Image Anal.

    (2002)
  • ShenD. et al.

    Very high-resolution morphometry using mass-preserving deformations and HAMMER elastic registration

    Neuroimage

    (2003)
  • ShiinoA. et al.

    Four subgroups of Alzheimer's disease based on patterns of atrophy using VBM and a unique pattern for early onset disease

    Neuroimage

    (2006)
  • StudholmeC. et al.

    A template free approach to volumetric spatial normalization of brain anatomy

    Pattern Recogn. Lett.

    (2004)
  • StudholmeC. et al.

    Deformation tensor morphometry of semantic dementia with quantitative validation

    Neuroimage

    (2004)
  • TeipelS.J. et al.

    Multivariate deformation-based analysis of brain atrophy to predict Alzheimer's disease in mild cognitive impairment

    Neuroimage

    (2007)
  • AljabarP. et al.

    Analysis of Growth in the Developing Brain Using Non-Rigid Registration

    IEEE Int. Symp. Biomed. Imaging

    (2006)
  • ApostolovaL.G. et al.

    Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps

    Arch. Neurol.

    (2006)
  • AshburnerJ. et al.

    Morphometry

  • BenjaminiY. et al.

    Controlling the false discovery rate: a practical and powerful approach to multiple testing

    J. R. Stat. Soc., B.

    (1995)
  • BrunC. et al.

    Comparison of Standard and Riemannian Elasticity for Tensor-Based Morphometry in HIV/AIDS

  • BullmoreE.T. et al.

    Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain

    IEEE Trans. Med. Imaging

    (1999)
  • CallenD.J. et al.

    Beyond the hippocampus: MRI volumetry confirms widespread limbic atrophy in AD

    Neurology

    (2001)
  • CarmichaelO.T. et al.

    Mapping ventricular changes related to dementia and mild cognitive impairment in a large community-based cohort

    IEEE ISBI

    (2006)
  • ChetelatG. et al.

    Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment

    Neuroreport

    (2002)
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