TY - JOUR
T1 - 3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry
AU - Hua, Xue
AU - Leow, Alex D.
AU - Lee, Suh
AU - Klunder, Andrea D.
AU - Toga, Arthur W.
AU - Lepore, Natasha
AU - Chou, Yi Yu
AU - Brun, Caroline
AU - Chiang, Ming Chang
AU - Barysheva, Marina
AU - Jack, Clifford R.
AU - Bernstein, Matt A.
AU - Britson, Paula J.
AU - Ward, Chadwick P.
AU - Whitwell, Jennifer L.
AU - Borowski, Bret
AU - Fleisher, Adam S.
AU - Fox, Nick C.
AU - Boyes, Richard G.
AU - Barnes, Josephine
AU - Harvey, Danielle
AU - Kornak, John
AU - Schuff, Norbert
AU - Boreta, Lauren
AU - Alexander, Gene E.
AU - Weiner, Michael W.
AU - Thompson, Paul M.
AU - the Alzheimer's Disease Neuroimaging Initiative, Alzheimer's Disease Neuroimaging Initiative
N1 - Funding Information:
Data used in preparing this article were obtained from the Alzheimer’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: Michael Weiner; NIH grant number U01 AG024904). ADNI is funded by the National Institute of Aging, the National Institute of Biomedical Imaging and Bioengineering (NIBIB), and the Foundation for the National Institutes of Health, through generous contributions from the following companies and organizations: Pfizer Inc., Wyeth Research, Bristol-Myers Squibb, Eli Lilly and Company, Glaxo- SmithKline, Merck & Co. Inc., AstraZeneca AB, Novartis Pharmaceuticals Corporation, the Alzheimer’s Association, Eisai Global Clinical Development, Elan Corporation plc, Forest Laboratories, and the Institute for the Study of Aging (ISOA), with participation from the U.S. Food and Drug Administration. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. Algorithm development for this study was also funded by the NIA, NIBIB, the National Library of Medicine, and the National Center for Research Resources (AG016570, EB01651, LM05639, RR019771 to PT). Author contributions were as follows: XH, AL, SL, AK, AT, NL, YC, MC, MB, RB, JB, NS, LB, and PT performed the image analyses; CJ, AD, MAB, PB, JG, CW, JW, BB, AF, NF, DH, JK, CS, GA, and MW contributed substantially to the image acquisition, study design, quality control, calibration and pre- processing, databasing and image analysis. We thank Anders Dale for his contributions to the image pre-processing and the ADNI project. Part of this work was undertaken at UCLH/UCL, which received a proportion of funding from the Department of Health’s NIHR Biomedical Research Centres funding scheme.
PY - 2008/5/15
Y1 - 2008/5/15
N2 - 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: 76.1 years+/- 7.7 SD). We warped each individual brain image (N = 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.
AB - 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: 76.1 years+/- 7.7 SD). We warped each individual brain image (N = 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.
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U2 - 10.1016/j.neuroimage.2008.02.010
DO - 10.1016/j.neuroimage.2008.02.010
M3 - Article
C2 - 18378167
AN - SCOPUS:42649102966
SN - 1053-8119
VL - 41
SP - 19
EP - 34
JO - NeuroImage
JF - NeuroImage
IS - 1
ER -