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Cited by 145Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsMedical Image Segmentation Techniques

Optimizing power to track brain degeneration in Alzheimer’s disease and mild cognitive impairment with tensor-based morphometry: an ADNI study of 515 subjects

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Hua, Xue, Lee, Suh, Yanovsky, Igor, Leow, Alex D., Chou, Yi-Yu, Ho, April J., Gutman, Boris, Toga, Arthur W. +9 more·2009·NeuroImage
DOI 10.1016/j.neuroimage.2009.07.011PMC PMC2971697
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Abstract

Tensor-based morphometry (TBM) is a powerful method to map the 3D profile of brain degeneration in Alzheimer's disease (AD) and mild cognitive impairment (MCI). We optimized a TBM-based image analysis method to determine what methodological factors, and which image-derived measures, maximize statistical power to track brain change. 3D maps, tracking rates of structural atrophy over time, were created from 1030 longitudinal brain MRI scans (1-year follow-up) of 104 AD patients (age: 75.7+/-7.2 years; MMSE: 23.3+/-1.8, at baseline), 254 amnestic MCI subjects (75.0+/-7.2 years; 27.0+/-1.8), and 157 healthy elderly subjects (75.9+/-5.1 years; 29.1+/-1.0), as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). To determine which TBM designs gave greatest statistical power, we compared different linear and nonlinear registration parameters (including different regularization functions), and different numerical summary measures derived from the maps. Detection power was greatly enhanced by summarizing changes in a statistically-defined region-of-interest (ROI) derived from an independent training sample of 22 AD patients. Effect sizes were compared using cumulative distribution function (CDF) plots and false discovery rate methods. In power analyses, the best method required only 48 AD and 88 MCI subjects to give 80% power to detect a 25% reduction in the mean annual change using a two-sided test (at alpha=0.05). This is a drastic sample size reduction relative to using clinical scores as outcome measures (619 AD/6797 MCI for the ADAS-Cog, and 408 AD/796 MCI for the Clinical Dementia Rating sum-of-boxes scores). TBM offers high statistical power to track brain changes in large, multi-site neuroimaging studies and clinical trials of AD.

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  • Among the three cognitive assessments, CDR-sob showed the strongest correlations with dMRI indices, in line with prior ADNI brain imaging studies.

    “Among the three cognitive assessments, CDR-sob showed the strongest correlations with dMRI indices, in line with prior ADNI brain imaging studies (Hua et al., 2009; Nir et al., 2013).”

    From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Hua et al., 2009

    The Results section (“Minimal sample size estimates and power analyses”; page unavailable) identifies CDR-SB as the best of the three clinical assessments, but ranks them by the sample sizes needed to detect change. It does not show that CDR-SB had the strongest correlations with dMRI indices; the imaging measure studied was tensor-based morphometry of structural MRI. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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Authors
Hua, X., Lee, S., Yanovsky, I., Leow, A. D., Chou, Y.-Y., Ho, A. J., Gutman, B., Toga, A. W., Jack, C. R., Bernstein, M. A., Reiman, E. M., Harvey, D. J., Kornak, J., Schuff, N., Alexander, G. E., Weiner, M. W., & Thompson, P. M.
Affiliations
  • University of California, Los Angeles (US)
  • Mayo Clinic (US)
  • University of Arizona (US)
  • University of California, Davis (US)
  • University of California, San Francisco (US)
  • San Francisco VA Medical Center (US)
Corresponding author
Thompson, Paul M.
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2009-12
Volume
48
Issue
4
Pages
668-681
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2009.07.011
PMC
PMC2971697
OpenAlex
W2063859734
Cited by
145
References
16
Citations by year
2026: 1 · 2025: 4 · 2024: 8 · 2023: 5 · 2022: 2 · 2021: 2 · 2020: 4 · 2019: 3 · 2018: 5 · 2017: 5 · 2016: 5 · 2015: 14 · 2014: 14 · 2013: 10 · 2012: 12
FWCI
12.99
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institute of Biomedical Imaging and Bioengineering
  • GlaxoSmithKline
  • U.S. National Library of Medicine
  • National Institute on Aging
  • National Center for Research Resources (AG016570, LM05639, RR019771, EB01651)
  • Pfizer
  • National Institutes of Health (U01 AG024904)
  • Alzheimer's Disease Neuroimaging Initiative
  • Foundation for the National Institutes of Health
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
tensor-based morphometry, Alzheimer's disease, mild cognitive impairment, statistical power, longitudinal MRI, image registration, regularization function, region of interest, sample size reduction, false discovery rate, ADNI
MeSH terms
Aged, Alzheimer Disease, Atrophy, Brain, Cognition Disorders, Follow-Up Studies, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Neuropsychological Tests, Time Factors, Linear Models, Databases, Factual, Nonlinear Dynamics, Disease Progression, Neurodegenerative Diseases, Imaging, Three-Dimensional

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