Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer's disease clinical trials

Xue Hua, Derrek P. Hibar, Christopher R K Ching, Christina P. Boyle, Priya Rajagopalan, Boris A. Gutman, Alex D. Leow, Arthur W. Toga, Clifford R. Jack, Danielle J Harvey, Michael W. Weiner, Paul M. Thompson

Research output: Contribution to journalArticle

79 Citations (Scopus)

Abstract

Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24. months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39. AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI.

Original languageEnglish (US)
Pages (from-to)648-661
Number of pages14
JournalNeuroImage
Volume66
DOIs
StatePublished - Feb 1 2013

Fingerprint

Neuroimaging
Sample Size
Alzheimer Disease
Clinical Trials
Magnetic Resonance Imaging
Brain
Apolipoprotein E4
Atrophy
Disease Progression
Healthy Volunteers
Biomarkers
Cognitive Dysfunction
Power (Psychology)
Therapeutics

Keywords

  • ADNI
  • Aging
  • Alzheimer's disease
  • Drug trial
  • Mild cognitive impairment
  • Tensor-based morphometry

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology

Cite this

Hua, X., Hibar, D. P., Ching, C. R. K., Boyle, C. P., Rajagopalan, P., Gutman, B. A., ... Thompson, P. M. (2013). Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer's disease clinical trials. NeuroImage, 66, 648-661. https://doi.org/10.1016/j.neuroimage.2012.10.086

Unbiased tensor-based morphometry : Improved robustness and sample size estimates for Alzheimer's disease clinical trials. / Hua, Xue; Hibar, Derrek P.; Ching, Christopher R K; Boyle, Christina P.; Rajagopalan, Priya; Gutman, Boris A.; Leow, Alex D.; Toga, Arthur W.; Jack, Clifford R.; Harvey, Danielle J; Weiner, Michael W.; Thompson, Paul M.

In: NeuroImage, Vol. 66, 01.02.2013, p. 648-661.

Research output: Contribution to journalArticle

Hua, X, Hibar, DP, Ching, CRK, Boyle, CP, Rajagopalan, P, Gutman, BA, Leow, AD, Toga, AW, Jack, CR, Harvey, DJ, Weiner, MW & Thompson, PM 2013, 'Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer's disease clinical trials', NeuroImage, vol. 66, pp. 648-661. https://doi.org/10.1016/j.neuroimage.2012.10.086
Hua, Xue ; Hibar, Derrek P. ; Ching, Christopher R K ; Boyle, Christina P. ; Rajagopalan, Priya ; Gutman, Boris A. ; Leow, Alex D. ; Toga, Arthur W. ; Jack, Clifford R. ; Harvey, Danielle J ; Weiner, Michael W. ; Thompson, Paul M. / Unbiased tensor-based morphometry : Improved robustness and sample size estimates for Alzheimer's disease clinical trials. In: NeuroImage. 2013 ; Vol. 66. pp. 648-661.
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