Mapping Alzheimer's disease progression in 1309 MRI scans

Power estimates for different inter-scan intervals

Xue Hua, Suh Lee, Derrek P. Hibar, Igor Yanovsky, Alex D. Leow, Arthur W. Toga, Clifford R. Jack, Matt A. Bernstein, Eric M. Reiman, Danielle J. Harvey, John Kornak, Norbert Schuff, Gene E Alexander, Michael W. Weiner, Paul M. Thompson

Research output: Contribution to journalArticle

61 Citations (Scopus)

Abstract

Neuroimaging centers and pharmaceutical companies are working together to evaluate treatments that might slow the progression of Alzheimer's disease (AD), a common but devastating late-life neuropathology. Recently, automated brain mapping methods, such as tensor-based morphometry (TBM) of structural MRI, have outperformed cognitive measures in their precision and power to track disease progression, greatly reducing sample size estimates for drug trials. In the largest TBM study to date, we studied how sample size estimates for tracking structural brain changes depend on the time interval between the scans (6-24months). We analyzed 1309 brain scans from 91 probable AD patients (age at baseline: 75.4±7.5 years) and 189 individuals with mild cognitive impairment (MCI; 74.6±7.1 years), scanned at baseline, 6, 12, 18, and 24months. Statistical maps revealed 3D patterns of brain atrophy at each follow-up scan relative to the baseline; numerical summaries were used to quantify temporal lobe atrophy within a statistically-defined region-of-interest. Power analyses revealed superior sample size estimates over traditional clinical measures. Only 80, 46, and 39 AD patients were required for a hypothetical clinical trial, at 6, 12, and 24months respectively, to detect a 25% reduction in average change using a two-sided test (α=0.05, power=80%). Correspondingly, 106, 79, and 67 subjects were needed for an equivalent MCI trial aiming for earlier intervention. A 24-month trial provides most power, except when patient attrition exceeds 15-16%/year, in which case a 12-month trial is optimal. These statistics may facilitate clinical trial design using voxel-based brain mapping methods such as TBM.

Original languageEnglish (US)
Pages (from-to)63-75
Number of pages13
JournalNeuroImage
Volume51
Issue number1
DOIs
StatePublished - May 2010

Fingerprint

Disease Progression
Alzheimer Disease
Magnetic Resonance Imaging
Sample Size
Brain Mapping
Atrophy
Brain
Clinical Trials
Temporal Lobe
Neuroimaging
Pharmaceutical Preparations
Power (Psychology)
Therapeutics

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology

Cite this

Hua, X., Lee, S., Hibar, D. P., Yanovsky, I., Leow, A. D., Toga, A. W., ... Thompson, P. M. (2010). Mapping Alzheimer's disease progression in 1309 MRI scans: Power estimates for different inter-scan intervals. NeuroImage, 51(1), 63-75. https://doi.org/10.1016/j.neuroimage.2010.01.104

Mapping Alzheimer's disease progression in 1309 MRI scans : Power estimates for different inter-scan intervals. / Hua, Xue; Lee, Suh; Hibar, Derrek P.; Yanovsky, Igor; Leow, Alex D.; Toga, Arthur W.; Jack, Clifford R.; Bernstein, Matt A.; Reiman, Eric M.; Harvey, Danielle J.; Kornak, John; Schuff, Norbert; Alexander, Gene E; Weiner, Michael W.; Thompson, Paul M.

In: NeuroImage, Vol. 51, No. 1, 05.2010, p. 63-75.

Research output: Contribution to journalArticle

Hua, X, Lee, S, Hibar, DP, Yanovsky, I, Leow, AD, Toga, AW, Jack, CR, Bernstein, MA, Reiman, EM, Harvey, DJ, Kornak, J, Schuff, N, Alexander, GE, Weiner, MW & Thompson, PM 2010, 'Mapping Alzheimer's disease progression in 1309 MRI scans: Power estimates for different inter-scan intervals', NeuroImage, vol. 51, no. 1, pp. 63-75. https://doi.org/10.1016/j.neuroimage.2010.01.104
Hua, Xue ; Lee, Suh ; Hibar, Derrek P. ; Yanovsky, Igor ; Leow, Alex D. ; Toga, Arthur W. ; Jack, Clifford R. ; Bernstein, Matt A. ; Reiman, Eric M. ; Harvey, Danielle J. ; Kornak, John ; Schuff, Norbert ; Alexander, Gene E ; Weiner, Michael W. ; Thompson, Paul M. / Mapping Alzheimer's disease progression in 1309 MRI scans : Power estimates for different inter-scan intervals. In: NeuroImage. 2010 ; Vol. 51, No. 1. pp. 63-75.
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