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

• Published in: NeuroImage (Orlando, Fla.) Vol. 66; pp. 648 - 661
• Main Authors: 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, Weiner, Michael W., Thompson, Paul M.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.02.2013; Elsevier; Elsevier Limited
• Subjects: ADNI; Adult and adolescent clinical studies; Aged; Aging; Alzheimer Disease - genetics; Alzheimer Disease - pathology; Alzheimer's disease; Apolipoproteins E - genetics; Atrophy; Bias; Biological and medical sciences; Biomarkers; Brain - pathology; Clinical trials; Clinical Trials as Topic; Cognitive Dysfunction - pathology; Data Interpretation, Statistical; Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases; Diffusion Tensor Imaging - instrumentation; Diffusion Tensor Imaging - methods; Discriminant analysis; Drug trial; Estimates; Female; Health care; Humans; Male; Medical imaging; Medical sciences; Methods; Mild cognitive impairment; Neurology; Organic mental disorders. Neuropsychology; Prospective Studies; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Research Design - standards; Software; Tensor-based morphometry; ADNI; VBM; Quarc; AD; MRI; Mild cognitive impairment; BBSI; stat-ROI; Drug trial; SPM; TE; MCI; QC; Tensor-based morphometry; TI; Aging; ApoE; MDT; MI; Alzheimer's disease; FSL; TBM; TR; Nervous system diseases; Senescence; Alzheimer disease; Cerebral disorder; Central nervous system disease; Degenerative disease; mild cognitive impairment; Morphometry
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2012.10.086

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 24months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39AD 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.