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

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 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.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Hua X,Hibar DP,Ching CR,Boyle CP,Rajagopalan P,Gutman BA,Leow AD,Toga AW,Jack CR Jr,Harvey D,Weiner MW,Thompson PM,Alzheimer's Disease Neuroimaging Initiative.

doi

10.1016/j.neuroimage.2012.10.086

subject

Has Abstract

pub_date

2013-02-01 00:00:00

pages

648-61

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(12)01090-7

journal_volume

66

pub_type

杂志文章

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