Unbiased Tensor-Based Morphometry: Improved Robustness and Sample Size Estimates for Alzheimer's Disease Clinical Trials.

Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA.
NeuroImage (Impact Factor: 6.36). 11/2012; 66. DOI: 10.1016/j.neuroimage.2012.10.086
Source: PubMed


Various neuroimaging measures are being evaluated for tracking Alzheimer's disease 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 ADNI-1 MRI dataset 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 and 95 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 vs. 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.

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Available from: Clifford R Jack, Jun 16, 2014
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    • "We used the expected scan interval or nominal scan interval in all the analyses. In the previous work, we found similar results in modeling the trend of brain atrophy using actual and expected scan intervals in the ADNI-1 study (Hua et al., 2013). "
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    ABSTRACT: The goal of this work was to assess statistical power to detect treatment effects in Alzheimer's disease (AD) clinical trials using magnetic resonance imaging (MRI)-derived brain biomarkers. We used unbiased tensor-based morphometry (TBM) to analyze n = 5,738 scans, from Alzheimer's Disease Neuroimaging Initiative 2 participants scanned with both accelerated and nonaccelerated T1-weighted MRI at 3T. The study cohort included 198 healthy controls, 111 participants with significant memory complaint, 182 with early mild cognitive impairment (EMCI) and 177 late mild cognitive impairment (LMCI), and 155 AD patients, scanned at screening and 3, 6, 12, and 24 months. The statistical power to track brain change in TBM-based imaging biomarkers depends on the interscan interval, disease stage, and methods used to extract numerical summaries. To achieve reasonable sample size estimates for potential clinical trials, the minimal scan interval was 6 months for LMCI and AD and 12 months for EMCI. TBM-based imaging biomarkers were not sensitive to MRI scan acceleration, which gave results comparable with nonaccelerated sequences. ApoE status and baseline amyloid-beta positron emission tomography data improved statistical power. Among healthy, EMCI, and LMCI participants, sample size requirements were significantly lower in the amyloid+/ApoE4+ group than for the amyloid-/ApoE4- group. ApoE4 strongly predicted atrophy rates across brain regions most affected by AD, but the remaining 9 of the top 10 AD risk genes offered no added predictive value in this cohort.
    Full-text · Article · Nov 2015 · Neurobiology of aging
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    • "More recently, with the development of deformable image registration techniques, automatic spatial normalization proves to be a fundamental procedure in brain morphometric pattern analysis, which allows quantitative comparisons among different subjects in a common space. Within the spatial normalization framework, a large number of brain morphometric analysis methods are developed for automatic AD/MCI diagnosis, e.g., deformation-based morphometry (DBM) [4] [11] [12] [13] [14], tensor-based morphometry (TBM) [6] [15] [16] [17] [18] [19] [20], and voxel-based morphometry (VBM) [21] [22] [23] [24] [25] [26]. "
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    ABSTRACT: Multi-template based brain morphometric pattern analysis using magnetic resonance imaging (MRI) has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multi-view morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multi-template based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality the underlying data distribution is actually not pre-known. In this paper, we propose an inherent structure based multi-view leaning (ISML) method using multiple templates for AD/MCI classification. Specifically, we first extract multi-view feature representations for subjects using multiple selected templates, and then cluster subjects within a specific class into several sub-classes (i.e., clusters) in each view space. Then, we encode those sub-classes with unique codes by considering both their original class information and their own distribution information, followed by a multi-task feature selection model. Finally, we learn an ensemble of view-specific support vector machine (SVM) classifiers based on their respectively selected features in each view, and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multi-template based methods.
    Full-text · Article · Nov 2015 · IEEE transactions on bio-medical engineering
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    • "Effective and accurate diagnosis of Alzheimer's disease and its prodromal stage, MCI, are crucial for drug trials, given the urgent need for treatments to resist or slow disease progression. Many neuroimaging studies have used anatomical measures derived from T1-weighted brain MRI, such as cortical thickness, and volumetric or shape measures of subregions of the brain, to differentiate AD or MCI from NC (Fan et al., 2008; Hua et al., 2008a,b; Gerardin et al., 2009; Magnin et al., 2009; Hua et al., 2010; Cuingnet et al., 2011; Westman et al., 2011; Hua et al., 2013; Gutman et al., 2015). Moreover, measures derived from functional imaging or cerebrospinal fluid (CSF) assays have also been used to help classify individuals with cognitive impairment vs. healthy controls (De Santi et al., 2001; Morris et al., 2001; Bouwman et al., 2007; Mattsson et al., 2009; Shaw et al., 2009; Fjell et al., 2010). "
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    ABSTRACT: Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.
    Full-text · Article · Aug 2015 · Frontiers in Neuroscience
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