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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    • "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.
    Frontiers in Neuroscience 08/2015; 9:257. DOI:10.3389/fnins.2015.00257 · 3.66 Impact Factor
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    • "In the literature, many single-atlas based morphometry pattern analysis methods, such as voxel-based morphometry (VBM) [Ashburner and Friston, 2000; Davatzikos et al., 2001, 2008; Thompson et al., 2001], deformation-based morphometry (DBM) [Ashburner et al., 1998; Chung et al., 2001; Gaser et al., 2001; Joseph et al., 2014], and tensorbased morphometry (TBM) [Kipps et al., 2005; Leow et al., 2006; Whitford et al., 2006], have been proposed and demonstrated promising results in AD diagnosis with different classification techniques [Bozzali et al., 2006; Fan et al., 2008; Frisoni et al., 2002; Hua et al., 2013]. Specifically, in these methods, after nonrigidly transforming each individual brain image onto a common atlas space, VBM measures the local tissue density of the original brain image directly, while DBM and TBM measure the local deformation and the Jacobian of local deformation, respectively. "
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    ABSTRACT: Multi-atlas based methods have been recently used for classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Compared with traditional single-atlas based methods, multiatlas based methods adopt multiple predefined atlases and thus are less biased by a certain atlas. However, most existing multiatlas based methods simply average or concatenate the features from multiple atlases, which may ignore the potentially important diagnosis information related to the anatomical differences among different atlases. In this paper, we propose a novel view (i.e., atlas) centralized multi-atlas classification method, which can better exploit useful information in multiple feature representations from different atlases. Specifically, all brain images are registered onto multiple atlases individually, to extract feature representations in each atlas space. Then, the proposed view-centralized multi-atlas feature selection method is used to select the most discriminative features from each atlas with extra guidance from other atlases. Next, we design a support vector machine (SVM) classifier using the selected features in each atlas space. Finally, we combine multiple SVM classifiers for multiple atlases through a classifier ensemble strategy for making a final decision. We have evaluated our method on 459 subjects [including 97 AD, 117 progressive MCI (p-MCI), 117 stable MCI (s-MCI), and 128 normal controls (NC)] from the Alzheimer's Disease Neuroimaging Initiative database, and achieved an accuracy of 92.51% for AD versus NC classification and an accuracy of 78.88% for p-MCI versus s-MCI classification. These results demonstrate that the proposed method can significantly outperform the previous multi-atlas based classification methods. Hum Brain Mapp, 2015. © 2014 Wiley Periodicals, Inc.
    Human Brain Mapping 01/2015; 36(5). DOI:10.1002/hbm.22741 · 5.97 Impact Factor
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    • "In particular, local differences in brain tissue volume are evaluated by computing highdimensional nonlinear deformations to adjust the anatomy of each individual to match a custom-built group-average template and successively comparing maps of the Jacobian determinant (|J|) of the deformation fields in order to estimate the degree of tissue contraction/expansion at each location/ voxel (Ashburner and Friston, 2003; Chung et al., 2001; Fox et al., 2001; Freeborough and Fox, 1998; Hua et al., 2009, 2011, 2013; Riddle et al., 2004; Studholme et al., 2001; Thompson et al., 2000). TBM has been proven to be an unbiased, robust, high-throughput imaging marker in Alzheimer's disease (AD) and MCI and is particularly suited for longitudinal studies (Hua et al., 2013). "
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    ABSTRACT: The presence of brain atrophy and its progression in early Parkinson's disease (PD) are still a matter of debate, particularly in patients without cognitive impairment. The aim of this longitudinal study was to assess whether PD patients who remain cognitively intact develop progressive atrophic changes in the early stages of the disease. For this purpose, we employed high-resolution T1-weighted MR imaging to compare 22 drug-naïve de novo PD patients without cognitive impairment to 17 age-matched control subjects, both at baseline and at three-year follow-up. We used tensor-based morphometry to explore the presence of atrophic changes at baseline and to compute yearly atrophy rates, after which we performed voxel-wise group comparisons using threshold-free cluster enhancement. At baseline, we did not observe significant differences in regional atrophy in PD patients with respect to control subjects. In contrast, PD patients showed significantly higher yearly atrophy rates in the prefrontal cortex, anterior cingulum, caudate nucleus, and thalamus when compared to control subjects. Our results indicate that even cognitively preserved PD patients show progressive cortical and subcortical atrophic changes in regions related to cognitive functions and that these changes are already detectable in the early stages of the disease. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
    Human Brain Mapping 08/2014; 35(8). DOI:10.1002/hbm.22449 · 5.97 Impact Factor
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