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

ABSTRACT 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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    • "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 · 6.92 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 · 6.92 Impact Factor
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    • "Genetic factors affect alcohol intake (Olfson and Bierut, 2012; Blaine et al., 2013; Meyers et al., 2013) but we do not know if the same genes affect brain structure and the rate of brain atrophy. Dynamic changes in the brain's lateral ventricles reveal the rate of brain atrophy as we age and reflect brain tissue loss with high effect sizes (Hua et al., 2013). Lateral ventricle expansion accompanies gray and white matter degeneration globally and in nearby subcortical regions (Ferrarini et al., 2008). "
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    ABSTRACT: A recent genome-wide association meta-analysis showed a suggestive association between alcohol intake in humans and a common single nucleotide polymorphism in the ras-specific guanine nucleotide releasing factor 2 gene. Here, we tested whether this variant - associated with lower alcohol consumption - showed associations with brain structure and longitudinal ventricular expansion over time, across two independent elderly cohorts, totaling 1,032 subjects. We first examined a large sample of 738 elderly participants with neuroimaging and genetic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI1). Then, we assessed the generalizability of the findings by testing this polymorphism in a replication sample of 294 elderly subjects from a continuation of the first ADNI project (ADNI2) to minimize the risk of reporting false positive results. The minor allele - previously linked with lower alcohol intake - was associated with larger volumes in various cortical regions, notably the medial prefrontal cortex and cingulate gyrus in both cohorts. Intriguingly, the same allele also predicted faster ventricular expansion rates in the ADNI1 cohort at 1- and 2-year follow up. Despite a lack of alcohol consumption data in this study cohort, these findings, combined with earlier functional imaging investigations of the same gene, suggest the existence of reciprocal interactions between genes, brain, and drinking behavior.
    Frontiers in Aging Neuroscience 12/2013; 5:93. DOI:10.3389/fnagi.2013.00093 · 2.84 Impact Factor
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