The Alzheimer's Disease neuroimaging initiative (ADNI): MRI methods

Mayo Clinic and Foundation, Rochester, Minnesota 55905, USA.
Journal of Magnetic Resonance Imaging (Impact Factor: 3.21). 05/2008; 27(4):685-91. DOI: 10.1002/jmri.21049
Source: PubMed


The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a longitudinal multisite observational study of healthy elders, mild cognitive impairment (MCI), and Alzheimer's disease. Magnetic resonance imaging (MRI), (18F)-fluorodeoxyglucose positron emission tomography (FDG PET), urine serum, and cerebrospinal fluid (CSF) biomarkers, as well as clinical/psychometric assessments are acquired at multiple time points. All data will be cross-linked and made available to the general scientific community. The purpose of this report is to describe the MRI methods employed in ADNI. The ADNI MRI core established specifications that guided protocol development. A major effort was devoted to evaluating 3D T(1)-weighted sequences for morphometric analyses. Several options for this sequence were optimized for the relevant manufacturer platforms and then compared in a reduced-scale clinical trial. The protocol selected for the ADNI study includes: back-to-back 3D magnetization prepared rapid gradient echo (MP-RAGE) scans; B(1)-calibration scans when applicable; and an axial proton density-T(2) dual contrast (i.e., echo) fast spin echo/turbo spin echo (FSE/TSE) for pathology detection. ADNI MRI methods seek to maximize scientific utility while minimizing the burden placed on participants. The approach taken in ADNI to standardization across sites and platforms of the MRI protocol, postacquisition corrections, and phantom-based monitoring of all scanners could be used as a model for other multisite trials.

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    • "These parameters were selected to be as consistent across sites while accommodating for different scanner types. We aimed to obtain a spatial resolution of 1 mm, as previous multisite studies such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) study [Jack et al., 2008] have suggested that a spatial resolution of 1 mm is desired for brain morphometric examinations. Furthermore, although the ADNI study used a slice thickness of 1.2 mm to accommodate sites with 1.5T scanners (1 mm thickness would yield very low SNR at 1.5T), all of the sites in our study are equipped with 3T MRI systems. "
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    ABSTRACT: In the last decade, many studies have used automated processes to analyze magnetic resonance imaging (MRI) data such as cortical thickness, which is one indicator of neuronal health. Due to the convenience of image processing software (e.g., FreeSurfer), standard practice is to rely on automated results without performing visual inspection of intermediate processing. In this work, structural MRIs of 40 healthy controls who were scanned twice were used to determine the test-retest reliability of FreeSurfer-derived cortical measures in four groups of subjects-those 25 that passed visual inspection (approved), those 15 that failed visual inspection (disapproved), a combined group, and a subset of 10 subjects (Travel) whose test and retest scans occurred at different sites. Test-retest correlation (TRC), intraclass correlation coefficient (ICC), and percent difference (PD) were used to measure the reliability in the Destrieux and Desikan-Killiany (DK) atlases. In the approved subjects, reliability of cortical thickness/surface area/volume (DK atlas only) were: TRC (0.82/0.88/0.88), ICC (0.81/0.87/0.88), PD (0.86/1.19/1.39), which represent a significant improvement over these measures when disapproved subjects are included. Travel subjects' results show that cortical thickness reliability is more sensitive to site differences than the cortical surface area and volume. To determine the effect of visual inspection on sample size required for studies of MRI-derived cortical thickness, the number of subjects required to show group differences was calculated. Significant differences observed across imaging sites, between visually approved/disapproved subjects, and across regions with different sizes suggest that these measures should be used with caution. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.
    Human Brain Mapping 05/2015; 36(9). DOI:10.1002/hbm.22856 · 5.97 Impact Factor
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    • "Such end-state databases are frequently organized to support a particular research topic. Three successful centralized systems are the Alzheimer's Disease Neuroimaging Initiative (ADNI, (Jack et al., 2008)), the National Database for Autism Research (NDAR, (Hall, Huerta, McAuliffe, & Farber, 2012; NIH, 2015)), and the Human Connectome Project (HCP, (Van Essen et al., 2013)). "
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    ABSTRACT: We describe the current state and future plans for a set of tools for scientific data management (SDM) designed to support scientific transparency and reproducible research. SDM has been in active use at our MRI Center for more than two years. We designed the system to be used from the beginning of a research project, which contrasts with conventional end-state databases that accept data as a project concludes. A number of benefits accrue from using scientific data management tools early and throughout the project, including data integrity as well as reuse of the data and of computational methods.
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    • "In our experiments, there are a total of 459 subjects, randomly selected from those scanned with a 1.5T scanner, including 97 AD, 128 NC, and 234 MCI (117 p-MCI and 117 s-MCI) subjects. The scanning parameters for the 1.5T MRI data used in this study can be found from [Jack et al., 2008]. It is worth noting that not all subjects with MRI data from the ADNI-1 database are used, because it requires much preprocessing time to register all subjects to multiple atlases (i.e., 10 atlases in this study). "
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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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