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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    • "To evaluate the efficacy of our method, we perform four groups of experiments: 1) AD vs. NC classification, 2) progressive MCI (pMCI) vs. stable MCI (sMCI) classification, 3) pMCI vs. NC classification, and 4) sMCI vs. NC classification. By using a 10-fold cross-validation strategy on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database[9], we achieve a significant performance improvement for each of these four classification tasks, compared with several state-of-the-art methods for AD/MCI diagnosis. It is worth noting that this work is different from our earlier work in[28]. "
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    ABSTRACT: As shown in the literature, methods based on multiple templates usually achieve better performance, compared to those using only a single template for processing medical images. However, most existing multi-template based methods simply average or concatenate multiple sets of features extracted from different templates, which potentially ignoring important structural information contained in the multi-template data. Accordingly, in this paper, we propose a novel relationship induced multi-template learning method for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI), by explicitly modeling structural information in the multi-template data. Specifically, we first nonlinearly register each brain's magnetic resonance (MR) image separately onto multiple pre-selected templates, and then extract multiple sets of features for this MR image. Next, we develop a novel feature selection algorithm by introducing two regularization terms to model the relationships among templates and among individual subjects. Using these selected features corresponding to multiple templates, we then construct multiple support vector machine (SVM) classifiers. Finally, an ensemble classification is used to combine outputs of all SVM classifiers, for achieving the final result. We evaluate our proposed method with 459 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 97 AD patients, 128 normal controls (NC), 117 progressive MCI (pMCI) patients, and 117 stable MCI (sMCI) patients. The experimental results demonstrate promising classification performance, compared with several state-of-the-art methods for multi-template based AD/MCI classification.
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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.
    No preview · Article · May 2015 · Human Brain Mapping
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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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    Full-text · Article · Feb 2015
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