Ways toward an early diagnosis in Alzheimer's disease: The Alzheimer's Disease Neuroimaging Initiative (ADNI)

By Susanne G. Mueller, Michael W. Weiner, L. Thal, Ronald C. Petersen, Clifford R. Jack Jr., William J. Jagust, John Q. Trojanowski, Arthur W. Toga, L. Beckett. Alzheimer's & Dementia, 2005

1,280 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Physiology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/71902c4ea32f24a3ae5e72cdbde40fa7d52db1cc

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Citations in the corpus

Cerebrospinal Fluid Biomarker Signature in Alzheimer’s Disease Neuroimaging Initiative Subjects (2009, methodology): “Discussion ADNI is a multisite, prospective clinical study funded by the National Institute on Aging, industry, and foundations (see Acknowledgements for details on sources for ADNI funding) with the following mission: (1) to develop standardized neuroimaging and biomarker methods for AD clinical tr”
The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods (2008, positive): “The Alzheimer's Disease Neuroimaging Initiative (ADNI) study is a longitudinal multisite observational study of elderly individuals with normal cognition, mild cognitive impairment (MCI), or AD (11,12).”
The Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception (2011, positive): “Mueller et al [38] and Weiner et al [3] reaffirmed the definition of an ideal biomarker formulated at the first meeting of the NIA working group on AD biomarkers, which proposed that an ideal AD biomarker should detect a fundamental feature of AD pathology; be minimally invasive, simple to analyze, ”
Efficient, Distributed and Interactive Neuroimaging Data Analysis Using the LONI Pipeline (2009, positive): “Using Automated Image Registration (Woods et al., 1998), we constructed a workﬂ ow to systematically create a whole brain atlas for use in describing the average brain anatomical structure in patients drawn from the ADNI series of Alzheimer’s subject MRI data contained in the LONI Image Data Archive”
Tensor-Based Morphometry as a Neuroimaging Biomarker for Alzheimer’s Disease: An MRI Study of 676 AD, MCI, and Normal Subjects (2008, positive): “…measures, that can (1) detect abnormal aging before neuronal loss is widespread; (2) gauge the level of structural brain degeneration in way that correlates with standard cognitive measures, and (3) predict future clinical decline, or imminent conversion from MCI to AD (Mueller et al., 2005b).”
Visualization of image data from cells to organisms (2010, neutral): “Both the Alzheimer’s Disease Neuroimaging Initiative (ADNI 68 ) and the International Consortium for Brain Mapping (ICBM 69 ) make use of the Laboratory of Neuro Imaging’s Image Data Archive (IDA) to store data for thousands of subjects, including MRI, positron emission tomography, magnetic…”
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration (2018, methodology): “We use a large-scale, multi-site dataset of 7829 T1-weighted brain MRI scans from eight publicly available datasets: ADNI [25], OASIS [22], ABIDE [12], ADHD200 [24], MCIC [15], PPMI [23], HABS [9], and Harvard GSP [16].”
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline (2019, positive): “Further, subjects never encountered during training from ADNI and OASIS-1, the Minimal Interval Resonance Imaging in Alzheimer ’ s Disease (MIRIAD) (Malone et al., 2013), the Human Con-nectome Project (HCP) (Van Essen et al., 2012), a multi-subject, multimodel neuroimaging dataset (MMND) (Wakeman an”
Clinical applications of the functional connectome (2013, positive): “Ideally such collaboration would be coordinated, such as the Alzheimer's Disease Neuroimaging Initiative (Mueller et al., 2005), in which variance in experimental procedures, such as scanning protocols, has been minimized.”
Relevant Feature Set Estimation with a Knock-out Strategy and Random Forests (2015, positive): “In the second experimentwe used the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (Mueller et al., 2005; Jack et al., 2008).”
Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. (2015, methodology): “To standardize image acquisition to better understand Alzheimer’s disease, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was launched in October 2004.(19,20) This was a landmark effort to standardize brain imaging across clinical centers in the United States and Canada.”
Open Access Series of Imaging Studies (OASIS): Longitudinal MRI Data in Nondemented and Demented Older Adults (2010, methodology): “Longitudinal measures of brain structure are emerging as tools for tracking progression of disease and as adjunct outcome measures in clinical trials (e.g., Jack et al., in press; Borroni, Premi, Di Luca, & Padovani, 2007; Chong, Lim, & Sahadevan, 2006; Mueller et al., 2005).”
The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry (2012, methodology): “…the multi-investigator, multi-center model, commonly employed by the pharmaceutical industry, has been effectively implemented by efforts such as the Biomedical Informatics Research Network (BIRN; Helmer et al., 2011) or the Alzheimer Disease Neuroimaging Initiative (ADNI; Mueller et al., 2005).”
Sparse Multi-Task Regression and Feature Selection to Identify Brain Imaging Predictors for Memory Performance (2011, background): “Further information can be found in [13] and at www.adni-info.org.”
Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains (2015, methodology): “In this section, the new method is applied to data from the Alzheimer’s Disease Neuroimaging Initiative study (ADNI), which aims at identifying biomarkers for accurate diagnosis of Alzheimer’s disease (AD) in an early stage (Mueller et al. 2005).”