Open Access Series of Imaging Studies (OASIS): Longitudinal MRI Data in Nondemented and Demented Older Adults

By Daniel S. Marcus, Anthony F. Fotenos, John G. Csernansky, John C. Morris, Randy L. Buckner. Journal of Cognitive Neuroscience, 2010

652 citations recorded by the source corpus.

Abstract The Open Access Series of Imaging Studies is a series of neuroimaging data sets that are publicly available for study and analysis. The present MRI data set consists of a longitudinal collection of 150 subjects aged 60 to 96 years all acquired on the same scanner using identical sequences. Each subject was scanned on two or more visits, separated by at least 1 year for a total of 373 imaging sessions. Subjects were characterized using the Clinical Dementia Rating (CDR) as either nondemented or with very mild to mild Alzheimer's disease. Seventy-two of the subjects were characterized as nondemented throughout the study. Sixty-four of the included subjects were characterized as demented at the time of their initial visits and remained so for subsequent scans, including 51 individuals with CDR 0.5 similar level of impairment to individuals elsewhere considered to have “mild cognitive impairment.” Another 14 subjects were characterized as nondemented at the time of their initial visit (CDR 0) and were subsequently characterized as demented at a later visit (CDR > 0). The subjects were all right-handed and include both men (n = 62) and women (n = 88). For each scanning session, three or four individual T1-weighted MRI scans were obtained. Multiple within-session acquisitions provide extremely high contrast to noise, making the data amenable to a wide range of analytic approaches including automated computational analysis. Automated calculation of whole-brain volume is presented to demonstrate use of the data for measuring differences associated with normal aging and Alzheimer's disease.

Subfields: Cognitive Neuroscience, Psychiatry and Mental health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a24edf0740a11f3b75a987137a38b13119d43e94

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Citations in the corpus

Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks (2018, methodology): “We use the Multi-Atlas Labelling Challenge (MALC) dataset [11], which is a part of OASIS [12], with 15 scans for training and 15 scans for testing consistent to the challenge instructions.”
Deep learning based brain tumor segmentation: a survey (2020, methodology): “Related to brain imaging analysis, the Open Access Series of Imaging Studies (OASIS) [120] is also established for providing crosssectional or longitudinal brain MRI dataset for normal aging and Alzheimers disease research.”
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline (2019, positive): “Oasis-1 (Marcus et al., 2007) and Oasis-2 (Marcus et al., 2010): The Open Access Series of Imaging Studies 1 and 2, both contain scans from a 1.5-”
Brain templates and atlases (2012, background): “The OASIS (Marcus et al., 2007, 2010) and ADNI Alzheimer's Disease (Weiner et al., 2010) databases (see below) also provide normative data from elderly control subjects.”
Data sharing in neuroimaging research (2012, methodology): “However, the proportion of such data currently available in public repositories (e.g., the fMRI Data Center, 1000 Functional Connectomes, Open fMRI, OASIS, ADNI, FBIRN) is less than a few percent.”
Deep Bayesian Active Learning with Image Data (2017, background): “Even though existing techniques for active learning have proven themselves useful in a variety of tasks, a major remaining challenge in active learning is its lack of scalability to high-dimensional data (Tong, 2001).”
Classification of Alzheimer’s Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling (2018, background): “Randy Buckner at the Howard Hughes Medical Institute (HHMI) at Harvard University, the Neuroinformatics Research Group (NRG) at Washington University School of Medicine, and the Biomedical Informatics Research Network (BIRN) [18, 19].”
MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection (2015, neutral): “As part of the MICCAI 2013 segmentation challenge, a dataset with training and testing images from OASIS project with reference label maps was created for evaluating multi-atlas label fusion algorithms in segmentation of mid-brain structures.”
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images (2017, neutral): “It is of great significance on diagnosis, progression assessment, and 4 treatment of a wide range of neurodegenerative diseases such as dementia and 5 Alzheimer’s disease (Petrella et al. (2003); Giorgio and De Stefano (2013)).”
Brain Age from the Electroencephalogram of Sleep (2018, background): “This definition is consistent with the one used in Brain Images of Normal Participants (BRAINS) (Job et al., 2017), Open Access Series of Imaging Studies (OASIS) (Marcus et al., 2010), and in line with (Steffener et al., 2016) and (Cole et al., 2015).”
A Review of Deep-Learning-Based Medical Image Segmentation Methods (2021, background): “Participants included 609 cognitively normal adults and 489 people at various stages of cognitive decline, ages 42 to 95 [111].”