Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults

By Daniel S. Marcus, Tracy H. Wang, Jamie Parker, John G. Csernansky, John C. Morris, Randy L. Buckner. Journal of Cognitive Neuroscience, 2007

2,264 citations recorded by the source corpus.

The Open Access Series of Imaging Studies is a series of magnetic resonance imaging data sets that is publicly available for study and analysis. The initial data set consists of a cross-sectional collection of 416 subjects aged 18 to 96 years. One hundred of the included subjects older than 60 years have been clinically diagnosed with very mild to moderate Alzheimer's disease. The subjects are all right-handed and include both men and women. For each subject, three or four individual T1-weighted magnetic resonance imaging scans obtained in single imaging sessions are included. Multiple within-session acquisitions provide extremely high contrast-to-noise ratio, making the data amenable to a wide range of analytic approaches including automated computational analysis. Additionally, a reliability data set is included containing 20 subjects without dementia imaged on a subsequent visit within 90 days of their initial session. Automated calculation of whole-brain volume and estimated total intracranial volume are 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/144457047be2820246fb41b9f552d14cc988d4c6

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Citations in the corpus

Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks (2018, methodology): “We trained our model using the OASIS database [7] that has only 416 sMRI data.”
Transformers in Medical Imaging: A Survey (2022, background): “Qualitative and quantitative results in terms of Dice similarity coefficient and negative Jacobian determinant on OASIS dataset [334] of MRI scans demonstrate the effectiveness of their proposed architecture.”
Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease (2009, positive): “The first cohort of 97 participants (‘training cohort’) was selected from the Open Access Series of Imaging Studies (OASIS) database (Marcus et al., 2007).”
VoxelMorph: A Learning Framework for Deformable Medical Image Registration (2018, methodology): “1) Dataset: We use a large-scale, multi-site, multistudy dataset of 3731 T1–weighted brain MRI scans from eight publicly available datasets: OASIS [69], ABIDE [70], ADHD200 [71], MCIC [72], PPMI [73], HABS [74], Harvard GSP [75], and the FreeSurfer Buckner40 [1].”
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces (2019, methodology): “We use a large-scale, multi-site dataset of 3731 T1weighted brain MRI scans from eight publicly available datasets: OASIS [45], ABIDE [24], ADHD200 [48], MCIC [29], PPMI [46], HABS [17], and Harvard GSP [33].”
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images (2017, positive): “, 2015), we tried our method on another larger dataset from OASIS project (Marcus et al., 2007) with 35 annotated MRIs on the task of hippocampus segmentation.”
Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation (2019, methodology): “The scans are compiled from eight databases: ADNI [54], OASIS [46], ABIDE [48], ADHD200 [49], MCIC [30], PPMI [47], HABS [19], and Harvard GSP [33]; the segmentation labels are computed using FreeSurfer [27].”
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy (2017, methodology): “We evaluate the segmentation on the dataset of the MICCAI Multi-Atlas Labeling challenge(1) (Landman and Warfield, 2012), which consists of T1-weighted MRI scans from 30 subjects of OASIS (Marcus et al., 2007).”
Handbook of Functional MRI Data Analysis: Index (2011, methodology): “Adult images obtained from OASIS cross-sectional dataset (Marcus et al., 2007).”
OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease (2019, methodology): “In the Trail Making Test participants were asked to make a trail by connecting a series of numbers (1-26) for part A and connecting a series of alternating numbers and letters (1-A-2-B) for part B.”
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Structural Brain Changes in Aging: Courses, Causes and Cognitive Consequences (2010, background): “Course Four studies found non-linear effects [32, 41 , 42, 46], and non-linear relationships were found for all five samples in a large multi-center study [19].”