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

Marcus, Daniel S.; Wang, Tracy H.; Parker, Jamie; Csernansky, John G.; Morris, John C.; Buckner, Randy L.

doi:10.1162/jocn.2007.19.9.1498

Cited by 1,491 publications

(1,009 citation statements)

References 36 publications

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“…The region extracting method was assessed using a subset of the OASIS database (Marcus et al., 2007). The group consisted of 136 brain MR cases, from which 66 were the control group (CN), 50 corresponded to mild cognitive impairment (MCI), and 20 were patients diagnosed with mild Alzheimer's disease.…”

Section: Methodsmentioning

confidence: 99%

Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease

et al. 2018

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PurposeThis work presents an automatic characterization of the Alzheimer's disease describing the illness as a multidirectional departure from a baseline defining the control state, being these directions determined by a distance between functional‐equivalent anatomical regions.MethodsAfter a brain parcellation, a region is described by its histogram of gray levels, and the Earth mover's distance establishes how close or far these regions are. The medoid of the control group is set as the reference and any brain is characterized by its set of distances to this medoid.EvaluationThis hypothesis was assessed by separating groups of patients with mild Alzheimer's disease and mild cognitive impairment from control subjects, using a subset of the Open Access Series of Imaging Studies (OASIS) database. An additional experiment evaluated the method generalization and consisted in training with the OASIS data and testing with the Minimal Interval Resonance Imaging in Alzheimer's disease (MIRIAD) database.ResultsClassification between controls and patients with AD resulted in an equal error rate of 0.1 (90% of sensitivity and specificity at the same time). The automatic ranking of regions resulting is in strong agreement with those regions described as important in clinical practice. Classification with different databases results in a sensitivity of 85% and a specificity of 91%.ConclusionsThis method automatically finds out a multidimensional expression of the AD, which is directly related to the anatomical changes in specific areas such as the hippocampus, the amygdala, the planum temporale, and thalamus.

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Section: Methodsmentioning

confidence: 99%

Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease

et al. 2018

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“…Observe that these streamlines bend and fan to follow the folds, qualitatively demonstrating the accuracy of the recovered curvature field. We used the OASIS database [7] to determine if our method could find curves whose length is significantly correlated with the presence of mild cognitive impairment in Alzheimer's disease (AD). The OASIS database consists of 97 healthy subjects and 92 subjects (aged 60 and above) affected with mild and very-mild dementia.…”

Section: Resultsmentioning

confidence: 99%

“…Directional filters are then generated by launching streamlines whose curvature is provided by this assignment. Using the OASIS database [7] we test our framework to determine how the shape of cortical folds in patients with cognitive impairment is affected. We are able to identify folds that undergo significant deformation in the temporal lobe, and also a stretch in length of the Cingulate gyrus below the Praecuneus area and below the sup frontal cortex in the left hemisphere.…”

Section: Introductionmentioning

confidence: 99%

A Texture Manifold for Curve-Based Morphometry of the Cerebral Cortex

Boucher

Evans

Siddiqi

2011

Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging

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Abstract. The cortical surface of the human brain is composed of folds that are juxtaposed alongside one another. Several methods have been proposed to study the shape of these folds, e.g., by first segmenting them on the cortical surface or by analysis via a continuous deformation of a common template. A major disadvantage of these methods is that, while they can localize shape differences, they cannot easily identify the directions in which they occur. The type of deformation that causes a fold to change in length is quite different from that which causes it to change in width. Furthermore, these two deformations may have a completely different biological interpretation. In this article we propose a method to analyze such deformations using directional filters locally adapted to the geometry of the folding pattern. Motivated by the texture flow literature in computer vision we recover flow fields that maintain a fixed angle with the orientation of folds, over a significant spatial extent. We then trace the flow fields to determine which correspond to the shape changes that are the most salient. Using the OASIS database, we demonstrate that in addition to known regions of atrophy, our method can find subtle but statistically significant shape deformations.

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“…The aim is to facilitate future discoveries in basic and clinical neuroscience [17]. OASIS is made available by the Washington University Alzheimer's Disease Research Center, Harvard University, the Neuroinformatics Research Group (NRG) at Washington University School of Medicine, and the Biomedical Informatics Research Network (BIRN) [17].…”

Section: Methodsmentioning

confidence: 99%

Robust Intensity Standardization in Brain Magnetic Resonance Images

2015

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The paper is focused on a tiSsue-Based Standardization Technique (SBST) of magnetic resonance (MR) brain images. Magnetic Resonance Imaging intensities have no fixed tissue-specific numeric meaning, even within the same MRI protocol, for the same body region, or even for images of the same patient obtained on the same scanner in different moments. This affects postprocessing tasks such as automatic segmentation or unsupervised/supervised classification methods, which strictly depend on the observed image intensities, compromising the accuracy and efficiency of many image analyses algorithms. A large number of MR images from public databases, belonging to healthy people and to patients with different degrees of neurodegenerative pathology, were employed together with synthetic MRIs. Combining both histogram and tissue-specific intensity information, a correspondence is obtained for each tissue across images. The novelty consists of computing three standardizing transformations for the three main brain tissues, for each tissue class separately. In order to create a continuous intensity mapping, spline smoothing of the overall slightly discontinuous piecewise-linear intensity transformation is performed. The robustness of the technique is assessed in a post hoc manner, by verifying that automatic segmentation of images before and after standardization gives a high overlapping (Dice index >0.9) for each tissue class, even across images coming from different sources. Furthermore, SBST efficacy is tested by evaluating if and how much it increases intertissue discrimination and by assessing gaussianity of tissue gray-level distributions before and after standardization. Some quantitative comparisons to already existing different approaches available in the literature are performed.

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

Cited by 1,491 publications

References 36 publications

Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease

Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease

A Texture Manifold for Curve-Based Morphometry of the Cerebral Cortex

Robust Intensity Standardization in Brain Magnetic Resonance Images

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