Subjects aged 60–96 years were selected from a larger database of individuals who had participated in MRI studies at Washington University, based on the availability of at least two separate visits in which clinical and MRI data were obtained, at least three acquired T1-weighted images per imaging session, and right-hand dominance. Subjects were obtained from the longitudinal pool of the Washington University Alzheimer Disease Research Center (ADRC). The ADRC‘s normal and cognitively impaired subjects were recruited primarily through media appeals and word of mouth, with 80% of subjects initiating contact with the center and the remainder being referred by physicians. All subjects participated in accordance with guidelines of the Washington University Human Studies Committee. Approval for public sharing of the anonymized data was also specifically obtained.
All subjects were screened for inclusion in this release. Each subject underwent the ADRC‘s full clinical assessment as described below. Subjects with a primary cause of dementia other than AD (e.g., vascular dementia, primary progressive aphasia), active neurologic or psychiatric illness (e.g. major depression), serious head injury, history of clinically meaningful stroke, and use of psychoactive drugs were excluded, as were subjects with gross anatomical abnormalities evident in their MRI images (e.g. large lesions, tumors). However, subjects with age-typical brain changes (e.g. mild atrophy, leukoaraiosis) were accepted. MRI acquisitions typically were obtained within one year before or after a subject‘s clinical assessment (mean = 111 days, range = 0 to 352 days). Twelve subjects with AD were scanned after a somewhat longer duration (mean = 653 days, range = 374 to 924 days) but were included because each had several previous clinical assessments with CDR scores greater than 0. Two subjects without dementia were scanned somewhat longer than one year prior to a clinical assessment (392 and 431 days) but were included because their subsequent clinical assessments continued to indicate no signs of dementia. Each subject was scanned on two or more separate occasions, with an average delay of 719 days (range = 183–1707 days) between visits. The final data set includes 150 subjects and 373 imaging sessions.
Portions of the clinical, demographic, and longitudinal image data obtained from subjects in this release have been used in previous publications (Buckner et al., 2004
; Salat et al., 2004
; Buckner et al., 2005
; Head et al., 2005
; Burns et al., 2005
; Fotenos et al., 2005
; Dickerson et al., 2007
; Fotenos et al., 2008
; Dickerson et al., 2008
; He et al., 2008
; Salat et al., 2008
). Many of the subjects were part of the cross-sectional OASIS data set (Marcus et al., 2007
) but have been assigned new random identifiers.
Dementia status was established and staged using the CDR scale. The determination of AD or nondemented control status is based solely on clinical methods, without reference to psychometric performance, and any potential alternative causes of dementia (known neurological, medical, or psychiatric disorders) must not contribute to dementia. The diagnosis of AD is based on clinical information (derived primarily from a collateral source) that the subject has experienced gradual onset and progression of decline in memory and other cognitive and functional domains. Specifically, the CDR is a dementia staging instrument that rates subjects for impairment in each of 6 domains: memory, orientation, judgment and problem solving, function in community affairs, home and hobbies, and personal care. Based on the collateral source and subject interview, a global CDR score is derived from individual ratings in each domain. A global CDR of 0 indicates no dementia and CDR of 0.5, 1, 2 and 3 represent very mild, mild, moderate, and severe dementia, respectively. These methods allow for the clinical diagnosis of AD in individuals with a CDR of 0.5 or greater, based on standard criteria, that is confirmed by histopathological examination in 93% of the individuals (Berg et al., 1998
), even for those in the earliest symptomatic stage (CDR 0.5) of AD who elsewhere may be considered to represent “mild cognitive impairment” (Storandt et al., 2006).
For each subject, 3 to 4 individual T1-weighted magnetization prepared rapid gradient echo (MP-RAGE) images were acquired on a 1.5-Tesla Vision scanner (Siemens, Erlangen, Germany) in a single imaging session. Head movement was minimized by cushioning and a thermoplastic face mask. Headphones were provided for communication. A vitamin E capsule was placed over the left forehead to provide a reference marker of anatomic side. Positioning was low in the head coil (toward the feet) to optimize imaging of the cerebral cortex. MP-RAGE parameters were empirically optimized for gray-white contrast (). The scanner and sequences were maintained across the duration of the study so the present data are not influenced by hardware upgrades or other instrument differences.
MR image acquisition details
For each subject, the individual scan files were converted from Siemens proprietary IMA format into 16-bit NiFTI1 format using a custom conversion program. Header fields with identifying information (patient ID, experiment date) were left blank. The images were then corrected for inter-scan head movement and spatially warped into the atlas space of Talairach and Tournoux (1988) using a rigid transformation that differs in process from the original piecewise scaling. The resulting transformation nonetheless places the brains in the same coordinate system and bounding box as the original atlas. The template atlas used here consisted of a combined young-and-old target previously generated from a representative sample of young (n
= 12) and nondemented old (n
= 12) adults. The use of a combined template has been shown to minimize the potential bias of an atlas normalization procedure to over-expand atrophied brains (Buckner et al., 2004
). Given the age range of the present sample, an old-only atlas target could have been employed. We chose to retain the young-and-old target to be comparable to our earlier report (Marcus et al., 2007a
For registration, a 12-parameter affine transformation was computed to minimize the variance between the first MP-RAGE image and the atlas target. The remaining MP-RAGE images were registered to the first (in-plane stretch allowed) and resampled via transform composition into a 1-mm isotropic image in atlas space. The result was a single, high-contrast, averaged MP-RAGE image in atlas space. Subsequent steps included skull removal by application of a loose-fitting atlas mask and correction for intensity inhomogeneity due to nonuniformity in the magnetic field. Intensity variation was corrected across contiguous regions, based on a quadratic inhomogeneity model fitted to data from a phantom.
Estimated Total Intracranial Volume (eTIV) and Normalized Whole-brain Volume (nWBV)
The procedures used for measuring intracranial and whole-brain volumes have been described previously (Buckner et al., 2004
; Fotenos et al., 2005
) and are identical to our earlier OASIS data release (Marcus et al., 2007a
). eTIV was computed by scaling the manually-measured intracranial volume of the atlas by the determinant of the affine transform connecting each individual‘s brain to the atlas. This method is minimally influenced and proportional to manually measured total intracranial volume.
nWBV was computed using the FAST program in the FSL software suite (www.fmrib.ox.ac.uk/fsl
). The image was first segmented to classify brain tissue as cerebral spinal fluid (CSF), gray, or white matter. The segmentation procedure iteratively assigned voxels to tissue classes based on maximum likelihood estimates of a hidden Markov random field model. The model used spatial proximity to constrain the probability with which voxels of a given intensity are assigned to each tissue class. Finally, nWBV was computed as the proportion of all voxels within the brain mask classified as tissue (either gray or white matter). The unit of normalized volume is percent, which represents the percentage of the total white and gray matter voxels within the estimated total intracranial volume (Fotenos et al., 2005
). To calculate atrophy rates, we estimated the slope of the line connecting nWBV measurements within each individual, divided by baseline nWBV, expressed as percent change per year. For example, in a participant with two scans, atrophy rate was computed as nWBV at scan 2 minus nWBV at scan 1, divided by the interval between measurements, divided by nWBV at scan 1, times 100. Analysis of covariance was again used to test for differences in atrophy rate based on age, sex, and dementia status.
All images in the data set were carefully screened for artifacts, acquisition problems, and processing errors. During the screening process, each image was viewed on a per-slice basis along the axis of acquisition. Typical flaws visible in the images included electronic noise resulting in bright lines through multiple slices, motion artifacts appearing as hazy bands across the image, poor head positioning resulting in wraparound artifacts, distortions from dental work, and limited image contrast. Images with severe flaws were excluded from the data set. A number of borderline images remain in the distribution, providing tool builders and testers with a realistic range of acquisition quality. In cases where individual scans were deemed unusable, the single scan was removed from the data set but the remainder of the subject‘s scans was included. Overall, 25 imaging sessions were excluded from the final release due to poor image quality.