Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults

• Published in: Journal of cognitive neuroscience Vol. 22; no. 12; p. 2677
• Main Authors: Marcus, Daniel S, Fotenos, Anthony F, Csernansky, John G, Morris, John C, Buckner, Randy L
• Format: Journal Article
• Language: English
• Published: United States 01-12-2010
• Subjects: Aged; Aged, 80 and over; Brain - pathology; Brain Mapping; Dementia - pathology; Female; Humans; Image Processing, Computer-Assisted; Longitudinal Studies; Magnetic Resonance Imaging; Male; Middle Aged
• ISSN: 1530-8898
• DOI: 10.1162/jocn.2009.21407

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.