Datasets for Special Series on Cognitive Reserve

The Sixth Annual Advanced Psychometrics Methods Workshop was held August 30 to September 4, 2009 at the University of Washington’s Friday Harbor Laboratories in the San Juan Islands, WA. The theme, Cognitive Reserve, focused on identifying variables that influence the association of neuropathology with cognitive function. Functional physiologic systems, for example, pulmonary, renal, hepatic, cardiac, are redundant and a considerable amount of tissue destruction must take place before the system is compromised to the point that signs and symptoms of dysfunction become clinically apparent. The nervous system, though functionally and structurally more complex, has long been recognized to exhibit some type of reserve. Evidence of cognitive reserve comes from a variety of sources. For example, several clinical–pathologic studies have reported widespread Alzheimer’s disease (AD) pathology among persons without obvious cognitive impairment suggesting the presence of factors that must influence cognition separate from neuropathology or influence the association of neuropathology with cognition (Crystal et al., 1993; Davis, Schmitt, Wekstein, & Markesbery, 1999; Katzman et al., 1988;Morris et al., 1996). Several factors have been reported to be associated with cognitive reserve such as years of formal education and occupation (Zhang et al., 1990; Stern et al., 1994). Studies providing data to this workshop included data on a) indicators of cognitive reserve, b) detailed cognitive function, and c) neuropathologic indices. Data were primarily from two studies: The Rush Religious Orders Study and the Rush Memory and Aging Project (MAP). Both are cohort studies of risk factors for common chronic conditions of aging that include organ donation at death. Together, more than 2,500 persons have undergone annual detailed clinical evaluations with up to 17 waves of data, and more than 850 brain autopsies have been performed. Prior work from these studies has reported the relation of neuropathology, including measures of AD pathology, cerebral infarctions, Lewy bodies, and amyloid angiopathy, to cognition (Arvanitakis, Leurgans, Barnes, Bennett, & Schneider, 2011; Arvanitakis, Leurgans, et al., 2010; Bennett, Schneider, Bienias, Evans, & Wilson, 2005; Bennett, Schneider, Arvanitakis, et al., 2006; Schneider, Wilson, Bienias, Evans, & Bennett, 2004; Schneider, Boyle, Arvanitakis, Bienias, & Bennett, 2007; Schneider, Arvanitakis, Bang, & Bennett, 2007; Schneider, Arvanitakis, Leurgans, & Bennett, 2009;Wilson, Leurgans, Boyle, Schneider, & Bennett, 2010). In addition, several experiential and psychological factors have been reported to be related to cognition separate from neuropathology such as neuroticism, loneliness, depression, and cognitive activities (Bennett, Wilson, Schneider, Bienias, & Arnold, 2004; Wilson et al., 2003; Wilson, Scherr, Schneider, Tang, & Bennett, 2007; Wilson, Arnold, Schneider, Li, & Bennett, 2007; Wilson, Krueger, et al., 2007). Furthermore, several factors have been reported to modify the relation of neuropathology to cognition including years of formal education, social networks, processing resources, conscientiousness, and sex (Barnes et al., 2005; Bennett, Schneider, Wilson, Bienias, & Arnold, 2005; Bennett, Schneider, Arnold, Tang, & Wilson, 2006; Bennett, Wilson, et al., 2009; Boyle, Wilson, Schneider, Bienias, & Bennett, 2008;Wilson, Schneider, Arnold, Bienias, & Bennett 2007). Finally, data collection methods include a large subset of data collected with identical methods in both studies allowing for data to be pooled for clinical and clinical–pathologic analyses in which larger samples are needed to enhance power for associations (Bennett, Schneider, Arvanitakis, et al., 2006; Bennett, Schneider, Aggarwal, et al., 2006b; Bennett, De Jager, Leurgans,& Schneider, 2009; Schneider, Arvanitakis, Bang, & Bennett, 2007; Wilson, Schneider, Boyle, et al., 2007). A third study, the Minority Aging Research Study, also contributed clinical data to the workshop. This is a cohort study of risk factors for common chronic conditions with more than 350 older African Americans. Data collection methods include a large subset of data collected with identical methods as those used in the Religious Orders Study and the Rush Memory and Aging Project, which include nearly 200 African Americans, allowing for data to be pooled for clinical analyses across race (Arvanitakis, Bennett, Wilson, & Barnes, 2010; Boyle, Barnes, Buchman, & Bennett, 2009; Boyle, Buchman, Barnes, James, & Bennett, 2010; Buchman, Boyle, Barnes, Leurgans, & Bennett, 2010). This special Series on Cognitive Reserve in the Journal of the International Neuropsychological Society highlights four manuscripts produced by conference participants. The first, by Jones, et al. examined latent variable modeling approaches for quantifying reserve, with an emphasis on their application to data from observational studies. The second paper, by Dowling, et al., used latent variable modeling to examine clinical–pathologic relationships with data from the Religious Orders Study and Rush Memory and Aging Project. The third paper, by Reed et al., also used a latent variable modeling approach to estimate domain-specific cognitive reserve and then sought to identify potential determinants of reserve using data from the same two studies. Finally, the paper by Fyffe et al., assessed differential item functioning, demographic characteristics, and factors associated with cognitive reserve to determine whether they account for racial differences in episodic memory performance using data from the Rush Memory and Aging Project and the Minority Aging Research Study.