ADNI data is made available to researchers around the world. As such, there are many active research projects accessing and applying the shared ADNI data. To further encourage Alzheimer’s disease research collaboration, and to help prevent duplicate efforts, the list below shows the specific research focus of the active ADNI investigations. This information is requested annually as a requirement for data access.
|Principal Investigator's Name:||catherine labrish|
|Proposed Analysis:||I am a co-reasearcher with N. Maritza Dowling of the University of Wisconson (who is principal investigator) and am applying for access to the ADNI data as part of my research activities with her. Her particulars are: N. Maritza Dowling Associate Scientist Department of Biostatistics & Medical Informatics University of Wisconsin, Madison e-mail: firstname.lastname@example.org Our proposed reaserch is: A challenge in the identification of risk groups based on neuropsychological assessments that reflect underlying pathology is the lack of uniformity in the selection of cognitive measures and norms making it difficult to compare results across studies. Recent studies have also shown that the use of complementary information such as cerebrospinal fluid (CSF) and neuroimaging biomarkers can significantly increase the accuracy in predicting AD before the emergence of measurable cognitive impairment (Mueller et al, 2005; DeMeyer et al, 2010). Using data from the Alzheimer’s Disease Neuroimage Initiative (ADNI) study we will employ a mixture model clustering approach to define homogeneous clusters or typologies of baseline amnestic MCI individuals based on associations among different neuropsychological tests measuring a common underlying memory construct. To describe the heterogeneity of the latent classes or “typologies” and explain differences across classes, we will model conversion to AD in both a 12- and a 24-month period as a distal outcome. APOE genotype, age, education, and gender will be added as covariates to increase the accuracy of the classification and study correlates of class membership. A second step in the analysis is to examine the relationship between CSF biomarker levels, total tau (t-tau), phosphorylated tau (p-tau), and Aβ42 across latent amnestic MCI subgroups.|
|Investigator's Name:||norman park|
|Proposed Analysis:||In partial fulfillment of the requirements for my doctoral degree in Psychology (specialization Quantitative Methods) at York University, Toronto, Canada, I am required to complete a minor area paper, applying advanced statistical methods to a substantive problem in psychology. My tentative proposal, for which I will require the continued use of ADNI data, follows. My work will be supervised by Dr. Norman Park, Associate Professor, Clinical Neuropsychology, York University, Toronto, Canada. I should note that ADNI data obtained to date is in a state ready for the analyses I propose to conduct. As Chang, et. al, (2010) note, information processing models of memory propose that memory involves three distinct yet related, processes:, encoding, retention and information retrieval, with retention defined as the process of maintaining encoded information over time in the absence of active rehearsal and encoding defined as the process of acquiring and storage of new information.n Retention is typically operationalized as performance on measures of delayed free recall of words lists presumably encoded during repeated presentation of the same word list over a number of trials, while encoding is typically operationalized as the increment in the total number of words learned over repeated presentation of the same list of words. Deficits in both learning and retention have been associated with AD, and, there are presently two schools of thought regarding which is implicated in prodromal AD (MCI) (Chang, et. al, 2010). The first maintains that encoding is at the heart of the memory deficits apparent in AD and its prodome (Greene, Baddeley, & Hodges, 1996; Grober & Kawas, 1997). A second maintains that it is retention, separate from encoding, that best characterizes the memory impairment associated with AD and its prodome (Hart, Kwentus, Harkins, & Taylor, 1988; Moss, Albert, Butters, & Payne, 1986). The capacity to learn new information is commonly assessed using learning over trials tasks in which individuals are presented with same word list over a limited number of trails (ie. Rey’s Auditory Learning Task (RAVLT)). Learning, as measured by these tasks, has traditionally been operationalized as the difference in the total number of words correctly recalled between the first and last trials or alternatively, as the rate of linear change in the number of words recalled over the total number of trials. These approaches assume that growth in the number of words retained over trials assumes a linear trajectory. In recent years however, a literature has emerged which suggests that learning, as measured using learning over trials tasks such the RAVLT, is non-linear (Jones, et. al., 2005; Poreh, 2005; Zhang, David, Salthouse and Tucker-Drob (2007); Zimprich & Rast, 2009). Using latent growth modeling, these studies have successfully demonstrated that among the cognitively unimpaired elderly, the pattern of growth in acquiring new information when performing learning over trials task is instead non-linear and best described as logarithmic (Jones, et. al., 2005; Poreh, 2005) or hyperbolic (Zimprich & Rast, 2009). These authors have also demonstrated that the parameters which define the shape of the learning curve, first, evidence considerable intra-individual heterogeneity (i.e. individual parameter estimates vary significantly around the mean parameter estimate for the group overall) ; and, second, may represent dissociable measures of learning and encoding. For example, Jones, et. al, (2005) demonstrated that the parameter associated with initial recall (i.e. recall on Trial1 of the RAVLT) was uncorrelated with the parameter associated with subsequent growth in words recalled (i.e. change in the number of words recalled between trials2 and trials5), suggesting that initial recall and subsequent growth in words recalled over subsequent trials are dissociable constructs. In a separate sample, Zimprich and Rast (2009) were able to demonstrate that three parameters, initial learning, asymptotic performance, and learning rate successfully captured the pattern of growth in learning over trials on a verbal learning task similar to the RAVLT. In a separate study, these same authors were also able to demonstrate that individuals with higher learning rates required more trials to achieve their asymptotic performance and that those with high initial recall also had higher asymptotic performance (Rast and Zimprich, 2009). Both studies were also able to successfully demonstrate that the intra-individual differences in parameters could be accounted for by covariates such as age, education, and cognitive performance measures such as speed of processing. Further, the extent to which these covariates explained intra-individual differences in parameters varied across parameters. For example, Jones and colleagues found that age failed to independently explain intra-individual differences in initial recall, although age was able to independently explain intra-individual differences in the subsequent increase in words learned following repeated presentation of the same word list. Similar results were obtained when speed of processing was included as a covariate in the model. Based on a preliminary review of the literature, I have been unable to identify any studies which have attempted to fit these kinds of models to learning over trials data obtained from those with either a diagnosis of MCI or DAT. ADNI data is well-suited to this task and will permit me to discern whether a similar set of parameters describe performance on initial recall and subsequent learning over trials (as measured using the RAVLT) for those with either a diagnosis of MCI or AD. Additional objectives of my study include: First, can differences in the parameters which define the growth curve underlying performance over repeated trials on the RAVLT across individuals successfully differentiate those with a diagnosis of MCI from the cognitively unimpaired elderly and those who have already progressed to AD at baseline. Consistent with Jones and colleagues (2005) do the estimated growth parameters suggest a dissociable pattern between performance on initial recall and subsequent learning over trials for those with a diagnosis of either MCI or AD? Second, are these parameters predictive of later conversion to AD among those diagnosed as MCI at baseline? Third, in an attempt to resolve the debate over whether encoding or retention best characterize the memory impairment inherent in AD and its prodome, do these parameters predict delayed recall and recognition performance in these groups? Fourth, what is the influence of other cognitive domains (eg. working memory, speed of processing, executive function) on the parameters which describe the learning curve in individuals with MCI and AD? Do these cognitive domains serve to moderate or perhaps mediate the relationship between learning performance and subsequent performance on measures of delayed recall and recognition? Are the effects of these covariates greater than those expected as a result of the normal aging process? Extensions of this study may include, but are not limited to assessing whether there are discernible changes over time in these growth parameters that might also be predictive of subsequent conversion from MCI to AD. If these growth parameters are predictive of subsequent performance on delayed free recall and recognition, does this relationship remain stable over time, or change among those with a diagnosis of MCI?. Are these changes predictive of subsequent conversion to AD? Finally, if these same growth parameters are influenced by cognitive domains such as speed of processing or working memory over and above that expected due to normal aging, do these associations also change over time and are these changes predictive of subsequent conversion to AD? References Chang, Y.L., Bondi, M.W., Fennema-Notestine, C., McEvoy, L. K., Hagler, D. J. Jr., Jacobson, M. W., Dale, A.M., & the Alzheimer’s Disease Neuroimaging Initiative (2010). Brain substrates of learning and retention in mild cognitive impairment diagnosis and progression to Alzheimer’s disease. Neuropsychologia, 48, 1237-1247. Greene, J. D., Baddeley, A. D., & Hodges, J. R. (1996). Analysis of the episodic memory deficit in early Alzheimer’s disease: Evidence from the doors and people test. Neuropsychologia, 34, 537–551. Grober, E, & Kawas, C. (1997). Learning and retention in preclinical and earlyAlzheimer’s disease. Psychology and Aging, 12, 183–188. Hart, R. P., Kwentus, J. A., Harkins, S. W., & Taylor, J. R. (1988). Rate of forgetting in mild Alzheimer’s-type dementia. Brain and Cognition, 7, 31–38. Jones, R. N., Rosenberg, A. L., Morris, J. N., Allaire, J. C., McCoy, K. J. M., Marsiske, M., . . . Malloy, P. F. (2005). A growth curve model of learning acquisition among cognitively normal older adults. Experimental Aging Research: An International Journal Devoted to the ScientificStudy of the Aging Process, 31, 291–312. Moss, M. B, Albert, M. S., Butters, N., & Payne, M. (1986). Differential patterns of memory loss among patients with Alzheimer’s disease, Huntington’s disease, and alcoholic Korsakoff’s syndrome. Archives of Neurology, 43, 239–246. Poreh, A. (2005). Analysis of Mean Learning of Normal Participants on the Rey Auditory–Verbal Learning Test. Psychological Measurement,17, 191-199. Zhang, Z., Davis, H. P., Salthouse, T. A., & Tucker-Drob, E. M. (2007). Correlates of individual, and age-related, differences in short-term learning. Learning and Individual Differences, 17, 231–240. Zimprich, D., & Rast, P. (2009). Verbal Learning Changes in Older Adults Across 18 Months. Aging, Neuropsychology, and Cognition, 16, 461–484.|