There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
Principal Investigator | |
Principal Investigator's Name: | Emily Edmonds |
Institution: | University of California, San Diego |
Department: | Psychiatry |
Country: | |
Proposed Analysis: | Our proposed analysis involves identifying distinct subtypes of mild cognitive impairment (MCI) and exploring underlying biomakers of each subtype using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. The classification of MCI has traditionally involved a distinction between amnestic and non-amnestic MCI. However, recent research has shown that MCI can be further differentiated into subtypes (Clark et al., 2013; Libon et al., 2010). Using cluster analysis of neuropsychological test performance, our group recently found four empirically-derived subtypes of MCI: an Amnensic, a Dysexecutive, a Visuospatial, and a Mixed subtype, each characterized by different performance on neuropsychological measures (Clark et al., 2013). These more nuanced MCI subtypes may have advantages over the conventional classification system, as the dichotomous amnestic/non-amnestic may obscure the heterogeneity of MCI by combining all patients with non-memory impairments into one group. We hope to validate these empirically-derived MCI subtypes by performing a statistical cluster analysis of cognitive test performance of ADNI participants classified as having MCI. Despite differences in the specific cognitive tests that were used to measure cognitive abilities, we hypothesize that we will find a cluster structure that is similar to the Clark et al., 2013 study. Such a finding would provide evidence for the stability and generalizability of the MCI subtypes. In order to further understand and characterize the MCI subtypes, the next step of the proposed project would be to examine potential underlying biomarkers. Such biomarkers could include neuroimaging findings (e.g., cortical thinning, white matter hyperintensities), CSF biomarkers, or the presence of the e4 allele of the apolipoprotein E gene. Further characterization of distinct prodromal dementia states, including the identification of underlying biomarkers, may have important clinical implications. Specifically, a better understanding of mild cognitive impairment could be valuable for identifying those individuals who are at risk for progression to dementia, thereby providing an opportunity for early interventions or future preventive treatment. References: Clark, L. R., Delano-Wood, L., Libon, D. J., McDonald, C. R., Nation, D. A., Bangen, K. J. et al. (2013). Are empirically derived subtypes of mild cognitive impairment consistent with conventional subtypes? Journal of the International Neuropsychological Society, 19, 1-11. Libon, D. J., Xie, S. X., Eppig, J., Wicas, G., Lamar, M., Lippa, C. et al. (2010). The heterogeneity of mild cognitive impairment: A neuropsychological analysis. Journal of the International Neuropsychological Society, 16, 84-93. Petersen, R. C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of Internal Medicine, 256, 183-194. Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L.-O. et al. (2004). Mild cognitive impairment – beyond controversies, towards a consensus: Report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine, 256, 240-246. |
Additional Investigators |