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: | Clinton Wright |
| Institution: | University of Miami |
| Department: | Neurology |
| Country: | |
| Proposed Analysis: | Alzheimer’s disease is the most common type of dementia. This neurodegenerative condition has influenced more than 36 million people worldwide and it is expected to increase to 115 million by 2050 (Alzheimer’s Assoc. 2012.). The insidious onset and progression of the symptoms make the diagnosis of AD challenging in the preclinical stages. Although the development of neuroimaging techniques and CSF biomarkers have enabled the measurement of brain atrophy associated with MCI and AD and ways to predict disease progression, there is discordance between brain pathology findings and cognitive tests performance (Stern et al.,2001; Risacher et al., 2013; Vemuri et al., 2011). The cognitive reserve hypothesis has been proposed to explain the dissociation between clinical manifestations versus brain damage. A recent study has shown that up to 30% of subjects who were cognitively normal in life were found at autopsy to have a pathological profile typically dominated by amyloid plaques consistent with AD diagnosis (Katzman et al., 1988; Crystal et al., 1993; Hulette et al., 1998;Price and Morris, 1999; Schmitt et al., 2000; Morris and Price,2001; Riley et al., 2002; Knopman et al., 2003). Further, numerous cross sectional studies have explored the correlation between education and Alzheimer’s disease biomarkers as well as cognitive test performance. However, more studies are needed to understand these relationships in terms of severity and progression of the disease in early stages. Specifically, further longitudinal research is needed to explore the influence of cognitive reserve in the rate of conversion from MCI to severe Alzheimer disease and the development of neuropsychiatry symptoms such as anxiety or depression. The purpose of our study is to assess the influence of cognitive reserve in the severity and progression of the disease in patients with MCI and AD compared to normal controls through a longitudinal study using the ADNI cohort. We would also like to explore the rate of conversion of of normal cognition and MCI to Alzheimer’s disease in a large group of patients. We will use American National Adult Reading Test (ANART) and education level as measures of cognitive reserve, a full neurocognitive battery to measure cognitive performance, and specific biomarkers (MRI, CSF, PET scan) to assess brain pathology. We propose to analyze the data using Machine learning methods References 1. Risacher, S. L., & Saykin, A. J. (2013). Neuroimaging and other biomarkers for Alzheimer's disease: the changing landscape of early detection. Annual review of clinical psychology, 9, 621. 2. Vemuri, P., Weigand, S. D., Przybelski, S. A., Knopman, D. S., Smith, G. E., Trojanowski, J. Q., ... & Jack, C. R. (2011). Cognitive reserve and Alzheimer's disease biomarkers are independent determinants of cognition. Brain, awr049. 3. Querbes, O., Aubry, F., Pariente, J., Lotterie, J. A., Démonet, J. F., Duret, V., ... & Celsis, P. (2009). Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve. Brain, 132(8), 2036-2047. 4. Ewers, M., Insel, P. S., Stern, Y., & Weiner, M. W. (2013). Cognitive reserve associated with FDG-PET in preclinical Alzheimer disease. Neurology, 80(13), 1194-1201. 5. Chong, M. S., Lim, W. S., & Sahadevan, S. (2006). Biomarkers in preclinical Alzheimer's disease. Current opinion in investigational drugs (London, England: 2000), 7(7), 600-607. 6. Reed, B. R., Mungas, D., Farias, S. T., Harvey, D., Beckett, L., Widaman, K., ... & DeCarli, C. (2010). Measuring cognitive reserve based on the decomposition of episodic memory variance. Brain, awq154. 7. Casanova, Ramon, Fang-Chi Hsu, Kaycee M. Sink, Stephen R. Rapp, Jeff D. Williamson, Susan M. Resnick, Mark A. Espeland, and Alzheimer's Disease Neuroimaging Initiative. "Alzheimer's Disease Risk Assessment Using Large-Scale Machine Learning Methods." PloS one 8, no. 11 (2013): e77949. |
| Additional Investigators | |
| Investigator's Name: | Ramon Casanova |
| Proposed Analysis: | The purpose of our study is to assess the influence of cognitive reserve in the severity and progression of the disease in patients with MCI and AD compared to normal controls through a longitudinal study using the ADNI cohort. We would also like to explore the rate of conversion of of normal cognition and MCI to Alzheimer’s disease in a large group of patients. We will use American National Adult Reading Test (ANART) and education level as measures of cognitive reserve, a full neurocognitive battery to measure cognitive performance, and specific biomarkers (MRI, CSF, PET scan) to assess brain pathology. We propose to analyze the data using Machine learning methods |
| Investigator's Name: | Yesica Campos |
| Proposed Analysis: | The purpose of our study is to assess the influence of cognitive reserve in the severity and progression of the disease in patients with MCI and AD compared to normal controls through a longitudinal study using the ADNI cohort. We would also like to explore the rate of conversion of of normal cognition and MCI to Alzheimer’s disease in a large group of patients. We will use American National Adult Reading Test (ANART) and education level as measures of cognitive reserve, a full neurocognitive battery to measure cognitive performance, and specific biomarkers (MRI, CSF, PET scan) to assess brain pathology. We propose to analyze the data using Machine learning methods |
| Investigator's Name: | Maria Virginia Diaz |
| Proposed Analysis: | The purpose of this study is to look cognitive trajectories in patients with mild cognitive impairment and subjective memory complains and how cognitive reserve modulates those trajectories |
