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: | Cameron Ferguson |
| Institution: | Cardiff University |
| Department: | Department of Psychology |
| Country: | |
| Proposed Analysis: | I plan to perform Bayesian network analyses of ADNI cognitive battery subtest level data for healthy controls (HC), MCI patients, mild Alzheimer’s patients (AZ), and moderate-to-severe AZ patients. For each group, the analyses will model the relative influence of individual cognitive processes on each other as they constitute global cognitive functioning at each study time point (e.g. 6 months, 12 months, 18 months, etc.) I will compute centrality indices (degree strength, closeness, and betweenness) to identify the most central nodes in the four networks to highlight which cognitive faculties are most central in producing the global cognitive functioning of each group at each time point. To infer whether the cognitive function networks differ from each other, they will be compared using network comparison tests (HC vs MCI, HC vs mild-AZ, MCI vs mild-AZ, HC vs moderate-to-severe-AZ, MCI vs moderate-to-severe-AZ, mild-AZ vs moderate-to-severe-AZ), which is a permutation test of the null hypothesis that network structures do not differ, with post hoc Bonferroni corrections for multiple comparisons. The primary aim of the study is exploratory; however, I hypothesise, that the HC and moderate-to-severe-AZ cognitive networks will significantly differ from each other. All analyses will be conducted with R in RStudio. My approach confers three advantages over the traditional latent variable-based understanding of neurocognition in healthy older adults, MCI, and AZ disease states. First, it will offer systematic and mathematically precise models of cognitive function in healthy, MCI, and AZ disease states to compliment established, verbal described neuropsychological profiles associated with them. Second, identifying the most central nodes in global cognitive function networks in three groups will contribute to understanding how specific cognitive processes interact to engender global cognitive (dys)function in health and two disease states. By extension, the study may outline fruitful avenues for neuropsychological and/or pharmacological interventions. Finally, the network psychometric approach is amenable to neuropsychological formulation, in which speculations about causal and/or maintenance factors regarding cognitive function are often made, while also providing a specified mathematical model in place of a verbalistic theory. |
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