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: | Sascha Gill |
| Institution: | University of Calgary |
| Department: | Neuroscience |
| Country: | |
| Proposed Analysis: | Background: Over 560,000 individuals in Canada are living with Alzheimer’s disease (AD) and it is anticipated that these numbers will increase by 66% in 2031. Research has shown that changes within the brain may begin up to 20 years before the onset of memory loss. It is therefore necessary to focus research on methods of early detection to provide opportunities for earlier intervention, treatment approaches, and improved patient outcomes. Neuropsychiatric symptoms (NPS) are common in AD and are associated with poor cognitive and functional outcome. Multiple lines of evidence suggest that NPS can be early markers of neurodegenerative disease, and may emerge in advance of or in concert with mild cognitive impairment (MCI). While several studies have identified brain abnormalities to be associated with these neuropsychiatric and behavioural symptoms in AD populations, evidence in pre-clinical populations is sparse. Mild-Behavioural Impairment (MBI) is a neurobehavioural syndrome that describes later-life onset of neuropsychiatric symptoms as an at-risk state for incident cognitive decline and dementia. MBI symptoms are divided into 5 neuropsychiatric domains: 1) drive/motivation; 2) emotional regulation; 3) impulse control; 4) social cognition; and 5) psychotic symptomatology. MBI is a novel approach for early identification of neurocognitive disorders, however, the neural correlates of MBI are not well characterized in pre-clinical and dementia populations. Several studies have identified decreased white matter integrity and volumetric changes in regions associated with neuropsychiatric and behavioural symptoms in AD populations, but, evidence in pre-clinical populations is sparse. Understanding neuroimaging correlates of NPS, specifically those within MBI domains, may help characterize the evolution of neurocognitive disorders and identify treatment targets. Objectives: 1) To assess the structural connectivity of NPS within the MBI domains, specifically focusing on large white matter tracts, in individuals with normal cognition, MCI and mild AD. 2) To utilize machine learning algorithms to identify volumetric changes associated with NPS in individuals with normal cognition, MCI, and mild AD. Hypothesis: We hypothesize that in individuals with normal cognition, MCI, and AD there will be greater structural changes in large white matter tracts and volumetric differences in participants with MBI symptoms compared to those without. Methods: We intend to take a cross-sectional approach to examine neural correlates of NPS, using the ADNI database. We will extract clinical, neuropsychological and imaging data to explore non-cognitive markers to track the progression of disease. Specifically, using diffusion tensor imaging (DTI) to observe large white matter tracts may provide insight into changes in structural connectivity associated with NPS. Additionally, machine learning algorithms will leverage structural T1 imaging to identify if volumetric brain changes associated with NPS are predictive of cognitive decline and dementia. NPS will be identified using the Neuropsychiatric Inventory Questionnaire (NPI-Q) from the ADNI database. The NPI-Q scores will be converted such that the NPS will be divided into the 5 MBI domains. DTI analysis will be performed using probabilistic tractography using FSL Software and T1 analysis will be performed using an established machine learning pipeline. Between group differences and correlational analyses, including analysis of variance (ANOVA), will be performed to explore neural correlates of MBI domains. Significance of project: In conclusion, investigating structural changes in pre-clinical and dementia populations will improve our understanding of the neural correlates underlying NPS. We hope to apply our findings clinically, by combining it with other biomarkers to identify people who may be at risk or showing early signs of dementia. Such individuals will also make for an ideal population to try new pharmacological approaches that could affect the course of AD. |
| Additional Investigators | |
| Investigator's Name: | Zahinoor Ismail |
| Proposed Analysis: | Primary Investigator / Supervisor |
| Investigator's Name: | Hung-Yu Chen |
| Proposed Analysis: | Exploring several biomarkers such as CSF and plasma tau and beta-amyloid associated with neuropsychiatric symptoms in predementia risk states |
