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: | Kyla-Louise Horne |
| Institution: | University of Otago |
| Department: | New Zealand Brain Research Institute |
| Country: | |
| Proposed Analysis: | The goals of this project are to use patterns of brain ageing as a context to understand neuro-degenerative diseases, specifically Parkinson’s disease (PD) and Alzheimer’s disease (AD). First, metrics of brain health, based upon BrainAGE, will be developed using healthy control data from freely available data-sets. The BrainAGE framework uses structural MRI scans to define a pattern of normal brain ageing; using an individual’s structural MRI scan, it can then predict this individual’s “brain age”. The difference between their chronological age and the model’s predicted age is termed BrainAGE score; positive BrainAGE scores are indicative of accelerated brain ageing (that is, predicted age > chronological age). The relative effects of PD and AD, including mild cognitive impairment associated within both conditions, will be quantified in terms of their BrainAGE and level of non-ageing related pathology, in a cross-sectional PD sample and an AD sample. The ability of these BrainAGE metrics to track cognitive decline will be assessed using existing data in our NZ Brain Research Institute (NZBRI) longitudinal PD sample. Finally, these metrics will be applied to a sub-group of PD patients who received amyloid PET scans to reveal the association between amyloid and these metrics. In terms of health outcomes, the primary focus of this study is to develop a robust MRI model of healthy ageing that can be used to quantify accelerated ageing and non-ageing related (i.e. disease-related) pathology. Currently it is difficult to evaluate the success of a new treatment in these progressive and heterogeneous populations. The critical benefit of these brain health metrics is that they provide a quantitative generalisable measure that is suitable both within and across studies. The hope is that these metrics will provide a suitable biomarker for cognitive decline. |
| Additional Investigators | |
| Investigator's Name: | Tracy Melzer |
| Proposed Analysis: | The goals of this project are to use patterns of brain ageing as a context to understand neuro-degenerative diseases, specifically Parkinson’s disease (PD) and Alzheimer’s disease (AD). First, metrics of brain health, based upon BrainAGE, will be developed using healthy control data from freely available datasets. The BrainAGE framework uses structural MRI scans to define a pattern of normal brain ageing; using an individual’s structural MRI scan, it can then predict this individual’s “brain age”. The difference between their chronological age and the model’s predicted age is termed BrainAGE score; positive BrainAGE scores are indicative of accelerated brain ageing (that is, predicted age > chronological age). The relative effects of PD and AD, including mild cognitive impairment associated within both conditions, will be quantified in terms of their BrainAGE and level of non-ageing related pathology, in a cross-sectional PD sample and AD sample. The ability of these BrainAGE metrics to track cognitive decline will be assessed using existing data in our NZ Brain Research Institute (NZBRI) longitudinal PD sample. Finally, these metrics will be applied to a sub-group of PD patients who received amyloid PET scans to reveal the association between amyloid and these metrics. In terms of health outcomes, the primary focus of this study is to develop a robust MRI model of healthy ageing that can be used to quantify accelerated ageing and non-ageing related (i.e. disease-related) pathology. Currently it is difficult to evaluate the success of a new treatment in these progressive and heterogeneous populations. The critical benefit of these brain health metrics is that they provide a quantitative generalisable measure that is suitable both within and across studies. The hope is that these metrics will provide a suitable biomarker for cognitive decline. |
| Investigator's Name: | John Dalrymple-Alford |
| Proposed Analysis: | The goals of this project are to use patterns of brain ageing as a context to understand neuro-degenerative diseases, specifically Parkinson’s disease (PD) and Alzheimer’s disease (AD). First, metrics of brain health, based upon BrainAGE, will be developed using healthy control data from freely available datasets. The BrainAGE framework uses structural MRI scans to define a pattern of normal brain ageing; using an individual’s structural MRI scan, it can then predict this individual’s “brain age”. The difference between their chronological age and the model’s predicted age is termed BrainAGE score; positive BrainAGE scores are indicative of accelerated brain ageing (that is, predicted age > chronological age). The relative effects of PD and AD, including mild cognitive impairment associated within both conditions, will be quantified in terms of their BrainAGE and level of non-ageing related pathology, in a cross-sectional PD sample and AD sample. The ability of these BrainAGE metrics to track cognitive decline will be assessed using existing data in our NZ Brain Research Institute (NZBRI) longitudinal PD sample. Finally, these metrics will be applied to a sub-group of PD patients who received amyloid PET scans to reveal the association between amyloid and these metrics. In terms of health outcomes, the primary focus of this study is to develop a robust MRI model of healthy ageing that can be used to quantify accelerated ageing and non-ageing related (i.e. disease-related) pathology. Currently it is difficult to evaluate the success of a new treatment in these progressive and heterogeneous populations. The critical benefit of these brain health metrics is that they provide a quantitative generalisable measure that is suitable both within and across studies. The hope is that these metrics will provide a suitable biomarker for cognitive decline. |
