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: | Carly Nelson |
| Institution: | Wake Forest School of Medicine Radiology |
| Department: | Radiology |
| Country: | |
| Proposed Analysis: | Alzheimer’s disease (AD) is the most common form of dementia. The disease is associated with changes in brain structures that support memory and higher cognitive functions. The pathophysiologic processes leading to AD begin well before the onset of clinically detectable symptoms. Currently, no medication or particular intervention has been clearly shown to delay or halt the progression of the disease. To slow the progression of AD through intervention, a non-invasive and expensive early detection method is key. Using magnetic resonance imaging (MRI) to detect very small anatomic changes in the brain could play an important role in early detection. The proposed multidisciplinary research project will involve the collaboration of investigators from diverse and complementary backgrounds (a biomechanical engineer, an MR physicist, a neuroradiologist and cognitive neuroscientists) to investigate early AD imaging biomarkers which can be derived from one of the most common structural MRI scans. We propose two specific aims to validate the feasibility of new structural MRI biomarkers derived from biomechanical methods to measure signs of early AD. Aim 1: Considering anatomic features of individual brains, such as orientations of cortical sulci and gyri, we will analyze three-dimensional deformations along these neuroanatomic orientations. Using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, we will compare the longitudinal trajectory of anatomic changes in brains of 301 cognitively normal individuals and 870 people with early AD (mild cognitive impairment), using images that were obtained over a 48-month period. We will compare the statistical power to detect abnormal brain degeneration patterns in early AD subjects between our new morphometry algorithm and conventional volumetric or cortical thickness measures. Aim 2: We will propose a statistical model to predict individual cognitive decline, considering substantial inter-subject variability in baseline characteristics and disease progression rates. Within the ADNI dataset, we will use longitudinal positron emission tomography (PET) images and MRI biomarkers, to determine the temporal ordering among MRI, PET, and cognitive biomarkers. The goal of these studies is to gauge the clinical utility of imaging biomarkers by correlating with longitudinal neuropsychological assessment data. Here, we will determine if single or multi-modality imaging biomarkers can predict cognitive decline before clinical onset. The large datasets available and the multidisciplinary team led by an Early-Stage Investigator will facilitate the likelihood of meaningful results to advance the field of AD diagnosis and prevention. This framework will be the foundation of continued work to create a new paradigm for use structural MRI biomarkers in longitudinal studies of AD. Once developed, our software programs will be shared through a public software development/sharing platform. |
| Additional Investigators |
