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: | Fabrizio Pizzagalli |
| Institution: | University of Southern California |
| Department: | Keck School of Medicine |
| Country: | |
| Proposed Analysis: | Alzheimer’s disease (AD) is a progressive neurodegenerative disorders leading to cognitive impairment. Magnetic resonance imaging (MRI) can be used to derive metrics of brain structure and function and offer a powerful method to assess disease burden in the brain. Statistical brain mapping methods, such as voxel-based morphometry, reveal correlations between gray matter atrophy, disease progression and cognitive impairment. I aim to identify new and reliable disease-related structural and functional biomarkers for AD prediction. Cortical gyrification and sulcal morphometry have been associated with cognitive performance in the elderly and in people with AD [Liu 2010, Liu 2012], and have been used to identify AD at early stage [Cai 2017]. Gyrification, or folding, of the brain’s cortical surface takes place during brain development, forming sulci (“fissures”) and gyri (“ridges”) and it is one of the fundamental features of human and non-human primate neuroanatomy [Rogers 2010, Kochunov 2010]. The mechanisms of brain folding are not fully understood [Van Essen 1997] but the pattern of brain gyri (or sulci) tend to delimit cortical areas with specific functions and are consistent across subjects [Ono 1990; Brodmann 1909; Talairach 1988]. The broad availability of brain scans led to the development of widely adopted tools for analyzing neuroimaging data, such as, among others, FreeSurfer (https://surfer.nmr.mgh.harvard.edu/) and BrainVISA (http://brainvisa.info/web/index.html). These tools allow us to compute standardized measures from neuroimaging data and to measure comparable brain features across different cohorts and imaging sites. This project focuses on extracting novel brain features, mapping brain gyrification, cortical atrophy and functional features in large clinically well-characterized cohorts to determine a set of new cortical biomarkers for early AD diagnosis and tracking progression. We will map biomarker trajectories with age in both healthy and cognitively impaired groups. We will analyze publicly available datasets, including: ADNI, AddNeuroMed, CoRR, OASIS, CamCAN and the UK Biobank for which our team already has access. |
| Additional Investigators |
