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: | Gagan Wig |
| Institution: | University of Texas at Dallas |
| Department: | Center for Vital Longevity |
| Country: | |
| Proposed Analysis: | The aim of the study is to investigate whether and how Alzheimer’s disease (AD) is associated with changes in large-scale resting-state brain networks in comparison to healthy elderly population, and how network-based disruptions in AD interact with cognition and clinical manifestations. We plan to examine 1) how brain networks differ across various levels of AD (Clincial Dementia Rating [CDR] > 0) and healthy elderly participants (CDR = 0), and 2) how the networks change longitudinally as the disease becomes more severe within an individual. We will relate our observations to measures of neuropathology (cortical grey-matter thinning/volume-loss, and amyloid and tau burden) and measures of cognitive abilities (such as memory, verbal intelligence and verbal fluency). We expect to find associations between the progression of AD and changes in patterns of functional brain networks, which will help us understand the progression and impact of the disease at the level of complex large-scale brain networks. We are also interested in how AD-related brain networks modulate the cognition of AD patients, which can help us understand the impact of AD progression and associated changes of brain networks on cognition. Preprocessing of anatomical images will include brain extraction, tissue segmentation to separate grey matter, white matter and cerebrospinal fluid (CSF), normalization, generation of cortical and subcortical segments, and creation of cortical surfaces (fsaverage and fs_LR) (Van Essen et al., 2012). Where available, regional and whole-cortical deposition of amyloid and tau (collected using positron emission tomography (PET)) will be measured by calculating individuals standardized uptake value ratio (relative to cerebellar grey matter). Resting-state blood oxygen level-dependent (BOLD) images will go through standard BOLD preprocessing: slice-time correction, realignment, and co-registration to the participants’ T1 weighted anatomical images. Additional processing for resting-state data includes global signal regression, nuisance regression (e.g., signals from white matter and CSF), and head motion correction (“scrubbing”; Power et al. 2014). Preprocessed PET and BOLD data will be mapped to individual’s fs_LR surfaces. To define resting-state brain networks , we will use data-driven approaches that have been developed by our group (Wig et al., 2014; Chan et al., 2014, Han et al., 2018). Specific steps include node creation, node-by-node correlation matrix construction, correlation coefficient calculation, and network community detection. Attributes of the functional networks will be analyzed at the whole-brain and node levels. Whole-brain network analyses will include computing system segregation (Chan et al. 2014), modularity (Newman, 2004). Node-level metrics will include computing clustering coefficient (Watts & Strogatz, 1998) and participation coefficient (PC) to quantify the connections of a specific node to other communities (Rubinov & Sporns, 2010). Large-scale network patterns will be examined across groups of subjects (e.g., healthy control vs. mild AD patients (CDR = 1)), but also within an individual as the severity of the disease changes. These network features will be directly related to local and global measures of neuropathology (cortical grey-matter thinning/volume-loss, and amyloid and tau burden) to determine how they relate to one another. |
| Additional Investigators |
