Bayesian methods for cortical surface neuroimaging data

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PROJECT SUMMARY Advanced Bayesian statistical methods for the analysis of functional magnetic resonance imaging (fMRI), developed in parent grant R01EB027119, produce accurate measures of functional brain organization in individual subjects. As a result, they are uniquely suited for advancing the development of fMRI-based brain biomarkers for Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI). Accurate biomarkers are needed for early diagnosis and for identification of clinical trial participants who are likely to exhibit sufficient decline across the trial to adequately test the intervention. Functional MRI-based biomarkers may serve as an inexpensive, non-invasive, and widely-available first-line screening measure before positron emission tomography (PET) imaging is used. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) was launched in 2004 to develop and validate biomarkers for AD clinical trials. A principal goal of ADNI-3, the latest iteration of ADNI, is to promote the development of diagnostic models and precision medicine approaches to identify patients for therapeutic clinical trials, including the use of resting-state fMRI (rs-fMRI). In this supplement project, we will apply the methods developed in the parent grant to extract features related to the topological functional organization of the brain, functional connectivity, and texture of functional networks. These features will be used to develop diagnostic and prognostic models to predict current disease status in ADNI-3. Additionally, we will utilize the overlap between the ADNI-2 and ADNI-3 cohorts to investigate conversion to MCI or AD. We will validate these models using a large independent holdout set to ensure rigor and generalizability in our results. This project leverages an existing long-term collaborative environment between Indiana University, Bloomington, and the University of Utah.