Accumulating evidence suggests that measuring loss of structural connectivity together with markers of core Alzheimer's disease (AD) pathology such as amyloid plaques and neurofibrillary tangles may facilitate identification of individuals with the greatest risk of progressing to dementia. The primary focus and overarching goal of this project is to improve prediction of cognitive decline in the preclinical stage, prior to irreversible disease stages by utilizing novel wavelets based multi-scale brain connectivity signatures (WaCS) and deriving mechanisms that characterize the propagation of plaque and tangle pathology in the brain over time. We will accomplish this goal by using longitudinal cognitive and diffusion weighted magnetic resonance and positron emission tomography (PET) imaging data acquired from asymptomatic middle-aged participants enrolled in the Wisconsin Registry for Alzheimer's Prevention study and the Wisconsin Alzheimer's Disease Research Center clinical core. The analyses will be driven by (a) the ?Wavelets on graphs? transforms which allow us to simultaneously operate on multiple scale representations of brain connectivity and yield very sensitive detection of weak but real disease-specific signals with rigorous statistical confidence and (b) novel algebraic formulations that parameterize the dynamics of molecular pathology progression at the subject-specific level using an exquisite spatial mapping of amyloid and tau pathology with PiB and MK6240 PET. The proposed project comprises of three aims.
Specific Aim 1 : Derive structural connectivity phenotypes of preclinical AD by extending wavelet based multi-scale representations of brain connectivity graphs with improved microstructural specificity offered by biophysical diffusion models beyond classical diffusion tensor imaging (DTI). We will utilize ?? these representations to identify statistical associations between connectivity, cerebrospinal fluid based AD biomarkers (e.g., A 42, phosphorylated tau), and cognitive trajectories.
Specific Aim 2 : Develop techniques to characterize the subject-specific propagation dynamics of amyloid and tau pathology as measured on PET images and evaluate their associations with structural connectivity, total pathology burden and cognitive scores.
Specific Aim 3 : Design frameworks for predicting cognitive trajectories at the individual level using structural connectivity and molecular pathology propagation networks. Significance: This project is expected to transform three distinct areas of AD research, it will (1) deliver the impact of multi-resolution analysis of brain connectivity networks derived from diffusion models beyond the classical DTI for preclinical AD, (2) characterize how amyloid plaque and tangle pathology propagate over time in a subject-specific manner in early AD stages and (3) how such information sources can be utilized to jointly predict endpoint diagnostic status at the level of individual subjects, for monitoring disease progression and as a first step towards criteria that could inform the design of secondary prevention trials.
This project will deploy novel algebraic methods for image analysis to characterize (i) changes in brain connectivity and (ii) dynamics of molecular pathology propagation in healthy asymptomatic individuals who are at risk of Alzheimer's disease (AD) and also have evidence of AD related pathology but are at a preclinical stage. The analysis will identify specific structural connections in the brain affected by highly specific tau and amyloid AD pathology: this will yield accurate mathematical models which use connectivity and longitudinal propagation of pathology markers for predicting clinical outcome measures such as longitudinal cognitive decline in cognitively healthy individuals. The joint connectivity/pathology AD biomarkers have applications in understanding the early AD processes in cognitively healthy at-risk individuals that could eventually inform the design of clinical trials and hypotheses driven studies focused on preclinical AD.
