Understanding network features of brain pathology is essential to reveal underlying pathological mechanisms of neurodegenerative diseases. We developed a novel graph regression model (GRM) for learning structural brain connectivity from neuroimaging datasets. The proposed GRM regards neuroimaging data as smooth signals defined on an unknown graph. This graph is then estimated through an optimization framework, which fits the graph to the data with an adjustable level of uniformity of the connection weights. Under the assumed data model, results based on simulated data illustrate that our approach can accurately reconstruct the underlying network, often with better reconstruction than those obtained by both sample correlation and L1-regularized partial correlation estimation. We applied our GRM approach to an Alzheimer’s Disease (AD) positron emission tomography (PET) dataset and evaluations performed upon neuroimaging data demonstrate that the connectivity patterns revealed by the GRM are easy to interpret and consistent with known pathology. Moreover, the hubs of the reconstructed networks match the cortical hubs given by functional MRI. The discriminative network features including both global connectivity measurements and degree statistics of specific nodes discovered from the AD and NC amyloid-beta networks provide new potential biomarkers for preclinical and clinical AD.
Hu, Chenhui, et al. "A spectral graph regression model for learning brain connectivity of alzheimer’s disease." PloS one 10.5 (2015): e0128136.