@article {254926,
	title = {A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer{\textquoteright}s Disease},
	journal = {PLOS ONE},
	volume = {10},
	number = {5},
	year = {2015},
	month = {2015},
	abstract = {Understanding network features of brain pathology is essential to reveal underpinnings\&nbsp;of neurodegenerative diseases. In this paper, we introduce a novel graph regression\&nbsp;model (GRM) for learning structural brain connectivity of Alzheimer{\textquoteright}s disease (AD)\&nbsp;measured by amyloid-β\&nbsp; deposits. The proposed GRM regards 11 C-labeled Pittsburgh\&nbsp;Compound-B (PiB) positron emission tomography (PET) imaging data as smooth\&nbsp;signals defined on an unknown graph. This graph is then estimated through an\&nbsp;optimization framework, which fits the graph to the data with an adjustable level of\&nbsp;uniformity of the connection weights. Under the assumed data model, results based on\&nbsp;simulated data illustrate that our approach can accurately reconstruct the underlying\&nbsp;network, often with better reconstruction than those obtained by both sample\&nbsp;correlation and l1 -regularized partial correlation estimation. Evaluations performed\&nbsp;upon PiB-PET imaging data of 30 AD and 40 elderly normal control (NC) subjects\&nbsp;demonstrate that the connectivity patterns revealed by the GRM are easy to interpret\&nbsp;and consistent with known pathology. Moreover, the hubs of the reconstructed\&nbsp;networks match the cortical hubs given by functional MRI. The discriminative network\&nbsp;features including both global connectivity measurements and degree statistics of\&nbsp;specific nodes discovered from the AD and NC amyloid-beta networks provide new\&nbsp;potential biomarkers for preclinical and clinical AD.},
	url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0128136},
	author = {Chenhui Hu and Lin Cheng and Jorge Sepulcre and Keith A. Johnson and Georges E. Fakhri and Yue M. Lu and Quanzheng Li}
}
