@conference {55846,
	title = {A graph theoretical regression model for brain connectivity learning of Alzheimer{\textquoteright}s disease},
	booktitle = {Proc. International Symposium on Biomedical Imaging (ISBI)},
	year = {2013},
	month = {7-11 Apr.},
	address = {San Francisco, CA},
	abstract = {Learning functional brain connectivity is essential to the\&nbsp;understanding of neurodegenerative diseases. In this paper,\&nbsp;we introduce a novel graph regression model (GRM) which\&nbsp;regards the imaging data as signals defined on a graph\&nbsp;and optimizes the fitness between the graph and the data,\&nbsp;with a sparsity level regularization. The proposed framework\&nbsp;features a nice interpretation in terms of low-pass\&nbsp;signals on graphs, and is more generic compared with\&nbsp;previous statistical models. Results based on the data illustrates that our approach can obtain a very close\&nbsp;reconstruction of the true network. We then apply the GRM\&nbsp;to learn the brain connectivity of Alzheimer{\textquoteright}s disease (AD).\&nbsp;Evaluations performed upon PET imaging data of 30 AD patients\&nbsp;demonstrate that the connectivity patterns discovered\&nbsp;are easy to interpret and consistent with known pathology.},
	author = {Chenhui Hu and Lin Cheng and Jorge Sepulcre and Georges El Fakhri and Yue M. Lu and Quanzheng Li}
}
