title: A spectral graph regression model for learning brain connectivity of Alzheimer's disease.
abstract: Understanding network features of brain pathology is essential to reveal underpinnings of neurodegenerative diseases. In this paper, we introduce a novel graph regression model (GRM) for learning structural brain connectivity of Alzheimer's disease (AD) measured by amyloid-β deposits. The proposed GRM regards 11C-labeled Pittsburgh Compound-B (PiB) positron emission tomography (PET) imaging 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. Evaluations performed upon PiB-PET imaging data of 30 AD and 40 elderly normal control (NC) subjects 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.
authors.affiliation: School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA.
authors.forename: Chenhui
authors.initials: C
authors.lastname: Hu
authors.affiliation: Department of Engineering, Trinity College, Hartford, Connecticut, USA.
authors.forename: Lin
authors.initials: L
authors.lastname: Cheng
authors.affiliation: Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
authors.forename: Jorge
authors.initials: J
authors.lastname: Sepulcre
authors.affiliation: Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
authors.forename: Keith A
authors.initials: KA
authors.lastname: Johnson
authors.affiliation: Center for Advanced Medical Imaging Science, Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
authors.forename: Georges E
authors.initials: GE
authors.lastname: Fakhri
authors.affiliation: School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA.
authors.forename: Yue M
authors.initials: YM
authors.lastname: Lu
authors.affiliation: Center for Advanced Medical Imaging Science, Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
authors.forename: Quanzheng
authors.initials: Q
authors.lastname: Li
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000378:metabolism / Q000473:pathology*; D016229:Amyloid beta-Peptides / Q000378:metabolism; D000814:Aniline Compounds; D001921:Brain / Q000378:metabolism / Q000473:pathology*; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D008297:Male; D008954:Models, Biological; D049268:Positron-Emission Tomography / Q000379:methods; D012016:Reference Values; D012044:Regression Analysis; D013844:Thiazoles
chemical_list: C475519:2-(4'-(methylamino)phenyl)-6-hydroxybenzothiazole; D016229:Amyloid beta-Peptides; D000814:Aniline Compounds; D013844:Thiazoles