title: Estimating functional brain networks by incorporating a modularity prior.
abstract: Functional brain network analysis has become one principled way of revealing informative organization architectures in healthy brains, and providing sensitive biomarkers for diagnosis of neurological disorders. Prior to any post hoc analysis, however, a natural issue is how to construct "ideal" brain networks given, for example, a set of functional magnetic resonance imaging (fMRI) time series associated with different brain regions. Although many methods have been developed, it is currently still an open field to estimate biologically meaningful and statistically robust brain networks due to our limited understanding of the human brain as well as complex noises in the observed data. Motivated by the fact that the brain is organized with modular structures, in this paper, we propose a novel functional brain network modeling scheme by encoding a modularity prior under a matrix-regularized network learning framework, and further formulate it as a sparse low-rank graph learning problem, which can be solved by an efficient optimization algorithm. Then, we apply the learned brain networks to identify patients with mild cognitive impairment (MCI) from normal controls. We achieved 89.01% classification accuracy even with a simple feature selection and classification pipeline, which significantly outperforms the conventional brain network construction methods. Moreover, we further explore brain network features that contributed to MCI identification, and discovered potential biomarkers for personalized diagnosis.
authors.affiliation: School of Mathematics, Liaocheng University, Liaocheng 252000, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
authors.forename: Lishan
authors.initials: L
authors.lastname: Qiao
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
authors.forename: Han
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
authors.forename: Minjeong
authors.initials: M
authors.lastname: Kim
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
authors.forename: Shenghua
authors.initials: S
authors.lastname: Teng
authors.affiliation: School of Mathematics, Liaocheng University, Liaocheng 252000, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
authors.forename: Limei
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea. Electronic address: dgshen@med.unc.edu.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000368:Aged; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods; D060825:Cognitive Dysfunction / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009415:Nerve Net / Q000000981:diagnostic imaging / Q000503:physiopathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Brain network; Classification; Functional magnetic resonance imaging (fMRI); Low-rank representation; Mild cognitive impairment (MCI); Modularity; Partial correlation; Pearson's correlation; Sparse representation