title: Constrained sparse functional connectivity networks for MCI classification.
abstract: Mild cognitive impairment (MCI) is difficult to diagnose due to its subtlety. Recent emergence of advanced network analysis techniques utilizing resting-state functional Magnetic Resonance Imaging (rs-fMRI) has made the understanding of neurological disorders more comprehensively at a whole-brain connectivity level. However, inferring effective brain connectivity from fMRI data is a challenging task, particularly when the ultimate goal is to obtain good control-patient classification performance. Incorporating sparsity into connectivity modeling can potentially produce results that are biologically more meaningful since most biologically networks are formed by a relatively few number of connections. However, this constraint, when applied at an individual level, will degrade classification performance due to inter-subject variability. To address this problem, we consider a constrained sparse linear regression model associated with the least absolute shrinkage and selection operator (LASSO). Specifically, we introduced sparsity into brain connectivity via l1-norm penalization, and ensured consistent non-zero connections across subjects via l2-norm penalization. Our results demonstrate that the constrained sparse network gives better classification performance than the conventional correlation-based network, indicating its greater sensitivity to early stage brain pathologies.
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors.forename: Chong-Yaw
authors.initials: CY
authors.lastname: Wee
authors.forename: Pew-Thian
authors.initials: PT
authors.lastname: Yap
authors.forename: Daoqiang
authors.initials: D
authors.lastname: Zhang
authors.forename: Lihong
authors.initials: L
authors.lastname: Wang
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000503:physiopathology*; D060825:Cognitive Dysfunction / Q000503:physiopathology*; D063132:Connectome / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009415:Nerve Net / Q000503:physiopathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity