title: A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI.
abstract: In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI,i.e., internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature.
authors.affiliation: Department of Brain and Cognitive Engineering, Korea University, Republic of Korea.
authors.forename: Heung-Il
authors.initials: HI
authors.lastname: Suk
authors.affiliation: Department of Brain and Cognitive Engineering, Korea University, Republic of Korea.
authors.forename: Seong-Whan
authors.initials: SW
authors.lastname: Lee
authors.affiliation: Department of Brain and Cognitive Engineering, Korea University, Republic of Korea ; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, USA.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen