title: State-space model with deep learning for functional dynamics estimation in resting-state fMRI.
abstract: Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach.
authors.affiliation: Department of Brain and Cognitive Engineering, Korea University, Republic of Korea. Electronic address: hisuk@korea.ac.kr.
authors.forename: Heung-Il
authors.initials: HI
authors.lastname: Suk
authors.affiliation: Department of Biomedical Engineering, National University of Singapore, Singapore.
authors.forename: Chong-Yaw
authors.initials: CY
authors.lastname: Wee
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, Department of Radiology, University of North Carolina at Chapel Hill, USA.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000368:Aged; D001921:Brain / Q000502:physiology*; D060825:Cognitive Dysfunction / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D009415:Nerve Net; D012146:Rest / Q000502:physiology
keywords: Deep learning; Dynamic functional connectivity; Hidden Markov model; Mild cognitive impairment; Resting-state functional magnetic resonance imaging