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.
| Original language | English |
|---|---|
| Pages (from-to) | 292-307 |
| Number of pages | 16 |
| Journal | NeuroImage |
| Volume | 129 |
| DOIs | |
| Publication status | Published - 2016 Apr 1 |
Fingerprint
Keywords
- Deep learning
- Dynamic functional connectivity
- Hidden Markov model
- Mild cognitive impairment
- Resting-state functional magnetic resonance imaging
ASJC Scopus subject areas
- Cognitive Neuroscience
- Neurology
Cite this
State-space model with deep learning for functional dynamics estimation in resting-state fMRI. / Suk, Heung-Il; Wee, Chong Yaw; Lee, Seong Whan; Shen, Dinggang.
In: NeuroImage, Vol. 129, 01.04.2016, p. 292-307.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - State-space model with deep learning for functional dynamics estimation in resting-state fMRI
AU - Suk, Heung-Il
AU - Wee, Chong Yaw
AU - Lee, Seong Whan
AU - Shen, Dinggang
PY - 2016/4/1
Y1 - 2016/4/1
N2 - 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.
AB - 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.
KW - Deep learning
KW - Dynamic functional connectivity
KW - Hidden Markov model
KW - Mild cognitive impairment
KW - Resting-state functional magnetic resonance imaging
UR - http://www.scopus.com/inward/record.url?scp=84957052106&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84957052106&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2016.01.005
DO - 10.1016/j.neuroimage.2016.01.005
M3 - Article
C2 - 26774612
AN - SCOPUS:84957052106
VL - 129
SP - 292
EP - 307
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
ER -