State-space model with deep learning for functional dynamics estimation in resting-state fMRI

By Heung-Il Suk, Chong-Yaw Wee, Seong-Whan Lee, Dinggang Shen. NeuroImage, 2016

298 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/1b7aef72aacbef7602f7c5c1a77de941074303fb

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In Cognitive Neuroscience

Citations in the corpus

Deep Learning Applications in Medical Image Analysis (2018, methodology): “[59] classified fMRI images into diagnoses of Healthy orMild Cognitive Impairment, using a stacked architecture of RBMs to learn hierarchal functional relationships between different brain regions.”
Deep Learning in Medical Image Analysis (2017, methodology): “…inspired its use to medical image analysis such as image segmentation (22, 23), image registration (24), image fusion (25), image annotation (26), computer-aided diagnosis and prognosis (27, 28, 29), lesion/landmark detection (30, 31, 32), and microscopic imaging analysis (33, 34), to name a few.”
Deep ensemble learning of sparse regression models for brain disease diagnosis (2017, methodology): “To this end, it has also been considered as one of the major tools in brain imaging data analysis ( Li et al., 2014b; Plis et al., 2014; Suk et al., 2015a; 2016b; Pereira et al., 2016; Brosch et al., 2016; Dou et al., 2016 ) by achieving promising performances.”
Recent advances and clinical applications of deep learning in medical image analysis (2021, background): “Autoencoder and its variants are once prevalent in analyzing medical images because of the advantage of automatically exploring representative patterns (Shin et al., 2013; Zhang et al., 2016b; Suk et al., 2016; Chen et al., 2017a).”
Deep learning based brain tumor segmentation: a survey (2020, background): “Disorder diagnosis is to extract pre-defined features from brain scan images and then classify feature representations into pre-specified disorders such as High-Grade-Glioma (HGG) vs Low-Grade-Glioma (LGG), Mild Cognitive Impairment (MCI) [12], Alzheimers Disease (AD) [13] and Schizophrenia [14].”
Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results (2020, background): “Deep learning models have shown their advantage in fMRI analysis [3], [4].”