TY - GEN
T1 - A two-stage DBN-based method to exploring functional brain networks in naturalistic paradigm FMRI
AU - Zhang, Yin
AU - Hu, Xintao
AU - He, Chunlin
AU - Wang, Xiangning
AU - Ren, Yudan
AU - Liu, Huan
AU - Wang, Liting
AU - Guo, Lei
AU - Liu, Tianming
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - FMRI using naturalistic paradigms such as watching movies has gained increasing interest in recent neuroimaging studies. Data-driven blind source separation (BSS) methods such as independent component analysis (ICA) are widely used to extract meaningful features in fMRI data. Recent studies have shown that BSS based on deep neural networks (DNNs) such as restricted Boltzmann machine (RBM) and deep belief network (DBN) outperform ICA. Those DNN-based methods interpret spatially aggregated fMRI time series of multiple subjects in group analysis to reduce model complexity, and only brain networks with group-wise temporal consistency can be identified. However, fMRI activities to naturalistic paradigm exhibit both group-wise consistency and inter-subject difference. To address this problem, we propose a two-stage DBN-based BSS method. In the first stage, a DBN model is trained using temporally aggregated fMRI time series of multiple subjects. In the second stage, subject-specific DBN models are initialized using model parameters learned in the first stage and are trained to converge using individual fMRI data to refine brain network identification. We use an fMRI dataset acquired using a movie stimulus to validate the proposed method.
AB - FMRI using naturalistic paradigms such as watching movies has gained increasing interest in recent neuroimaging studies. Data-driven blind source separation (BSS) methods such as independent component analysis (ICA) are widely used to extract meaningful features in fMRI data. Recent studies have shown that BSS based on deep neural networks (DNNs) such as restricted Boltzmann machine (RBM) and deep belief network (DBN) outperform ICA. Those DNN-based methods interpret spatially aggregated fMRI time series of multiple subjects in group analysis to reduce model complexity, and only brain networks with group-wise temporal consistency can be identified. However, fMRI activities to naturalistic paradigm exhibit both group-wise consistency and inter-subject difference. To address this problem, we propose a two-stage DBN-based BSS method. In the first stage, a DBN model is trained using temporally aggregated fMRI time series of multiple subjects. In the second stage, subject-specific DBN models are initialized using model parameters learned in the first stage and are trained to converge using individual fMRI data to refine brain network identification. We use an fMRI dataset acquired using a movie stimulus to validate the proposed method.
KW - Blind source separation
KW - Deep belief network
KW - FMRI
UR - http://www.scopus.com/inward/record.url?scp=85073900593&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2019.8759376
DO - 10.1109/ISBI.2019.8759376
M3 - 会议稿件
AN - SCOPUS:85073900593
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1594
EP - 1597
BT - ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Y2 - 8 April 2019 through 11 April 2019
ER -