TY - GEN
T1 - Latent source mining of fMRI data via deep belief network
AU - Li, Lei
AU - Hu, Xintao
AU - Huang, Heng
AU - He, Chunlin
AU - Wang, Liting
AU - Han, Junwei
AU - Quo, Lei
AU - Zhang, Wei
AU - Liu, Tianming
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/23
Y1 - 2018/5/23
N2 - Blind source separation (BSS) is one of the fundamental techniques for resolving meaningful features in functional magnetic resonance imaging (fMRI). BSS methods based on unsupervised shallow models (e.g., restricted Boltzmann machine, RBM) have improved fMRI BSS compared to conventional matrix factorization models (e.g., independent component analysis (ICA)). In machine learning field, it is widely accepted that deeper models (e.g., deep belief network, DBN) are more powerful in latent feature learning and data representation. Thus, in this paper we propose a BSS model based on DBN with two hidden layers of RBM. In addition, we apply the model to fMRI time series for BSS instead of fMRI volumes as proposed in previous studies, such that the parameter searching space is significantly pruned and large-scale training samples of fMRI time series are available. Our experimental results on an fMRI dataset acquired with a movie stimulus showed that the proposed model is capable of identifying not only latent components related to distinct brain networks, but also the ones related to functional interactions across different networks.
AB - Blind source separation (BSS) is one of the fundamental techniques for resolving meaningful features in functional magnetic resonance imaging (fMRI). BSS methods based on unsupervised shallow models (e.g., restricted Boltzmann machine, RBM) have improved fMRI BSS compared to conventional matrix factorization models (e.g., independent component analysis (ICA)). In machine learning field, it is widely accepted that deeper models (e.g., deep belief network, DBN) are more powerful in latent feature learning and data representation. Thus, in this paper we propose a BSS model based on DBN with two hidden layers of RBM. In addition, we apply the model to fMRI time series for BSS instead of fMRI volumes as proposed in previous studies, such that the parameter searching space is significantly pruned and large-scale training samples of fMRI time series are available. Our experimental results on an fMRI dataset acquired with a movie stimulus showed that the proposed model is capable of identifying not only latent components related to distinct brain networks, but also the ones related to functional interactions across different networks.
KW - Blind source separation
KW - Deep belief network
KW - FMRI
UR - http://www.scopus.com/inward/record.url?scp=85048138530&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048138530&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2018.8363646
DO - 10.1109/ISBI.2018.8363646
M3 - Conference contribution
AN - SCOPUS:85048138530
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 595
EP - 598
BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PB - IEEE Computer Society
T2 - 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Y2 - 4 April 2018 through 7 April 2018
ER -