TY - JOUR
T1 - Modeling hierarchical brain networks via volumetric sparse deep belief network
AU - Dong, Qinglin
AU - Ge, Fangfei
AU - Ning, Qiang
AU - Zhao, Yu
AU - Lv, Jinglei
AU - Huang, Heng
AU - Yuan, Jing
AU - Jiang, Xi
AU - Shen, Dinggang
AU - Liu, Tianming
N1 - Funding Information:
Manuscript received February 13, 2019; revised May 22, 2019 and August 29, 2019; accepted September 28, 2019. Date of publication October 23, 2019; date of current version May 20, 2020. The work of X. Jiang was supported by the National Natural Science Foundation of China under Grants 61703073 and 61976045. The work of T Liu
Publisher Copyright:
© 1964-2012 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - It has been recently shown that deep learning models such as convolutional neural networks (CNN), deep belief networks (DBN) and recurrent neural networks (RNN), exhibited remarkable ability in modeling and representing fMRI data for the understanding of functional activities and networks because of their superior data representation capability and wide availability of effective deep learning tools. For example, spatial and/or temporal patterns of functional brain activities embedded in fMRI data can be effectively characterized and modeled by a variety of CNN/DBN/RNN deep learning models as shown in recent studies. However, it has been rarely investigated whether it is possible to directly infer hierarchical brain networks from volumetric fMRI data using deep learning models such as DBN. The perceived difficulties of such studies include very large number of input variables, very large number of training parameters, the lack of effective software tools, the challenge of results interpretation, and etc. To bridge these technical gaps, we designed a novel volumetric sparse deep belief network (VS-DBN) model and implemented it through the popular TensorFlow open source platform to reconstruct hierarchical brain networks from volumetric fMRI data based on the Human Connectome Project (HCP) 900 subjects release. Our experimental results showed that a large number of interpretable and meaningful brain networks can be robustly reconstructed from HCP 900 subjects in a hierarchical fashion, and importantly, these brain networks exhibit reasonably good consistency and correspondence across multiple HCP task-based fMRI datasets. Our work contributed a new general deep learning framework for inferring multiscale volumetric brain networks and offered novel insights into the hierarchical organization of functional brain architecture.
AB - It has been recently shown that deep learning models such as convolutional neural networks (CNN), deep belief networks (DBN) and recurrent neural networks (RNN), exhibited remarkable ability in modeling and representing fMRI data for the understanding of functional activities and networks because of their superior data representation capability and wide availability of effective deep learning tools. For example, spatial and/or temporal patterns of functional brain activities embedded in fMRI data can be effectively characterized and modeled by a variety of CNN/DBN/RNN deep learning models as shown in recent studies. However, it has been rarely investigated whether it is possible to directly infer hierarchical brain networks from volumetric fMRI data using deep learning models such as DBN. The perceived difficulties of such studies include very large number of input variables, very large number of training parameters, the lack of effective software tools, the challenge of results interpretation, and etc. To bridge these technical gaps, we designed a novel volumetric sparse deep belief network (VS-DBN) model and implemented it through the popular TensorFlow open source platform to reconstruct hierarchical brain networks from volumetric fMRI data based on the Human Connectome Project (HCP) 900 subjects release. Our experimental results showed that a large number of interpretable and meaningful brain networks can be robustly reconstructed from HCP 900 subjects in a hierarchical fashion, and importantly, these brain networks exhibit reasonably good consistency and correspondence across multiple HCP task-based fMRI datasets. Our work contributed a new general deep learning framework for inferring multiscale volumetric brain networks and offered novel insights into the hierarchical organization of functional brain architecture.
KW - Deep belief network (dbn)
KW - Hierarchical brain network
KW - Task fmri
UR - http://www.scopus.com/inward/record.url?scp=85085351703&partnerID=8YFLogxK
U2 - 10.1109/TBME.2019.2945231
DO - 10.1109/TBME.2019.2945231
M3 - Article
C2 - 31647417
AN - SCOPUS:85085351703
VL - 67
SP - 1739
EP - 1748
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 6
M1 - 8880543
ER -