@inproceedings{b231eb323f0d4db6bd38521054b14f4e,
title = "Simultaneous Spatial-Temporal Decomposition of Connectome-Scale Brain Networks by Deep Sparse Recurrent Auto-Encoders",
abstract = "Exploring the spatial patterns and temporal dynamics of human brain activities has long been a great topic, yet development of a unified spatial-temporal model for such purpose is still challenging. To better understand brain networks based on fMRI data and inspired by the success in applying deep learning for brain encoding/decoding, we propose a novel deep sparse recurrent auto-encoder (DSRAE) in an unsupervised spatial-temporal way to learn spatial and temporal patterns of brain networks jointly. The proposed DSRAE has been validated on the publicly available human connectome project (HCP) fMRI datasets with promising results. To our best knowledge, the proposed DSRAE is among the early unified models that can extract connectome-scale spatial-temporal networks from 4D fMRI data simultaneously.",
keywords = "Auto-encoder, Recurrent neural network, Task-based fMRI",
author = "Qing Li and Qinglin Dong and Fangfei Ge and Ning Qiang and Yu Zhao and Han Wang and Heng Huang and Xia Wu and Tianming Liu",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 26th International Conference on Information Processing in Medical Imaging, IPMI 2019 ; Conference date: 02-06-2019 Through 07-06-2019",
year = "2019",
doi = "10.1007/978-3-030-20351-1_45",
language = "English",
isbn = "9783030203504",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "579--591",
editor = "Chung, {Albert C.S.} and Gee, {James C.} and Yushkevich, {Paul A.} and Siqi Bao",
booktitle = "Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings",
address = "Germany",
}