TY - GEN
T1 - Identify Hierarchical Structures from Task-Based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net
AU - Zhang, Wei
AU - Zhao, Lin
AU - Li, Qing
AU - Zhao, Shijie
AU - Dong, Qinglin
AU - Jiang, Xi
AU - Zhang, Tuo
AU - Liu, Tianming
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - The hierarchical organization of brain function has been an established concept in the neuroscience field. Recently, these hierarchical organizations have been extensively investigated in terms of how such hierarchical functional networks are constructed in the human brain via a variety of deep learning models. However, a key problem of how to determine the optimal neural architecture (NA), e.g., hyper parameters, of deep model has not been solved yet. To address this question, in this work, a novel Hybrid Spatiotemporal Neural Architecture Search Net (HS-NASNet) is proposed by jointly using Evolutionary Optimizer, Deep Belief Networks (DBN) and Deep LASSO to reasonably determine the NA, thus revealing the latent hierarchical spatiotemporal features based on the Human Connectome Project (HCP) 900 fMRI datasets. Briefly, this HS-NASNet can automatically search the global optimal NA of DBN given the search space, and then the optimized DBN can extract the weights between two adjacent layers of the optimal NA, which are then treated as the hierarchical temporal dictionaries for Deep LASSO to identify the corresponding hierarchical spatial maps. Our results demonstrate that the optimized deep model can achieve accurate fMRI signal reconstruction and identify spatiotemporal functional networks exhibiting multiscale properties that can be well characterized and interpreted based on current neuroscience knowledge.
AB - The hierarchical organization of brain function has been an established concept in the neuroscience field. Recently, these hierarchical organizations have been extensively investigated in terms of how such hierarchical functional networks are constructed in the human brain via a variety of deep learning models. However, a key problem of how to determine the optimal neural architecture (NA), e.g., hyper parameters, of deep model has not been solved yet. To address this question, in this work, a novel Hybrid Spatiotemporal Neural Architecture Search Net (HS-NASNet) is proposed by jointly using Evolutionary Optimizer, Deep Belief Networks (DBN) and Deep LASSO to reasonably determine the NA, thus revealing the latent hierarchical spatiotemporal features based on the Human Connectome Project (HCP) 900 fMRI datasets. Briefly, this HS-NASNet can automatically search the global optimal NA of DBN given the search space, and then the optimized DBN can extract the weights between two adjacent layers of the optimal NA, which are then treated as the hierarchical temporal dictionaries for Deep LASSO to identify the corresponding hierarchical spatial maps. Our results demonstrate that the optimized deep model can achieve accurate fMRI signal reconstruction and identify spatiotemporal functional networks exhibiting multiscale properties that can be well characterized and interpreted based on current neuroscience knowledge.
KW - Deep Belief Network
KW - Evolutionary optimization
KW - Hierarchical organization
KW - LASSO
KW - Neural architecture search
KW - Task-based fMRI
UR - http://www.scopus.com/inward/record.url?scp=85075645187&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075645187&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32248-9_83
DO - 10.1007/978-3-030-32248-9_83
M3 - Conference contribution
AN - SCOPUS:85075645187
SN - 9783030322472
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 745
EP - 753
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -