標題: Unravelling the Spatio-Temporal Neurodynamics of Rhythm Encoding-reproduction Networks by a Novel fMRI Autoencoder
作者: Kao, Chia-Hsiang
Yang, Ching-Ju
Cheng, Li-Kai
Yu, Hsin-Yen
Chen, Yong-Sheng
Hsieh, Jen-Chuen
Chen, Li-Fen
資訊工程學系
Department of Computer Science
公開日期: 1-一月-2019
摘要: Visualization of how the external stimuli are processed dynamically in the brain would help understanding the neural mechanisms of functional segregation and integration. The present study proposed a novel temporal autoencoder to estimate the neurodynamics of functional networks involved in rhythm encoding and reproduction. A fully-connected two-layer autoencoder was proposed to estimate the temporal dynamics in functional magnetic resonance image recordings. By minimizing the reconstruction error between the predicted next time sample and the corresponding ground truth next time sample, the system was trained to extract spatial patterns of functional network dynamics without any supervision effort. The results showed that the proposed model was able to extract the spatial patterns of task-related functional dynamics as well as the interactions between them. Our findings suggest that artificial neural networks would provide a useful tool to resolve temporal dynamics of neural processing in the human brain.
URI: http://hdl.handle.net/11536/152473
ISBN: 978-1-5386-7921-0
ISSN: 1948-3546
期刊: 2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER)
起始頁: 615
結束頁: 618
顯示於類別:會議論文