Unravelling the Spatio-Temporal Neurodynamics of Rhythm Encoding-reproduction Networks by a Novel fMRI Autoencoder

Chia Hsiang Kao, Ching Ju Yang, Li Kai Cheng, Hsin Yen Yu, Yong-Sheng Chen, Jen Chuen Hsieh, Li Fen Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PublisherIEEE Computer Society
Pages615-618
Number of pages4
ISBN (Electronic)9781538679210
DOIs
StatePublished - 16 May 2019
Event9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States
Duration: 20 Mar 201923 Mar 2019

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2019-March
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference9th International IEEE EMBS Conference on Neural Engineering, NER 2019
CountryUnited States
CitySan Francisco
Period20/03/1923/03/19

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