Two-stage spatial temporal deep learning framework for functional brain network modeling

Yu Zhao, Haixing Dai, Wei Zhang, Fangfei Ge, Tianming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations


Resting state functional magnetic resonance imaging (rsfMRI) data provides a unique window for the investigation of the human brain's intrinsic functional mechanism. However, it is still an open question how to analyze and model functional brain connectivity networks via rsfMRI data due to the variability of different individual brains, noisy signals from rsfMRI data, and technical limitations of current rsfMRI data decomposition methods. In this work, we proposed a two-stage deep learning framework for both temporal and spatial analysis of functional brain networks with an application on autism spectrum disorder (ASD) rsfMRI data. This framework tackled the abovementioned challenges in these aspects: reducing noises in rsfMRI raw data, establishing functional network correspondence across various individual brains, and composing multiple functional networks into a compact representation. In general, our proposed framework offers a novel scheme for comprehensive and systematic spatial-temporal resting state network modeling. Our experimental results on the ABIDE ASD dataset showed promising results in discovering discriminative functional networks compared with traditional analysis. Furthermore, our work provided a new insight into ASD that ASD's functional activity abnormalities tend to be more composite and systematic, other than being localized.

Original languageEnglish (US)
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538636411
StatePublished - Apr 2019
Externally publishedYes
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: Apr 8 2019Apr 11 2019

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019


  • Autism
  • Composite networks
  • Deep learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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