Modeling hierarchical spatial and temporal patterns of naturalistic fmri volume via volumetric deep belief network with neural architecture search

Yudan Ren, Zeyang Tao, Wei Zhang, Tianming Liu

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

2 Scopus citations

Abstract

The functional magnetic resonance imaging under naturalistic paradigm (NfMRI) exhibited promising ability in approximating the functional activities of brain in real life. Deep learning models such as convolutional neural network (CNN), convolutional autoencoder (CAE) and deep belief network (DBN) have shown notable performance in identifying temporal patterns and functional brain networks (FBNs) from fMRI data, in which most of these studies directly modelled the functional brain activities embedded in fMRI data. However, the hierarchical temporal and spatial organization of brain function under naturalistic condition has been rarely investigated and it is unknown whether it is possible to directly derive hierarchical FBNs from volumetric fMRI data using deep learning models. In addition, due to the high dimensionality of fMRI volume images and very large number of training parameters, the manual design of neural architecture for deep learning model is time-consuming and not optimal, thus awaiting further advances in automatic searching framework to learn optimal network architecture for deep learning model. To tackle these problems, we proposed a deep belief network (DBN) and neural architecture search (NAS) combined framework (Volumetric NAS-DBN) to directly model the fMRI volume images under naturalistic condition. Our results demonstrated that the DBN with optimal architecture can effectively characterize hierarchical organization of spatial distribution and temporal responses from volumetric fMRI data under naturalistic condition.

Original languageEnglish (US)
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PublisherIEEE Computer Society
Pages130-134
Number of pages5
ISBN (Electronic)9781665412469
DOIs
StatePublished - Apr 13 2021
Externally publishedYes
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: Apr 13 2021Apr 16 2021

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2021-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Country/TerritoryFrance
CityNice
Period4/13/214/16/21

Keywords

  • Deep belief network
  • Hierarchical functional brain networks
  • Naturalistic fMRI
  • Neural architecture search

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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