Exploring Brain Hemodynamic Response Patterns via Deep Recurrent Autoencoder

Shijie Zhao, Yan Cui, Yaowu Chen, Xin Zhang, Wei Zhang, Huan Liu, Junwei Han, Lei Guo, Li Xie, Tianming Liu

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

Abstract

For decades, task-based functional MRI (tfMRI) has been widely used in exploring functional brain networks and modeling brain activities. A variety of brain activity analysis methods for tfMRI data have been developed. However, these methods are mainly shallow models and are limited in faithfully modeling the complex spatial-temporal diverse and concurrent functional brain activities. Recently, recurrent neural networks (RNNs) demonstrate great superiority in modeling temporal dependency signals and autoencoder models have been proven to be effective in automatically estimating the optimal representations of the original data. These characteristics meet the requirement of modeling hemodynamic response patterns in tfMRI data. In order to take the advantages of both models, we proposed a novel unsupervised framework of deep recurrent autoencoder (DRAE) for modeling tfMRI data in this work. The basic idea of the DRAE model is to combine the deep recurrent neural network and autoencoder to automatically characterize the meaningful functional brain networks and corresponding diverse and complex hemodynamic response patterns underlying tfMRI data simultaneously. The proposed DRAE model has been tested on the motor tfMRI dataset of HCP 900 subjects release and all seven tfMRI datasets of HCP Q1 release. Extensive experimental results demonstrated the great superiority of the proposed method.

Original languageEnglish (US)
Title of host publicationMultimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy - 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsDajiang Zhu, Jingwen Yan, Heng Huang, Li Shen, Paul M. Thompson, Carl-Fredrik Westin, Xavier Pennec, Sarang Joshi, Mads Nielsen, Stefan Sommer, Tom Fletcher, Stanley Durrleman
PublisherSpringer
Pages66-74
Number of pages9
ISBN (Print)9783030332259
DOIs
StatePublished - 2019
Externally publishedYes
Event4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 17 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11846 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer Assisted Intervention, MICCAI 2019
Country/TerritoryChina
CityShenzhen
Period10/17/1910/17/19

Keywords

  • Autoencoder
  • Brain network
  • Deep learning
  • Hemodynamic response pattern
  • RNN
  • Task fMRI

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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