A Unified Multi-Modality Fusion Framework for Deep Spatio-Spectral-Temporal Feature Learning in Resting-State fMRI Denoising

Minjoo Lim, Keun Soo Heo, Jun Mo Kim, Bogyeong Kang, Weili Lin, Han Zhang, Dinggang Shen, Tae Eui Kam

Research output: Contribution to journalArticlepeer-review

Abstract

Resting-state functional magnetic resonance imaging (rs-fMRI) is a commonly used functional neuroimaging technique to investigate the functional brain networks. However, rs-fMRI data are often contaminated with noise and artifacts that adversely affect the results of rs-fMRI studies. Several machine/deep learning methods have achieved impressive performance to automatically regress the noise-related components decomposed from rs-fMRI data, which are expressed as the pairs of a spatial map and its associated time series. However, most of the previous methods individually analyze each modality of the noise-related components and simply aggregate the decision-level information (or knowledge) extracted from each modality to make a final decision. Moreover, these approaches consider only the limited modalities making it difficult to explore class-discriminative spectral information of noise-related components. To overcome these limitations, we propose a unified deep attentive spatio-spectral-temporal feature fusion framework. We first adopt a learnable wavelet transform module at the input-level of the framework to elaborately explore the spectral information in subsequent processes. We then construct a feature-level multi-modality fusion module to efficiently exchange the information from multi-modality inputs in the feature space. Finally, we design confidence-based voting strategies for decision-level fusion at the end of the framework to make a robust final decision. In our experiments, the proposed method achieved remarkable performance for noise-related component detection on various rs-fMRI datasets.

Original languageEnglish
Pages (from-to)2067-2078
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Volume28
Issue number4
DOIs
Publication statusPublished - 2024 Apr 1

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Resting-state fMRI
  • convolutional neural network
  • deep learning
  • denoising
  • multi-modality fusion

ASJC Scopus subject areas

  • Health Information Management
  • Health Informatics
  • Electrical and Electronic Engineering
  • Computer Science Applications

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