Learning Dynamic Connectivity with Residual-Attention Network for Autism Classification in 4D fMRI Brain Images

Kyoung Won Park, Seok Jun Bu, Sung Bae Cho

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

4 Citations (Scopus)

Abstract

Diagnosing autism spectrum disorder (ASD) is still challenging because of its complex disorder and insufficient evidence to diagnose. A recent research in psychiatry perspective demonstrates that there are no obvious reasons for ASD. However, considering a hypothesis that abnormalities in the superior temporal sulcus (STS) connected with visual cortex regions can be a critical sign of ASD, a model is required to exploit the brain functional connectivity between STS and visual cortex to reinforce the neurobiological evidence. This paper proposes a deep learning model composed of attention and convolutional recurrent neural networks that can select and extract the time-series pattern of dynamic connectivity between the two regions within the brain based on observations. By integration of extracting autism disorder features from dynamic connectivity through attention with the structure containing interlayer connections to preserve the functional connectivity loss within a neural network, the model extracts the connectivity between STS and visual cortex, leading to the increase of generalization performance. Experiments with 800 patients’ fMRI imaging data known as ABIDE (Autism Brain Imaging Data Exchange) and 10-fold cross-validation to compare its performance show that the proposed model outperforms the state-of-the-art performance by achieving a 4.90% improvement in the ASD classification. Additionally, the proposed method is analyzed by visualizing dynamic brain connectivity of the neural network layers.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - 22nd International Conference, IDEAL 2021, Proceedings
EditorsDavid Camacho, Peter Tino, Richard Allmendinger, Hujun Yin, Antonio J. Tallón-Ballesteros, Ke Tang, Sung-Bae Cho, Paulo Novais, Susana Nascimento
PublisherSpringer Science and Business Media Deutschland GmbH
Pages387-396
Number of pages10
ISBN (Print)9783030916077
DOIs
Publication statusPublished - 2021
Event22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021 - Virtual, Online
Duration: 2021 Nov 252021 Nov 27

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13113 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021
CityVirtual, Online
Period21/11/2521/11/27

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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