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.
|Title of host publication||Intelligent Data Engineering and Automated Learning - 22nd International Conference, IDEAL 2021, Proceedings|
|Editors||David Camacho, Peter Tino, Richard Allmendinger, Hujun Yin, Antonio J. Tallón-Ballesteros, Ke Tang, Sung-Bae Cho, Paulo Novais, Susana Nascimento|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||10|
|Publication status||Published - 2021|
|Event||22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021 - Virtual, Online|
Duration: 2021 Nov 25 → 2021 Nov 27
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021|
|Period||21/11/25 → 21/11/27|
Bibliographical noteFunding Information:
Acknowledgement. This work was partially supported by an IITP grant funded by the Korean government (MSIT) (No. 2020–0-01361, Artificial Intelligence Graduate School Program (Yonsei University)) and Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government (21ZS1100, Core Technology Research for Self-Improving Integrated Artificial Intelligence System).
© 2021, Springer Nature Switzerland AG.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)