Dynamic multi-site graph convolutional network for autism spectrum disorder identification. (May 2023)

• Record Type: Journal Article
• Authors: Cui, Weigang; Du, Junling; Sun, Mingyi; Zhu, Shimao; Zhao, Shijie; Peng, Ziwen; Tan, Li; Li, Yang
• Is Part Of: Computers in biology and medicine. Volume 157(2023)
• Publication Date: 2023-05
• Subjects: Austim spectrum disorder -- Graph convolutional network -- Functional magnetic resonance imaging -- Sliding window -- Multi-site learning; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106749
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Multi-site learning has attracted increasing interests in autism spectrum disorder (ASD) identification tasks by its efficacy on capturing data heterogeneity of neuroimaging taken from different medical sites. However, existing multi-site graph convolutional network (MSGCN) often ignores the correlations between different sites, and may obtain suboptimal identification results. Moreover, current feature extraction methods characterizing temporal variations of functional magnetic resonance imaging (fMRI) signals require the time series to be of the same length and cannot be directly applied to multi-site fMRI datasets. To address these problems, we propose a dual graph based dynamic multi-site graph convolutional network (DG-DMSGCN) for multi-site ASD identification. First, a sliding-window dual-graph convolutional network (SW-DGCN) is introduced for feature extraction, simultaneously capturing temporal and spatial features of fMRI data with different series lengths. Then we aggregate the features extracted from multiple medical sites through a novel dynamic multi-site graph convolutional network (DMSGCN), which effectively considers the correlations between different sites and is beneficial to improve identification performance. We evaluate the proposed DG-DMSGCN on public ABIDE I dataset containing data from 17 medical sites. The promising results obtained by our framework outperforms the state-of-the-art methods with increase in identification accuracy, indicating that it has a potential clinical prospect for practical ASD diagnosis. Our codes are available on https://github.com/Junling-Du/DG-DMSGCN . Highlights: A DG-DMSGCN framework is proposed for multi-site ASD identification, which outperforms the state-of-the-art methods. A SW-DGCN method is proposed and can be applied to the feature extraction of BOLD time series with different data lengths. A novel multi-site graph learning method named DMSGCN is introduced and shows a promising ASD identification performance.