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基于脑功能和结构数据的 ASD 诊断和感兴趣区识别的双线性感知融合算法。

Bilinear Perceptual Fusion Algorithm Based on Brain Functional and Structural Data for ASD Diagnosis and Regions of Interest Identification.

机构信息

College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.

College of Business, Hunan Normal University, Changsha, 410081, China.

出版信息

Interdiscip Sci. 2024 Dec;16(4):936-950. doi: 10.1007/s12539-024-00651-w. Epub 2024 Sep 10.

Abstract

Autism spectrum disorder (ASD) is a serious mental disorder with a complex pathogenesis mechanism and variable presentation among individuals. Although many deep learning algorithms have been used to diagnose ASD, most of them focus on a single modality of data, resulting in limited information extraction and poor stability. In this paper, we propose a bilinear perceptual fusion (BPF) algorithm that leverages data from multiple modalities. In our algorithm, different schemes are used to extract features according to the characteristics of functional and structural data. Through bilinear operations, the associations between the functional and structural features of each region of interest (ROI) are captured. Then the associations are used to integrate the feature representation. Graph convolutional neural networks (GCNs) can effectively utilize topology and node features in brain network analysis. Therefore, we design a deep learning framework called BPF-GCN and conduct experiments on publicly available ASD dataset. The results show that the classification accuracy of BPF-GCN reached 82.35%, surpassing existing methods. This demonstrates the superiority of its classification performance, and the framework can extract ROIs related to ASD. Our work provides a valuable reference for the timely diagnosis and treatment of ASD.

摘要

自闭症谱系障碍(ASD)是一种严重的精神障碍,其发病机制复杂,个体表现多样。尽管许多深度学习算法已被用于诊断 ASD,但它们大多侧重于单一模态的数据,导致信息提取有限,稳定性差。在本文中,我们提出了一种利用多模态数据的双线性感知融合(BPF)算法。在我们的算法中,根据功能和结构数据的特点,使用不同的方案来提取特征。通过双线性操作,捕获每个感兴趣区域(ROI)的功能和结构特征之间的关联。然后,将这些关联用于整合特征表示。图卷积神经网络(GCN)可以有效地利用脑网络分析中的拓扑和节点特征。因此,我们设计了一个名为 BPF-GCN 的深度学习框架,并在公开的 ASD 数据集上进行了实验。结果表明,BPF-GCN 的分类准确率达到 82.35%,超过了现有方法。这证明了其分类性能的优越性,并且该框架可以提取与 ASD 相关的 ROI。我们的工作为 ASD 的及时诊断和治疗提供了有价值的参考。

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