Kim Young-Geun, Ravid Orren, Zhang Xinyuan, Kim Yoojean, Neria Yuval, Lee Seonjoo, He Xiaofu, Zhu Xi
bioRxiv. 2023 Sep 13:2023.09.13.557591. doi: 10.1101/2023.09.13.557591.
Resting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used to study brain function in psychiatric disorders, yielding insight into brain organization. However, the high dimensionality of the rs-fMRI data presents challenges, and requires dimensionality reduction before applying machine learning techniques. Neural networks, specifically variational autoencoders (VAEs), have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity patterns, addressing the complex nonlinear structure of rs-fMRI. However, interpreting those latent representations remains a challenge. This paper aims to address this gap by creating explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD).
One-thousand one hundred and fifty participants (601 HC and 549 patients with ASD) were included in the analysis. We extracted functional connectivity correlation matrices from the preprocessed rs-fMRI data using Power atlas with 264 ROIs. Then VAEs were trained in an unsupervised fashion. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures.
We quantified the latent contribution scores for the ASD and control groups at the network level. We found that both ASD and control groups share the top network connectivity that contribute to all estimated latent components. For example, latent 0 was driven by resting state functional connectivity patterns (rsFC) within ventral attention network in both the ASD and control. However, significant differences in the latent contribution scores between the ASD and control groups were discovered within the ventral attention network in latent 0 and the sensory/somatomotor network in latent 2.
This study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC features as estimated latent representation changes, enabling an explainable deep learning model to better understand the underlying neural mechanism of ASD.
静息态功能磁共振成像（rs-fMRI）已被用于研究精神疾病中的脑功能，有助于深入了解大脑组织。然而，rs-fMRI数据的高维度带来了挑战，在应用机器学习技术之前需要进行降维。神经网络，特别是变分自编码器（VAE），在提取静息态功能连接模式的低维潜在表示方面发挥了重要作用，解决了rs-fMRI的复杂非线性结构问题。然而，解释这些潜在表示仍然是一个挑战。本文旨在通过创建可解释的VAE模型并使用自闭症谱系障碍（ASD）的rs-fMRI数据测试其效用，来弥补这一差距。
1150名参与者（601名健康对照和549名ASD患者）纳入分析。我们使用包含264个感兴趣区域（ROI）的Power图谱从预处理后的rs-fMRI数据中提取功能连接相关矩阵。然后以无监督方式训练VAE。最后，我们引入潜在贡献分数来解释估计表示与原始rs-fMRI脑测量之间的关系。
我们在网络水平上量化了ASD组和对照组的潜在贡献分数。我们发现ASD组和对照组都共享对所有估计潜在成分有贡献的顶级网络连接。例如，潜在成分0在ASD组和对照组中均由腹侧注意网络内的静息态功能连接模式（rsFC）驱动。然而，在潜在成分0的腹侧注意网络和潜在成分2的感觉/躯体运动网络中，发现ASD组和对照组之间的潜在贡献分数存在显著差异。
本研究引入潜在贡献分数来解释VAE识别的非线性模式。这些分数有效地捕捉了每个观察到的rsFC特征随估计潜在表示变化的变化，使可解释的深度学习模型能够更好地理解ASD的潜在神经机制。
