Graph signal processing (GSP) is a framework that enables the generalization of signal processing to multivariate signals described on graphs. In this paper, we present an approach based on Graph Fourier Transform (GFT) and machine learning for the analysis of resting-state functional magnetic resonance imaging (rs-fMRI). For each subject, we use rs-fMRI time series to compute several descriptive statistics in regions of interest (ROI). Next, these measures are considered as signals on an averaged structural graph built using tractography of the white matter of the brain, defined using the same ROI. GFT of these signals is computed using the structural graph as a support, and the obtained feature vectors are subsequently benchmarked in a supervised learning setting. Further analysis suggests that GFT using structural connectivity as a graph and the standard deviation of fMRI time series as signals leads to more accurate supervised classification using a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange) when compared to several other statistical metrics. Moreover, the proposed approach outperforms several approaches, based on using functional connectomes or complex functional network measures as features for classification.

图信号处理 (GSP) 是一个框架，可以将信号处理推广到图上描述的多元信号。在本文中，我们提出了一种基于图傅立叶变换 (GFT) 和机器学习的方法，用于分析静息态功能磁共振成像 (rs-fMRI)。对于每个主题，我们使用 rs-fMRI 时间序列来计算感兴趣区域 (ROI) 中的几个描述性统计数据。接下来，这些测量被视为使用大脑白质纤维束成像构建的平均结构图上的信号，使用相同的 ROI 定义。使用结构图作为支持计算这些信号的 GFT，然后将获得的特征向量在监督学习设置中进行基准测试。进一步的分析表明 GFT 使用结构连接作为图形和 fMRI 时间序列的标准偏差作为信号导致使用称为 ABIDE（自闭症脑成像数据交换）的全球多站点数据库的更准确的监督分类与几个相比其他统计指标。此外，所提出的方法优于几种基于使用功能连接组或复杂功能网络度量作为分类特征的方法。