Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran.
Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran.
Comput Biol Med. 2022 Jul;146:105643. doi: 10.1016/j.compbiomed.2022.105643. Epub 2022 May 17.
Graph signal processing (GSP) is a subset of signal processing, allowing for the analysis of functional magnetic resonance imaging (fMRI) data in the topological domain of the brain. One of the most important and popular tools of GSP is graph Fourier transform (GFT), which can analyze the brain signals in different graph frequency bands. This paper has analyzed the resting-state fMRI (rfMRI) data of two sites using the GFT tool to discover new knowledge about autism spectrum disorder (ASD) and find features discriminating between ASD subjects and typical controls (TCs). The results were reported for both structural and functional atlases with different numbers of regions of interest (ROIs). For the ASD group, the signal energy concentrations in low and somewhat high-frequency bands declined by increasing age in most well-known brain networks. The changes of signal energy levels in different graph frequency bands were less for ASD subjects in comparison to TC ones. This result seems to reflect the difficulty in dynamic switching, which in turn leads to lower behavioral flexibility in the ASD group. In the low graph frequency band, the segregation of brain ROIs and brain networks increased with the age of ASD subjects. For TCs, growing up led to the integration of brain ROIs and segregation of brain networks in low and high-frequency bands, respectively. In the low-frequency band, the growing process was accompanied by lower activation and higher isolation of ASD brain networks. In addition, the segregation of salient-ventral attention network and dorsal attention network of ASD subjects grew with age. The structural atlas results indicated the reduced segregation of ASD subjects' default mode network in the high graph frequency band. The cross-frequency functional connectivity analysis showed that high-frequency signals of the right precentral gyrus and right precuneus posterior cingulate cortex had connections with almost all the low-frequency ROIs so that all connections were dramatically different between ASD and TC. The results of different scenarios at different graph frequency bands demonstrate that the combinatorial usage of functional and structural data through GSP can open a new avenue to investigate ASD.
图信号处理 (GSP) 是信号处理的一个分支，允许在大脑的拓扑域中分析功能磁共振成像 (fMRI) 数据。GSP 中最重要和最流行的工具之一是图傅里叶变换 (GFT)，它可以分析不同图频带中的大脑信号。本文使用 GFT 工具分析了两个地点的静息态 fMRI (rfMRI) 数据，以发现关于自闭症谱系障碍 (ASD) 的新知识，并找到区分 ASD 受试者和典型对照组 (TCs) 的特征。结果报告了具有不同感兴趣区域 (ROIs) 数量的结构和功能图谱。对于 ASD 组，在大多数知名脑网络中，随着年龄的增长，低和中高频带的信号能量浓度下降。与 TC 相比，ASD 受试者在不同图频带中的信号能量水平变化较小。这一结果似乎反映了动态切换的困难，这反过来又导致 ASD 组的行为灵活性降低。在低频带中，随着 ASD 受试者年龄的增长，大脑 ROI 和脑网络的分离增加。对于 TC，随着年龄的增长，大脑 ROI 逐渐融合，大脑网络在低和高频带中逐渐分离。在低频带中，生长过程伴随着 ASD 大脑网络的低激活和高隔离。此外，ASD 受试者的突显腹侧注意网络和背侧注意网络的分离随着年龄的增长而增加。结构图谱的结果表明，在高频带中，ASD 受试者的默认模式网络的分离减少。跨频功能连接分析表明，右侧中央前回和右侧楔前叶后扣带回皮质的高频信号与几乎所有低频 ROI 都有连接，因此 ASD 和 TC 之间的所有连接都有显著差异。不同图频带不同场景的结果表明，通过 GSP 组合使用功能和结构数据可以为研究 ASD 开辟新途径。
