The study of structure-function relationships in the brain has been an active area of research in neuroscience. The availability of brain imaging data that captures the structural connectivity and functional co-activation of the brain regions has led to the study of multimodal technical frameworks that can help disentangle the mechanisms linking cognitive abilities and brain structural alterations. This paper analyzes the diffusion and resting state functional magnetic resonance imaging (dMRI and rs-fMRI) data collected from a population consisting of former athletes with a history of multiple concussions and healthy controls with no reported history of concussion. For each subject, the structural connectome is represented by a graph with its nodes associated with cortical brain regions and the adjacency matrix derived from dMRI. Each cortical brain region is associated with a blood oxygen level dependent (BOLD) signal derived from fMRI. This paper uses the tools from graph signal processing (GSP) to select the brain regions of interest (ROIs) that have significant statistical differences in the extracted high and low graph frequency components of the region specific BOLD signal across former athletes and healthy controls, where the graph frequencies represent the extent of spatial variations of the BOLD signal across the brain. The selected ROIs have also been previously identified to be affected in the existing clinical studies on traumatic brain injuries (TBI). Furthermore, the dynamic functional connectivity profiles of the selected ROIs are determined by leveraging the high and low graph frequency components of the BOLD signal and a sliding window based approach. Interestingly, the graph frequency functional connectivity profiles reveal unique characteristics that are not apparent in the unimodal dynamic functional connectivity profiles based on fMRI. Our analysis reveals statistically significant differences in the dwell times in multiple dynamic graph frequency functional connectivity states for the two groups of subjects. Therefore, the results presented in this paper underline the significance of graph signal processing tools for multimodal analysis of brain imaging data and also provide promising direction for applications in clinical research and medical diagnosis.

中文翻译：

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基于图信号处理的多发性脑震荡前运动员多模式动态脑连通性分析

大脑中结构-功能关系的研究一直是神经科学研究的活跃领域。捕获大脑区域的结构连通性和功能共激活的大脑成像数据的可用性，导致了对多模式技术框架的研究，该框架可以帮助弄清联系认知能力和大脑结构改变的机制。本文分析了弥散和静止状态功能磁共振成像（dMRI和rs-fMRI）数据，该数据是从具有多次脑震荡史的前运动员和没有脑震荡史的健康对照人群中收集的。对于每个对象，结构连接体均由一个图表示，其结点与皮质脑区域和源自dMRI的邻接矩阵相关。每个皮层大脑区域都与源自fMRI的血氧水平依赖性（BOLD）信号相关。本文使用来自图形信号处理（GSP）的工具来选择感兴趣的大脑区域（ROI），这些区域在前运动员和健康对照组之间的区域特定BOLD信号的提取的高和低图形频率分量中具有显着的统计差异，其中图表频率代表大脑中BOLD信号的空间变化程度。先前在有关颅脑外伤（TBI）的现有临床研究中也已确定了所选的ROI。此外，通过利用BOLD信号的高和低图形频率分量以及基于滑动窗口的方法来确定所选ROI的动态功能连通性配置文件。有趣的是，图频率功能连通性图揭示了基于fMRI的单峰动态功能连通性图不明显的独特特征。我们的分析揭示了两组对象在多个动态图频率功能连接状态下的停留时间的统计显着差异。因此，本文提出的结果强调了图形信号处理工具对于脑成像数据的多峰分析的重要性，也为在临床研究和医学诊断中的应用提供了有希望的方向。我们的分析揭示了两组对象在多个动态图频率功能连接状态下的停留时间的统计显着差异。因此，本文提出的结果强调了图形信号处理工具对于脑成像数据的多峰分析的重要性，也为在临床研究和医学诊断中的应用提供了有希望的方向。我们的分析揭示了两组对象在多个动态图频率功能连接状态下的停留时间的统计显着差异。因此，本文提出的结果强调了图形信号处理工具对于脑成像数据的多峰分析的重要性，也为在临床研究和医学诊断中的应用提供了有希望的方向。