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从视觉Oddball同步脑电图-功能磁共振成像数据中对任务相关网络进行盲可视化:频谱模型还是时空频谱模型?

Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model?

作者信息

Labounek René, Wu Zhuolin, Bridwell David A, Brázdil Milan, Jan Jiří, Nestrašil Igor

机构信息

Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States.

Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States.

出版信息

Front Neurol. 2021 Apr 26;12:644874. doi: 10.3389/fneur.2021.644874. eCollection 2021.

Abstract

Various disease conditions can alter EEG event-related responses and fMRI-BOLD signals. We hypothesized that event-related responses and their clinical alterations are imprinted in the EEG spectral domain as event-related (spatio)spectral patterns (ERSPat). We tested four EEG-fMRI fusion models utilizing EEG power spectra fluctuations (i.e., absolute spectral model - ASM; relative spectral model - RSM; absolute spatiospectral model - ASSM; and relative spatiospectral model - RSSM) for fully automated and blind visualization of task-related neural networks. Two (spatio)spectral patterns (high band and low band) demonstrated significant negative linear relationship ( < 0.05) to the frequent stimulus and three patterns (two low and bands, and narrow band) demonstrated significant positive relationship ( < 0.05) to the target stimulus. These patterns were identified as ERSPats. EEG-fMRI F-map of each model showed strong engagement of insula, cuneus, precuneus, basal ganglia, sensory-motor, motor and dorsal part of fronto-parietal control (FPCN) networks with fast HRF peak and noticeable trough. ASM and RSSM emphasized spatial statistics, and the relative power amplified the relationship to the frequent stimulus. For the model, we detected a reduced HRF peak amplitude and a magnified HRF trough amplitude in the frontal part of the FPCN, default mode network (DMN) and in the frontal white matter. The frequent-related patterns visualized less significant and distinct suprathreshold spatial associations. Each model showed strong involvement of lateralized left-sided sensory-motor and motor networks with simultaneous basal ganglia co-activations and reduced HRF peak and amplified HRF trough in the frontal part of the FPCN and DMN. The ASM model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For , , and bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were observed for ASM and RSSM.

摘要

多种疾病状态可改变脑电图(EEG)事件相关反应和功能磁共振成像血氧水平依赖(fMRI-BOLD)信号。我们假设事件相关反应及其临床改变在EEG频谱域中以事件相关(时空)频谱模式(ERSPat)的形式被印记下来。我们测试了四种EEG-fMRI融合模型,利用EEG功率谱波动(即绝对频谱模型 - ASM;相对频谱模型 - RSM;绝对时空频谱模型 - ASSM;以及相对时空频谱模型 - RSSM)来对任务相关神经网络进行全自动和盲态可视化。两种(时空)频谱模式(高频带和低频带)与频繁刺激呈现显著负线性关系(P < 0.05),三种模式(两个低频带和窄带)与目标刺激呈现显著正相关关系(P < 0.05)。这些模式被确定为ERSPat。每个模型的EEG-fMRI F图显示岛叶、楔叶、楔前叶、基底神经节、感觉运动、运动以及额顶控制网络(FPCN)背侧部分有强烈参与,具有快速的血流动力学反应函数(HRF)峰值和明显的波谷。ASM和RSSM强调空间统计,相对功率放大了与频繁刺激的关系。对于该模型,我们在FPCN、默认模式网络(DMN)的额叶部分以及额叶白质中检测到HRF峰值幅度降低和HRF波谷幅度放大。频繁相关模式可视化显示出不太显著和清晰的超阈值空间关联。每个模型都显示出左侧化的感觉运动和运动网络强烈参与,同时基底神经节共同激活,并且在FPCN和DMN的额叶部分HRF峰值降低和HRF波谷放大。ASM模型在FPCN背侧部分保留了与目标相关的EEG-fMRI关联。对于高频、低频和窄带,所有模型在预期区域提供了高局部F统计量。观察到ASM和RSSM的EEG-fMRI关联最为稳健。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da34/8107237/0a464925c7aa/fneur-12-644874-g0001.jpg

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