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从 TMS 诱发的 EEG 中提取脑连接特征。

Brain Connectivity Signature Extractions from TMS Invoked EEGs.

机构信息

Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21227, USA.

Maryland Psychiatric Research Center, University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD 21201, USA.

出版信息

Sensors (Basel). 2023 Apr 18;23(8):4078. doi: 10.3390/s23084078.

Abstract

(1) Background: The correlations between brain connectivity abnormality and psychiatric disorders have been continuously investigated and progressively recognized. Brain connectivity signatures are becoming exceedingly useful for identifying patients, monitoring mental health disorders, and treatment. By using electroencephalography (EEG)-based cortical source localization along with energy landscape analysis techniques, we can statistically analyze transcranial magnetic stimulation (TMS)-invoked EEG signals, for obtaining connectivity among different brain regions at a high spatiotemporal resolution. (2) Methods: In this study, we analyze EEG-based source localized alpha wave activity in response to TMS administered to three locations, namely, the left motor cortex (49 subjects), left prefrontal cortex (27 subjects), and the posterior cerebellum, or vermis (27 subjects) by using energy landscape analysis techniques to uncover connectivity signatures. We then perform two sample -tests and use the (5 × 10) Bonferroni corrected -valued cases for reporting six reliably stable signatures. (3) Results: Vermis stimulation invoked the highest number of connectivity signatures and the left motor cortex stimulation invoked a sensorimotor network state. In total, six out of 29 reliable, stable connectivity signatures are found and discussed. (4) Conclusions: We extend previous findings to localized cortical connectivity signatures for medical applications that serve as a baseline for future dense electrode studies.

摘要

(1) 背景:大脑连接异常与精神疾病之间的相关性不断被研究并逐步得到认可。大脑连接特征对于识别患者、监测精神健康障碍和治疗变得越来越有用。通过使用基于脑电图 (EEG) 的皮质源定位和能量景观分析技术,我们可以对经颅磁刺激 (TMS) 诱发的 EEG 信号进行统计分析,以在高时空分辨率下获得不同脑区之间的连接。(2) 方法:在这项研究中,我们通过能量景观分析技术分析了对左运动皮层(49 名受试者)、左前额叶皮层(27 名受试者)和后小脑或蚓部(27 名受试者)进行 TMS 刺激时基于 EEG 的源定位 alpha 波活动,以揭示连接特征。然后我们进行了两个样本检验,并使用(5×10)的 Bonferroni 校正检验值报告了六个可靠稳定的特征。(3) 结果:蚓部刺激引起了最多的连接特征,而左运动皮层刺激引起了感觉运动网络状态。总共发现并讨论了 29 个可靠稳定连接特征中的六个。(4) 结论:我们将之前的发现扩展到局部皮质连接特征,为医疗应用提供了基线,为未来的密集电极研究奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbab/10146617/5abbc2f682c4/sensors-23-04078-g001.jpg

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