Preliminary Study on the Neural Mechanisms of Headache Based on SEEG Data: A Case Study

Fankai Sun,Nan Zheng, Liang Zhang

2024 AUSTRALIAN & NEW ZEALAND CONTROL CONFERENCE, ANZCC(2024)

引用 0|浏览1
暂无评分
摘要
Headache is one of the most common symptoms worldwide, which not only burdens individuals but also exerts significant pressure on the socioeconomic aspects of society. The neural mechanisms underlying headache attacks are complex and often difficult to diagnose accurately. Traditional Electroencephalography and functional magnetic resonance imaging techniques have limitations in exploring headache attacks due to their limited spatial and temporal resolution capabilities. In contrast, Stereo-electroencephalography has advantages in spatial and temporal resolution. This study, for the first time, used Stereo-electroencephalography technology to detect and differentiate the occurrence process and did a preliminary study on neural mechanisms of headaches. By applying wavelet transform for time-frequency domain analysis and using PSI for connectivity analysis, we were able to find clear evidence of changes in brain activity levels and connectivity between brain regions such as the amygdala caused by headache attacks. These findings contribute to a deeper understanding of the neural mechanisms of headache attacks and provide new clues for the diagnosis and treatment of headaches.
更多
查看译文
关键词
Neural Mechanisms,Stereoelectroencephalography Data,Spatial Resolution,Brain Regions,Brain Activity,Wavelet Transform,Connectivity Analysis,Changes In Connectivity,Headache Attacks,Treatment Of Headache,Level Of Brain Activity,Clues For Diagnosis,Neural Activity,High Spatial Resolution,Information Flow,Data Pre-processing,Fast Fourier Transform,Electrical Activity,Temporoparietal Junction,Magnetoencephalography,Epilepsy Patients,Continuous Wavelet Transform,Migraine Patients,Lateral Temporal Lobe,Positive And Negative Values,Event-related Spectral Perturbation,Time-frequency Analysis,Lateral Lobe,Morlet Wavelet,Deep Brain Regions
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要