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目的既往报道认为癫痫猝死(Sudden unexpected death in epilepsy,SUDEP)的直接原因可能是痫性发作相关的心肺功能障碍,但越来越多的研究却发现这种心肺功能的改变很可能是继发于痫性发作后的弥漫性脑功能抑制。但目前痫性发作后脑功能抑制的神经网络基础尚不清楚。探讨运用血氧水平依赖(Blood oxygen level dependent,BOLD)的静息态功能磁共振技术对SUDEP高危人群的神经网络特征进行研究。方法 2012年9月-2013年3月四川大学华西医院神经内科癫痫专科门诊就诊患者纳入SUDEP高危患者13例,低危患者12例,进行静息态磁共振数据采集。采用低频振幅(Amplitude of low-frequency fluctuations,ALFF)作为测量指标,比较两组被试患者的局部静息态脑活动差异。结果与低危患者相比,SUDEP高危组患者的ALFF值在右侧背外侧额上回、左侧眶部额上回、左侧岛叶及左侧丘脑的部分区域降低,而在右侧内侧和旁扣带回、右侧补充运动区及左侧丘脑的部分区域增高。结论研究发现SUDEP高危患者确有功能神经网络的异常存在。从神经网络的角度展开对SUDEP的机制研究,为SUDEP的预警提供了可能的影像学标记。
PURPOSE: It has been previously reported that the direct cause of sudden death in epilepsy (SUDEP) may be a cardio-pulmonary dysfunction associated with seizures, but more and more studies have found that this change in cardiopulmonary function is likely to be secondary to Diffuse brain function suppression after seizures. However, the basis of neural network for inhibiting brain function after epileptic seizure is still unclear. To explore the neural network characteristics of high-risk SUDEP population by resting state functional magnetic resonance (RF) technique with blood oxygen level dependent (BOLD). Methods From September 2012 to March 2013, 13 patients with high-risk SUDEP and 12 patients with low-risk were enrolled in Department of Neurology, West China Hospital, Sichuan University, and their resting magnetic resonance data were collected. Amplitude of low-frequency fluctuations (ALFF) was used as a measure to compare the differences in local resting brain activity between the two groups. Results Compared with low-risk patients, patients with high-risk SUDEP had lower ALFF values in the right dorsolateral frontal gyrus, supraorbital orbital upper frontal gyrus, left insula and left thalamus in some areas, while in the right medial And next to the cingulate back, right side of the motor area and the left part of the thalamus increased. Conclusions The study found that high-risk patients with SUDEP do have functional neural network abnormalities. The research on the mechanism of SUDEP from the perspective of neural network provides possible imaging markers for SUDEP’s early warning.