应用脑电非线性分析观察吞咽失用症的皮质电活动

Observation on cortical electric activation in swallowing apraxia with eletroencephalography nonlinear dynamics analysis

目的:应用脑电非线性分析观察吞咽失用症的大脑皮质电活动变化。方法:利用脑电非线性分析观察1例左侧额颞顶梗死后吞咽失用患者卒中后4周,安静闭眼、反射性吞咽、自主性吞咽3种状态下的大脑皮质电活动,同时进行吞咽失用的功能评价;计算脑电图非线性指数的近似熵(ApEn);记录3种状态下6名健康者的脑电图(EEG)作为正常对照。结果:脑电显示,与安静闭眼比较,正常对照组反射性吞咽时C3、C4、P3、P4、T6的ApEns值显著增高,自主性吞咽时除上述导联ApEn值增高外,F4和T5的ApEns值也显著增高。自主性吞咽时F4、T6的ApEns值比反射性吞咽增高。吞咽失用患者反射性吞咽时C3、P3的ApEns值增高,T6的ApEn值降低;自主性吞咽时,T6的ApEn值降低;自主性吞咽时的C3、P3、T5的ApEn数值比反射性吞咽时低。结论:吞咽失用患者自主性吞咽时左侧中央、顶、后颞大脑皮质的兴奋性不但没有升高,反而比安静闭眼和反射性吞咽时还低,符合吞咽失用的临床特征。应用脑电非线性分析可以观察到吞咽失用症吞咽任务相关的大脑半球的脑电变化。

Objective：To investigate the changes of cortical electric activation in the patient with swallowing apraxia by electroencephalography（EEG） nonlinear dynamics analysis.Method：One subject with swallowing apraxia caused by left hemisphere infarction was involved in the study.EEG was recorded under three conditions：eyes closed,reflexive swallowing and volitional swallowing.EEG nonlinear indice approximate entropy（ApEn） was calculated.The EEG of 6 healthy subjects was recorded in 3 different conditions as control.Result：Compared with eyes-closed condition,the ApEns of control group increased obviously in C3,C4,P3,P4 and T6 for reflexive swallowing task,and additional F4 and T5 for volitional swallowing task.ApEns of control group in F4 and T6 for volitional swallowing task was higher than those for reflexive swallowing task.ApEns of the swallowing-apraxia patient for reflexive swallowing increased in C3 and P3 and decreased in T6,yet ApEn for volitional swallowing did not change but decreased in T6.ApEns of patient in C3,P3 and T5 for volitional swallowing task were lower than those for reflexive swallowing task.Conclusion：Corresponding with the clinical characteristic of swallowing apraxia,the excitability of cortex in patient with swallowing apraxia did not increase but decreased on the left central,parietal and inferior temporal.The changes of cortical electric activations in hemisphere correlated with swallowing tasks could be observed with EEG nonlinear dynamics analysis