1.Wavelet package decomposition and power spectrum analysis were adopted to analyze neural firings and information transmission. We found that a Parkinson’s disease group could be separated from a control group on the basis of related wavelet coefficients.
2.Based on the combination of the inference of wavelet coefficients, the activities of globus palli-dus neurons were weakened.
3.Microelectrode recording of neural firing signals could help us understand the pathogenesis of Parkinson’s disease. As well as detecting changes in neural firing before and after disease attack, this method could help distinguish targets for stimulation from the neighborhood of the globus pal-lidus, and confirm the target location precisely.
1.实验结果证实利用小波包变化和功率谱分析对神经元放电规律和其神经元活动信息传递进行分析,发现小波包变换系数可以较好地区分帕金森病组与正常组。
2.实验结果证实结合小波包变换系数的物理涵义,发现在帕金森病状态苍白球神经元活动减弱。
3.利用微电极记录神经元放电信号,可研究帕金森病发病机制,除了鉴别发病前后神经元放电规律变化,还可以用于苍白球或丘脑底核切除手术靶点与临近区域的区分,以更好地确认靶点。
4.作者认为,病理放电参数降低特征可以作为帕金森病评估的指标之一。