大鼠大腦皮質體感覺區神經信號與後肢觸覺刺激之關聯

腦部神經信號是研究腦部功能的一項重要的訊息。以往的研究中已證實不同的腦皮質區域對映不同的功能，因此記錄與分析該區的神經信號，將有助於了解該功能的運作機制。本研究的目的是自行開發一套神經的記錄與分析系統，藉以分析大鼠大腦皮質體感覺區神經信號與後肢觸覺刺激之關聯。整個系統包括了擷取、放大、記錄與分析四個部分。擷取部分是利用微線電極植入腦皮質擷取微小的神經信號。放大部分則以儀表放大為前級，再輔以後級放大與濾波電路，藉以取出所需的神經信號。記錄部分的功用是將放大後類比的神經信號數位化存入電腦。分析部分旨在找出信號的特徵，進而能逐漸了解腦部各功能的運作機制。研究的方法是以不同尖銳程度的刺激工具刺激大鼠的後肢，同時記錄其被刺激後的大腦皮質後肢體感覺區的神經信號，經由適當的信號分析找出其中的關聯性。由實驗中發現，後肢刺激與否能產生不同的信號振幅與聲音(頻率)，因此利用非線性能量操作(NLEO)的方式由信號中檢測出其被刺激的時間點，再以此時間點為基準點前後各取一段適當時間是為各種不同刺激的樣本信號，這些不同刺激的樣本信號透過ICA及DDIA找出特徵，最後再分類得出結果。由結果顯示，分辨有無刺激可達80%以上，分辨不同尖銳程度的刺激物則為40%~70%，另外也發現刺激左、右後肢會得到類似的結果。因此本研究所描述的系統架構，及自行建立的各項系統(硬體、軟體)，將有助於運用在腦神經科學的研究。

關鍵字

初級體感覺區；微電極；頻譜相減；胞外記錄；腦機介面；非線性能量操作；獨立成分分析；動態維度增加演算法

並列摘要

Brain neural signal is an important message of studying the brain function. Previous studying found the different cortex regions mapped the different function. Therefore, recording and analyzing the neural signal in the somatosensory cortex will help to understand the mechanism of sensory function. The goal of this research is to represent the relationship between the neural signal of primary somatosensory cortex (S1) and tactile stimuli in rats by using a homemade system. The system includes four parts: neural signal acquisition, amplifying, recording, and analysis. In the acquisition part, we use multichannel electrodes of microwire to pick up the neural signal of brain cortex of rats. The amplifying part is used to filter and boost signal. The recording part is used to store the amplified data into computer for further processing. As for the analysis part, we use some digital signal processing and statistics techniques to extract the underlying information hidden in the neural signal. In the study, we use the different sharp stimulant to stimulate the hindlimb of rat, and recording the response signals from S1HL. Then analyze the relationships between these neural signals and the stimuli. The analytic processes are to utilize the nonlinear energy operator (NLEO) to detect exactly the response time of evoked potential by stimulation of hindlimb in rat. Then signals are separated into each section according to the response time. Further, we combine independent component analysis (ICA) and dynamic dimension increasing algorithm (DDIA) to extract features of signals of all sections. Lastly these features are classified by K-means. The results have shown the accuracy above 80% in distinguishing the hindlimb is stimulated or not. then to distinguish from different stimulants is 40% to 70%. Moreover, the properties of neural signals of S1HL on cortex of both hemispheres are similar. These results indicate that the techniques develop in this study would benefit researchers in neuroscience studies.

並列關鍵字

Primary somatosensory cortex (S1) ； Microelectrode ； Spectral subtraction ； Extracellular recording ； Brain-machine interface (BMI) ； Nonlinear energy operator (NLEO) ； Independent component analysis (ICA) ； Dynamic dimension increasing algorithm (DDIA)

參考文獻

[12] Chia-Chi Liao, Functional connectivity of the secondary somatosensory cortex of the rat, Master. Thesis, National Taiwan University, ROC, 2004.

[33] Chih-Jen Lan, Using ICA to Distinguish the Signal of Primary Motor Cortex of Rats, Master. Thesis, National Taipei University of Technology, ROC, 2007.

[1] Lebedev, M.A. and M.A. Nicolelis, “Brain-machine interfaces: past, present and future,” Trends in Neurosci, vol. 29, no. 9, 2006, pp. 536-546.

[2] Green JD., “A simple microelectrode for recording from the central nervous system,” Nature, 1958 Oct 4;182(4640):962.

[3] Schmidt EM, Bak MJ, McIntosh JS., “Long-term chronic recording from cortical neurons,” Exp Neurol, 1976 Sep;52(3):496-506.

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大鼠大腦皮質體感覺區神經信號與後肢觸覺刺激之關聯

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