CAJ | 학술논문

为了方便提取静息态默认网络,降低功能核磁共振(fMRI)数据复杂度,克服独立成分分析只适合于源信号线性混合的限制,提出了特征降维和非线性变换的框架.首先采用主成分分析对fMRI信号的时间维度进行降维,将原始维度为153 594×128的fMRI数据降至153 594×5,以达到降低计算复杂度的目的,并保留95%的信息成分.然后利用基于高斯核的非线性独立成分分析即核独立成分分析来分析静息态fMRI数据并提取默认网络.实验结果表明,在分析静息态fMRI数据的过程中,核独立成分分析不仅能准确提取默认网络,而且降低了噪声,所得到的结果优于普通独立成分分析.
위료방편제취정식태묵인망락,강저공능핵자공진(fMRI)수거복잡도,극복독립성분분석지괄합우원신호선성혼합적한제,제출료특정강유화비선성변환적광가.수선채용주성분분석대fMRI신호적시간유도진행강유,장원시유도위153 594×128적fMRI수거강지153 594×5,이체도강저계산복잡도적목적,병보류95%적신식성분.연후이용기우고사핵적비선성독립성분분석즉핵독립성분분석래분석정식태fMRI수거병제취묵인망락.실험결과표명,재분석정식태fMRI수거적과정중,핵독립성분분석불부능준학제취묵인망락,이차강저료조성,소득도적결과우우보통독립성분분석.
In order to facilitate the extraction of the default mode network(DMN),reduce the data complexity of the functional magnetic resonance imaging(fMRI)and overcome the restriction of the linearity of the mixing process encountered with the independent component analysis(ICA), a framework of dimensionality reduction and nonlinear transformation is proposed.First, the principal component analysis(PCA)is applied to reduce the time dimension 153 594×128 of the fMRI data to 153 594×5 for simplifying complexity computation and obtaining 95% of the information.Secondly,a new kemel-based nonlinear ICA method referred as the kernel ICA(KICA)based on the Gaussian kernel is introduced to analyze the resting-state fMRI data and extract the DMN.Experimental results show that the KICA provides a better performance for the resting-state fMRI data analysis compared with the classical ICA.Furthermore,the DMN is accurately extracted and the noise is reduced.