Objective To design a fast method based on wavelet analysis for fMRI data. Methods Lifting wavelet decomposition instead of stationary wavelet decomposition was utilized to separate paradigm responsive signal and confound ones in fMRI data, while frequency analysis was used to find out the wavelet scales in which paradigm responsive signal existed, then reconstructed signal from these scales was subjected to correlation analysis for actived pixels. Results Analyzing visual fMRI data revealed that when the significant level was α＜10-6, the proposed method gave more sensitive results than correlation analysis, but process time decreased on a large scale compared with the one based on the stationary wavelet transform. At the mean time, the proposed method only used 24 timepoints of data for wavelet reconstruction while one based on stationary wavelet transform used 256 timepoints of data. Conclusion The proposed method is the fast one based on wavelet transform for analyzing fMRI data, which also gives an effective technique for compressing fMRI data.%目的 构建一种快速的fMRI数据小波分析方法.方法 用提升小波变换代替平稳小波变换分解fMRI数据,以分离其实验响应信号和干扰信号,再由频谱分析识别实验响应信号所在的小波尺度,并只对实验响应信号所在的小波尺度进行重构,最后对重构信号进行相关分析来检测激活.结果 分析视觉实验数据显示,在显著性水平为α＜10-6时,本文基于提升小波变换的方法比未去噪的相关分析方法更灵敏,而消耗时间比基于平稳小波变换的方法大幅度减少.同时本文方法只需24个数据点即可进行小波重构,而基于平稳小波变换的方法则需要256个数据点.结论 本文为fMRI数据提出了一种既能快速分析又能有效压缩的小波分析方法.
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