去噪方法在大鼠静息态fMRI中的比较研究

Comparative Study of Denoising Methods Based on Head MotionCorrection in Resting-State fMRI of Rats

在神经科学研究中,静息态功能磁共振成像(rs-fMRI)信号不仅反映了神经活动,还保留了头动、生理运动、设备等带来的噪声,这将导致静息态功能连接的错误估计。为了得到准确的功能连接网络,分析噪声来源、运动校正方法,并研究独立成分分析、频率滤波和CompCor等信号回归的去噪方法及其在啮齿动物中的应用。通过评价信噪比(SNR)发现,CompCor和独立成分分析去噪后的图像有较高的SNR,可替代有争议的去噪方法。由于噪声是复杂且相互影响的,最大程度消除噪声而不影响感兴趣区域的信号仍是静息态数据预处理研究的方向。

In neuroscience research,resting-state functional magnetic resonance imaging(rs-fMRI)not only reflects neural activity,but al⁃so preserves the noise caused by head movement,physiology,and hardware,which leads to erroneous estimates of resting-state functional connectivity.In order to obtain an accurate functional connectivity network,this paper analyzes the source of noise,introduces motion correc⁃tion methods,and studies denoising methods such as independent component analysis,frequency filtering,and CompCor signal regression and their applications in rodents.By evaluating the signal-to-noise ratio(SNR),we found that images denoised by CompCor and independent component analysis have higher SNR and can be an alternative to controversial denoising methods.Although denoising has been developed rel⁃atively rapidly,noise is complex and interacts with each other,and eliminating noise to the greatest extent without affecting the signal of the re⁃gion of interest is still the research direction of static state data preprocessing.