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Abstract：
Resting-state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence-based and correlation-based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.
摘要：
静息态功能连通性(FC)广泛应用于功能磁共振成像(fMRI)的多变量模式分析,包括确定推定的大脑功能边界的位置，预测个体表型，诊断临床精神疾病。然而,从频率的角度对功能相互作用的分析给予了有限的关注。在这项研究中,通过将基于一致性和基于相关性的FC与两个机器学习任务进行对比，我们观察到,相对于时间相关性,在频域测量FC有助于识别更精细的功能子区域,并获得更好的模式辨别能力.这项研究证明了在fMRI分析中相干性的可行性，结果表明，在频域中对功能相互作用进行建模可以提供比时域中更丰富的信息，这可能为功能神经影像学的分析提供新的视角。
