College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China.
Systems Engineering Institute, Academy of Military Sciences, Beijing, China.
Hum Brain Mapp. 2024 Jul 15;45(10):e26726. doi: 10.1002/hbm.26726.
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.
静息态功能连接（FC）广泛应用于功能磁共振成像（fMRI）的多变量模式分析，包括识别假定的大脑功能边界的位置、预测个体表型和诊断临床精神疾病。然而，从频率角度分析功能相互作用的研究还很有限。在这项研究中，我们通过对比基于相干性和基于相关性的功能连接与两个机器学习任务，观察到在频域中测量功能连接有助于识别更精细的功能子区域，并相对于时间相关性实现更好的模式区分能力。本研究证明了相干性在 fMRI 分析中的可行性，结果表明，在频域中建模功能相互作用可能比在时域中提供更丰富的信息，这可能为功能神经影像学的分析提供新的视角。
