Wang Xinbo, Wang Qing, Zhang Peiwen, Qian Shufang, Liu Shiyu, Liu Dong-Qiang
Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, China.
Neuroinformatics. 2021 Jan;19(1):23-38. doi: 10.1007/s12021-020-09463-x.
It has been reported that resting state fluctuation amplitude (RSFA) exhibits extremely large inter-site variability, which limits its application in multisite studies. Although global normalization (GN) based approaches are efficient in reducing the site effects, they may cause spurious results. In this study, our purpose was to find alternative strategies to minimize the substantial site effects for RSFA, without the risk of introducing artificial findings. We firstly modified the ALFF algorithm so that it is conceptually validated and insensitive to data length, then found that (a) global mean amplitude of low-frequency fluctuation (ALFF) covaried only with BOLD signal intensity, while global mean fractional ALFF (fALFF) was significantly correlated with TRs across different sites; (b) The inter-site variations in raw RSFA values were significant across the entire brain and exhibited similar trends between gray matter and white matter; (c) For ALFF, signal intensity rescaling could dramatically reduce inter-site variability by several orders, but could not fully removed the globally distributed inter-site variability. For fALFF, the global site effects could be completely removed by TR controlling; (d) Meanwhile, the magnitude of the inter-site variability of fALFF could also be reduced to an acceptable level, as indicated by the detection power of fALFF in multisite data quite close to that in monosite data. Thus our findings suggest GN based harmonization methods could be replaced with only controlling for confounding factors including signal scaling, TR and full-band power.
据报道，静息态波动幅度（RSFA）表现出极大的位点间变异性，这限制了其在多站点研究中的应用。尽管基于全局归一化（GN）的方法在减少位点效应方面很有效，但它们可能会导致虚假结果。在本研究中，我们的目的是找到替代策略，以最小化RSFA的显著位点效应，同时避免引入人为结果的风险。我们首先修改了低频振幅（ALFF）算法，使其在概念上得到验证且对数据长度不敏感，然后发现：（a）低频波动的全局平均振幅（ALFF）仅与血氧水平依赖（BOLD）信号强度共变，而全局平均分数低频振幅（fALFF）在不同位点与重复时间（TRs）显著相关；（b）原始RSFA值的位点间差异在整个大脑中都很显著，且在灰质和白质之间呈现出相似的趋势；（c）对于ALFF，信号强度重新缩放可以将位点间变异性显著降低几个数量级，但不能完全消除全局分布的位点间变异性。对于fALFF，通过TR控制可以完全消除全局位点效应；（d）同时，fALFF位点间变异性的大小也可以降低到可接受的水平，这体现在多站点数据中fALFF的检测能力与单站点数据中的相当接近。因此，我们的研究结果表明，基于GN的协调方法可以仅通过控制包括信号缩放、TR和全频段功率在内的混杂因素来替代。
