School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China.
Department of Radiology and Biomedical Imaging, Yale University, New Haven, 300 Cedar Street, New Haven, 06510 CT, United States.
Cereb Cortex. 2023 Feb 20;33(5):2011-2020. doi: 10.1093/cercor/bhac189.
Resting-state functional connectivity (RSFC) has been widely adopted for individualized trait prediction. However, multiple confounding factors may impact the predicted brain-behavior relationships. In this study, we investigated the impact of 4 confounding factors including time series length, functional connectivity (FC) type, brain parcellation choice, and variance of the predicted target. The data from Human Connectome Project including 1,206 healthy subjects were employed, with 3 cognitive traits including fluid intelligence, working memory, and picture vocabulary ability as the prediction targets. We compared the prediction performance under different settings of these 4 factors using partial least square regression. Results demonstrated appropriate time series length (300 time points) and brain parcellation (independent component analysis, ICA100/200) can achieve better prediction performance without too much time consumption. FC calculated by Pearson, Spearman, and Partial correlation achieves higher accuracy and lower time cost than mutual information and coherence. Cognitive traits with larger variance among subjects can be better predicted due to the well elaboration of individual variability. In addition, the beneficial effects of increasing scan duration to prediction partially arise from the improved test-retest reliability of RSFC. Taken together, the study highlights the importance of determining these factors in RSFC-based prediction, which can facilitate standardization of RSFC-based prediction pipelines going forward.
静息态功能连接（RSFC）已被广泛应用于个体特质预测。然而，多种混杂因素可能会影响预测的脑-行为关系。在这项研究中，我们调查了 4 种混杂因素的影响，包括时间序列长度、功能连接（FC）类型、脑区划分选择和预测目标的方差。使用人类连接组计划的数据，包括 1206 名健康受试者，将 3 种认知特征（流体智力、工作记忆和图片词汇能力）作为预测目标。我们使用偏最小二乘回归比较了这 4 个因素在不同设置下的预测性能。结果表明，适当的时间序列长度（300 个时间点）和脑区划分（独立成分分析，ICA100/200）可以在不消耗太多时间的情况下获得更好的预测性能。与互信息和相干性相比，皮尔逊、斯皮尔曼和偏相关计算的 FC 具有更高的准确性和更低的时间成本。由于个体变异性的详细描述，具有较大个体间方差的认知特征可以得到更好的预测。此外，增加扫描时间对预测的有益影响部分来自于 RSFC 测试-重测信度的提高。总之，该研究强调了确定这些因素在基于 RSFC 的预测中的重要性，这有助于未来基于 RSFC 的预测管道的标准化。
