Determining four confounding factors in individual cognitive traits prediction with functional connectivity: an exploratory study
Determining four confounding factors in individual cognitive traits prediction with functional connectivity: an exploratory study
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确定具有功能连接的个体认知特征预测中的四个混杂因素：一项探索性研究
DOI：
10.1093/cercor/bhac189
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Sui, Jing
中科院分区：
文献类型：
--
作者：
Feng, Pujie;Jiang, Rongtao;Wei, Lijiang;Calhoun, Vince D;Jing, Bin;Li, Haiyun;Sui, Jing
静息状态功能连接性（RSFC）已被广泛用于个体化特质预测。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个混杂因素的影响，包括时间序列长度，功能连接（FC）类型，脑分区的选择，和预测目标的方差。本研究采用人类连接组计划（Human Connectome Project）1,206名健康受试者的数据，以流体智力、工作记忆和图片词汇能力3项认知特质作为预测指标。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）和脑分组（独立成分分析，ICA 100/200）可以达到更好的预测性能，而不会太多的时间消耗。Pearson、斯皮尔曼计算的FC和偏相关比互信息和相干性具有更高的准确性和更低的时间成本。由于个体差异性的充分发挥，个体间差异较大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RSFC的重测信度的提高。总之，该研究强调了在基于RSFC的预测中确定这些因素的重要性，这可以促进基于RSFC的预测管道的标准化。
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.