Determining four confounding factors in individual cognitive traits prediction with functional connectivity: an exploratory study. (15th May 2022)

• Record Type: Journal Article
• Authors: Feng, Pujie; Jiang, Rongtao; Wei, Lijiang; Calhoun, Vince D; Jing, Bin; Li, Haiyun; Sui, Jing
• Is Part Of: Cerebral cortex. Volume 33:Number 5(2023)
• Page Start: 2011
• Page End: 2020
• Publication Date: 2022-05-15
• Subjects: cognitive trait -- confounding factors -- functional connectivity -- ICA -- individualized prediction; Cerebral cortex -- Periodicals; Brain -- Periodicals; 612.825
• DOI: 10.1093/cercor/bhac189
• Languages: English
• ISSNs: 1047-3211

Abstract

Abstract: 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.