Increasing the reliability of functional connectivity by predicting long-scan functional connectivity based on short-scan functional connectivity: model exploration, explanation, validation, and application.

Abstract

Functional connectivity (FC) is a widely used imaging parameter of functional magnetic resonance imaging (fMRI). However, low reliability has been a concern among researchers, particularly in small-sample-size studies. Previous studies have shown that FC based on longer fMRI scans was more reliable, therefore, a feasible solution is to predict long-scan FCs using existing short-scan FCs. This study explored three different generalized linear models (GLMs) using the human connectome project (HCP) dataset. We found that the GLM based on individual short-scan FC could effectively predict long-scan individual FC value, while GLMs based on whole-brain FCs and dynamic FC performed better in predicting long-scan summed FC value of whole brain. The models were explained through visualization of weights in models. Besides, the differences in three GLMs could be explained as differences in distribution features of FC matrices predicted by them. Results were validated in different datasets, including the Consortium for Reliability and Reproducibility (CoRR) project and our local dataset. These models could be applied to improve the test-retest reliability of FC and to improve the performance of connectome-based predictive models (CPM). In conclusion, we developed three GLMs that could be used to predict long-scan FC from short-scan FC, and these models were robust across different datasets and could be applied to improve the test-retest reliability of FC and the performance of CPM.