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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Data availability HCP and CoRR data are available in public database, and our local data are available in https://www.nitrc.org/projects/socialbrain_22/
Funding This study received financial support from the National Natural Science Foundation of China [grant numbers 81771815, 81801676]. This study received financial support from Hovering Program of Air Force Medical University [grant numbers axjhww], the Talent Foundation of Tangdu Hospital [grant numbers 2018BJ003, 2018MZ012].
Conflict of interest disclosure The authors declare that they have no competing interests. All participants provided written informed consent.
Ethics approval statement This study was approved by the institutional review board of Tangdu hospital, Air Force Medical University. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and complied with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Patient consent statement All the subjects in our local dataset provided written informed consent.
http://fcon_1000.projects.nitrc.org/indi/CoRR/html/index.html