Abstract
Resting‐state functional connectivity (rsFC) methods have been used in basic and clinical research as a way to visualize and quantify functional brain networks. Although psychiatric disease biomarker research and development for diagnostic and therapeutic targeting and subtyping is underway, establishing a practical biomarker remains a challenge. In this study, to comprehensively and quantitatively assess the impact of various factors related to biomarker development on FC, we have analyzed a large travel subject data set from the Brain Mind/Beyonds Project (BMB, 2018‐2023) and a multi‐disorder data set measured in SRPBS (2012‐2018) , consisting of total 2,100 runs of the functional magnetic resonance imaging (fMRI) data during a 10‐minute eye‐open resting sate experiment. The results showed that the two main non‐disease factors, intra‐individual inter‐trial and inter‐individual variability, were the two most important factors, with disease variability being as large as the measurement factors, scanner and measurement protocol‐to‐protocol variability. On the other hand, there were a small number of connectvity in which disease variability was the largest. When the brain networks with the highest variability for each factor were examined, it was observed that different networks were affected with overlap between the factors. These results suggest that for reliable biomarker development, it is necessary to select the connectivity with large disease variability and small other factors, and to develop experimental and analytical methods to reduce intra‐ and inter‐individual variability.
