@article{oai:repo.qst.go.jp:00075796,
 author = {Yamashita, Ayumu and Yahata, Noriaki and Itahashi, Takashi and Lisi, Giuseppe and Yamada, Takashi and Ichikawa, Naho and Takamura, Masahiro and Yoshihara, Yujiro and Kunimatsu, Akira and Okada, Naohiro and Yamagata, Hirotaka and Matsuo, Koji and Hashimoto, Ryuichiro and Okada, Go and Sakai, Yuki and Morimoto, Jun and Narumoto, Jin and Shimada, Yasuhiro and Kasai, Kiyoto and Kato, Nobumasa and Takahashi, Hidehiko and Okamoto, Yasumasa and C Tanaka, Saori and Kawato, Mitsuo and Yamashita, Okito and Imamizu, Hiroshi and Yahata, Noriaki},
 issue = {4},
 journal = {PLoS biology},
 month = {Apr},
 note = {When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.},
 title = {Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias.},
 volume = {17},
 year = {2019}
}