論文

査読有り 本文へのリンクあり
2019年

Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias

PLoS Biology
  • Ayumu Yamashita
  • ,
  • Noriaki Yahata
  • ,
  • Takashi Itahashi
  • ,
  • Giuseppe Lisi
  • ,
  • Takashi Yamada
  • ,
  • Naho Ichikawa
  • ,
  • Masahiro Takamura
  • ,
  • Yujiro Yoshihara
  • ,
  • Akira Kunimatsu
  • ,
  • Naohiro Okada
  • ,
  • Hirotaka Yamagata
  • ,
  • Koji Matsuo
  • ,
  • Ryuichiro Hashimoto
  • ,
  • Go Okada
  • ,
  • Yuki Sakai
  • ,
  • Jun Morimoto
  • ,
  • Jin Narumoto
  • ,
  • Yasuhiro Shimada
  • ,
  • Kiyoto Kasai
  • ,
  • Nobumasa Kato
  • ,
  • Hidehiko Takahashi
  • ,
  • Yasumasa Okamoto
  • ,
  • Saori C. Tanaka
  • ,
  • Mitsuo Kawato
  • ,
  • Okito Yamashita
  • ,
  • Hiroshi Imamizu

17
4
開始ページ
e3000042
終了ページ
記述言語
掲載種別
研究論文(学術雑誌)
DOI
10.1371/journal.pbio.3000042

© 2019 Yamashita et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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.

リンク情報
DOI
https://doi.org/10.1371/journal.pbio.3000042
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/30998673
Scopus
https://www.scopus.com/record/display.uri?eid=2-s2.0-85065016805&origin=inward
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85065016805&origin=inward 本文へのリンクあり
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