Display options
Share it on

Sci Data. 2017 Feb 14;4:170017. doi: 10.1038/sdata.2017.17.

Longitudinal test-retest neuroimaging data from healthy young adults in southwest China.

Scientific data

Wei Liu, Dongtao Wei, Qunlin Chen, Wenjing Yang, Jie Meng, Guorong Wu, Taiyong Bi, Qinglin Zhang, Xi-Nian Zuo, Jiang Qiu

Affiliations

  1. Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.
  2. Faculty of Psychology, Southwest University, Chongqing 400715, China.
  3. Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen 6525 EZ, The Netherlands.
  4. Key Laboratory of Behavioral Science, Laboratory for Functional Connectome and Development and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
  5. Department of Psychology, School of Education Science, Guangxi Teachers Education University, Nanning 530000, China.

PMID: 28195583 PMCID: PMC5308199 DOI: 10.1038/sdata.2017.17

Abstract

Multimodal magnetic resonance imaging (mMRI) has been widely used to map the structure and function of the human brain, as well as its behavioral associations. However, to date, a large sample with a long-term longitudinal design and a narrow age-span has been lacking for the assessment of test-retest reliability and reproducibility of brain-behavior correlations, as well as the development of novel causal insights into these correlational findings. Here we describe the SLIM dataset, which includes brain and behavioral data across a long-term retest-duration within three and a half years, mMRI scans provided a set of structural, diffusion and resting-state functional MRI images, along with rich samples of behavioral assessments addressed-demographic, cognitive and emotional information. Together with the Consortium for Reliability and Reproducibility (CoRR), the SLIM is expected to accelerate the reproducible sciences of the human brain by providing an open resource for brain-behavior discovery sciences with big-data approaches.

Conflict of interest statement

The authors declare no competing financial interests.

References

  1. Neuroimage. 2016 Jun;133:408-16 - PubMed
  2. Neuroimage. 2014 May 15;92:1-7 - PubMed
  3. Neuroimage. 2013 Oct 15;80:62-79 - PubMed
  4. Sci Data. 2015 Jul 07;2:150031 - PubMed
  5. Mol Psychiatry. 2014 Jun;19(6):659-67 - PubMed
  6. PLoS One. 2014 Aug 22;9(8):e104989 - PubMed
  7. Proc Natl Acad Sci U S A. 2014 Apr 22;111(16):6058-62 - PubMed
  8. Cortex. 2014 Feb;51:92-102 - PubMed
  9. Neuroimage. 2012 Jan 2;59(1):431-8 - PubMed
  10. Neuroimage. 2012 Feb 1;59(3):2142-54 - PubMed
  11. Nature. 2015 Oct 15;526(7573):371-9 - PubMed
  12. Neuroimage. 2013 Aug 1;76:183-201 - PubMed
  13. Arch Gen Psychiatry. 1961 Jun;4:561-71 - PubMed
  14. Nat Neurosci. 2015 Nov;18(11):1664-71 - PubMed
  15. Science. 2016 Apr 8;352(6282):216-20 - PubMed
  16. Nat Rev Neurosci. 2011 Apr;12 (4):231-42 - PubMed
  17. Neuroimage. 2016 Jan 1;124(Pt B):1149-54 - PubMed
  18. Neuroimage. 2013 Nov 15;82:403-15 - PubMed
  19. Neuroimage. 2017 Jan;144(Pt B):262-269 - PubMed
  20. Nature. 2012 Apr 04;484(7392):24-6 - PubMed
  21. J Digit Imaging. 2006 Jun;19(2):140-7 - PubMed
  22. Sci Data. 2014 Dec 09;1:140049 - PubMed
  23. Magn Reson Med. 2009 Aug;62(2):365-72 - PubMed
  24. IEEE Trans Med Imaging. 1997 Dec;16(6):903-10 - PubMed
  25. Nat Neurosci. 2015 Nov;18(11):1565-7 - PubMed
  26. Comput Biomed Res. 1996 Jun;29(3):162-73 - PubMed
  27. Soc Cogn Affect Neurosci. 2015 Feb;10(2):191-8 - PubMed

MeSH terms

Publication Types