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Brain Structure and Function

, Volume 224, Issue 2, pp 553–566 | Cite as

Reconfiguration patterns of large-scale brain networks in motor imagery

  • Tao Zhang
  • Fei Wang
  • Mengchen Li
  • Fali Li
  • Ying Tan
  • Yangsong Zhang
  • Hang Yang
  • Bharat BiswalEmail author
  • Dezhong Yao
  • Peng XuEmail author
Original Article
  • 191 Downloads

Abstract

Motor imagery (MI) is a multidimensional cognitive ability which recruited multiple brain networks. However, how connections and interactions are adjusted among distributed networks during MI remains unknown. To investigate these issues, we analyze the reconfiguration patterns of large-scale networks for different MI states. In our work, we explored the specific patterns of large-scale functional network organization from rest to different MI tasks using group independent component analysis (ICA), and evaluated the potential relationships between MI and the patterns of large-scale networks. The results indicate that task-related large-scale networks show the balanced relation between the within- and between-network connectivities during MI, and reveal the somatomotor network and dorsal attention network play critical roles in switching context-specific MI, and also demonstrate the change of large-scale networks organization toward effective topology could facilitate MI performance. Moreover, based on the large-scale network connectivities, we could differentiate an individual’s three states (i.e., left-hand MI, right-hand MI and rest) with an 72.73% accuracy using a multi-variant pattern analysis, suggesting that the specific patterns of large-scale network can also provide potential biomarkers to predict an individual’s behavior. Our findings contribute to the further understanding of the neural mechanisms underlying MI from large-scale network patterns and provide new biomarkers to predict the individual’s behaviors.

Keywords

Motor imagery Large-scale network ICA Machine learning 

Notes

Funding

This work was supported in part by grants from the National Natural Science Foundation of China (#61522105, #81330032); Sichuan Science and Technology Program, Grant/Award Number: 2018JY0526.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study involving human participants have been approved by the local ethics committee of the institute and are in agreement with the 1964 Helsinki declaration and its later amendments.

Informed consent

All participants gave their informed consent to participate in the study.

Supplementary material

429_2018_1786_MOESM1_ESM.docx (1.3 mb)
Supplementary material 1 (DOCX 1295 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science InstituteUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.Xihua UniversityChengduChina
  3. 3.Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
  4. 4.School of Computer Science and TechnologySouthwest University of Science and TechnologyMianyangChina
  5. 5.Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkUSA

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