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Brain Structure and Function

, Volume 223, Issue 4, pp 1849–1862 | Cite as

Plasticity in deep and superficial white matter: a DTI study in world class gymnasts

  • Feng Deng
  • Ling Zhao
  • Chunlei Liu
  • Min Lu
  • Shufei Zhang
  • Huiyuan Huang
  • Lixiang Chen
  • Xiaoyan Wu
  • Chen Niu
  • Yuan He
  • Jun Wang
  • Ruiwang Huang
Original Article

Abstract

Brain white matter (WM) could be generally categorized into two types, deep and superficial WM. Studies combining these two types WM are important for a better understanding of brain plasticity induced by motor training. In this study, we applied both univariate and multivariate approaches to study gymnastic training-induced plasticity in brain WM. Specifically, we acquired diffusion tensor imaging data from 13 world class gymnasts and 14 non-athlete normal controls, reconstructed brain deep and superficial WM tracts, estimated and compared their fractional anisotropy (FA) difference between the two groups. Taking FA values as the features, we applied logistic regression and support vector machine to distinguish the gymnasts from the controls. Compared to the controls, the gymnasts showed lower FA in four regional deep WM tracts, including the occipital lobe portion of left inferior fronto-occipital fasciculus (IFOF.L), occipital and temporal lobe portion of right inferior longitudinal fasciculus (ILF.R), insular cortex portion of right uncinate fasciculus (UF.R), and parietal lobe portion of right arcuate fasciculus (AF.R). Meanwhile, we found lower FA in the superficial U-shaped tracts within the frontal lobe in the gymnasts compared to the controls. In addition, we detected that mean FA in either the AF.R or the U-shaped tracts connecting the left pars triangularis and superior frontal gyrus was negatively correlated with years of training in the gymnasts. Classification analyses indicated FA in deep WM hold higher potential to distinguish the gymnasts from the controls. Overall, our findings provide a more complete picture of training-induced plasticity in brain WM.

Keywords

Neuroplasticity Tractography Logistic regression Support vector machine (SVM) 

Abbreviations

ATR

anterior thalamic radiation

CST

corticospinal tract

CGC

cingulum cingulate

IFOF

Inferior fronto-occipital fasciculus

ILF

Inferior longitudinal fasciculus

SLF

Superior longitudinal fasciculus

UF

Uncinate fasciculus

AF

Arcuate fasciculus

Tr

Pars triangularis

SF

Superior frontal

RMF

Rostral middle frontal

Op

Pars opercularis

WCGs

World class gymnasts

NCs

Non-athlete normal controls

FA

Fractional anisotropy

WM

Gray matter

GM

Gray matter

Notes

Acknowledgements

This work was supported by funding from the National Natural Science Foundation of China (Grant Numbers: 81371535, 81271548, 81428013, and 81471654).

Compliance with ethical standards

Conflict of interest

We declare that we have no competing financial interests.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Review Board of the State Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University (BNU) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

429_2017_1594_MOESM1_ESM.docx (213 kb)
Supplementary material 1 (DOCX 213 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Feng Deng
    • 1
  • Ling Zhao
    • 1
  • Chunlei Liu
    • 2
    • 3
  • Min Lu
    • 4
  • Shufei Zhang
    • 1
  • Huiyuan Huang
    • 1
  • Lixiang Chen
    • 1
  • Xiaoyan Wu
    • 1
  • Chen Niu
    • 1
  • Yuan He
    • 1
  • Jun Wang
    • 5
  • Ruiwang Huang
    • 1
  1. 1.Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, School of PsychologySouth China Normal UniversityGuangzhouPeople’s Republic of China
  2. 2.Department of Electrical Engineering and Computer SciencesUniversity of CaliforniaBerkeleyUSA
  3. 3.Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyUSA
  4. 4.CAS Key Laboratory of Mental Health, Institute of PsychologyChinese Academy of SciencesBeijingPeople’s Republic of China
  5. 5.State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingPeople’s Republic of China

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