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Analyzing brain structural differences among undergraduates with different grades of self-esteem using multiple anatomical brain network

  • Peng, Bo1, 2, 3
  • Pang, Gaofeng4
  • Saxena, Aditya5
  • Liu, Yan1, 2, 3
  • Hu, Baohua1, 2, 3
  • Wang, Suhong4
  • Dai, Yakang1, 2, 3
  • 1 Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences,
  • 2 Suzhou Key Laboratory of Medical and Health Information Technology, Suzhou, China
  • 3 Jinan Guoke Medical Engineering Technology Development Co., LTD, Jinan, China
  • 4 The Third Affiliated Hospital of Soochow University,
  • 5 Trauma Center, Khandwa District Hospital, Khandwa, India
Published Article
BioMedical Engineering OnLine
Springer (Biomed Central Ltd.)
Publication Date
Feb 12, 2021
DOI: 10.1186/s12938-021-00853-z
PMID: 33579302
PMCID: PMC7881471
PubMed Central


Background Self-esteem is the individual evaluation of oneself. People with high self-esteem grade have mental health and can bravely cope with the threats from the environment. With the development of neuroimaging techniques, researches on cognitive neural mechanisms of self-esteem are increased. Existing methods based on brain morphometry and single-layer brain network cannot characterize the subtle structural differences related to self-esteem. Method To solve this issue, we proposed a multiple anatomical brain network based on multi-resolution region of interest (ROI) template to study the brain structural connections of self-esteem. The multiple anatomical brain network consists of ROI features and hierarchal brain network features that are extracted from structural MRI. For each layer, we calculated the correlation relationship between pairs of ROIs. In order to solve the high-dimensional problem caused by the large amount of network features, feature selection methods ( t -test, mRMR, and SVM-RFE) are adopted to reduce the number of features while retaining discriminative information to the maximum extent. Multi-kernel SVM is employed to integrate the various types of features by appropriate weight coefficient. Result The experimental results show that the proposed method can improve classification accuracy to 97.26% compared with single-layer brain network. Conclusions The proposed method provides a new perspective for the analysis of brain structural differences of self-esteem, which also has potential guiding significance in other researches involved brain cognitive activity and brain disease diagnosis.

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