Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method
Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method.Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter vo...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2017-05-01
|
| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fncom.2017.00037/full |
| _version_ | 1811297140225343488 |
|---|---|
| author | Bo Peng Bo Peng Bo Peng Jieru Lu Aditya Saxena Zhiyong Zhou Tao Zhang Suhong Wang Yakang Dai |
| author_facet | Bo Peng Bo Peng Bo Peng Jieru Lu Aditya Saxena Zhiyong Zhou Tao Zhang Suhong Wang Yakang Dai |
| author_sort | Bo Peng |
| collection | DOAJ |
| description | Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method.Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii) similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs) with appropriate weighting factor.Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features that are related to self-esteem spread over occipital lobe, frontal lobe, parietal lobe, limbic lobe, temporal lobe, and central region, mainly involving white matter and cortical thickness. The most discriminating similarity features are distributed in both the right and left hemisphere, including frontal lobe, occipital lobe, limbic lobe, parietal lobe, and central region, which conveys information of structural connections between different brain regions.Conclusion: By using ROI features and similarity features to exam self-esteem related brain morphometry, this paper provides a pilot evidence that self-esteem is linked to specific ROIs and structural connections between different brain regions. |
| first_indexed | 2024-04-13T05:59:36Z |
| format | Article |
| id | doaj.art-7b7f42236c45482ea0ee808a1e79062e |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-13T05:59:36Z |
| publishDate | 2017-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-7b7f42236c45482ea0ee808a1e79062e2022-12-22T02:59:29ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882017-05-011110.3389/fncom.2017.00037238432Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification MethodBo Peng0Bo Peng1Bo Peng2Jieru Lu3Aditya Saxena4Zhiyong Zhou5Tao Zhang6Suhong Wang7Yakang Dai8Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesSuzhou, ChinaUniversity of Chinese Academy of SciencesBeijing, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of SciencesChangchun, ChinaSchool of Information Science and Engineering, Changzhou UniversityChangzhou, ChinaTrauma Center, Khandwa District HospitalKhandwa, IndiaSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesSuzhou, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of SciencesChangchun, ChinaDepartment of Neuroscience, The Third Affiliated Hospital of Soochow UniversityChangzhou, ChinaSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesSuzhou, ChinaPurpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method.Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii) similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs) with appropriate weighting factor.Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features that are related to self-esteem spread over occipital lobe, frontal lobe, parietal lobe, limbic lobe, temporal lobe, and central region, mainly involving white matter and cortical thickness. The most discriminating similarity features are distributed in both the right and left hemisphere, including frontal lobe, occipital lobe, limbic lobe, parietal lobe, and central region, which conveys information of structural connections between different brain regions.Conclusion: By using ROI features and similarity features to exam self-esteem related brain morphometry, this paper provides a pilot evidence that self-esteem is linked to specific ROIs and structural connections between different brain regions.http://journal.frontiersin.org/article/10.3389/fncom.2017.00037/fullself-esteemmagnetic resonance imaging (MRI)multilevel ROI featuresbrain connectionsmulti-kernel support vector machine |
| spellingShingle | Bo Peng Bo Peng Bo Peng Jieru Lu Aditya Saxena Zhiyong Zhou Tao Zhang Suhong Wang Yakang Dai Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method Frontiers in Computational Neuroscience self-esteem magnetic resonance imaging (MRI) multilevel ROI features brain connections multi-kernel support vector machine |
| title | Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method |
| title_full | Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method |
| title_fullStr | Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method |
| title_full_unstemmed | Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method |
| title_short | Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method |
| title_sort | examining brain morphometry associated with self esteem in young adults using multilevel roi features based classification method |
| topic | self-esteem magnetic resonance imaging (MRI) multilevel ROI features brain connections multi-kernel support vector machine |
| url | http://journal.frontiersin.org/article/10.3389/fncom.2017.00037/full |
| work_keys_str_mv | AT bopeng examiningbrainmorphometryassociatedwithselfesteeminyoungadultsusingmultilevelroifeaturesbasedclassificationmethod AT bopeng examiningbrainmorphometryassociatedwithselfesteeminyoungadultsusingmultilevelroifeaturesbasedclassificationmethod AT bopeng examiningbrainmorphometryassociatedwithselfesteeminyoungadultsusingmultilevelroifeaturesbasedclassificationmethod AT jierulu examiningbrainmorphometryassociatedwithselfesteeminyoungadultsusingmultilevelroifeaturesbasedclassificationmethod AT adityasaxena examiningbrainmorphometryassociatedwithselfesteeminyoungadultsusingmultilevelroifeaturesbasedclassificationmethod AT zhiyongzhou examiningbrainmorphometryassociatedwithselfesteeminyoungadultsusingmultilevelroifeaturesbasedclassificationmethod AT taozhang examiningbrainmorphometryassociatedwithselfesteeminyoungadultsusingmultilevelroifeaturesbasedclassificationmethod AT suhongwang examiningbrainmorphometryassociatedwithselfesteeminyoungadultsusingmultilevelroifeaturesbasedclassificationmethod AT yakangdai examiningbrainmorphometryassociatedwithselfesteeminyoungadultsusingmultilevelroifeaturesbasedclassificationmethod |