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Bo Peng
SIBET, CAS, China
Corresponding Author
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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 multiple anatomical brain network method based on multi-resolution region of interest (ROI) template to study the structural connections of self-esteem. The multiple anatomical brain network consist 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 structure differences of self-esteem, which also has potential guiding significance in other researches involved brain cognitive activity and brain disease diagnosis.
Keywords
structural magnetic resonance imaging, multi-resolution ROI, hierarchal brain network, machine learning method, self-esteem
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CURRENT STATUS: Published
View the published article at BioMedical Engineering OnLine.
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Posted 10 Feb, 2021
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Editor assigned
On 21 Jan, 2021
Editorial decision: Accept
On 21 Jan, 2021
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On 21 Jan, 2021
Editor invited
On 21 Jan, 2021
No comments provided
Editorial decision: Minor revision
On 13 Dec, 2020
Review #1 received
Received 04 Dec, 2020
Review #2 received
Received 26 Nov, 2020
Review #3 received
Received 26 Nov, 2020
Reviewer #3 agreed
On 19 Nov, 2020
Reviewer #2 agreed
On 19 Nov, 2020
Reviewers invited
Invitations sent on 16 Nov, 2020
Reviewer #1 agreed
On 16 Nov, 2020
Editor assigned
On 21 Oct, 2020
Submission checks complete
On 20 Oct, 2020
Editor invited
On 20 Oct, 2020
No comments provided
Review #1 received
Received 11 Sep, 2020
Editorial decision: Major revision
On 11 Sep, 2020
Review #4 received
Received 28 Aug, 2020
Review #3 received
Received 25 Aug, 2020
Review #2 received
Received 24 Aug, 2020
Reviewer #3 agreed
On 21 Aug, 2020
Reviewer #4 agreed
On 21 Aug, 2020
Reviewer #2 agreed
On 20 Aug, 2020
Editor assigned
On 18 Aug, 2020
Reviewers invited
Invitations sent on 18 Aug, 2020
Reviewer #1 agreed
On 18 Aug, 2020
Submission checks complete
On 17 Aug, 2020
Editor invited
On 17 Aug, 2020
No comments provided
Editor assigned
On 12 Aug, 2020
Editorial decision: Revise before peer review
On 12 Aug, 2020
First submitted
On 11 Aug, 2020
Submission checks complete
On 11 Aug, 2020
Editor invited
On 11 Aug, 2020
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Biomedical Engineering
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A new preprint design is coming!
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See the published version of this preprint at BioMedical Engineering OnLine.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Bo Peng
SIBET, CAS, China
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BioMedical Engineering OnLine.
Version 4
Posted 10 Feb, 2021
No community comments so far
Editor assigned
On 21 Jan, 2021
Editorial decision: Accept
On 21 Jan, 2021
Submission checks complete
On 21 Jan, 2021
Editor invited
On 21 Jan, 2021
No comments provided
Editorial decision: Minor revision
On 13 Dec, 2020
Review #1 received
Received 04 Dec, 2020
Review #2 received
Received 26 Nov, 2020
Review #3 received
Received 26 Nov, 2020
Reviewer #3 agreed
On 19 Nov, 2020
Reviewer #2 agreed
On 19 Nov, 2020
Reviewers invited
Invitations sent on 16 Nov, 2020
Reviewer #1 agreed
On 16 Nov, 2020
Editor assigned
On 21 Oct, 2020
Submission checks complete
On 20 Oct, 2020
Editor invited
On 20 Oct, 2020
No comments provided
Review #1 received
Received 11 Sep, 2020
Editorial decision: Major revision
On 11 Sep, 2020
Review #4 received
Received 28 Aug, 2020
Review #3 received
Received 25 Aug, 2020
Review #2 received
Received 24 Aug, 2020
Reviewer #3 agreed
On 21 Aug, 2020
Reviewer #4 agreed
On 21 Aug, 2020
Reviewer #2 agreed
On 20 Aug, 2020
Editor assigned
On 18 Aug, 2020
Reviewers invited
Invitations sent on 18 Aug, 2020
Reviewer #1 agreed
On 18 Aug, 2020
Submission checks complete
On 17 Aug, 2020
Editor invited
On 17 Aug, 2020
No comments provided
Editor assigned
On 12 Aug, 2020
Editorial decision: Revise before peer review
On 12 Aug, 2020
First submitted
On 11 Aug, 2020
Submission checks complete
On 11 Aug, 2020
Editor invited
On 11 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biomedical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BioMedical Engineering OnLine.
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 multiple anatomical brain network method based on multi-resolution region of interest (ROI) template to study the structural connections of self-esteem. The multiple anatomical brain network consist 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 structure differences of self-esteem, which also has potential guiding significance in other researches involved brain cognitive activity and brain disease diagnosis.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
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