Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method.

Bo Peng,Zhiyong Zhou,Aditya Saxena,Jieru Lu,Suhong Wang,Tao Zhang,Yakang Dai

doi:10.3389/fncom.2017.00037

Abstract

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.

Highlights

Self-esteem is defined as the degree that people evaluate and accept themselves (Wang and Ollendick, 2001), which has effects on human health, average lifetime, and life satisfaction (Baumeister et al, 2003)
Multilevel regions of interest (ROIs) features consist of ROI features and similarity features, which are extracted from T1-weighted structural brain magnetic resonance (MR) images
The classification performance using different feature types between high self-esteem group and low self-esteem group is listed in Table 2, including classification accuracy (ACC), sensitivity (SEN), specificity (SPE), area under receiver operating characteristic curve (AUC), F-score (F), Youden’s index (Y), balanced accuracy (BAC), and paired t-test results on classification accuracy

Summary

Introduction

Self-esteem is defined as the degree that people evaluate and accept themselves (Wang and Ollendick, 2001), which has effects on human health, average lifetime, and life satisfaction (Baumeister et al, 2003). High self-esteem is associated with positive attitudes and behaviors, such as happiness, interpersonal success, ability to overcome difficulties, and healthy lifestyle (Baumeister et al, 2003). Based on brain MR images, most researchers use volumetric or cortical analysis method to study self-esteem related brain morphometry (Agroskin et al, 2014). Onoda et al (2010) find out that differences in brain connections are existed between low selfesteem group and high self-esteem group Except for these volumetric studies, cortical measurement is used for selfesteem (Somerville et al, 2010). More studies are required to further explain the relationship between self-esteem and brain morphometry

Methods

Results

Discussion

Conclusion