TY - JOUR
T1 - Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning
AU - Chen, Zhi Hong
AU - Yan, Tao
AU - Wang, Er Lei
AU - Jiang, Hong
AU - Tang, Yi Qian
AU - Yu, Xi
AU - Zhang, Jian
AU - Liu, Chang
AU - Rostro-Gonzalez, Horacio
N1 - Publisher Copyright:
© 2020 ZhiHong Chen et al.
PY - 2020
Y1 - 2020
N2 - Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. This study proposed an ML framework based on coarse-to-fine feature selection. The proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. The method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. The experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. The identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.
AB - Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. This study proposed an ML framework based on coarse-to-fine feature selection. The proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. The method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. The experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. The identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.
UR - http://www.scopus.com/inward/record.url?scp=85083030018&partnerID=8YFLogxK
U2 - 10.1155/2020/6405930
DO - 10.1155/2020/6405930
M3 - Article
C2 - 32300361
AN - SCOPUS:85083030018
SN - 1687-5265
VL - 2020
JO - Computational Intelligence and Neuroscience
JF - Computational Intelligence and Neuroscience
M1 - 6405930
ER -