TY - JOUR
T1 - Discriminative analysis of schizophrenia patients using an integrated model combining 3D CNN with 2D CNN
T2 - A multimodal MR image and connectomics analysis
AU - Guo, Haiman
AU - Jian, Shuyi
AU - Zhou, Yubin
AU - Chen, Xiaoyi
AU - Chen, Jinbiao
AU - Zhou, Jing
AU - Huang, Yuanyuan
AU - Ma, Guolin
AU - Li, Xiaobo
AU - Ning, Yuping
AU - Wu, Fengchun
AU - Wu, Kai
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2024/1
Y1 - 2024/1
N2 - Objective: Few studies have applied deep learning to the discriminative analysis of schizophrenia (SZ) patients using the fusional features of multimodal MRI data. Here, we proposed an integrated model combining a 3D convolutional neural network (CNN) with a 2D CNN to classify SZ patients. Method: Structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data were acquired for 140 SZ patients and 205 normal controls. We computed structural connectivity (SC) from the sMRI data as well as functional connectivity (FC), amplitude of low-frequency fluctuation (ALFF), and regional homogeneity (ReHo) from the rs-fMRI data. The 3D images of T1, ReHo, and ALFF were used as the inputs for the 3D CNN model, while the SC and FC matrices were used as the inputs for the 2D CNN model. Moreover, we added squeeze and excitation blocks (SE-blocks) to each layer of the integrated model and used a support vector machine (SVM) to replace the softmax classifier. Results: The integrated model proposed in this study, using the fusional features of the T1 images, and the matrices of FC, showed the best performance. The use of the SE-blocks and SVM classifiers significantly improved the performance of the integrated model, in which the accuracy, sensitivity, specificity, area under the curve, and F1-score were 89.86%, 86.21%, 92.50%, 89.35%, and 87.72%, respectively. Conclusions: Our findings indicated that an integrated model combining 3D CNN with 2D CNN is a promising method to improve the classification performance of SZ patients and has potential for the clinical diagnosis of psychiatric diseases.
AB - Objective: Few studies have applied deep learning to the discriminative analysis of schizophrenia (SZ) patients using the fusional features of multimodal MRI data. Here, we proposed an integrated model combining a 3D convolutional neural network (CNN) with a 2D CNN to classify SZ patients. Method: Structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data were acquired for 140 SZ patients and 205 normal controls. We computed structural connectivity (SC) from the sMRI data as well as functional connectivity (FC), amplitude of low-frequency fluctuation (ALFF), and regional homogeneity (ReHo) from the rs-fMRI data. The 3D images of T1, ReHo, and ALFF were used as the inputs for the 3D CNN model, while the SC and FC matrices were used as the inputs for the 2D CNN model. Moreover, we added squeeze and excitation blocks (SE-blocks) to each layer of the integrated model and used a support vector machine (SVM) to replace the softmax classifier. Results: The integrated model proposed in this study, using the fusional features of the T1 images, and the matrices of FC, showed the best performance. The use of the SE-blocks and SVM classifiers significantly improved the performance of the integrated model, in which the accuracy, sensitivity, specificity, area under the curve, and F1-score were 89.86%, 86.21%, 92.50%, 89.35%, and 87.72%, respectively. Conclusions: Our findings indicated that an integrated model combining 3D CNN with 2D CNN is a promising method to improve the classification performance of SZ patients and has potential for the clinical diagnosis of psychiatric diseases.
KW - Brain connectivity
KW - Classification
KW - Convolutional neural network
KW - Integrated model
KW - Schizophrenia
UR - http://www.scopus.com/inward/record.url?scp=85180603155&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85180603155&partnerID=8YFLogxK
U2 - 10.1016/j.brainresbull.2023.110846
DO - 10.1016/j.brainresbull.2023.110846
M3 - Article
C2 - 38104672
AN - SCOPUS:85180603155
SN - 0361-9230
VL - 206
JO - Brain Research Bulletin
JF - Brain Research Bulletin
M1 - 110846
ER -