TY - GEN
T1 - Discriminating schizophrenia from normal controls using resting state functional network connectivity
T2 - 2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017
AU - Yan, Weizheng
AU - Plis, Sergey
AU - Calhoun, Vince D.
AU - Liu, Shengfeng
AU - Jiang, Rongtao
AU - Jiang, Tian Zi
AU - Sui, Jing
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/5
Y1 - 2017/12/5
N2 - Deep learning has gained considerable attention in the scientific community, breaking benchmark records in many fields such as speech and visual recognition [1]. Motivated by extending advancement of deep learning approaches to brain imaging classification, we propose a framework, called 'deep neural network (DNN)+ layer-wise relevance propagation (LRP)', to distinguish schizophrenia patients (SZ) from healthy controls (HCs) using functional network connectivity (FNC). 1100 Chinese subjects of 7 sites are included, each with a 50<50 FNC matrix resulted from group ICA on resting-state fMRI data. The proposed DNN+LRP not only improves classification accuracy significantly compare to four state-of-the-art classification methods (84% vs. less than 79%, 10 folds cross validation) but also enables identification of the most contributing FNC patterns related to SZ classification, which cannot be easily traced back by general DNN models. By conducting LRP, we identified the FNC patterns that exhibit the highest discriminative power in SZ classification. More importantly, when using leave-one-site-out cross validation (using 6 sites for training, 1 site for testing, 7 times in total), the cross-site classification accuracy reached 82%, suggesting high robustness and generalization performance of the proposed method, promising a wide utility in the community and great potentials for biomarker identification of brain disorders.
AB - Deep learning has gained considerable attention in the scientific community, breaking benchmark records in many fields such as speech and visual recognition [1]. Motivated by extending advancement of deep learning approaches to brain imaging classification, we propose a framework, called 'deep neural network (DNN)+ layer-wise relevance propagation (LRP)', to distinguish schizophrenia patients (SZ) from healthy controls (HCs) using functional network connectivity (FNC). 1100 Chinese subjects of 7 sites are included, each with a 50<50 FNC matrix resulted from group ICA on resting-state fMRI data. The proposed DNN+LRP not only improves classification accuracy significantly compare to four state-of-the-art classification methods (84% vs. less than 79%, 10 folds cross validation) but also enables identification of the most contributing FNC patterns related to SZ classification, which cannot be easily traced back by general DNN models. By conducting LRP, we identified the FNC patterns that exhibit the highest discriminative power in SZ classification. More importantly, when using leave-one-site-out cross validation (using 6 sites for training, 1 site for testing, 7 times in total), the cross-site classification accuracy reached 82%, suggesting high robustness and generalization performance of the proposed method, promising a wide utility in the community and great potentials for biomarker identification of brain disorders.
KW - Deep neural network
KW - Functional network connectivity
KW - Layer-wise relevance propagation
KW - Schizophrenia
UR - http://www.scopus.com/inward/record.url?scp=85042335303&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85042335303&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2017.8168179
DO - 10.1109/MLSP.2017.8168179
M3 - Conference contribution
AN - SCOPUS:85042335303
T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
SP - 1
EP - 6
BT - 2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings
A2 - Ueda, Naonori
A2 - Chien, Jen-Tzung
A2 - Matsui, Tomoko
A2 - Larsen, Jan
A2 - Watanabe, Shinji
PB - IEEE Computer Society
Y2 - 25 September 2017 through 28 September 2017
ER -