Automatic classification of first-episode, drug-naive schizophrenia with multi-modal magnetic resonance imaging_Journal of Biomedical Engineering

Authors：

YANG Yongzhe ^1,2,3 , ZHANG Yue ² , WU Fengchun ^4,5 , LU Xiaobing ^4,5 , NING Yuping ^4,5 , HUANG Biao ³ , DU Xin ² , LI Chengwei ² , WANG Kaixi ² , WU Xiaoming ² ,  WU Kai ^2,5

1. School of Medicine, South China University of Technology (SCUT), Guangzhou 510006, P.R.China;
2. Department of Biomedical Engineering, School of Materials Science and Engineering, SCUT, Guangzhou 510006, P.R.China;
3. Department of Radiology, Guangdong General Hospital, Guangzhou 510080, P.R.China;
4. Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)/Guangzhou Brain Hospital (GBH), Guangzhou 510370, P.R.China;
5. GBH-SCUT Joint Research Centre for Neuroimaging, Guangzhou 510370, P.R.China;

Corresponding author：

WU Kai, Email: kaiwu@scut.edu.cn

Keywords：

first-episode schizophrenia; multi-modal magnetic resonance imaging; support vector machine; recursive feature elimination

DOI：

10.7507/1001-5515.201607084

Video：

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A great number of studies have demonstrated the structural and functional abnormalities in chronic schizophrenia (SZ) patients. However, few studies analyzed the differences between first-episode, drug-naive SZ (FESZ) patients and normal controls (NCs). In this study, we recruited 44 FESZ patients and 56 NCs, and acquired their multi-modal magnetic resonance imaging (MRI) data, including structural and resting-state functional MRI data. We calculated gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low frequency fluctuation (ALFF), and degree centrality (DC) of 90 brain regions, basing on an automated anatomical labeling (AAL) atlas. We then applied these features into support vector machine (SVM) combined with recursive feature elimination (RFE) to discriminate FESZ patients from NCs. Our results showed that the classifier using the combination of ReHo and ALFF as input features achieved the best performance (an accuracy of 96.97%). Moreover, the most discriminative features for classification were predominantly located in the frontal lobe. Our findings may provide potential information for understanding the neuropathological mechanism of SZ and facilitate the development of biomarkers for computer-aided diagnosis of SZ patients.

Citation： YANG Yongzhe, ZHANG Yue, WU Fengchun, LU Xiaobing, NING Yuping, HUANG Biao, DU Xin, LI Chengwei, WANG Kaixi, WU Xiaoming, WU Kai. Automatic classification of first-episode, drug-naive schizophrenia with multi-modal magnetic resonance imaging. Journal of Biomedical Engineering, 2017, 34(5): 674-680. doi: 10.7507/1001-5515.201607084 Copy

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2.	Glahn D C, Laird A R, Ellison-Wright I, et al. Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis. Biol Psychiatry, 2008, 64(9): 774-781.
3.	Gupta C N, Calhoun V D, Rachakonda S A, et al. Patterns of gray matter abnormalities in schizophrenia based on an international mega-analysis. Schizophr Bull, 2015, 41(5): 1133-1142.
4.	Xu Lai, Groth K M, Pearlson G, et al. Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia. Hum Brain Mapp, 2009, 30(3): 711-724.
5.	Antonova E, Kumari V, Morris R, et al. The relationship of structural alterations to cognitive deficits in schizophrenia: a voxel-based morphometry study. Biol Psychiatry, 2005, 58(6): 457-467.
6.	Kreczmanski P, Heinsen H, Mantua V, et al. Volume, neuron density and total neuron number in five subcortical regions in schizophrenia. Brain, 2007, 130(Pt 3): 678-692.
7.	Xu Yongjie, Zhuo Chuanjun, Qin Wen, et al. Altered spontaneous brain activity in schizophrenia: a meta-analysis and a large-sample study. Biomed Res Int, 2015(2015): Article ID 204628.
8.	Yu Rongjun, Hsieh M H, Wang H L, et al. Frequency dependent alterations in regional homogeneity of baseline brain activity in schizophrenia. PLoS One, 2013, 8(3): e57516.
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10.	Zhang Wenjing, Deng Wei, Yao Li, et al. Brain structural abnormalities in a group of never-medicated patients with long-term schizophrenia. Am J Psychiatry, 2015, 172(10): 995-1003.
11.	Nakamura M, Salisbury D F, Hirayasu Y, et al. Neocortical gray matter volume in first-episode schizophrenia and first-episode affective psychosis: a cross-sectional and longitudinal MRI study. Biol Psychiatry, 2007, 62(7): 773-783.
12.	Dai Zhengjia, Yan Chaogan, Wang Zhiqun, et al. Discriminative analysis of early Alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier (M3) Neuroimage, 2012, 59(3): 2187-2195.
13.	Nieuwenhuis M, van Haren N E, Hulshoff Pol H E, et al. Classification of schizophrenia patients and healthy controls from structural MRI scans in two large Independent samples. Neuroimage, 2012, 61(3): 606-612.
14.	Chi Minyue, Guo Shengwen, Ning Yuping, et al. Discriminative analysis of major depressive disorder and anxious depression using support vector machine. J Comput Theor Nanosci, 2015, 12(7, SI): 1395-1401.
15.	Shen Hui, Wang Lubin, Liu Yadong, et al. Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. Neuroimage, 2010, 49(4): 3110-3121.
16.	Ding Xiaoyu, Yang Yihong, Stein E A, et al. Multivariate classification of smokers and nonsmokers using SVM-RFE on structural MRI images. Hum Brain Mapp, 2015, 36(12): 4869-4879.
17.	Raichle M E, Mintun M A. Brain work and brain imaging. Annu Rev Neurosci, 2006, 29: 449-476.
18.	Zang Yufeng, Jiang Tianzi, Lu Yingli, et al. Regional homogeneity approach to fMRI data analysis. Neuroimage, 2004, 22(1): 394-400.
19.	Cherkassky V. The nature of statistical learning theory~. IEEE Trans Neural Netw, 1997, 8(6): 1564.
20.	Golland P, Fischl B. Permutation tests for classification: towards statistical significance in image-based studies. Inf Process Med Imaging, 2003, 18: 330-341.
21.	Meisenzahl E M, Koutsouleris N, Bottlender R, et al. Structural brain alterations at different stages of schizophrenia: A voxel-based morphometric study. Schizophr Res, 2008, 104(1/3): 44-60.
22.	Nielsen M O, Rostrup E, Wulff S, et al. Improvement of brain reward abnormalities by antipsychotic monotherapy in schizophrenia. Arch Gen Psychiatry, 2012, 69(12): 1195-1204.
23.	Keedy S K, Rosen C, Khine T, et al. An fMRI study of visual attention and sensorimotor function before and after antipsychotic treatment in first-episode schizophrenia. Psychiatry Res, 2009, 172(1): 16-23.
24.	Zhou Yuan, Liang Meng, Jiang Tianzi, et al. Functional dysconnectivity of the dorsolateral prefrontal cortex in first-episode schizophrenia using resting-state fMRI. Neurosci Lett, 2007, 417(3): 297-302.
25.	Shad M U, Muddasani S, Keshavan M S. Prefrontal subregions and dimensions of insight in first-episode schizophrenia--A pilot study. Psychiatry Res, 2006, 146(1): 35-42.
26.	Ohtani T, Bouix S, Lyall A E, et al. Abnormal white matter connections between medial frontal regions predict symptoms in patients with first episode schizophrenia. Cortex, 2015, 71: 264-276.
27.	Zhou Yuan, Fan Lingzhong, Qiu Chenxiang, et al. Prefrontal cortex and the dysconnectivity hypothesis of schizophrenia. Neurosci Bull, 2015, 31(2): 207-219.

1. Friston K J. Schizophrenia and the disconnection hypothesis. Acta Psychiatr Scand Suppl, 1999, 99(395): 68-79.
2. Glahn D C, Laird A R, Ellison-Wright I, et al. Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis. Biol Psychiatry, 2008, 64(9): 774-781.
3. Gupta C N, Calhoun V D, Rachakonda S A, et al. Patterns of gray matter abnormalities in schizophrenia based on an international mega-analysis. Schizophr Bull, 2015, 41(5): 1133-1142.
4. Xu Lai, Groth K M, Pearlson G, et al. Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia. Hum Brain Mapp, 2009, 30(3): 711-724.
5. Antonova E, Kumari V, Morris R, et al. The relationship of structural alterations to cognitive deficits in schizophrenia: a voxel-based morphometry study. Biol Psychiatry, 2005, 58(6): 457-467.
6. Kreczmanski P, Heinsen H, Mantua V, et al. Volume, neuron density and total neuron number in five subcortical regions in schizophrenia. Brain, 2007, 130(Pt 3): 678-692.
7. Xu Yongjie, Zhuo Chuanjun, Qin Wen, et al. Altered spontaneous brain activity in schizophrenia: a meta-analysis and a large-sample study. Biomed Res Int, 2015(2015): Article ID 204628.
8. Yu Rongjun, Hsieh M H, Wang H L, et al. Frequency dependent alterations in regional homogeneity of baseline brain activity in schizophrenia. PLoS One, 2013, 8(3): e57516.
9. Shenton M E, Dickey C C, Frumin M, et al. A review of MRI findings in schizophrenia. Schizophr Res, 2001, 49(1/2): 1-52.
10. Zhang Wenjing, Deng Wei, Yao Li, et al. Brain structural abnormalities in a group of never-medicated patients with long-term schizophrenia. Am J Psychiatry, 2015, 172(10): 995-1003.
11. Nakamura M, Salisbury D F, Hirayasu Y, et al. Neocortical gray matter volume in first-episode schizophrenia and first-episode affective psychosis: a cross-sectional and longitudinal MRI study. Biol Psychiatry, 2007, 62(7): 773-783.
12. Dai Zhengjia, Yan Chaogan, Wang Zhiqun, et al. Discriminative analysis of early Alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier (M3) Neuroimage, 2012, 59(3): 2187-2195.
13. Nieuwenhuis M, van Haren N E, Hulshoff Pol H E, et al. Classification of schizophrenia patients and healthy controls from structural MRI scans in two large Independent samples. Neuroimage, 2012, 61(3): 606-612.
14. Chi Minyue, Guo Shengwen, Ning Yuping, et al. Discriminative analysis of major depressive disorder and anxious depression using support vector machine. J Comput Theor Nanosci, 2015, 12(7, SI): 1395-1401.
15. Shen Hui, Wang Lubin, Liu Yadong, et al. Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. Neuroimage, 2010, 49(4): 3110-3121.
16. Ding Xiaoyu, Yang Yihong, Stein E A, et al. Multivariate classification of smokers and nonsmokers using SVM-RFE on structural MRI images. Hum Brain Mapp, 2015, 36(12): 4869-4879.
17. Raichle M E, Mintun M A. Brain work and brain imaging. Annu Rev Neurosci, 2006, 29: 449-476.
18. Zang Yufeng, Jiang Tianzi, Lu Yingli, et al. Regional homogeneity approach to fMRI data analysis. Neuroimage, 2004, 22(1): 394-400.
19. Cherkassky V. The nature of statistical learning theory~. IEEE Trans Neural Netw, 1997, 8(6): 1564.
20. Golland P, Fischl B. Permutation tests for classification: towards statistical significance in image-based studies. Inf Process Med Imaging, 2003, 18: 330-341.
21. Meisenzahl E M, Koutsouleris N, Bottlender R, et al. Structural brain alterations at different stages of schizophrenia: A voxel-based morphometric study. Schizophr Res, 2008, 104(1/3): 44-60.
22. Nielsen M O, Rostrup E, Wulff S, et al. Improvement of brain reward abnormalities by antipsychotic monotherapy in schizophrenia. Arch Gen Psychiatry, 2012, 69(12): 1195-1204.
23. Keedy S K, Rosen C, Khine T, et al. An fMRI study of visual attention and sensorimotor function before and after antipsychotic treatment in first-episode schizophrenia. Psychiatry Res, 2009, 172(1): 16-23.
24. Zhou Yuan, Liang Meng, Jiang Tianzi, et al. Functional dysconnectivity of the dorsolateral prefrontal cortex in first-episode schizophrenia using resting-state fMRI. Neurosci Lett, 2007, 417(3): 297-302.
25. Shad M U, Muddasani S, Keshavan M S. Prefrontal subregions and dimensions of insight in first-episode schizophrenia--A pilot study. Psychiatry Res, 2006, 146(1): 35-42.
26. Ohtani T, Bouix S, Lyall A E, et al. Abnormal white matter connections between medial frontal regions predict symptoms in patients with first episode schizophrenia. Cortex, 2015, 71: 264-276.
27. Zhou Yuan, Fan Lingzhong, Qiu Chenxiang, et al. Prefrontal cortex and the dysconnectivity hypothesis of schizophrenia. Neurosci Bull, 2015, 31(2): 207-219.

Journal of Biomedical Engineering

Automatic classification of first-episode, drug-naive schizophrenia with multi-modal magnetic resonance imaging

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