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Characterization of Post-traumatic Stress Disorder Using Resting-State fMRI with a Multi-level Parametric Classification Approach

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Abstract

Functional neuroimaging studies have found intra-regional activity and inter-regional connectivity alterations in patients with post-traumatic stress disorder (PTSD). However, the results of these studies are based on group-level statistics and therefore it is unclear whether PTSD can be discriminated at single-subject level, for instance using the machine learning approach. Here, we proposed a novel framework to identify PTSD using multi-level measures derived from resting-state functional MRI (fMRI). Specifically, three levels of measures were extracted as classification features: (1) regional amplitude of low-frequency fluctuations (univariate feature), which represents local spontaneous synchronous neural activity; (2) temporal functional connectivity (bivariate feature), which represents the extent of similarity of local activity between two regions, and (3) spatial functional connectivity (multivariate feature), which represents the extent of similarity of temporal correlation maps between two regions. Our method was evaluated on 20 PTSD patients and 20 demographically matched healthy controls. The experimental results showed that the features of each level could successfully discriminate PTSD patients from healthy controls. Furthermore, the combination of multi-level features using multi-kernel learning can further improve the classification performance. Specifically, the classification accuracy obtained by the proposed framework was 92.5 %, which was an increase of at least 5 and 17.5 % from the two-level and single-level feature based methods, respectively. Particularly, the limbic structure and prefrontal cortex provided the most discriminant features for classification, consistent with results reported in previous studies. Together, this study demonstrated for the first time that patients with PTSD can be identified at the individual level using resting-state fMRI data. The promising classification results indicated that this method may provide a complementary approach for improving the clinical diagnosis of PTSD.

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Acknowledgments

The authors thank the three anonymous reviewers for the constructive suggestions. This work was supported by the 973 project (2012CB517901), the Natural Science Foundation of China (Nos. 61125304, 61035006, 61273361, 30870696 and 81301279), the China Scholarship Council (No. 2011607033), the Scholarship Award for Excellent Doctoral Student granted by Ministry of Education (No. A03003023901010), the Fundamental Research Funds for the Central Universities (ZYGX2013Z004), the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20120185110028), the Starting Fund of Third Military University (TMMU2009XHG01), Natural Science Foundation of Chongqing (CSTC2009BB5019) and the Key Technology R&D Program of Sichuan Province (2012SZ0159, Science & Technology Department of Sichuan Province).

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Authors and Affiliations

  1. Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611054, Sichuan, People’s Republic of China

    Feng Liu, Yifeng Wang, Zhiliang Long, Heng Chen, Meiling Li, Xujun Duan & Huafu Chen

  2. Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, People’s Republic of China

    Feng Liu

  3. Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing, 400038, People’s Republic of China

    Bing Xie

  4. Department of Anatomy, Third Military Medical University, Chongqing, 400038, People’s Republic of China

    Bing Xie

  5. Mental Health Center, The First Affiliated Hospital, Guangxi Medical University, Nanning, 410011, Guangxi, People’s Republic of China

    Wenbin Guo

  6. Department of Psychiatry, University of Cape Town, Cape Town, 7925, South Africa

    Jean-Paul Fouche

  7. Department of Human Biology, University of Cape Town, Cape Town, 7925, South Africa

    Jean-Paul Fouche

  8. Department of Psychiatry, University of Stellenbosch, Cape Town, 7925, South Africa

    Jean-Paul Fouche

  9. School of Sciences, Tianjin Polytechnic University, Tianjin, 300130, People’s Republic of China

    Wenqin Wang

  10. School of Electrical Engineering and Information, Sichuan University, Chengdu, 610065, People’s Republic of China

    Jiang Zhang

  11. Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, 400038, People’s Republic of China

    Mingguo Qiu

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  1. Feng Liu
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Correspondence to Huafu Chen.

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Feng Liu and Bing Xie contributed equally to this work.

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Liu, F., Xie, B., Wang, Y. et al. Characterization of Post-traumatic Stress Disorder Using Resting-State fMRI with a Multi-level Parametric Classification Approach. Brain Topogr 28, 221–237 (2015). https://doi.org/10.1007/s10548-014-0386-2

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