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Aberrant white matter microstructure evaluation by automated fiber quantification in typhoon-related post-traumatic stress disorder

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Abstract

Super typhoons can lead to post-traumatic stress disorder (PTSD), which can adversely affect a person’s mental health after a disaster. Neuroimaging studies suggest that patients with PTSD may have post-exposure abnormalities of the white matter. However, little is known about these defects, if they are localized to specific regions of the white matter fibers, or whether they may be potential biomarkers for PTSD. Typhoon survivors with PTSD (n = 27), trauma-exposed controls (TEC) (n = 33), and healthy controls (HCs) (n = 30) were enrolled. We used automated fiber quantification (AFQ) to process the participants’ DTI and compared diffusion metrics among the three groups. To evaluate diagnostic value, we used support vector machine (SVM) and a random forest (RF) classifier to build a machine learning model. White matter fiber segmentation between the three groups was found to be statistically significant for the fractional anisotropy (FA) value of the right anterior thalamic radiation (ATR) (26—50 nodes) and right uncinate fasciculus (UF) (60—72 nodes) (FDR correction, p < 0.05). By analyzing the characteristics of the machine learning model, the two most important variables were the right ATR and right UF for differentiating PTSD and trauma-exposed controls (TEC) from the healthy controls (HC). In addition, the left cingulum cingulate and left UF were the most critical variables in the differentiation of PTSD and TEC. AFQ with machine learning can localize abnormalities in specific regions of white matter fibers. These regions may be used as a diagnostic biomarker for PTSD.

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Data availability

The datasets used during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank all the participants for their time and effort.

Funding

This study was supported by the National Nature Science Foundation of China (Grant No. 81971602, 82271977 for FC, Grant No. 82160327 for TL, and Grant No. 81871346 for WYH), Key Science and Technology Project of Hainan Province (ZDYF2021SHFZ239, ZDKJ2021042), Innovative research project for postgraduate students in Hainan Province (Qhyb2021-59 for YYZ), the Hainan Academician Innovation Platform Fund, and the Hainan Province Clinical Medical Center.

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Authors and Affiliations

  1. Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Hainan Medical University, No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People’s Republic of China

    Yiying Zhang, Huijuan Chen, Yihao Guo & Feng Chen

  2. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China

    Rongfeng Qi, Jun Ke, Qiang Xu, Yuan Zhong & Guangming Lu

  3. Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China

    Jun Ke

  4. School of Psychology, Nanjing Normal University, Nanjing, 210002, Jiangsu, China

    Yuan Zhong

  5. MR Collaboration, Siemens Healthineers Ltd, Beijing, 100000, China

    Yanglei Wu

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  1. Yiying Zhang
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Contributions

YYZ, HC and FC contributed to the conception and design of this study. YYZ, HC, RQ, JK, and FC performed the statistical analysis and wrote the manuscript. YYZ, HC, RQ, JK, QX, YZ, and GL performed the experiments. YYZ wrote the first draft of the manuscript. YW and YG have substantively revised the work. All authors contributed to manuscript revision and read and approved the submitted version.

Corresponding author

Correspondence to Feng Chen.

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This study was approved by the Ethics Committee of Hainan General Hospital of Hainan Medical University and the Ethics Committee of The Second Xiangya Hospital of Central South University.

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Zhang, Y., Chen, H., Qi, R. et al. Aberrant white matter microstructure evaluation by automated fiber quantification in typhoon-related post-traumatic stress disorder. Brain Imaging and Behavior 17, 213–222 (2023). https://doi.org/10.1007/s11682-022-00755-1

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