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Multi-Atlas Based Early Prediction of Post-Traumatic Stress Disorder

Published: 13 October 2018 Publication History

Abstract

Individuals may develop into post-traumatic stress disorder (PTSD) within days to months after trauma exposure. However, if early intervention is taken, PTSD can be well prevented or delayed. This paper proposes a machine learning method associated with multiple atlases upon the multi-modal structural MRI images to predict high-risk patients with PTSD. Specifically, 72 participants underwent T1 and DTI scans within 48 hours after suffering traffic accidents. Thirty-two subjects were eventually diagnosed with PTSD. We first extracted T1 and DTI features within the regions-of-interest (ROIs) parcellated by multiple atlases. Those features were then fed into feature selection and classification to build an optimal model through which we can predict whether a new trauma-exposed subject would develop into PTSD. Our results demonstrated high performance (AUC: 0.8484, ACC: 0.8750), indicating the proposed method can produce an accurate and robust prediction of PTSD.

References

[1]
R. Yehuda, "Post-Traumatic Stress Disorder," The New England Journal of Medicine, vol. 346, no. 2, pp. 108--114, 2002.
[2]
R. C. Kessler, A. Sonnega, E. Bromet, M. Hughes, and C. B. Nelson, "Posttraumatic stress disorder in the National Comorbidity Survey," Archives of general psychiatry, vol. 52, no. 12, pp. 1048--1060, 1995/12//, 1995.
[3]
N. N. Jordan, C. W. Hoge, S. K. Tobler, J. Wells, G. J. Dydek, and W. E. Egerton, "Mental health impact of 9/11 Pentagon attack: validation of a rapid assessment tool," American journal of preventive medicine, vol. 26, no. 4, pp. 284--293, 2004/05//, 2004.
[4]
K. Magruder, T. Serpi, R. Kimerling, A. M. Kilbourne, J. F. Collins, Y. Cypel, S. M. Frayne, J. Furey, G. D. Huang, T. Gleason, M. J. Reinhard, A. Spiro, and H. Kang, "Prevalence of Posttraumatic Stress Disorder in Vietnam-Era Women Veterans: The Health of Vietnam-Era Women's Study (HealthVIEWS)," JAMA Psychiatry, vol. 72, no. 11, pp. 1127--34, Nov, 2015.
[5]
S. Charitaki, P. Pervanidou, J. Tsiantis, G. Chrousos, and G. Kolaitis, "Post-traumatic stress reactions in young victims of road traffic accidents," European Journal of Psychotraumatology, vol. 8, no. sup4, 2017, 2017.
[6]
R. K. Pitman, and D. L. Delahanty, "Conceptually driven pharmacologic approaches to acute trauma," CNS spectrums, vol. 10, no. 2, pp. 99--106, 2005/02//, 2005.
[7]
E. P. Slade, J. D. Gottlieb, W. Lu, P. T. Yanos, S. Rosenberg, S. M. Silverstein, S. K. Minsky, and K. T. Mueser, "Cost-Effectiveness of a PTSD Intervention Tailored for Individuals With Severe Mental Illness," Psychiatric services (Washington, D.C.), vol. 68, no. 12, pp. 1225--1231, 2017/12//, 2017.
[8]
N. P. Roberts, N. J. Kitchiner, J. Kenardy, and J. I. Bisson, "Systematic review and meta-analysis of multiple-session early interventions following traumatic events," The American journal of psychiatry, vol. 166, no. 3, pp. 293--301, 2009/03//, 2009.
[9]
G. Schelling, J. Briegel, B. Roozendaal, C. Stoll, H. B. Rothenhäusler, and H. P. Kapfhammer, "The effect of stress doses of hydrocortisone during septic shock on posttraumatic stress disorder in survivors," Biological psychiatry, vol. 50, no. 12, pp. 978--985, 2001/12//, 2001.
[10]
C. R. Brewin, B. Andrews, and J. D. Valentine, "Meta-analysis of risk factors for posttraumatic stress disorder in trauma-exposed adults," Journal of consulting and clinical psychology, vol. 68, no. 5, pp. 748--766, 2000/10//, 2000.
[11]
Q. Noirhomme, R. Brecheisen, D. Lesenfants, G. Antonopoulos, and S. Laureys, ""Look at my classifier's result": Disentangling unresponsive from (minimally) conscious patients," NeuroImage, vol. 145, no. Pt B, pp. 288--303, 2017/01//, 2017.
[12]
Y. Zhou, Z. Wang, L.-d. Qin, J.-q. Wan, Y.-w. Sun, S.-s. Su, W.-n. Ding, and J.-r. Xu, "Early altered resting-state functional connectivity predicts the severity of post-traumatic stress disorder symptoms in acutely traumatized subjects," PloS one, vol. 7, no. 10, pp. e46833, 2012, 2012.
[13]
A. Karl, M. Schaefer, L. S. Malta, D. Dörfel, N. Rohleder, and A. Werner, "A meta-analysis of structural brain abnormalities in PTSD," Neuroscience and biobehavioral reviews, vol. 30, no. 7, pp. 1004--1031, 2006, 2006.
[14]
Z. Wang, and Z. Xiao, Magnetic resonance imaging study of hippocampus structural alterations in post-traumatic stress disorder: A brief review (translated version), 2010.
[15]
L. Li, M. Wu, Y. Liao, L. Ouyang, M. Du, D. Lei, L. Chen, L. Yao, X. Huang, and Q. Gong, "Grey matter reduction associated with posttraumatic stress disorder and traumatic stress," Neuroscience and biobehavioral reviews, vol. 43, pp. 163--172, 2014/06//, 2014.
[16]
L. Zhang, Y. Zhang, L. Li, Z. Li, W. Li, N. Ma, C. Hou, Z. Zhang, Z. Zhang, L. Wang, L. Duan, and G. Lu, "Different white matter abnormalities between the first-episode, treatment-naive patients with posttraumatic stress disorder and generalized anxiety disorder without comorbid conditions," Journal of affective disorders, vol. 133, no. 1--2, pp. 294--299, 2011/09//, 2011.
[17]
I. R. Galatzer-Levy, K. I. Karstoft, A. Statnikov, and A. Y. Shalev, "Quantitative forecasting of PTSD from early trauma responses: a Machine Learning application," J Psychiatr Res, vol. 59, pp. 68--76, Dec, 2014.
[18]
N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, and M. Joliot, "Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain," NeuroImage, vol. 15, no. 1, pp. 273--289, 2002/01/01/, 2002.
[19]
A. Klein, and J. Tourville, "101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol," Frontiers in Neuroscience, vol. 6, no. 171, 2012-December-05, 2012.
[20]
R. S. Desikan, F. Ségonne, B. Fischl, B. T. Quinn, B. C. Dickerson, D. Blacker, R. L. Buckner, A. M. Dale, R. P. Maguire, B. T. Hyman, M. S. Albert, and R. J. Killiany, "An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest," NeuroImage, vol. 31, no. 3, pp. 968--980, 2006/07//, 2006.
[21]
J. G. Sled, A. P. Zijdenbos, and A. C. Evans, "A nonparametric method for automatic correction of intensity nonuniformity in MRI data," IEEE transactions on medical imaging, vol. 17, no. 1, pp. 87--97, 1998/02//, 1998.
[22]
Y. Dai, Y. Wang, L. Wang, G. Wu, F. Shi, D. Shen, and I. Alzheimer's Disease Neuroimaging, "aBEAT: A Toolbox for Consistent Analysis of Longitudinal Adult Brain MRI," PLoS ONE, vol. 8, no. 4, pp. e60344, 04/03--10/26/received 02/25/accepted, 2013.
[23]
G. Li, J. Nie, and D. Shen, "Consistent reconstruction of cortical surfaces from longitudinal brain MR images," in Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II, Toronto, Canada, 2011, pp. 671--679.
[24]
G. Li, J. Nie, L. Wang, F. Shi, W. Lin, J. H. Gilmore, and D. Shen, "Mapping region-specific longitudinal cortical surface expansion from birth to 2 years of age," Cerebral cortex (New York, N.Y.: 1991), vol. 23, no. 11, pp. 2724--2733, 2013/11//, 2013.
[25]
G. Li, J. Nie, L. Wang, F. Shi, J. H. Gilmore, W. Lin, and D. Shen, "Measuring the dynamic longitudinal cortex development in infants by reconstruction of temporally consistent cortical surfaces," NeuroImage, vol. 90, pp. 266--279, 2014/04//, 2014.
[26]
C.-Y. Wee, P.-T. Yap, W. Li, K. Denny, J. N. Browndyke, G. G. Potter, K. A. Welsh-Bohmer, L. Wang, and D. Shen, "Enriched white matter connectivity networks for accurate identification of MCI patients," NeuroImage, vol. 54, no. 3, pp. 1812--1822, 2011/02//, 2011.
[27]
F.-C. Yeh, T. D. Verstynen, Y. Wang, J. C. Fernández-Miranda, and W.-Y. I. Tseng, "Correction: Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy," PloS one, vol. 9, no. 1, 2014, 2014.
[28]
R. Tibshirani, "Regression Shrinkage and Selection via the Lasso," Journal of the Royal Statistical Society. Series B (Methodological), vol. 58, no. 1, pp. 267--288, 1996.

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  • (2020)Machine Learning in Mental HealthACM Transactions on Computer-Human Interaction10.1145/339806927:5(1-53)Online publication date: 17-Aug-2020

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    cover image ACM Other conferences
    ISICDM 2018: Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine
    October 2018
    166 pages
    ISBN:9781450365338
    DOI:10.1145/3285996
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    • University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

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    Published: 13 October 2018

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    Author Tags

    1. Post-traumatic stress disorder (PTSD)
    2. classification
    3. feature selection
    4. multiple atlases

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    • (2020)Machine Learning in Mental HealthACM Transactions on Computer-Human Interaction10.1145/339806927:5(1-53)Online publication date: 17-Aug-2020

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