Abstract
Classification and prediction of posttraumatic stress disorder (PTSD) based on brain imaging measures is important because it could aid in PTSD diagnosis and clinical management of PTSD. The goal of the present study was to test the effectiveness of using cortical morphological measures (i.e. volume, thickness, and surface area) to classify PTSD cases and controls on 3571 individuals from the ENIGMA-Psychiatric Genomics Consortium PTSD Workgroup, the largest PTSD neuroimaging dataset to date. We constructed 6 feature sets from different demographic variables (age and sex) and cortical morphological measures and used four machine learning algorithms for classification: logistic regression, random forest, support vector machine, and multi-layer perceptron. We found that classifiers trained using only cortical morphological measures (any one of volume, thickness, or surface area) performed better than classifiers trained using only demographic variables. Among all 6 feature sets, combining demographic variables and all three cortical morphological measures yielded the best prediction accuracy, with area under the receiver operating characteristic curve (ROC AUC) scores ranging from 0.615 for logistic regression to 0.648 for random forest. These findings suggest that using cortical morphological measures only has modest prediction power for PTSD classification. Future studies that wish to produce clinically and practically significant findings should consider using whole brain morphological measures, as well as incorporating other neuroimaging modalities and relevant clinical and behavioral symptoms.
B. O'Leary and C.-H. Shih---Contributed equally.
Contributing authors are listed in https://drive.google.com/file/d/1wO-WeUYGB_gWbFh5LIQsUP7H7ckEjeo1/view.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sareen, J.: Posttraumatic stress disorder in adults: impact, comorbidity, risk factors, and treatment. Can. J. Psychiatry 59, 460–467 (2014). https://doi.org/10.1177/070674371405900902
Liberzon, I., Wang, X., Xie, H.: Brain structural abnormalities in posttraumatic stress disorder and relations with sleeping problems. In: Vermetten, E., Germain, A., Neylan, T.C. (eds.) Sleep and Combat-Related Post Traumatic Stress Disorder, pp. 145–167. Springer, New York (2018). https://doi.org/10.1007/978-1-4939-7148-0_12
Eckart, C., Stoppel, C., et al.: Structural alterations in lateral prefrontal, parietal and posterior midline regions of men with chronic posttraumatic stress disorder. J. Psychiatry Neurosci. 36, 176 (2011)
Rauch, S.L., et al.: Selectively reduced regional cortical volumes in post-traumatic stress disorder. NeuroReport 14, 913–916 (2003)
Kitayama, N., Quinn, S., Bremner, J.D.: Smaller volume of anterior cingulate cortex in abuse-related posttraumatic stress disorder. J. Affect. Disord. 90, 171–174 (2006)
Chao, L., Weiner, M., Neylan, T.: Regional cerebral volumes in veterans with current versus remitted posttraumatic stress disorder. Psychiatry Res. Neuroimaging 213, 193–201 (2013)
Liberzon, I., Abelson, J.L.: Context processing and the neurobiology of post-traumatic stress disorder. Neuron 92, 14–30 (2016). https://doi.org/10.1016/j.neuron.2016.09.039
Garfinkel, S.N., et al.: Impaired contextual modulation of memories in PTSD: an fMRI and psychophysiological study of extinction retention and fear renewal. J. Neurosci. 34, 13435–13443 (2014)
Greco, J.A., Liberzon, I.: Neuroimaging of fear-associated learning. Neuropsychopharmacol. 41, 320–334 (2016)
Kessler, R.C., et al.: How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? an exploratory study in the WHO World Mental Health Surveys. World Psychiatry 13, 265–274 (2014)
Ditlevsen, D.N., Elklit, A.: The combined effect of gender and age on post traumatic stress disorder: do men and women show differences in the lifespan distribution of the disorder? Ann. Gen. Psychiatry 9, 32 (2010). https://doi.org/10.1186/1744-859X-9-32
Galatzer-Levy, I.R., Karstoft, K.-I., Statnikov, A., Shalev, A.Y.: Quantitative forecasting of PTSD from early trauma responses: a machine learning application. J. Psychiatry Res. 59, 68–76 (2014)
Mor, N.S., Dardeck, K.L.: Quantitative forecasting of risk for PTSD using ecological factors: a deep learning application. J. Soc. Behav. Health Sci. 12, 4 (2018)
Choi, J.S., Lee, E., Suk, Hl: Regional abnormality representation learning in structural MRI for AD/MCI diagnosis. In: Shi, Y., Suk, Hl, Liu, M. (eds.) MLMI 2018. LNCS, vol. 11046, pp. 64–72. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00919-9_8
A. Nunes, et al: Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA bipolar disorders working group. Mol. Psychiatry, 1–14 (2018). https://doi.org/10.1038/s41380-018-0228-9
Lee, J.S., et al.: Machine learning-based individual assessment of cortical atrophy pattern in alzheimer’s disease spectrum: development of the classifier and longitudinal evaluation. Sci. Rep. 8, 4161 (2018)
Menikdiwela, M., Nguyen, C., Shaw, M.: Deep learning on brain cortical thickness data for disease classification. In: 2018 Digital Image Computing: Techniques and Applications (DICTA), pp. 1–5. IEEE (2018)
Ramos-Lima, L.F., Waikamp, V., Antonelli-Salgado, T., Passos, I.C., Freitas, L.H.M.: The use of machine learning techniques in trauma-related disorders: a systematic review. J. Psychiatr. Res. 121, 159–172 (2020). https://doi.org/10.1016/j.jpsychires.2019.12.001
Gosnell, S.N., Fowler, J.C., Salas, R.: Classifying suicidal behavior with resting-state functional connectivity and structural neuroimaging. Acta Psychiatry, Scand (2019)
Kessler, R.C., et al.: Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Mol. Psychiatry. 22, 544–551 (2017)
Fischl, B.: FreeSurfer. NeuroImage. 62, 774–781 (2012). https://doi.org/10.1016/j.neuroimage.2012.01.021
Desikan, R.S., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage. 31, 968–980 (2006). https://doi.org/10.1016/j.neuroimage.2006.01.021
Genetics Protocols « ENIGMA, (n.d.). http://enigma.ini.usc.edu/protocols/genetics-protocols/. Accessed 15 June 2020
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67(2), 301–320 (2005)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Kingma, D.P., Ba, J., Adam: A method for stochastic optimization. ArXiv14126980 Cs (2017). http://arxiv.org/abs/1412.6980
Hajek, T., Cooke, C., Kopecek, M., Novak, T., Hoschl, C., Alda, M.: Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study. J. Psychiatry Neurosci. JPN. 40, 316–324 (2015). https://doi.org/10.1503/jpn.140142
Costafreda, S.G., Chu, C., Ashburner, J., Fu, C.H.: Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS ONE 4, e6353 (2009)
Gong, Q., et al.: Prognostic prediction of therapeutic response in depression using high-field MR imaging. Neuroimage. 55, 1497–1503 (2011)
Ecker, C., et al.: Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Neuroimage 49, 44–56 (2010)
Logue, M.W., et al.: Smaller hippocampal volume in posttraumatic stress disorder: a multisite ENIGMA-PGC study: subcortical volumetry results from posttraumatic stress disorder consortia. Biol. Psychiatry 83, 244–253 (2018). https://doi.org/10.1016/j.biopsych.2017.09.006
Wshah, S., Skalka, C., Price, M.: Predicting posttraumatic stress disorder risk: a machine learning approach. JMIR Ment. Health. 6, e13946 (2019). https://doi.org/10.2196/13946
Calhoun, V.D., Sui, J.: Multimodal fusion of brain imaging data: a key to finding the missing link(s) in complex mental illness. Biol. Psychiatry Cogn. Neurosci. Neuroimaging. 1, 230–244 (2016). https://doi.org/10.1016/j.bpsc.2015.12.005
Uludağ, K., Roebroeck, A.: General overview on the merits of multimodal neuroimaging data fusion. NeuroImage. 102, 3–10 (2014). https://doi.org/10.1016/j.neuroimage.2014.05.018
Author information
Authors and Affiliations
Consortia
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
O’Leary, B. et al. (2020). Classification of PTSD and Non-PTSD Using Cortical Structural Measures in Machine Learning Analyses—Preliminary Study of ENIGMA-Psychiatric Genomics Consortium PTSD Workgroup. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-59277-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59276-9
Online ISBN: 978-3-030-59277-6
eBook Packages: Computer ScienceComputer Science (R0)