Multimodal Imaging-Based Classification of PTSD Using Data-Driven Computational Approaches: A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium
Authors: Zhu Xi , Kim Yoojean , Ravid Orren , He Xiaofu , Suarez-Jimenez Benjamin , Zilcha-Mano Sigal , Lazarov Amit , Lee Seonjoo , Abdallah Chadi G. , Angstadt Michael , Averill Christopher L. , Baird C. Lexi , Baugh Lee A. , Blackford Jennifer U. , Bomyea Jessica , Bruce Steven E. , Bryant Richard A. , Cao Zhihong , Choi Kyle , Cisler Josh , Cotton Andrew S. , Daniels Judith K. , Davenport Nicholas D. , Davidson Richard J. , DeBellis Michael D. , Dennis Emily L. , Densmore Maria , deRoon-Cassini Terri , Disner Seth G. , Hage Wissam El , Etkin Amit , Fani Negar , Fercho Kelene A. , Fitzgerald Jacklynn , Forster Gina L. , Frijling Jessie L. , Geuze Elbert , Gonenc Atilla , Gordon Evan M. , Gruber Staci , Grupe Daniel W , Guenette Jeffrey P. , Haswell Courtney C. , Herringa Ryan J. , Herzog Julia , Hofmann David Bernd , Hosseini Bobak , Hudson Anna R. , Huggins Ashley A. , Ipser Jonathan C. , Jahanshad Neda , Jia-Richards Meilin , Jovanovic Tanja , Kaufman Milissa L. , Kennis Mitzy , King Anthony , Kinzel Philipp , Koch Saskia B. J. , Koerte Inga K. , Koopowitz Sheri M. , Korgaonkar Mayuresh S. , Krystal John H. , Lanius Ruth , Larson Christine L. , Lebois Lauren A. M. , Li Gen , Liberzon Israel , Lu Guang Ming , Luo Yifeng , Magnotta Vincent A. , Manthey Antje , Maron-Katz Adi , May Geoffery , McLaughlin Katie , Mueller Sven C. , Nawijn Laura , Nelson Steven M. , Neufeld Richard W.J. , Nitschke Jack B , O’Leary Erin M. , Olatunji Bunmi O. , Olff Miranda , Peverill Matthew , Phan K. Luan , Qi Rongfeng , Quidé Yann , Rektor Ivan , Ressler Kerry , Riha Pavel , Ross Marisa , Rosso Isabelle M. , Salminen Lauren E. , Sambrook Kelly , Schmahl Christian , Shenton Martha E. , Sheridan Margaret , Shih Chiahao , Sicorello Maurizio , Sierk Anika , Simmons Alan N. , Simons Raluca M. , Simons Jeffrey S. , Sponheim Scott R. , Stein Murray B. , Stein Dan J. , Stevens Jennifer S. , Straube Thomas , Sun Delin , Théberge Jean , Thompson Paul M. , Thomopoulos Sophia I. , van der Wee Nic J.A. , van der Werff Steven J.A. , van Erp Theo G. M. , van Rooij Sanne J. H. , van Zuiden Mirjam , Varkevisser Tim , Veltman Dick J. , Vermeiren Robert R.J.M. , Walter Henrik , Wang Li , Wang Xin , Weis Carissa , Winternitz Sherry , Xie Hong , Zhu Ye , Wall Melanie , Neria Yuval , Morey Rajendra A. 
Published: December 2022

Background

Current clinical assessments of Posttraumatic stress disorder (PTSD) rely solely on subjective symptoms and experiences reported by the patient, rather than objective biomarkers of the illness. Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. Here we aimed to classify individuals with PTSD versus controls using heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.

Methods

We analyzed brain MRI data from 3,527 structural-MRI; 2,502 resting state-fMRI; and 1,953 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls (TEHC and HC) using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.

Results

We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history across all three modalities (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.

Conclusion

Our findings highlight the promise offered by machine learning methods for the diagnosis of patients with PTSD. The utility of brain biomarkers across three MRI modalities and the contribution of DVAE models for improving generalizability offers new insights into neural mechanisms involved in PTSD.

Significance

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Classifying PTSD from trauma-unexposed healthy controls (HC) using three imaging modalities performed well (∼75% AUC), but performance suffered markedly when classifying PTSD from trauma-exposed healthy controls (TEHC) using three imaging modalities (∼60% AUC).

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Using deep learning for feature reduction (denoising variational auto-encoder; DVAE) dramatically reduced the number of features with no concomitant performance degradation.

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Utilizing denoising variational autoencoder (DVAE) models improves generalizability across heterogeneous multi-site data compared with the traditional machine learning frameworks