Department of Medicine Publications


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

Document Type


Publication Date






First Page


Last Page


URL with Digital Object Identifier


BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.

METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls 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 in each modality (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: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Find in your library



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.