دورية أكاديمية
Neuroimaging-Based Classification of PTSD Using Data-Driven Computational Approaches: A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium
| العنوان: | Neuroimaging-Based Classification of PTSD Using Data-Driven Computational Approaches: A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium |
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| المؤلفون: | 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 BJ, Koerte, Inga K, Koopowitz, Sheri M, Korgaonkar, Mayuresh S, Krystal, John H, Lanius, Ruth, Larson, Christine L, Lebois, Lauren AM, Li, 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 WJ, 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 JA, van der Werff, Steven JA, van Erp, Theo GM, van Rooij, Sanne JH, van Zuiden, Mirjam, Varkevisser, Tim, Veltman, Dick J, Vermeiren, Robert RJM, Walter, Henrik, Wang, Li, Wang, Xin, Weis, Carissa, Winternitz, Sherry, Xie, Hong, Zhu, Ye, Wall, Melanie, Neria, Yuval, Morey, Rajendra A, Quide, Yann |
| المصدر: | urn:ISSN:1053-8119 ; urn:ISSN:1095-9572 ; NeuroImage, 283, 120412-120412 |
| بيانات النشر: | Elsevier |
| سنة النشر: | 2023 |
| المجموعة: | UNSW Sydney (The University of New South Wales): UNSWorks |
| مصطلحات موضوعية: | Clinical Research, Biomedical Imaging, Networking and Information Technology R&D (NITRD), Anxiety Disorders, Mental Health, Neurosciences, Post-Traumatic Stress Disorder (PTSD), Brain Disorders, Neurological, 3 Good Health and Well Being, Humans, Stress Disorders, Post-Traumatic, Reproducibility of Results, Big Data, Neuroimaging, Magnetic Resonance Imaging, Brain, Classification, Deep learning, Machine learning, Multimodal MRI, Posttraumatic stress disorder, anzsrc-for: 11 Medical and Health Sciences, anzsrc-for: 17 Psychology and Cognitive Sciences |
| الوصف: | 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 ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | unknown |
| العلاقة: | http://hdl.handle.net/1959.4/unsworks_84777Test; https://unsworks.unsw.edu.au/bitstreams/15a5b0bf-4d8d-4a5d-839a-461c8f26601f/downloadTest; https://doi.org/10.1016/j.neuroimage.2023.120412Test |
| DOI: | 10.1016/j.neuroimage.2023.120412 |
| الإتاحة: | https://doi.org/10.1016/j.neuroimage.2023.120412Test http://hdl.handle.net/1959.4/unsworks_84777Test https://unsworks.unsw.edu.au/bitstreams/15a5b0bf-4d8d-4a5d-839a-461c8f26601f/downloadTest |
| حقوق: | open access ; https://purl.org/coar/access_right/c_abf2Test ; CC BY-NC-ND ; https://creativecommons.org/licenses/by-nc-nd/4.0Test/ ; free_to_read ; This is an open access article published by Elsevier distributed under the terms of the Creative Commons CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0Test/), which permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed. © 2023 The Author(s). View the final version at this link: https://doi.org/10.1016/j.neuroimage.2023.120412Test |
| رقم الانضمام: | edsbas.ADF0A66B |
| قاعدة البيانات: | BASE |
| DOI: | 10.1016/j.neuroimage.2023.120412 |
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