ZHU, Xi, 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, El Hage WISSAM, 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 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 LEARY, Bunmi O OLATUNJI, Miranda OLFF, Matthew PEVERILL, K Luan PHAN, Rongfeng QI, Yann QUIDE, 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, Paul M THOMPSON, Sophia I THOMOPOULOS, 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, 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 a Rajendra A MOREY. Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium. Neuroimage. San Diego: ACADEMIC PRESS INC ELSEVIER SCIENCE, 2023, roč. 283, Jan, s. 1-13. ISSN 1053-8119. Dostupné z: https://dx.doi.org/10.1016/j.neuroimage.2023.120412.
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Základní údaje
Originální název Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium
Autoři ZHU, Xi, 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, El Hage WISSAM, 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 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 LEARY, Bunmi O OLATUNJI, Miranda OLFF, Matthew PEVERILL, K Luan PHAN, Rongfeng QI, Yann QUIDE, 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, Paul M THOMPSON, Sophia I THOMOPOULOS, 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, 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 a Rajendra A MOREY.
Vydání Neuroimage, San Diego, ACADEMIC PRESS INC ELSEVIER SCIENCE, 2023, 1053-8119.
Další údaje
Originální jazyk angličtina
Typ výsledku Článek v odborném periodiku
Obor 30103 Neurosciences
Stát vydavatele Kanada
Utajení není předmětem státního či obchodního tajemství
WWW URL
Impakt faktor Impact factor: 5.700 v roce 2022
Organizační jednotka Středoevropský technologický institut
Doi http://dx.doi.org/10.1016/j.neuroimage.2023.120412
UT WoS 001109390600001
Klíčová slova anglicky Posttraumatic stress disorder; Multimodal MRI; Machine learning; Deep learning; Classification
Štítky CF MAFIL, rivok
Příznaky Mezinárodní význam, Recenzováno
Změnil Změnila: Mgr. Eva Dubská, učo 77638. Změněno: 13. 3. 2024 06:14.
Anotace
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.
VytisknoutZobrazeno: 24. 7. 2024 14:13