Faculty Collaboration Database

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium. Neuroimage 2023 Dec 01;283:120412

Date

10/20/2023

Pubmed ID

37858907

Pubmed Central ID

PMC10842116

DOI

10.1016/j.neuroimage.2023.120412

Scopus ID

2-s2.0-85174730385 (requires institutional sign-in at Scopus site)

Abstract

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.

Author List

Zhu X, Kim Y, Ravid O, He X, Suarez-Jimenez B, Zilcha-Mano S, Lazarov A, Lee S, Abdallah CG, Angstadt M, Averill CL, Baird CL, Baugh LA, Blackford JU, Bomyea J, Bruce SE, Bryant RA, Cao Z, Choi K, Cisler J, Cotton AS, Daniels JK, Davenport ND, Davidson RJ, DeBellis MD, Dennis EL, Densmore M, deRoon-Cassini T, Disner SG, Hage WE, Etkin A, Fani N, Fercho KA, Fitzgerald J, Forster GL, Frijling JL, Geuze E, Gonenc A, Gordon EM, Gruber S, Grupe DW, Guenette JP, Haswell CC, Herringa RJ, Herzog J, Hofmann DB, Hosseini B, Hudson AR, Huggins AA, Ipser JC, Jahanshad N, Jia-Richards M, Jovanovic T, Kaufman ML, Kennis M, King A, Kinzel P, Koch SBJ, Koerte IK, Koopowitz SM, Korgaonkar MS, Krystal JH, Lanius R, Larson CL, Lebois LAM, Li G, Liberzon I, Lu GM, Luo Y, Magnotta VA, Manthey A, Maron-Katz A, May G, McLaughlin K, Mueller SC, Nawijn L, Nelson SM, Neufeld RWJ, Nitschke JB, O'Leary EM, Olatunji BO, Olff M, Peverill M, Phan KL, Qi R, Quidé Y, Rektor I, Ressler K, Riha P, Ross M, Rosso IM, Salminen LE, Sambrook K, Schmahl C, Shenton ME, Sheridan M, Shih C, Sicorello M, Sierk A, Simmons AN, Simons RM, Simons JS, Sponheim SR, Stein MB, Stein DJ, Stevens JS, Straube T, Sun D, Théberge J, Thompson PM, Thomopoulos SI, van der Wee NJA, van der Werff SJA, van Erp TGM, van Rooij SJH, van Zuiden M, Varkevisser T, Veltman DJ, Vermeiren RRJM, Walter H, Wang L, Wang X, Weis C, Winternitz S, Xie H, Zhu Y, Wall M, Neria Y, Morey RA

Authors

Carissa W. Tomas PhD Assistant Professor in the Institute for Health and Equity department at Medical College of Wisconsin
Terri A. deRoon Cassini PhD Center Director, Professor in the Surgery department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Brain
Humans
Magnetic Resonance Imaging
Neuroimaging
Reproducibility of Results
Stress Disorders, Post-Traumatic