Browsing by Author "Sun, Delin"

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    Age-dependent white matter disruptions after military traumatic brain injury: Multivariate analysis results from ENIGMA brain injury.
    (Human brain mapping, 2022-06) Bouchard, Heather C; Sun, Delin; Dennis, Emily L; Newsome, Mary R; Disner, Seth G; Elman, Jeremy; Silva, Annelise; Velez, Carmen; Irimia, Andrei; Davenport, Nicholas D; Sponheim, Scott R; Franz, Carol E; Kremen, William S; Coleman, Michael J; Williams, M Wright; Geuze, Elbert; Koerte, Inga K; Shenton, Martha E; Adamson, Maheen M; Coimbra, Raul; Grant, Gerald; Shutter, Lori; George, Mark S; Zafonte, Ross D; McAllister, Thomas W; Stein, Murray B; Thompson, Paul M; Wilde, Elisabeth A; Tate, David F; Sotiras, Aristeidis; Morey, Rajendra A
    Mild Traumatic brain injury (mTBI) is a signature wound in military personnel, and repetitive mTBI has been linked to age-related neurogenerative disorders that affect white matter (WM) in the brain. However, findings of injury to specific WM tracts have been variable and inconsistent. This may be due to the heterogeneity of mechanisms, etiology, and comorbid disorders related to mTBI. Non-negative matrix factorization (NMF) is a data-driven approach that detects covarying patterns (components) within high-dimensional data. We applied NMF to diffusion imaging data from military Veterans with and without a self-reported TBI history. NMF identified 12 independent components derived from fractional anisotropy (FA) in a large dataset (n = 1,475) gathered through the ENIGMA (Enhancing Neuroimaging Genetics through Meta-Analysis) Military Brain Injury working group. Regressions were used to examine TBI- and mTBI-related associations in NMF-derived components while adjusting for age, sex, post-traumatic stress disorder, depression, and data acquisition site/scanner. We found significantly stronger age-dependent effects of lower FA in Veterans with TBI than Veterans without in four components (q < 0.05), which are spatially unconstrained by traditionally defined WM tracts. One component, occupying the most peripheral location, exhibited significantly stronger age-dependent differences in Veterans with mTBI. We found NMF to be powerful and effective in detecting covarying patterns of FA associated with mTBI by applying standard parametric regression modeling. Our results highlight patterns of WM alteration that are differentially affected by TBI and mTBI in younger compared to older military Veterans.
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    Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium.
    (NeuroImage, 2023-12) 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, 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 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

    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.
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    Posttraumatic Stress Disorder Symptom Network Analysis in U.S. Military Veterans: Examining the Impact of Combat Exposure.
    (Frontiers in psychiatry, 2018-01) Phillips, Rachel D; Wilson, Sarah M; Sun, Delin; VA Mid-Atlantic MIRECC Workgroup; Morey, Rajendra
    Recent work inspired by graph theory has begun to conceptualize mental disorders as networks of interacting symptoms. Posttraumatic stress disorder (PTSD) symptom networks have been investigated in clinical samples meeting full diagnostic criteria, including military veterans, natural disaster survivors, civilian survivors of war, and child sexual abuse survivors. Despite reliable associations across reported networks, more work is needed to compare central symptoms across trauma types. Additionally, individuals without a diagnosis who still experience symptoms, also referred to as subthreshold cases, have not been explored with network analysis in veterans. A sample of 1,050 Iraq/Afghanistan-era U.S. military veterans (851 males, mean age = 36.3, SD = 9.53) meeting current full-criteria PTSD (n = 912) and subthreshold PTSD (n = 138) were assessed with the Structured Clinical Interview for DSM-IV Disorders (SCID). Combat Exposure Scale (CES) scores were used to group the sample meeting full-criteria into high (n = 639) and low (n = 273) combat exposure subgroups. Networks were estimated using regularized partial correlation models in the R-package qgraph, and robustness tests were performed with bootnet. Frequently co-occurring symptom pairs (strong network connections) emerged between two avoidance symptoms, hypervigilance and startle response, loss of interest and detachment, as well as, detachment and restricted affect. These associations replicate findings reported across PTSD trauma types. A symptom network analysis of PTSD in a veteran population found significantly greater overall connectivity in the full-criteria PTSD group as compared to the subthreshold PTSD group. Additionally, novel findings indicate that the association between intrusive thoughts and irritability is a feature of the symptom network of veterans with high levels of combat exposure. Mean node predictability is high for PTSD symptom networks, averaging 51.5% shared variance. With the tools described here and by others, researchers can help refine diagnostic criteria for PTSD, develop more accurate measures for assessing PTSD, and eventually inform therapies that target symptoms with strong network connections to interrupt interconnected symptom complexes and promote functional recovery.
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    Widespread Cortical Thickness Is Associated With Neuroactive Steroid Levels.
    (Frontiers in Neuroscience, 2019-01) Morey, Rajendra A; Davis, Sarah L; Haswell, Courtney C; Naylor, Jennifer C; Kilts, Jason D; Szabo, Steven T; Shampine, Larry J; Parke, Gillian J; Sun, Delin; Swanson, Chelsea A; Wagner, Henry R; Mid-Atlantic MIRECC Workgroup; Marx, Christine E
    Background:Neuroactive steroids are endogenous molecules with regenerative and neuroprotective actions. Both cortical thickness and many neuroactive steroid levels decline with age and are decreased in several neuropsychiatric disorders. However, a systematic examination of the relationship between serum neuroactive steroid levels and in vivo measures of cortical thickness in humans is lacking. Methods:Peripheral serum levels of seven neuroactive steroids were assayed in United States military veterans. All (n = 143) subsequently underwent high-resolution structural MRI, followed by parcellelation of the cortical surface into 148 anatomically defined regions. Regression modeling was applied to test the association between neuroactive steroid levels and hemispheric total gray matter volume as well as region-specific cortical thickness. False discovery rate (FDR) correction was used to control for Type 1 error from multiple testing. Results:Neuroactive steroid levels of allopregnanolone and pregnenolone were positively correlated with gray matter thickness in multiple regions of cingulate, parietal, and occipital association cortices (r = 0.20-0.47; p < 0.05; FDR-corrected). Conclusion:Positive associations between serum neuroactive steroid levels and gray matter cortical thickness are found in multiple brain regions. If these results are confirmed, neuroactive steroid levels and cortical thickness may help in monitoring the clinical response in future intervention studies of neuroregenerative therapies.