Browsing by Author "Dennis, Emily L"

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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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    Altered white matter microstructural organization in posttraumatic stress disorder across 3047 adults: results from the PGC-ENIGMA PTSD consortium.
    (Molecular psychiatry, 2021-08) Dennis, Emily L; Disner, Seth G; Fani, Negar; Salminen, Lauren E; Logue, Mark; Clarke, Emily K; Haswell, Courtney C; Averill, Christopher L; Baugh, Lee A; Bomyea, Jessica; Bruce, Steven E; Cha, Jiook; Choi, Kyle; Davenport, Nicholas D; Densmore, Maria; du Plessis, Stefan; Forster, Gina L; Frijling, Jessie L; Gonenc, Atilla; Gruber, Staci; Grupe, Daniel W; Guenette, Jeffrey P; Hayes, Jasmeet; Hofmann, David; Ipser, Jonathan; Jovanovic, Tanja; Kelly, Sinead; Kennis, Mitzy; Kinzel, Philipp; Koch, Saskia BJ; Koerte, Inga; Koopowitz, Sheri; Korgaonkar, Mayuresh; Krystal, John; Lebois, Lauren AM; Li, Gen; Magnotta, Vincent A; Manthey, Antje; May, Geoff J; Menefee, Deleene S; Nawijn, Laura; Nelson, Steven M; Neufeld, Richard WJ; Nitschke, Jack B; O'Doherty, Daniel; Peverill, Matthew; Ressler, Kerry J; Roos, Annerine; Sheridan, Margaret A; Sierk, Anika; Simmons, Alan; Simons, Raluca M; Simons, Jeffrey S; Stevens, Jennifer; Suarez-Jimenez, Benjamin; Sullivan, Danielle R; Théberge, Jean; Tran, Jana K; van den Heuvel, Leigh; van der Werff, Steven JA; van Rooij, Sanne JH; van Zuiden, Mirjam; Velez, Carmen; Verfaellie, Mieke; Vermeiren, Robert RJM; Wade, Benjamin SC; Wager, Tor; Walter, Henrik; Winternitz, Sherry; Wolff, Jonathan; York, Gerald; Zhu, Ye; Zhu, Xi; Abdallah, Chadi G; Bryant, Richard; Daniels, Judith K; Davidson, Richard J; Fercho, Kelene A; Franz, Carol; Geuze, Elbert; Gordon, Evan M; Kaufman, Milissa L; Kremen, William S; Lagopoulos, Jim; Lanius, Ruth A; Lyons, Michael J; McCauley, Stephen R; McGlinchey, Regina; McLaughlin, Katie A; Milberg, William; Neria, Yuval; Olff, Miranda; Seedat, Soraya; Shenton, Martha; Sponheim, Scott R; Stein, Dan J; Stein, Murray B; Straube, Thomas; Tate, David F; van der Wee, Nic JA; Veltman, Dick J; Wang, Li; Wilde, Elisabeth A; Thompson, Paul M; Kochunov, Peter; Jahanshad, Neda; Morey, Rajendra A
    A growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3047 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1426 individuals with PTSD and 1621 controls (2174 males/873 females) between ages 18-83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohen's d = -0.11, p = 0.0055). The tapetum connects the left and right hippocampus, for which structure and function have been consistently implicated in PTSD. Results were consistent even after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network.
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    ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.
    (Translational psychiatry, 2020-03) Thompson, Paul M; Jahanshad, Neda; Ching, Christopher RK; Salminen, Lauren E; Thomopoulos, Sophia I; Bright, Joanna; Baune, Bernhard T; Bertolín, Sara; Bralten, Janita; Bruin, Willem B; Bülow, Robin; Chen, Jian; Chye, Yann; Dannlowski, Udo; de Kovel, Carolien GF; Donohoe, Gary; Eyler, Lisa T; Faraone, Stephen V; Favre, Pauline; Filippi, Courtney A; Frodl, Thomas; Garijo, Daniel; Gil, Yolanda; Grabe, Hans J; Grasby, Katrina L; Hajek, Tomas; Han, Laura KM; Hatton, Sean N; Hilbert, Kevin; Ho, Tiffany C; Holleran, Laurena; Homuth, Georg; Hosten, Norbert; Houenou, Josselin; Ivanov, Iliyan; Jia, Tianye; Kelly, Sinead; Klein, Marieke; Kwon, Jun Soo; Laansma, Max A; Leerssen, Jeanne; Lueken, Ulrike; Nunes, Abraham; Neill, Joseph O'; Opel, Nils; Piras, Fabrizio; Piras, Federica; Postema, Merel C; Pozzi, Elena; Shatokhina, Natalia; Soriano-Mas, Carles; Spalletta, Gianfranco; Sun, Daqiang; Teumer, Alexander; Tilot, Amanda K; Tozzi, Leonardo; van der Merwe, Celia; Van Someren, Eus JW; van Wingen, Guido A; Völzke, Henry; Walton, Esther; Wang, Lei; Winkler, Anderson M; Wittfeld, Katharina; Wright, Margaret J; Yun, Je-Yeon; Zhang, Guohao; Zhang-James, Yanli; Adhikari, Bhim M; Agartz, Ingrid; Aghajani, Moji; Aleman, André; Althoff, Robert R; Altmann, Andre; Andreassen, Ole A; Baron, David A; Bartnik-Olson, Brenda L; Marie Bas-Hoogendam, Janna; Baskin-Sommers, Arielle R; Bearden, Carrie E; Berner, Laura A; Boedhoe, Premika SW; Brouwer, Rachel M; Buitelaar, Jan K; Caeyenberghs, Karen; Cecil, Charlotte AM; Cohen, Ronald A; Cole, James H; Conrod, Patricia J; De Brito, Stephane A; de Zwarte, Sonja MC; Dennis, Emily L; Desrivieres, Sylvane; Dima, Danai; Ehrlich, Stefan; Esopenko, Carrie; Fairchild, Graeme; Fisher, Simon E; Fouche, Jean-Paul; Francks, Clyde; Frangou, Sophia; Franke, Barbara; Garavan, Hugh P; Glahn, David C; Groenewold, Nynke A; Gurholt, Tiril P; Gutman, Boris A; Hahn, Tim; Harding, Ian H; Hernaus, Dennis; Hibar, Derrek P; Hillary, Frank G; Hoogman, Martine; Hulshoff Pol, Hilleke E; Jalbrzikowski, Maria; Karkashadze, George A; Klapwijk, Eduard T; Knickmeyer, Rebecca C; Kochunov, Peter; Koerte, Inga K; Kong, Xiang-Zhen; Liew, Sook-Lei; Lin, Alexander P; Logue, Mark W; Luders, Eileen; Macciardi, Fabio; Mackey, Scott; Mayer, Andrew R; McDonald, Carrie R; McMahon, Agnes B; Medland, Sarah E; Modinos, Gemma; Morey, Rajendra A; Mueller, Sven C; Mukherjee, Pratik; Namazova-Baranova, Leyla; Nir, Talia M; Olsen, Alexander; Paschou, Peristera; Pine, Daniel S; Pizzagalli, Fabrizio; Rentería, Miguel E; Rohrer, Jonathan D; Sämann, Philipp G; Schmaal, Lianne; Schumann, Gunter; Shiroishi, Mark S; Sisodiya, Sanjay M; Smit, Dirk JA; Sønderby, Ida E; Stein, Dan J; Stein, Jason L; Tahmasian, Masoud; Tate, David F; Turner, Jessica A; van den Heuvel, Odile A; van der Wee, Nic JA; van der Werf, Ysbrand D; van Erp, Theo GM; van Haren, Neeltje EM; van Rooij, Daan; van Velzen, Laura S; Veer, Ilya M; Veltman, Dick J; Villalon-Reina, Julio E; Walter, Henrik; Whelan, Christopher D; Wilde, Elisabeth A; Zarei, Mojtaba; Zelman, Vladimir; ENIGMA Consortium
    This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.
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    ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide.
    (Neuroimage, 2017-01-15) Thompson, Paul M; Andreassen, Ole A; Arias-Vasquez, Alejandro; Bearden, Carrie E; Boedhoe, Premika S; Brouwer, Rachel M; Buckner, Randy L; Buitelaar, Jan K; Bulayeva, Kazima B; Cannon, Dara M; Cohen, Ronald A; Conrod, Patricia J; Dale, Anders M; Deary, Ian J; Dennis, Emily L; de Reus, Marcel A; Desrivieres, Sylvane; Dima, Danai; Donohoe, Gary; Fisher, Simon E; Fouche, Jean-Paul; Francks, Clyde; Frangou, Sophia; Franke, Barbara; Ganjgahi, Habib; Garavan, Hugh; Glahn, David C; Grabe, Hans J; Guadalupe, Tulio; Gutman, Boris A; Hashimoto, Ryota; Hibar, Derrek P; Holland, Dominic; Hoogman, Martine; Hulshoff Pol, Hilleke E; Hosten, Norbert; Jahanshad, Neda; Kelly, Sinead; Kochunov, Peter; Kremen, William S; Lee, Phil H; Mackey, Scott; Martin, Nicholas G; Mazoyer, Bernard; McDonald, Colm; Medland, Sarah E; Morey, Rajendra A; Nichols, Thomas E; Paus, Tomas; Pausova, Zdenka; Schmaal, Lianne; Schumann, Gunter; Shen, Li; Sisodiya, Sanjay M; Smit, Dirk JA; Smoller, Jordan W; Stein, Dan J; Stein, Jason L; Toro, Roberto; Turner, Jessica A; van den Heuvel, Martijn P; van den Heuvel, Odile L; van Erp, Theo GM; van Rooij, Daan; Veltman, Dick J; Walter, Henrik; Wang, Yalin; Wardlaw, Joanna M; Whelan, Christopher D; Wright, Margaret J; Ye, Jieping; ENIGMA Consortium
    In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) - a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date - of schizophrenia and major depression - ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others(1), ENIGMA's genomic screens - now numbering over 30,000 MRI scans - have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants - and genetic variants in general - may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures - from tens of thousands of people - that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.
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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.