Browsing by Author "Dennis, Emily L."

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder
    (Wiley, 2019-05) Salminen, Lauren E.; Morey, Rajendra A.; Riedel, Brandalyn C.; Jahanshad, Neda; Dennis, Emily L.; Thompson, Paul M.; Radiology and Imaging Sciences, School of Medicine
    Posttraumatic stress disorder (PTSD) is a heterogeneous condition associated with a range of brain imaging abnormalities. Early life stress (ELS) contributes to this heterogeneity, but we do not know how a history of ELS influences traditionally defined brain signatures of PTSD. Here, we used a novel machine learning method – evolving partitions to improve classification (EPIC) – to identify shared and unique structural neuroimaging markers of ELS and PTSD in 97 combat-exposed military veterans. METHODS: We used EPIC with repeated cross-validation (CV) to determine how combinations of cortical thickness, surface area, and subcortical brain volumes could contribute to classification of PTSD (n = 40) versus controls (n = 57), and classification of ELS within the PTSD (ELS+ n = 16; ELS− n = 24) and control groups (ELS+ n = 16; ELS− n = 41). Additional inputs included intracranial volume, age, sex, adult trauma, and depression. RESULTS: On average, EPIC classified PTSD with 69% accuracy (SD = 5%), and ELS with 64% accuracy in the PTSD group (SD = 10%), and 62% accuracy in controls (SD = 6%). EPIC selected unique sets of individual features that classified each group with 75–85% accuracy in post hoc analyses; combinations of regions marginally improved classification from the individual atlas-defined brain regions. Across analyses, surface area in the right posterior cingulate was the only variable that was repeatedly selected as an important feature for classification of PTSD and ELS. CONCLUSIONS: EPIC revealed unique patterns of features that distinguished PTSD and ELS in this sample of combat-exposed military veterans, which may represent distinct biotypes of stress-related neuropathology.
  • Thumbnail Image
    Item
    Age‐dependent white matter disruptions after military traumatic brain injury: Multivariate analysis results from ENIGMA brain injury
    (Wiley, 2022) 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.; Psychiatry, School of Medicine
    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.
  • Thumbnail Image
    Item
    ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide
    (Elsevier, 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; Pol, Hilleke E. Hulshoff; 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 J.A.; Smoller, Jordan W.; Stein, Dan J.; Stein, Jason L.; Toro, Roberto; Turner, Jessica A.; Heuvel, Martijn P. van den; Heuvel, Odile L. van den; Erp, Theo G.M. van; Rooij, Daan van; Veltman, Dick J.; Walter, Henrik; Wang, Yalin; Wardlaw, Joanna M.; Whelan, Christopher D.; Wright, Margaret J.; Ye, Jieping; ENIGMA Consortium; Radiology and Imaging Sciences, School of Medicine
    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 others1, 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.