Age-dependent white matter disruptions after military traumatic brain injury: Multivariate analysis results from ENIGMA brain injury

Authors

Heather C. Bouchard, Duke University & University of Nebraska-LincolnFollow
Delin Sun, Duke University
Emily L. Dennis, University of Utah School of Medicine
Mary R. Newsome, Michael E. DeBakey VA Medical Center
Seth G. Disner, VA Midwest Health Care Network
Jeremy Elman, Department of Psychiatry
Annelise Silva, Brigham and Women's Hospital
Carmen Velez, University of Utah School of Medicine
Andrei Irimia, University of Southern California
Nicholas D. Davenport, VA Midwest Health Care Network
Scott R. Sponheim, VA Midwest Health Care Network
Carol E. Franz, Department of Psychiatry
William S. Kremen, Department of Psychiatry
Michael J. Coleman, Brigham and Women's Hospital
M. Wright Williams, Michael E. DeBakey VA Medical Center
Elbert Geuze, University Medical Center Utrecht
Inga K. Koerte, Brigham and Women's Hospital
Martha E. Shenton, Brigham and Women's Hospital
Maheen M. Adamson, VA Medical Center
Raul Coimbra, Department of Surgery
Gerald Grant, Stanford University Medical Center
Lori Shutter, University of Pittsburgh School of Medicine
Mark S. George, Medical University of South Carolina
Ross D. Zafonte, Brigham and Women's Hospital
Thomas W. McAllister, Geisel School of Medicine at Dartmouth
Murray B. Stein, Department of Psychiatry
Paul M. Thompson, Keck School of Medicine of USC
Elisabeth A. Wilde, University of Utah School of Medicine
David F. Tate, University of Utah School of Medicine
Aristeidis Sotiras, Washington University School of Medicine in St. Louis
Rajendra A. Morey, Duke University

Date of this Version

6-1-2022

Document Type

Article

Citation

Hum Brain Mapp. 2022;43:2653–2667.

Comments

This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Abstract

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.