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

Heather C Bouchard; Delin Sun; Emily L Dennis; Mary R Newsome; Seth G Disner; Jeremy Elman; Annelise Silva; Carmen Velez; Andrei Irimia; Nicholas D Davenport; Scott R Sponheim; Carol E Franz; William S Kremen; Michael J Coleman; M Wright Williams; Elbert Geuze; Inga K Koerte; Martha E Shenton; Maheen M Adamson; Raul Coimbra; Gerald Grant; Lori Shutter; Mark S George; Ross D Zafonte; Thomas W McAllister; Murray B Stein; Paul M Thompson; Elisabeth A Wilde; David F Tate; Aristeidis Sotiras; Rajendra A Morey

doi:10.1002/hbm.25811

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

Hum Brain Mapp. 2022 Jun 1;43(8):2653-2667. doi: 10.1002/hbm.25811. Epub 2022 Mar 15.

Authors

Heather C Bouchard^{1

2

3}, Delin Sun^{1

2}, Emily L Dennis^{4

5}, Mary R Newsome^{6

7}, Seth G Disner^{8

9}, Jeremy Elman^{10

11}, Annelise Silva¹², Carmen Velez^{4

13}, Andrei Irimia^{14

15}, Nicholas D Davenport^{8

9}, Scott R Sponheim^{8

9}, Carol E Franz^{10

11}, William S Kremen^{10

11

16}, Michael J Coleman¹², M Wright Williams^{6

17}, Elbert Geuze^{18

19}, Inga K Koerte¹², Martha E Shenton¹², Maheen M Adamson^{20

21}, Raul Coimbra²², Gerald Grant²³, Lori Shutter²⁴, Mark S George²⁵, Ross D Zafonte²⁶, Thomas W McAllister²⁷, Murray B Stein^{10

28}, Paul M Thompson^{29

30

31

32

33

34

35

36}, Elisabeth A Wilde^{4

6

13}, David F Tate^{4

13}, Aristeidis Sotiras³⁶, Rajendra A Morey^{1

2}

Affiliations

¹ Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, North Carolina, USA.
² Mid-Atlantic Mental Illness Research Education and Clinical Center, Durham VA Medical Center, Durham, North Carolina, USA.
³ Center for Brain, Biology & Behavior, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.
⁴ Department of Neurology, University of Utah, Salt Lake City, Utah, USA.
⁵ Department of Radiology, Stanford University, Stanford, California, USA.
⁶ Michael E. DeBakey VA Medical Center, Houston, Texas, USA.
⁷ H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, Texas, USA.
⁸ Minneapolis VA Health Care System, Minneapolis, Minnesota, USA.
⁹ Department of Psychiatry, University of Minnesota Medical School, Minneapolis, Minnesota, USA.
¹⁰ Department of Psychiatry, University of California San Diego, La Jolla, California, USA.
¹¹ Center for Behavior Genetics of Aging, University of California, San Diego, San Diego, California, USA.
¹² Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Boston, Massachusetts, USA.
¹³ George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, Utah, USA.
¹⁴ Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA.
¹⁵ Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA.
¹⁶ Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, California, USA.
¹⁷ Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas, USA.
¹⁸ Department of Psychiatry, University Medical Center, Utrecht, Netherlands.
¹⁹ Brain Research & Innovation Centre, Ministry of Defence, Utrecht, Netherlands.
²⁰ Rehabilitation Service, VA Palo Alto, Palo Alto, California, USA.
²¹ Neurosurgery, Stanford School of Medicine, Stanford, California, USA.
²² Department of Surgery, University of California San Diego, La Jolla, California, USA.
²³ Department of Neurosurgery, Stanford University Medical Center, Palo Alto, California, USA.
²⁴ Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
²⁵ Department of Psychiatry, Medical University of South Carolina, Charleston, South Carolina, USA.
²⁶ Spaulding Rehabilitation Hospital, Massachusetts General Hospital, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
²⁷ Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA.
²⁸ Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, California, USA.
²⁹ Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California, USA.
³⁰ Department of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, University of Southern California (USC), Los Angeles, California, USA.
³¹ Department of Pediatrics, USC, Los Angeles, California, USA.
³² Department of Psychiatry, USC, Los Angeles, California, USA.
³³ Department of Radiology, USC, Los Angeles, California, USA.
³⁴ Department of Engineering, USC, Los Angeles, California, USA.
³⁵ Department of Ophthalmology, USC, Los Angeles, California, USA.
³⁶ Department of Radiology and Institute for Informatics, School of Medicine, Washington University St. Louis, St. Louis, Missouri, 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.

Keywords: ENIGMA; diffusion MRI; mTBI; military; nonnegative matrix factorization; traumatic brain injury.

Publication types

Meta-Analysis
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Brain / diagnostic imaging
Brain Concussion* / diagnostic imaging
Brain Injuries* / etiology
Brain Injuries, Traumatic* / complications
Brain Injuries, Traumatic* / diagnostic imaging
Humans
Military Personnel*
Multivariate Analysis
Stress Disorders, Post-Traumatic* / complications
Veterans*
White Matter* / diagnostic imaging

Abstract

Publication types

MeSH terms

Grants and funding