Altered lateralization of the cingulum in deployment-related traumatic brain injury: An ENIGMA military-relevant brain injury study
Dennis, Emily L.; Newsome, Mary R.; Lindsey, Hannah M.; Adamson, Maheen; Austin, Tara A.; Disner, Seth G.; Eapen, Blessen C.; Esopenko, Carrie; Franz, Carol E.; Geuze, Elbert; Haswell, Courtney; Hinds, Sidney R.; Hodges, Cooper B.; Irimia, Andrei; Kenney, Kimbra; Koerte, Inga K.; Kremen, William S.; Levin, Harvey S.; Morey, Rajendra A.; Ollinger, John; Rowland, Jared A.; Scheibel, Randall S.; Shenton, Martha E.; Sullivan, Danielle R.; Talbert, Leah D.; Thomopoulos, Sophia I.; Troyanskaya, Maya; Walker, William C.; Wang, Xin; Ware, Ashley L.; Werner, John Kent; Williams, Wright; Thompson, Paul M.; Tate, David F.; Wilde, Elisabeth A.
(2023) Human Brain Mapping, volume 44, issue 5, pp. 1888 - 1900
(Article)
Abstract
Traumatic brain injury (TBI) in military populations can cause disruptions in brain structure and function, along with cognitive and psychological dysfunction. Diffusion magnetic resonance imaging (dMRI) can detect alterations in white matter (WM) microstructure, but few studies have examined brain asymmetry. Examining asymmetry in large samples may increase sensitivity to
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detect heterogeneous areas of WM alteration in mild TBI. Through the Enhancing Neuroimaging Genetics Through Meta-Analysis Military-Relevant Brain Injury working group, we conducted a mega-analysis of neuroimaging and clinical data from 16 cohorts of Active Duty Service Members and Veterans (n = 2598). dMRI data were processed together along with harmonized demographic, injury, psychiatric, and cognitive measures. Fractional anisotropy in the cingulum showed greater asymmetry in individuals with deployment-related TBI, driven by greater left lateralization in TBI. Results remained significant after accounting for potentially confounding variables including posttraumatic stress disorder, depression, and handedness, and were driven primarily by individuals whose worst TBI occurred before age 40. Alterations in the cingulum were also associated with slower processing speed and poorer set shifting. The results indicate an enhancement of the natural left laterality of the cingulum, possibly due to vulnerability of the nondominant hemisphere or compensatory mechanisms in the dominant hemisphere. The cingulum is one of the last WM tracts to mature, reaching peak FA around 42 years old. This effect was primarily detected in individuals whose worst injury occurred before age 40, suggesting that the protracted development of the cingulum may lead to increased vulnerability to insults, such as TBI.
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Keywords: DTI, military, traumatic brain injury, Anatomy, Radiological and Ultrasound Technology, Radiology Nuclear Medicine and imaging, Neurology, Clinical Neurology
ISSN: 1065-9471
Publisher: Wiley-Liss Inc.
Note: Funding Information: This work was supported by the NIH R61NS120249 K99NS096116; U.S. Army Medical Research and Materiel Command (USAMRMC) award 13129004; VA RR&D IK2RX002922; South Central VA Healthcare Network Veterans Health Administration; VA RR&D SPIRE Award I21RX001608; DOD PRARP; Dutch Ministry of Defence; R01AG058822 R01NS100973, DoD contract W81XWH-18-1-0413, a grant from the James J. and Sue Femino Foundation, a Hanson-Thorell Research Scholarship, the USC CURVE & SURE programs, and the Leonard Davis School of Gerontology; R01NS100952; European Research Council (ERC) (Starting Grant 804326); VA Rehabilitation Research and Development Service (VA RR&D) I01RX003443 I01RX003442; VA Health Services Research and Development Service Research Career Scientist Award IK6HX002608; Merit Review Award Number I01 CX001820 from the United States (U.S.) Department of Veterans Affairs Clinical Sciences R&D (CSRD) Service; VA CSR&D Career Development Award VA Career Development Award 5IK2CX001508; VA CSR&D Career Development Award 1IK2CX001772-01; Defense and Veterans Brain Injury Centers, the U.S. Army Medical Research and Materiel Command (USAMRMC); W81XWH-13-2-0025 and the Chronic Effects of Neurotrauma Consortium (CENC); PT108802-SC104835 U54EB020403 K99 MH119314; (NIMH); NARSAD 27786. This work was supported by the Assistant Secretary of Defense for Health Affairs endorsed by the Department of Defense, through the Psychological Health/Traumatic Brain Injury Research Program LongTerm Impact of Military Relevant Brain Injury Consortium (LIMBIC) Award W81XWH18PH/TBIRPLIMBIC under Awards No. W81XWH1920067 W81XWH1320095, and by the U.S. Department of Veterans Affairs Awards No. I01 CX002097, I01 CX002096, I01 HX003155, I01 RX003444, I01 RX003443, I01 RX003442, I01 CX001135, I01 CX001246, I01 RX001774, I01 RX001135, I01 RX002076, I01 RX001880, I01 RX002172, I01 RX002173, I01 RX002171, I01 RX002174 I01 RX002170. Our analysis included 16 cohorts of Veteran and Active Duty SMs, totaling 1775 participants with TBI history and 823 comparison participants without TBI history. Of the 1775 participants, 1080 had at least one deployment-related injury, 480 had history of nonmilitary TBI only, and 215 did not have information regarding injury context (military vs. nonmilitary). While the majority of cohorts focused on Iraq/Afghanistan Veterans and SMs, two focused on Vietnam Veterans, a significantly older population. Across cohorts, the age range was 18–85 (M = 41.7 ± 12.7) years. Table 1 provides demographic and clinical details on the cohorts. Inclusion and exclusion criteria for each study are in Table S1. All participants provided institutional review board-approved written informed consent. The group with a history of TBI was significantly younger than the group without TBI history (M = 40.6 vs. 43.8 years, p = 3.6 × 10−8) and had a greater proportion of males (91% vs. 85%, p = 2.8 × 10−5). The gender-related differences match existing epidemiological trends in military TBI, and the age difference is likely related to military rank and thus potential exposure to TBI. Note: Numbers are shown for total sample, TBI (any) and control groups, and male/female. The age range, average age in years with SD, sample TBI severity, and the TBI scale collected are also listed. Abbreviation: TBI, traumatic brain injury. Our analysis included 16 cohorts of Veteran and Active Duty SMs, totaling 1775 participants with TBI history and 823 comparison participants without TBI history. Of the 1775 participants, 1080 had at least one deployment-related injury, 480 had history of nonmilitary TBI only, and 215 did not have information regarding injury context (military vs. nonmilitary). While the majority of cohorts focused on Iraq/Afghanistan Veterans and SMs, two focused on Vietnam Veterans, a significantly older population. Across cohorts, the age range was 18–85 (M = 41.7 ± 12.7) years. Table 1 provides demographic and clinical details on the cohorts. Inclusion and exclusion criteria for each study are in Table S1. All participants provided institutional review board-approved written informed consent. The group with a history of TBI was significantly younger than the group without TBI history (M = 40.6 vs. 43.8 years, p = 3.6 × 10−8) and had a greater proportion of males (91% vs. 85%, p = 2.8 × 10−5). The gender-related differences match existing epidemiological trends in military TBI, and the age difference is likely related to military rank and thus potential exposure to TBI. Note: Numbers are shown for total sample, TBI (any) and control groups, and male/female. The age range, average age in years with SD, sample TBI severity, and the TBI scale collected are also listed. Abbreviation: TBI, traumatic brain injury. Details on injuries were collected using a range of tools (see Tables 1 and S2). From these disparate scales, we extracted common variables, as detailed in Note S1. Acquisition parameters are provided in Table S3. Preprocessing, including eddy current correction, echo-planar imaging-induced distortion correction, and tensor fitting, was completed at the University of Utah for sites sharing raw data, or locally for others. Recommended protocols and quality control procedures are available on the ENIGMA-DTI and Neuroimaging Informatics Tools and Resources Clearinghouse webpages. These procedures were recommended, but coordination of preprocessing schemes accommodated site- and acquisition-specific pipelines. Once tensors were estimated, they were mapped and projected onto the ENIGMA-DTI template, and averaged within regions of interest (ROIs; http://enigma.ini.usc.edu/protocols/dti-protocols/) in a TBSS-based approach (Smith et al., 2006). Further details and ROI abbreviations are in Note S2. In the seven sites for whom raw data was shared, we extracted motion parameters from the eddy current correction step to determine if motion played a role in our case–control findings. We compared rotation and translation averaged across the X, Y, and Z axes, finding no significant between-group differences (all ps >.05). We calculated FA lateralization index and asymmetry for each lateralized ROI: Lateralization index=FALeft−FARight/0.5*FALeft+FARight where asymmetry was the absolute value of the lateralization index. Significant effects with asymmetry were followed post hoc with examinations of the lateralization index. Asymmetry was the primary measure as it would detect alterations irrespective of direction. Mega-analysis was performed on individual-level ROI data. Linear mixed effects models were implemented with lme in R 3.1.3. Nested random effects controlled for cohort and site, as some studies included multiple data collection sites. Age and gender were included as covariates in all analyses. The average correlation in asymmetry between all ROIs was r =.13. For multiple comparisons correction, we used a modified Bonferroni threshold, following recent ENIGMA analyses (Dennis et al., 2019) to calculate the effective number of independent tests based on the observed correlation structure between alternate responses. The equation of Li and Ji (Li & Ji, 2005) yielded an effective number of tests of Veff = 16, yielding a significance threshold of p <.05/16 =.003125. Results that did not pass correction for multiple comparisons (.05 > p >.003125) are reported in the Supplement for completeness, but not interpreted. Across analyses, Cohen's d statistics are reported for group comparisons and unstandardized betas (b) are reported for linear regressions. We calculated corrected p-values using the following equation: Padj=1−1−pVeff where p is the unadjusted p-value. Corrected p-values are shown for primary analyses. We used FDR to correct p-values for post hoc analyses. We first conducted analyses to determine whether a quadratic age term, age2, should be included in statistical models along with age and gender, as age has a nonlinear effect on FA (Kochunov et al., 2012). The effect of this term upon the regression was not significant, so age2 was not included in subsequent analyses. Our primary analysis compared participants with a history of deployment-related TBI to those with no history of TBI, excluding individuals reporting a history of only nonmilitary TBI and participants whose records did not specify the source of TBI. Deployment-related TBI included both combat and noncombat injuries. As a specificity analysis, we examined group differences between participants with a history of nonmilitary TBI to a non-TBI group as well as individuals with a history of blast-related TBI to the non-TBI group. Supplementary analyses on participant subgroups, interactions, injury variables, and symptom inventories are summarized in Note S3. Across the 16 cohorts included in this study, seven collected the trail making test (TMT), including 1613 participants, 676 of whom had a history of deployment-related TBI. TMT Part A measures visual search and motor speed and Part B measures set shifting (Sánchez-Cubillo et al., 2009). Participants with Part A or Part B completion times greater than 3 SD above the study-wide mean were winsorized to 3 SD above the mean. The views expressed in this article are those of the author(s) and do not reflect the official policy of the Department of Army/Navy/Air Force, Department of Defense, or US Government. Inga K. Koerte receives funding for a collaborative project unrelated to this article and serves as a paid scientific advisor for Abbott. She receives royalties for book chapters. Her spouse is an employee at Siemens AG. Paul M. Thompson received a research grant from Biogen, Inc., for research unrelated to this article. Funding Information: The views expressed in this article are those of the author(s) and do not reflect the official policy of the Department of Army/Navy/Air Force, Department of Defense, or US Government. Inga K. Koerte receives funding for a collaborative project unrelated to this article and serves as a paid scientific advisor for Abbott. She receives royalties for book chapters. Her spouse is an employee at Siemens AG. Paul M. Thompson received a research grant from Biogen, Inc., for research unrelated to this article. Funding Information: This work was supported by the NIH R61NS120249 K99NS096116; U.S. Army Medical Research and Materiel Command (USAMRMC) award 13129004; VA RR&D IK2RX002922; South Central VA Healthcare Network Veterans Health Administration; VA RR&D SPIRE Award I21RX001608; DOD PRARP; Dutch Ministry of Defence; R01AG058822 R01NS100973, DoD contract W81XWH‐18‐1‐0413, a grant from the James J. and Sue Femino Foundation, a Hanson‐Thorell Research Scholarship, the USC CURVE & SURE programs, and the Leonard Davis School of Gerontology; R01NS100952; European Research Council (ERC) (Starting Grant 804326); VA Rehabilitation Research and Development Service (VA RR&D) I01RX003443 I01RX003442; VA Health Services Research and Development Service Research Career Scientist Award IK6HX002608; Merit Review Award Number I01 CX001820 from the United States (U.S.) Department of Veterans Affairs Clinical Sciences R&D (CSRD) Service; VA CSR&D Career Development Award VA Career Development Award 5IK2CX001508; VA CSR&D Career Development Award 1IK2CX001772‐01; Defense and Veterans Brain Injury Centers, the U.S. Army Medical Research and Materiel Command (USAMRMC); W81XWH‐13‐2‐0025 and the Chronic Effects of Neurotrauma Consortium (CENC); PT108802‐SC104835 U54EB020403 K99 MH119314; (NIMH); NARSAD 27786. This work was supported by the Assistant Secretary of Defense for Health Affairs endorsed by the Department of Defense, through the Psychological Health/Traumatic Brain Injury Research Program LongTerm Impact of Military Relevant Brain Injury Consortium (LIMBIC) Award W81XWH18PH/TBIRPLIMBIC under Awards No. W81XWH1920067 W81XWH1320095, and by the U.S. Department of Veterans Affairs Awards No. I01 CX002097, I01 CX002096, I01 HX003155, I01 RX003444, I01 RX003443, I01 RX003442, I01 CX001135, I01 CX001246, I01 RX001774, I01 RX001135, I01 RX002076, I01 RX001880, I01 RX002172, I01 RX002173, I01 RX002171, I01 RX002174 I01 RX002170. Publisher Copyright: © 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
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