- Email: [email protected]
Brainstem atrophy in Gulf War Illness
Journal Pre-proof Brainstem Atrophy in Gulf War Illness Yu Zhang (Conceptualization) (Methodology) (Validation) (Formal analysis) (Investigation)Data Curation) (Writing - original draft) (Visualization), Timothy Avery (Validation) (Investigation) (Resources) (Writing - review and editing), Andrei A. Vakhtin (Software) (Validation) (Investigation) (Writing - review and editing), Danielle C. Mathersul (Investigation) (Writing - review and editing), Eric Tranvinh (Investigation) (Resources) (Writing - review and editing), Max Wintermark (Investigation) (Resources) (Writing review and editing), Payam Massaband (Resources) (Writing review and editing), J. Wesson. Ashford (Conceptualization) (Writing - review and editing) (Supervision) (Funding acquisition), Peter J. Bayley (Conceptualization) (Supervision) (Writing - review and editing), Ansgar J. Furst (Conceptualization) (Methodology) (Software) (Data curation) (Writing - review and editing) (Supervision) (Project administration)
PII:
S0161-813X(20)30028-0
DOI:
https://doi.org/10.1016/j.neuro.2020.02.006
Reference:
NEUTOX 2591
To appear in:
Neurotoxicology
Received Date:
5 October 2019
Revised Date:
30 January 2020
Accepted Date:
16 February 2020
Please cite this article as: Zhang Y, Avery T, Vakhtin AA, Mathersul DC, Tranvinh E, Wintermark M, Massaband P, Ashford JW, Bayley PJ, Furst AJ, Brainstem Atrophy in Gulf War Illness, Neurotoxicology (2020), doi: https://doi.org/10.1016/j.neuro.2020.02.006
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
Brainstem Atrophy in Gulf War Illness
Yu Zhang, MD1, Timothy Avery, PhD1,2, Andrei A. Vakhtin, PhD1,2, Danielle C. Mathersul, PhD1,2, Eric Tranvinh, MD3, Max Wintermark, MD1,3, Payam Massaband, MD4,5, J. Wesson.
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Ashford, MD, PhD1,2, Peter J. Bayley, PhD1,2, Ansgar J. Furst, PhD1,2,6,7
War Related Illness and Injury Study Center (WRIISC), Veterans Affairs Palo Alto Health
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Care System
Psychiatry and Behavioral Sciences, Stanford University School of Medicine
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Neuroradiology, Stanford University School of Medicine
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Radiology, VA Palo Alto Health Care System
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Radiology, Stanford University School of Medicine
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Neurology and Neurological Sciences, Stanford University
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Polytrauma System of Care (PSC), VA Palo Alto Health Care System
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Corresponding author: Yu Zhang, MD. War Related Illness and Injury Study Center (WRIISC). VA Palo Alto Health Care System. 3801 Miranda Ave, Mailcode 151Y. Palo Alto,
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CA 94304-1290, USA. Tel: +1 650 4935000 xt 64575. Fax: +1 650 8523297. E-mails: [email protected]; [email protected]
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Highlights:
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The multiple chronic symptoms developed after 1990-1991 Gulf War, which is known as GWI, has no validated definition or validated diagnostic marker. Existing neuroimaging studies have been limited by small sample sizes, inconsistent GWI definition. Using automated brain MRI volumetric measurement in a large GWI cohort, this study identified subcortical atrophy in GWI. More importantly, this study detected a greatest brainstem abnormality in GWI veterans. This finding will promote further investigations of GWI into the brainstem substructure. A novel observation of brainstem volume in correlation with fatigue, memory loss, depression and breath difficulties in GWI demonstrated that the brainstem is a key structure that related to the GWI core symptoms.
Abstract
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Background: Gulf War illness (GWI) is a condition that affects about 30% of veterans who
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served in the 1990-91 Persian Gulf War. Given its broad symptomatic manifestation, including chronic pain, fatigue, neurological, gastrointestinal, respiratory, and skin problems, it is of
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interest to examine whether GWI is associated with changes in the brain. Existing neuroimaging studies, however, have been limited by small sample sizes, inconsistent GWI diagnosis criteria,
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and potential comorbidity confounds.
Objectives: Using a large cohort of US veterans with GWI, we assessed regional brain volumes for their associations with GWI, and quantified the relationships between any regional volumetric changes and GWI symptoms.
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Methods: Structural magnetic resonance imaging (MRI) scans from 111 veterans with GWI (Age=49±6, 88% Male) and 59 healthy controls (age=51±9, 78% male) were collected at the California War Related Illness and Injury Study Center (WRIISC-CA) and from a multicenter study of the Parkinson’s Progression Marker Initiative (PPMI), respectively. Individual MRI volumes were segmented and parcellated using FreeSurfer. Regional volumes of 19 subcortical, 2
68 cortical, and 3 brainstem structures were evaluated in the GWI cohort relative to healthy controls. The relationships between regional volumes and GWI symptoms were also assessed. Results: We found significant subcortical atrophy, but no cortical differences, in the GWI group relative to controls, with the largest effect detected in the brainstem, followed by the ventral diencephalon and the thalamus. In a subsample of 58 veterans with GWI who completed the Chronic Fatigue Scale (CFS) inventory of Centers for Disease Control and Prevention (CDC),
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smaller brainstem volumes were significantly correlated with increased severities of fatigue and
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depressive symptoms.
Conclusion: The findings suggest that brainstem volume may be selectively affected by GWI,
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and that the resulting atrophy could in turn mediate or moderate GWI-related symptoms such as
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research focusing on GWI pathology.
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fatigue and depression. Consequently, the brain stem should be carefully considered in future
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Keywords: Gulf War; Chronic multi-symptom illness; Magnetic resonance imaging; Brain
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volumetric analysis; Brainstem; Fatigue
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Introduction From August 1990 to June 1991, nearly 700,000 veterans served in Operation Desert Shield/Desert Storm (ODS/DS). After returning from combat, about 25% to 32% of the veterans developed multiple chronic symptoms, including widespread pain, persistent problems with memory, mood, and cognition, fatigue, respiratory problems, gastrointestinal symptoms, skin
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abnormalities, and other medical problems (White et al., 2016). This constellation of symptoms is often referred to as Gulf War Illness (GWI; The 2014 report by the Department of Veterans
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Affairs (VA) Research Advisory Committee (RAC) on Gulf War Veterans' Illnesses,
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https://www.va.gov/RAC-GWVI/RACReport2014Final.pdf) and has been endorsed for use in clinical and research diagnoses of chronic multisymptom illness (CMI) by the Centers for
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Disease Control and Prevention (CDC) (Fukuda et al., 1998). A similar definition of GWI, the Kansas GWI criteria, is frequently used in clinical and research settings and includes broader
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symptom sets and exclusionary criteria (Steele, 2000). Symptoms of GWI are variable, and the potential mechanisms underlying neuronal damage or dysfunction of the brain are unknown.
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However, several different factors have been implicated in GWI, including exposure to neurotoxicants (Golomb, 2008; Valk & van der Knaap, 1992) and immunologic or genetic conditions. (Georgopoulos et al., 2017; Georgopoulos et al., 2016; Meuer, Schlossman, &
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Reinherz, 1982).
Symptoms of GWI remain persistent over time for the majority of affected veterans, with
few experiencing improvement or recovery (Gwini, Forbes, Kelsall, Ikin, & Sim, 2015; Hotopf et al., 2003; Ozakinci, Hallman, & Kipen, 2006). Previous GWI research has sought to identify the epidemiology of GWI, the conditions in theater that may cause GWI, GWI-related nervous system dysfunctions, pathophysiological mechanisms underlying GWI, and experimental models 4
of GWI and its causation (White et al., 2016). Identification of GWI-related central nervous system damage will be essential to facilitate understanding of the pathophysiologic mechanisms, and eventually benefit strategies for effective treatment of GWI. A recent study employing structural magnetic resonance imaging (MRI) and automated volumetric measurement reported brain atrophy in subcortical regions such as the brainstem,
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cerebellum, and the thalamus in 17 veterans with GWI relative to 24 controls (Christova et al., 2017). Another study (Rayhan et al., 2013) found that a subgroup of 10 GWI patients with
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orthostatic tachycardia after exercise exhibited volume decreases in the left lingual gyrus, right
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pons, and right medulla of the brainstem in comparison with 10 controls. Both studies, however, had relatively small sample sizes, which may have limited the generalizability of the GWI-
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related structural abnormalities. Apart from these reports, there is a general lack of robust evidence to support regional atrophy patterns as a potential indicator of GWI. Furthermore, it
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remains unclear if and how such patterns specifically relate to GWI symptoms.
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In this study, we assessed potential regional brain atrophy patterns associated with GWI in a large clinical cohort. We tested potential associations between regional subcortical atrophy levels and each GWI symptomatic domain to explore the role of structural changes underpinning
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GWI.
Methods
Participants
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Gulf War illness cohort: All veterans underwent MRI between 2011 and 2017 at the California War Related Illness and Injury Study Center (WRIISC-CA) at the Veterans Affairs Palo Alto Health Care System (VAPAHCS). This study was approved by the Stanford University and VAPAHCS Institutional Review Boards, and written informed consent to analyze clinical data was obtained from all participants. All participants underwent an interview by a clinical psychologist, neurologist and psychiatrist at WRIISC-CA. Self-reported information, including
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the chronic fatigue scale (CFS) and review of systems (ROS) was also obtained. Table 1 shows
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the criteria for participant selection to the GWI cohort. Inclusion criteria for the GWI participants were: 1) enlistment in the US armed forces, deployed to the Persian Gulf War between August
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1990 and 1991, and exposed to combat of Persian Gulf War; 2) symptom persistence for at least
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the past six months; and 3) symptoms that met both CDC/CMI (Fukuda et al., 1998) and Kansas (Steele, 2000) case definitions of GWI. One hundred and eleven veterans met all of the above
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GWI criteria (Table 1).
CDC/CMI (Fukuda et al., 1998) Inclusion Criteria Served in Operation Desert Shield/Desert Storm Meet at least 2 of the below symptom items Symptoms present in the last 6 months Combat duration August 1990 – 1991 Combat location Saudi Arabia; Kuwait; Iraq (1) Fatigue and sleep problems Symptom items (2) Mood and cognitive, neurologic problems (3) Musculoskeletal pains
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Definition
Symptom duration 6 months and longer Self-reports: Chronic fatigue scale (CFS)[2], Symptom assessments for the review of systems (ROS)[3] present study Clinician’s report if self-report is missing Meet Kansas Exclusions but included
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Kansas (Steele, 2000) Served in Operation Desert Shield/Desert Storm Meet at least 3 of the below symptom items Symptoms present in the last 6 months August 1990 – 1991 Saudi Arabia; Kuwait; Iraq (1) Fatigue and sleep problems (2) Mood and cognitive, neurologic problems (3) Pain symptoms (4) Gastrointestinal symptoms (5) Respiratory symptoms (6) Skin symptoms Chronic [1] Self-reports: Chronic fatigue scale (CFS)[2], review of systems (ROS)[3] Clinician’s report if self-report is missing Non-neurological disorders: history of cancer, heart disease, HIV, diabetes, Lyme’s disease, lupus, and hepatitis
Current neurological disorders: Current neurological disorders: multiple sclerosis (MS), amyotrophic lateral multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), epilepsy, bipolar, and sclerosis (ALS), epilepsy, bipolar, and schizophrenia schizophrenia [1] Kansas criteria calls for symptoms present in past 12 months; however, in this study, the self-report scales asked about symptom presence in past 6 months. [2] CFS = CDC Chronic Fatigue Scale (CFS) Symptom Inventory (http://www.cdc.gov/cfs/case-definition/) [3] ROS = review of systems (ROS); questionnaire designed at the WRIISC, inquiring about 47 symptoms over the past year. Meet Kansas Exclusions and excluded
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Table 1. Gulf War Illness Criteria for subject’s selection.
Control: Because nearly all of the WRIISC veterans presented more than one chronic
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symptom that potentially meet CDC/CMI criteria, no imaging data of healthy veterans
(regardless of combat locations) was obtained in WRIISC-CA. In this study, the MRI and
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demographic data of healthy control (HC) group was collected from a multi-center Parkinson’s
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Progression Marker Initiative (PPMI) study repository. The PPMI is a 5-year observational, international, longitudinal study supported by the Michael J. Fox foundation. PPMI recruited 30-
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80 years old HC individuals at multiple centers with similar structural MRI protocols who were free of neuropsychiatric disorders with preserved activities of daily living. PPMI allows sharing
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of MRI images and demographic information with qualified researchers and investigators through LONI IDA (Image & Data Archive powered by the Laboratory of Neuro Imaging, https://ida.loni.usc.edu). All PPMI protocols were approved by the institutional review board of each participating study center and all PPMI participants provided written informed consent. To
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match the demographic and imaging features with the veterans with GWI recruited in our clinic, HC participants were selected from PPMI if: 1) participants were between 35 and 70 years old; 2) MRI was collected on 3-Tesla scanners; and 3) volume voxel sizes ranged between 0.7 and 1.3 mm3. Using these criteria, MRI scans of 59 HC participants were selected from PPMI (Table 2). 7
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Information GWI HC Number 111 59 Age 49 ± 6 51 ± 9 Sex (men:women) 98:13 46:13 Education (years) * 14 ± 2 15 ± 3 Ethnicity (W:B:L:O)[1]* 91:6:3:5 45:4:1:0 Handedness (right:left:ambidextrous)* 96:8:2 47:8:2 Combat Expose 1990-91 Persian Gulf War none Duration in Combat (months)* 6 ± 2.6 — Symptoms meet CDC CMI 111 — Symptoms meet Kansas GWI 111 — Symptoms meet Kansas Exclusions 55 — TBI (none:mild:moderate)[2] 52:49:10 none Current PTSD (yes:no)[3]* 71:32 none Current Depression (MDD:DPD:none)[4]* 32:35:36 none Alcohol abuse (yes:no)[5]* 58:50 — MRI data Source WRIISC-CA PPMI Estimated total intracranial volume (milliliter) 1545.6 1545.3 [1] Ethnicity: W=White/Caucasian, B=Black/African American, L=Latino/Hispanic, O=Other races including American Indian and Hawaiian Pacific [2] TBI: history of TBI, defined from self-reports and neuropsychiatric interview. [3] Current PTSD: determined from DSM-IV-TR Axis I Disorders. [4] Current Depression: MDD=Major Depressive Disorder, DPD=Depressive Disorder. Both are determined from DSM-IV-TR Axis I Disorders. [5] Alcohol abuse: clinician’s reports for substances abuse. * Missing information: 9 educational information, 8 Ethnic information, 5 Handedness information, 10 Combat duration information, 8 clinical diagnoses for PTSD and depression, and 3 alcohol information were missing. — information not obtained. No significant group difference was observed in terms of age, sex, years of education, handedness, and eTIV.
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Table 2. Demographic and clinical characteristics of participants included in analyses.
MR Image Acquisition and Volume Estimation T1-weighted MRI (T1WI) scans of all participants were acquired from 3-Tesla MRI scanners by three different vendors (Supplementary Table S1). The acquired 3-dimensional T1WI data were
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processed with cortical reconstruction, noise and distortion correction, volume segmentation, intensity normalization, and parcellations using the current stable release FreeSurfer version 6.0.0 software package (http://surfer.nmr.mgh.harvard.edu/) (Fischl et al., 2002; Fischl et al., 2004). A Freesurfer standard parcellation atlas (the Desikan-Killiany atlas) with anatomically labeled regions of interest (ROIs) was then spatially warped from the Talairach template onto 8
each individual T1WI. Quantitative volume measures in 68 left and right cortical regions, 19 left and right subcortical regions, and three brainstem subregions, as well as the estimated total intracranial volume (eTIV) were extracted from all parcellated brain regions of each T1WI (see supplementary Figure S1 and supplementary Table S2). Note, none of these volume measures were affected by the FreeSurfer version 6 -related measurement bug (http://freesurfer.net/fswiki/BrainVolStatsFixed). The resulting segmentation and parcellation
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maps of each individual T1WI were checked for accuracy by a neuroanatomical expert (Y.Z.),
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blinded to individual’s information. Because visually incorrect parcellation was identified in
bilateral temporal cortices of only one GWI individual, volume outcomes measures in all cortical
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regions of this case were excluded from further analyses. Although FreeSurfer processing and
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analyses are considered generally reliable across different data collection sites regardless of MRI systems used (Jack et al., 2008; Jovicich et al., 2013), we still examined FreeSurfer volume
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outcomes of this data from multiple vendors and scan protocols, to determine whether the variations in scanners and voxel sizes affected the FreeSurfer volume measures, and in which
Statistics
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case the data would require statistical adjustments.
To account for the different T1WI acquisitions in the multicenter HC data, we performed
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the statistical analyses based on parcellated ROI volumes instead of voxel-based morphometry. All statistics were performed in SPSS (IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY: IBM Corp.). Prior to testing for any potential GWI-related effects, we performed a multivariate analysis of covariance (MANCOVA) for each confounding factor to test which variables were significantly associated with brain volume, thus requiring consideration in the statistical design. In this MANCOVA model, volumes of 87 cortical and subcortical regions (19 9
subcortical regions and 68 cortical regions, separately) were used as multiple dependent variables together with each confounding factor (e.g., age, sex, eTIV, etc.). Supplementary Table S3 summarized the relationship between each confounding factors and ROI measures. For both GWI and HC cohorts, eTIV, age and voxel size were significantly associated with ROI volume measures. In particular, voxel size that result from different T1WI parameters, rather than scanner differences, was the major factor that affect Freesurfer measurement (Supplementary
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Figure 2). On the other hand, in the GWI cohort, Kansas exclusion criteria, TBI severity, PTSD
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or depression status, alcohol abuse did not show significant correlation with regional brain
volumes, therefore these factors were not included in the Pearson’s correlation models. Because
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eTIV yielded an extremely strong association with regional volume measures, we expressed each
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regional brain volume as a proportion that normalized by eTIV, in order to better correct this confounding effect on an individual basis. For other factors that also significantly affect regional
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FreeSurfer volume measures, we regressed out the effects of age, gender, and voxel size by taking the residuals from the following two regression models below.
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1) Group differences in volumes of multiple regions (e.g., the cortical or subcortical regions, or total regions) between GWI and HC were tested based on MANCOVA models in which the normalized volume in multiple regions was the dependent variable, group was the
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main effect, and age, sex, and voxel sizes were the covariates. The MANCOVA models were performed initially to rule out the non-significant dependent variables and covariates from subsequent ANCOVA tests. For the MANCOVA analyses, the significance threshold was set to family-wise error rate of P-corr < 0.05. 2) Group differences in volume of a single region were tested using ANCOVA, in which the normalized volume was the dependent variable, group was the main effect, and age, sex, and 10
voxel sizes were the covariates. For ANCOVA analyses of group differences (test No.=14), significance was set to a false discovery rate adjusted critical p < 0.0268 based on Benjamini– Hochberg procedure (Benjamini & Hochberg, 1995). Effect sizes (Cohen's d) for group differences were determined using the d-value standardized coefficient (Rosnow, Rosenthal, & Rubin, 2000) from the following formula:
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d = 2 ∗ 𝑇𝑣𝑎𝑙𝑢𝑒/√𝑑𝑒𝑔𝑟𝑒𝑒𝑠 𝑜𝑓 𝐹𝑟𝑒𝑒𝑑𝑜𝑚
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3) Pearson’s correlation coefficients between normalized volumes and symptom severity
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(intensity and frequency) were assessed in a subset of 64 veterans with GWI who had completed the CFS Symptom Inventory. To avoid overfitting in subsequent linear regressions, we did not
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include age and voxel size in the correlation models. Instead, partial correlation models by adding age and voxel size as controlling covariates were performed separately to test if the
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observed significant correlations were altered after controlling for these covariates. For correlation analyses (test No.=128), significance was set to a false discovery rate adjusted critical
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Results
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p < 0.0252.
The group characteristics are summarized in Table 2. No significant differences between
groups were seen in terms of age, sex, years of education, handedness, and global head size as measured by eTIV. The Mean ± Std. volumes (mm3) in each group of all FreeSurfer parcellations can be found in supplementary Table S2.
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Figure 1 shows the group differences in volumes of multiple left/right/total cortical and subcortical regions. Consistent with a previous report (Christova et al., 2017), the MANCOVA analyses demonstrated that veterans with GWI had smaller left, right, or total subcortical volumes relative to HC. In contrast, there were no significant group differences in volumes in the
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left, right, or the total cortical regions.
Figure 1. Group differences in overall cortical and subcortical volumes. Scatter and line plots of eTIV normalized volumes (Z-Residuals=standardized residuals when age, sex, and voxel-size effects were regressed out).
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In addition, there were no significant interactions with age, sex, or laterality for the
observed group differences in subcortical volumes (see Supplementary Table S4). The subsequent ANCOVA analyses of the group differences in individual subcortical volumes were performed on regional volumes in both left and right hemispheres.
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Mean volumes and ANCOVA results of group differences for each subcortical region and brainstem subregion are summarized in Table 3. The group differences in volumes of each subcortical region are shown in Figure 2. Three out of 10 subcortical regions showed significant group differences: 1) the brainstem showed the most prominent volume loss (p=0.0004, Cohen’s d=-0.57) in the GWI group relative to HC; 2) the ventral diencephalon, which anatomically overlaps with the midbrain, also showed volume decreases (p=0.008, Cohen’s d =-0.42) in the
[1] *
GWI vs. HC Cohen’s d effect size p-values
0.7308 (0.0727) 0.2022 (0.0250) 0.1379 (0.0115) 0.0527 (0.0039) 0.0941 (0.0079) 0.0456 (0.0052) 0.0639 (0.0066) 0.0254 (0.0027) 0.0549 (0.0047) 0.0223 (0.0025)
0.7325 (0.0729) 0.2002 (0.0330) 0.1442 (0.0144) 0.0535 (0.0052) 0.0966 (0.0098) 0.0441 (0.0059) 0.0621 (0.0081) 0.0262 (0.0032) 0.0539 (0.0058) 0.0226 (0.0025)
-0.15 -0.07 -0.57** -0.42** -0.38** 0.20 0.20 -0.22 0.05 -0.23
0.32 0.63 0.0004 0.008 0.015 0.22 0.20 0.16 0.76 0.14
0.0413 (0.0045) 0.1025 (0.0117) 0.0308 (0.0038) 0.1765 (0.0188)
-0.54** -0.27 -0.99** -0.53**
0.001 0.08 0.0000 0.001
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HC Normalized volume [1] Mean (SD) (%)
0.0359 (0.0028) 0.1007 (0.0009) 0.0271 (0.0032) 0.1691 (0.0130)
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Subcortical regions Cerebellar Cortex Cerebellar White Matter Brainstem Ventral Diencephalon Thalamus Caudate Putamen Pallidum Hippocampus Amygdala Brainstem subregions Midbrain Pons Medulla Total Brainstem
GWI Normalized volume [1] Mean (SD) (%)
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Group
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(p=0.015, Cohen’s d =-0.38) in the veterans with GWI relative to HC.
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GWI cohort relative to HC; and 3) the thalamus showed a marginally significant volume loss
Normalized volume = Regional volume (mm3) / eTIV (mm3) × 100. 0.0268 ≤ uncorrected p <0.05, ** False discovery rate (FDR)-adjusted p< 0.0268
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Table 3. Summary of the mean (SD) volumes and ANCOVA results of group differences in each individual subcortical region, and the brainstem subregions.
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Figure 2. Group differences in volumes of subcortical regions. A, anatomical distribution of regions with group differences, colors indicate the Cohen’s d in each displayed region. B, scatter plots of eTIV normalized volumes (Z-Residuals=standardized residuals when age, sex, and voxel-size effects were regressed out).
The group differences in volumes of the three brainstem subregions are shown in Figure
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3. Compared to HC, GWI patients had significantly smaller midbrain volumes (p=0.001, Cohen’s d=-0.54), a non-significantly smaller pons (p=0.08, Cohen’s d=-0.27), and significantly
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smaller medulla volumes (p<0.001, Cohen’s d=-0.99).
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Figure 3. Group differences in volumes of the three brainstem subregions. A, color labels the Cohen’s d in each brainstem subregion. B, scatter and line plots of eTIV normalized volumes (ZResiduals=standardized residuals when age, sex, and voxel-size effects were regressed out).
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Table 4 shows Pearson’s correlations between brainstem volumes (including the eTIV normalized total brainstem, midbrain, pons and medulla) and each GWI symptom intensity and
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frequency in the GWI subsample (N = 64) without adjusting for covariates, such as age, sex, and voxel sizes. Figure 4 depicts distributions of brainstem volumes along none/mild, moderate and severe intensity levels in four most robust relationships: total brainstem volume and fatigue, medulla volume and shortness of breath, midbrain volume and memory loss, midbrain volume
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and depression. Smaller total brainstem volumes were significantly correlated with increased fatigue intensity (r=-0.36, p=0.003) and frequency (r=-0.30, p=0.017), and were also correlated with increased depression intensity (r=-0.33, p=0.008). For brainstem subregions, specifically, atrophy in midbrain correlated significantly with increased intensity (r=-0.33, p=0.007) and frequency (r=-0.29, p=0.021) of memory impairment, also the depression intensity (r=-0.37, 15
p=0.003); was significantly associated with increased fatigue intensity (r=-0.35, p=0.005) and frequency (r=-0.31, p=0.014); in addition, atrophy in the medulla was substantially correlated with increased severity (r=-0.38, p=0.002) and frequency (r=-0.35, p=0.005) of respiratory difficulties. There was also a strong correlation between medulla atrophy and increased abdominal pain (r=-0.30, p=0.015). After controlling for covariates, the correlations between
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brainstem (total and each subregion) volumes and symptomatic intensities remained significant. Table 4. Pearson’s correction coefficient (r) and significance (p) between brainstem volumes and GWI symptom intensities and frequencies in 64 veterans with GWI. Stat Total Brainstem Midbrain Volume Pons Volume Medulla Volume Volume[1] Intensity Frequency Intensity Frequency Intensity Frequency Intensity Frequency Fatigue Fatigue r -0.36** -0.30** -0.26* -0.15 -0.35** -0.31** -0.20 -0.16 (p) (0.003) (0.017) (0.040) (n.s.) (0.005) (0.014) (n.s.) (n.s.) Sleep Unrefreshing r -0.13 -0.00 -0.09 0.01 -0.09 0.02 -0.18 -0.07 Sleep (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Sleeping r -0.12 -0.06 -0.14 -0.06 -0.12 -0.04 -0.03 -0.04 problems (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Pain Muscle pains r 0.04 0.05 -0.01 0.03 0.07 0.09 0.01 -0.08 (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Joint pains r -0.00 -0.09 0.02 -0.07 0.06 -0.03 -0.16 -0.24 (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Cognition Memory r -0.27* -0.24 -033** -0.29** -0.23 -0.22 -0.07 -0.06 problem (p) (0.036) (n.s.) (0.007) (0.021) (n.s.) (n.s.) (n.s.) (n.s.) Concentration r -0.18 -0.15 -0.17 -0.14 -0.16 -0.12 -0.10 -0.11 problem (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Mood Depression r -0.33** -0.20 -0.37** -0.27* -0.28* -0.15 -0.20 -0.14 (p) (0.008) (n.s.) (0.003) (0.031) (0.028) (n.s.) (n.s.) (n.s.) Neurologic Headaches r -0.00 -0.02 -0.07 -0.04 0.04 0.01 -0.05 -0.05 (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Sensitive to r 0.09 0.07 -0.04 -0.00 0.19 0.15 -0.21 -0.18 light (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Feeling feverish r -0.15 -0.22 -0.08 -0.13 -0.13 -0.18 -0.16 -0.23 or Chills (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) G.I. Diarrhea r -0.01 -0.02 0.12 0.10 0.01 -0.03 -0.18 -0.10 (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Nausea r -0.11 -0.08 -0.15 -0.11 -0.08 -0.05 -0.10 -0.09 (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Abdominal Pain r -0.17 -0.19 -0.08 -0.13 -0.11 -0.16 -0.30** -0.20 (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (0.015) (n.s.) Respiratory Shortness of r -0.14 -0.13 -0.22 -0.13 -0.01 -0.02 -0.38** -0.35** breath (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (0.002) (0.005) Sinus or nasal r -0.01 -0.03 -0.07 -0.05 -0.04 -0.02 -0.09 -0.01 problems (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) [1] Total brainstem volume = a summarized volume of the three brainstem subregions, and normalized by the eTIV.
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Symptom Symptoms Domains
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Intensity = self-reported symptom intensity in the past 6 months from the CSF inventory: 0=none, 1=mild, 2=moderate, 3=severe. Frequency = self-reported symptom frequency in the past 6 months from the CSF inventory: 0=never, 1=little of the time, 2=some of the time, 3=most of the time, 4=all of the time * 0.0252 ≤ uncorrected p <0.05, ** False discovery rate (FDR)-adjusted p< 0.0252, n.s. p>0.05
Figure 4. Examples of significant correlations between normalized brainstem volume and intensity levels of the GWI s symptoms, in relations between: total brainstem volume and fatigue, medulla volume and shortness of breath, midbrain volume and memory loss, midbrain volume and depression.
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Discussion Using a cohort of 111 veterans with GWI, we identified a significant reduction in subcortical brain volumes relative to 59 controls. The most atrophy was observed in the brainstem, ventral diencephalon, and thalamus. The study further demonstrated that reduced brainstem volumes are associated with chronic fatigue and depressive mood, which are core
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symptoms of GWI.
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Brainstem atrophy in GWI
The finding that GWI is associated with volume loss in the brainstem, as evidenced by
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our Freesurfer volumetric approach in a large sample, mirrors previous observations in veterans
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with GWI (Christova et al., 2017) and veterans with GWI who also demonstrated exertional tachycardia (Rayhan et al., 2013). Previous functional imaging studies have provided evidence
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that GWI is characterized by reduced brainstem metabolism measured by N-acetylaspartate (NAA) from MR spectroscopy (Haley, Marshall, et al., 2000; Weiner et al., 2011). The brainstem contains major autonomic and neurotransmitter pathways and nuclei that regulate the sympathetic and parasympathetic nervous systems and play important roles in sensation, motion,
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cardiovascular system control, respiratory control, pain sensitivity control, alertness, awareness, and consciousness (Simic et al., 2017). Several studies (Haley et al., 2004; Rayhan et al., 2013) have suggested that GWI symptoms, such as chronic diarrhea, dizziness, and fatigue, as well as changes in cardiovascular indices are linked to autonomic dysregulation. Furthermore, potential exposure to neurotoxic substances in GWI (Brimfield, 2012) is also thought to produce an accumulation of certain neurotransmitters (e.g., acetylcholine released partly from the 18
brainstem), and lead to neuroinflammation. Consequently, evidence of brainstem structural deficiencies alludes to the presence of autonomic dysfunctions and dysregulated neurotransmission that may produce GWI symptoms. Substructures inside the brainstem, such as the red nucleus, substantia nigra, periaqueductal gray area, locus coeruleus, reticular formation, and white matter fibers that
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interconnect these nuclei, are differentially implicated in autonomic control (Al-Khazraji & Shoemaker, 2018). These structures are difficult to delineate and quantitatively measure on
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conventional MRI acquisitions because of the limited contrast and resolution in small structures.
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Although this study showed that brainstem atrophy in GWI was most severe in the medulla, followed by the midbrain, and lastly a trend in the pons, the current MRI scan protocol does not
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afford us the necessary resolution to explore substructures in these important regions. Analytic
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pathways are underway.
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techniques that aim to distinguish and extract these functionally relevant brainstem nuclei and
Atrophy of other brain regions in GWI
Apart from brainstem atrophy, (Christova et al., 2017). also reported atrophy of the
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cerebellum, amygdala, and diencephalon in veterans with GWI. Previous studies, mostly employing functional imaging, have also reported that GWI veterans showed abnormal function in the basal ganglia, including putamen and globus pallidus (Haley, Fleckenstein, et al., 2000; Weiner et al., 2011), caudate nuclei (Calley et al., 2010), thalamus (Haley et al., 2009; Liu et al., 2011), and hippocampus (Li et al., 2011). In line with the previous studies, our study found that brain atrophy of GWI was notable in selective regions including the brainstem, midbrain ventral 19
diencephalon, and the thalamus. In particular, this selective atrophic pattern was greater in the brainstem than in the ventral diencephalon and the thalamus, suggesting that GWI-related atrophy exhibits a roughly inferior-to-superior gradient of regional vulnerability. Furthermore, this pattern resembles a degeneration pattern of neurological, cognitive, and mood dysfunctions (Grinberg, Rueb, & Heinsen, 2011), and therefore may contribute to our understanding of the
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neuropathology underlying GWI. It is noteworthy that we did not observe any abnormal cerebellar atrophy in GWI as
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reported by two previous studies (Christova et al., 2017; Rayhan et al., 2013). Given our sample
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size and careful sample selection, it unlikely that cerebellar atrophy is the most characteristic signs of GWI. This discrepancy may reflect effects of confounding variables, such as alcohol
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consumption, that should be considered in future investigations.
Brainstem structural associations with GWI symptoms
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A novel finding of this study is the association between brainstem atrophy and severity of chronic fatigue and depressive mood. This finding suggests that damaged neural structures in the brainstem are related to chronic symptom complaints in GWI. Moreover, linking clinical
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symptoms with alterations in these key brain structures provides important clues concerning potential disease modifying therapies. We found a robust correlation between reduced volume of the total brainstem
(particularly in pons) and increased fatigue severity (i.e. the intensity and frequency). Regardless of etiology, early conventional (MRI) studies (Dickinson, 1997) demonstrated that small, 20
discrete, and patchy brainstem and subcortical lesions can often be seen in individuals with CFS. More recent research has confirmed that individuals with CFS demonstrate regional, but not global atrophy, particularly in the brainstem, midbrain, and basal ganglia (Barnden et al., 2018; Finkelmeyer et al., 2018; Rocca et al., 2014). Our finding is also supported by previous observations that brainstem white matter atrophy is significantly correlated with the duration of
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fatigue in individuals with CFS (Barnden et al., 2011). GWI symptoms commonly include neurological complaints about memory,
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concentration, and dysregulated mood. Our finding of a significant correlation between
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brainstem atrophy with a memory loss and depression severity suggests that memory and mood dysfunctions are results from brainstem structural damages. Although the brain areas, such as
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amygdala, the hippocampus, the cerebellum, and the prefrontal cortex have been considered functioning the memory and emotions, the brainstem is also important because it modulates the
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memory and mood processes by communicating cerebral neurons via neurotransmitters such as epinephrine, dopamine, serotonin, glutamate, and acetylcholine (Myhrer, 2003). Several previous
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reports could be evidenced: a midbrain atrophy was identified in patients with dementia; abnormal signals in brainstem raphe nuclei (Supprian et al., 2004), and an overall decrease in brainstem volume (Soriano-Mas et al., 2011) were found in patients with depression. However,
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further investigations of the relationship between GW exposures, brainstem dysfunction/damage, and related neurobehavioral impairments are needed. The observation of the correlation between increased severity of shortness of breath and
medullary volume loss is particularly robust. The neuroanatomical correlates of respiratory dysregulation and dysfunction are in line with the literature, as the medulla is known to be one of the key structures regulating respiratory and other major autonomic functions (Harper, Woo, & 21
Alger, 2000; Pilowsky, 2014). Exposure to airborne toxins during the Gulf War have frequently led to direct pulmonary symptoms such as breathing difficulties. The finding of dyspnea related medulla damages suggests these respiratory difficulties may stem from a neurotoxic response in the brain stem in veterans with GWI. The observation that medullar atrophy correlates with abdominal pain is also interesting and is supported by evidence suggesting the brainstem is responsible for receiving and relaying gastrointestinal sensations (Travagli, Hermann, Browning,
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& Rogers, 2006). These findings merit further validation studies using improved imaging
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resolution and advanced analytic methods.
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Limitations
This study has some limitations that should be considered: 1) Although comparing
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veterans with GWI with veteran controls would be ideal, we were unable to obtain a sufficient number of symptom-free veterans and instead used a well-described civilian cohort as the control
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group. 2) It necessary to account for inter-scan heterogeneities and confounds in analytical models where multi-center data are pooled. Although in this study we used regression models to account voxel sizes, which we detected as a major confounding factor, we were unable to
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establish inter-scan harmonization approaches in absent of a group of control participants who were scanned using every scanner and T1WI protocol. 3) We did not perform the analyses at voxel-level, because the whole-brain template approaches, such as VBM, are potentially susceptible to image registration errors that may introduce spurious results. Technical improvements are needed to improve the accuracy of voxel-based approaches. Future directions 22
Our findings, together with previous GWI studies, raise the possibility that neuronal abnormalities in deep brain structures, especially the brainstem, may be key to a better understanding of this complex disorder. In this context, multi-modal imaging markers that allow detailed assessment of brainstem structures – such as functional nuclei and white matter pathways – may prove particularly useful. We recently reported that increased pain levels are associated with disrupted fibers which are connected through brainstem pain centers such as the
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periaqueductal gray, locus coeruleus and nucleus raphe magnus, and suggest the microstructural
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measurement in brainstem fibers could be an indicator for pain and pain regularization in
veterans with chronic pain (Zhang et al., 2019). Future studies using multimodal imaging
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analyses of brainstem pain-centers and pain-pathways in GWI-related pain will shed more light
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on this possibility.
Exposure to various neurotoxicants has been proposed as a contributor to GWI. Previous
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MRI analyses have been used to examine structural damage in relation to specific exposures such as suspected sarin/cyclosarin (Chao, Abadjian, Hlavin, Meyerhoff, & Weiner, 2011; Chao,
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Kriger, Buckley, Ng, & Mueller, 2014; Chao, Rothlind, Cardenas, Meyerhoff, & Weiner, 2010; Chao & Zhang, 2018; Chao, Zhang, & Buckley, 2015; Heaton et al., 2007), Khamisiyah smoke plume (Chao, Raymond, Leo, & Abadjian, 2017), and chemical alarms (Chao, 2016) and found
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associated atrophy in whole-brain tissues, cortical lobes, and the hippocampus. Future imaging studies should also consider the effects of exposures to other substances implicated in GWI, such as exposures to chemical weapons (nerve agents, sarin, etc.), burn-pits and smoke oils, side effects of taking cholinergic agents (e.g., pyridostigmine bromide, PB), with respect to brainstem dysfunction/damage.
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Conclusion This study demonstrated atrophy of deep brain regions – including the brainstem, ventral diencephalon, and thalamus – in veterans with GWI and detected significant correlations between brainstem atrophy and symptom severities of chronic fatigue and depressed mood among individuals with GWI. These findings build upon past studies by refining the specific
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brain regions implicated in GWI, and encourage further investigations of GWI-related symptoms
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and neurotoxic exposures in relation to abnormalities in brainstem structures and pathways.
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CRediT Author Statement
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Yu Zhang: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Visualization
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Timothy Avery: Validation, Investigation, Resources, Writing - Review & Editing Andrei A. Vakhtin: Software, Validation, Investigation, Writing - Review & Editing
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Danielle C. Mathersul: Investigation, Writing - Review & Editing Eric Tranvinh: Investigation, Resources, Writing - Review & Editing Max Wintermark: Investigation, Resources, Writing - Review & Editing Payam Massaband: Resources, Writing - Review & Editing
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J. Wesson. Ashford: Conceptualization, Writing - Review & Editing, Supervision, Funding acquisition
Peter J. Bayley: Conceptualization, Supervision, Writing - Review & Editing Ansgar J. Furst: Conceptualization, Methodology, Software, Data Curation, Writing - Review & Editing, Supervision, Project administration 24
Funding None of the authors have commercial or financial involvements that might present a conflict of interest in connection with this manuscript.
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This study was supported by the WRIISC, a National VA Post-Deployment Health Resource and the Department of Veterans Affairs, Office of Academic Affiliations WRIISC
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Fellowship program.
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Declaration of interests
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Acknowledgement
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☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
The authors would like to thank all veterans for volunteering to participate in this project. Without their generous support this research would not have been possible.
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Further, the authors thank Ms. Stacy Moeder for administrating the California War
Related Illness and Injury Study Center (WRIISC-CA) research programs, Mr. Dr. Miguel T. Robinson and Mr. Gerald E. Robinson for managing the WRIISC-CA database of veterans’ selfreport questionnaires.
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A part of the data used in this article was obtained from the Parkinson’s Progression Markers Initiative (PPMI) database. PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson's Research and funding partners, including a consortium of industry players, non-profit organizations and private individuals. Please visit
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PPMI website (http://www.ppmi-info.org/) for details regarding the PPMI sponsors.
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29
PII:
S0161-813X(20)30028-0
DOI:
https://doi.org/10.1016/j.neuro.2020.02.006
Reference:
NEUTOX 2591
To appear in:
Neurotoxicology
Received Date:
5 October 2019
Revised Date:
30 January 2020
Accepted Date:
16 February 2020
Please cite this article as: Zhang Y, Avery T, Vakhtin AA, Mathersul DC, Tranvinh E, Wintermark M, Massaband P, Ashford JW, Bayley PJ, Furst AJ, Brainstem Atrophy in Gulf War Illness, Neurotoxicology (2020), doi: https://doi.org/10.1016/j.neuro.2020.02.006
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
Brainstem Atrophy in Gulf War Illness
Yu Zhang, MD1, Timothy Avery, PhD1,2, Andrei A. Vakhtin, PhD1,2, Danielle C. Mathersul, PhD1,2, Eric Tranvinh, MD3, Max Wintermark, MD1,3, Payam Massaband, MD4,5, J. Wesson.
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Ashford, MD, PhD1,2, Peter J. Bayley, PhD1,2, Ansgar J. Furst, PhD1,2,6,7
War Related Illness and Injury Study Center (WRIISC), Veterans Affairs Palo Alto Health
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Care System
Psychiatry and Behavioral Sciences, Stanford University School of Medicine
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Neuroradiology, Stanford University School of Medicine
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Radiology, VA Palo Alto Health Care System
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Radiology, Stanford University School of Medicine
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Neurology and Neurological Sciences, Stanford University
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Polytrauma System of Care (PSC), VA Palo Alto Health Care System
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Corresponding author: Yu Zhang, MD. War Related Illness and Injury Study Center (WRIISC). VA Palo Alto Health Care System. 3801 Miranda Ave, Mailcode 151Y. Palo Alto,
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CA 94304-1290, USA. Tel: +1 650 4935000 xt 64575. Fax: +1 650 8523297. E-mails: [email protected]; [email protected]
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Highlights:
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The multiple chronic symptoms developed after 1990-1991 Gulf War, which is known as GWI, has no validated definition or validated diagnostic marker. Existing neuroimaging studies have been limited by small sample sizes, inconsistent GWI definition. Using automated brain MRI volumetric measurement in a large GWI cohort, this study identified subcortical atrophy in GWI. More importantly, this study detected a greatest brainstem abnormality in GWI veterans. This finding will promote further investigations of GWI into the brainstem substructure. A novel observation of brainstem volume in correlation with fatigue, memory loss, depression and breath difficulties in GWI demonstrated that the brainstem is a key structure that related to the GWI core symptoms.
Abstract
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Background: Gulf War illness (GWI) is a condition that affects about 30% of veterans who
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served in the 1990-91 Persian Gulf War. Given its broad symptomatic manifestation, including chronic pain, fatigue, neurological, gastrointestinal, respiratory, and skin problems, it is of
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interest to examine whether GWI is associated with changes in the brain. Existing neuroimaging studies, however, have been limited by small sample sizes, inconsistent GWI diagnosis criteria,
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and potential comorbidity confounds.
Objectives: Using a large cohort of US veterans with GWI, we assessed regional brain volumes for their associations with GWI, and quantified the relationships between any regional volumetric changes and GWI symptoms.
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Methods: Structural magnetic resonance imaging (MRI) scans from 111 veterans with GWI (Age=49±6, 88% Male) and 59 healthy controls (age=51±9, 78% male) were collected at the California War Related Illness and Injury Study Center (WRIISC-CA) and from a multicenter study of the Parkinson’s Progression Marker Initiative (PPMI), respectively. Individual MRI volumes were segmented and parcellated using FreeSurfer. Regional volumes of 19 subcortical, 2
68 cortical, and 3 brainstem structures were evaluated in the GWI cohort relative to healthy controls. The relationships between regional volumes and GWI symptoms were also assessed. Results: We found significant subcortical atrophy, but no cortical differences, in the GWI group relative to controls, with the largest effect detected in the brainstem, followed by the ventral diencephalon and the thalamus. In a subsample of 58 veterans with GWI who completed the Chronic Fatigue Scale (CFS) inventory of Centers for Disease Control and Prevention (CDC),
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smaller brainstem volumes were significantly correlated with increased severities of fatigue and
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depressive symptoms.
Conclusion: The findings suggest that brainstem volume may be selectively affected by GWI,
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and that the resulting atrophy could in turn mediate or moderate GWI-related symptoms such as
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research focusing on GWI pathology.
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fatigue and depression. Consequently, the brain stem should be carefully considered in future
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Keywords: Gulf War; Chronic multi-symptom illness; Magnetic resonance imaging; Brain
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volumetric analysis; Brainstem; Fatigue
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Introduction From August 1990 to June 1991, nearly 700,000 veterans served in Operation Desert Shield/Desert Storm (ODS/DS). After returning from combat, about 25% to 32% of the veterans developed multiple chronic symptoms, including widespread pain, persistent problems with memory, mood, and cognition, fatigue, respiratory problems, gastrointestinal symptoms, skin
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abnormalities, and other medical problems (White et al., 2016). This constellation of symptoms is often referred to as Gulf War Illness (GWI; The 2014 report by the Department of Veterans
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Affairs (VA) Research Advisory Committee (RAC) on Gulf War Veterans' Illnesses,
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https://www.va.gov/RAC-GWVI/RACReport2014Final.pdf) and has been endorsed for use in clinical and research diagnoses of chronic multisymptom illness (CMI) by the Centers for
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Disease Control and Prevention (CDC) (Fukuda et al., 1998). A similar definition of GWI, the Kansas GWI criteria, is frequently used in clinical and research settings and includes broader
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symptom sets and exclusionary criteria (Steele, 2000). Symptoms of GWI are variable, and the potential mechanisms underlying neuronal damage or dysfunction of the brain are unknown.
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However, several different factors have been implicated in GWI, including exposure to neurotoxicants (Golomb, 2008; Valk & van der Knaap, 1992) and immunologic or genetic conditions. (Georgopoulos et al., 2017; Georgopoulos et al., 2016; Meuer, Schlossman, &
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Reinherz, 1982).
Symptoms of GWI remain persistent over time for the majority of affected veterans, with
few experiencing improvement or recovery (Gwini, Forbes, Kelsall, Ikin, & Sim, 2015; Hotopf et al., 2003; Ozakinci, Hallman, & Kipen, 2006). Previous GWI research has sought to identify the epidemiology of GWI, the conditions in theater that may cause GWI, GWI-related nervous system dysfunctions, pathophysiological mechanisms underlying GWI, and experimental models 4
of GWI and its causation (White et al., 2016). Identification of GWI-related central nervous system damage will be essential to facilitate understanding of the pathophysiologic mechanisms, and eventually benefit strategies for effective treatment of GWI. A recent study employing structural magnetic resonance imaging (MRI) and automated volumetric measurement reported brain atrophy in subcortical regions such as the brainstem,
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cerebellum, and the thalamus in 17 veterans with GWI relative to 24 controls (Christova et al., 2017). Another study (Rayhan et al., 2013) found that a subgroup of 10 GWI patients with
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orthostatic tachycardia after exercise exhibited volume decreases in the left lingual gyrus, right
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pons, and right medulla of the brainstem in comparison with 10 controls. Both studies, however, had relatively small sample sizes, which may have limited the generalizability of the GWI-
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related structural abnormalities. Apart from these reports, there is a general lack of robust evidence to support regional atrophy patterns as a potential indicator of GWI. Furthermore, it
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remains unclear if and how such patterns specifically relate to GWI symptoms.
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In this study, we assessed potential regional brain atrophy patterns associated with GWI in a large clinical cohort. We tested potential associations between regional subcortical atrophy levels and each GWI symptomatic domain to explore the role of structural changes underpinning
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GWI.
Methods
Participants
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Gulf War illness cohort: All veterans underwent MRI between 2011 and 2017 at the California War Related Illness and Injury Study Center (WRIISC-CA) at the Veterans Affairs Palo Alto Health Care System (VAPAHCS). This study was approved by the Stanford University and VAPAHCS Institutional Review Boards, and written informed consent to analyze clinical data was obtained from all participants. All participants underwent an interview by a clinical psychologist, neurologist and psychiatrist at WRIISC-CA. Self-reported information, including
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the chronic fatigue scale (CFS) and review of systems (ROS) was also obtained. Table 1 shows
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the criteria for participant selection to the GWI cohort. Inclusion criteria for the GWI participants were: 1) enlistment in the US armed forces, deployed to the Persian Gulf War between August
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1990 and 1991, and exposed to combat of Persian Gulf War; 2) symptom persistence for at least
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the past six months; and 3) symptoms that met both CDC/CMI (Fukuda et al., 1998) and Kansas (Steele, 2000) case definitions of GWI. One hundred and eleven veterans met all of the above
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GWI criteria (Table 1).
CDC/CMI (Fukuda et al., 1998) Inclusion Criteria Served in Operation Desert Shield/Desert Storm Meet at least 2 of the below symptom items Symptoms present in the last 6 months Combat duration August 1990 – 1991 Combat location Saudi Arabia; Kuwait; Iraq (1) Fatigue and sleep problems Symptom items (2) Mood and cognitive, neurologic problems (3) Musculoskeletal pains
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Definition
Symptom duration 6 months and longer Self-reports: Chronic fatigue scale (CFS)[2], Symptom assessments for the review of systems (ROS)[3] present study Clinician’s report if self-report is missing Meet Kansas Exclusions but included
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Kansas (Steele, 2000) Served in Operation Desert Shield/Desert Storm Meet at least 3 of the below symptom items Symptoms present in the last 6 months August 1990 – 1991 Saudi Arabia; Kuwait; Iraq (1) Fatigue and sleep problems (2) Mood and cognitive, neurologic problems (3) Pain symptoms (4) Gastrointestinal symptoms (5) Respiratory symptoms (6) Skin symptoms Chronic [1] Self-reports: Chronic fatigue scale (CFS)[2], review of systems (ROS)[3] Clinician’s report if self-report is missing Non-neurological disorders: history of cancer, heart disease, HIV, diabetes, Lyme’s disease, lupus, and hepatitis
Current neurological disorders: Current neurological disorders: multiple sclerosis (MS), amyotrophic lateral multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), epilepsy, bipolar, and sclerosis (ALS), epilepsy, bipolar, and schizophrenia schizophrenia [1] Kansas criteria calls for symptoms present in past 12 months; however, in this study, the self-report scales asked about symptom presence in past 6 months. [2] CFS = CDC Chronic Fatigue Scale (CFS) Symptom Inventory (http://www.cdc.gov/cfs/case-definition/) [3] ROS = review of systems (ROS); questionnaire designed at the WRIISC, inquiring about 47 symptoms over the past year. Meet Kansas Exclusions and excluded
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Table 1. Gulf War Illness Criteria for subject’s selection.
Control: Because nearly all of the WRIISC veterans presented more than one chronic
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symptom that potentially meet CDC/CMI criteria, no imaging data of healthy veterans
(regardless of combat locations) was obtained in WRIISC-CA. In this study, the MRI and
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demographic data of healthy control (HC) group was collected from a multi-center Parkinson’s
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Progression Marker Initiative (PPMI) study repository. The PPMI is a 5-year observational, international, longitudinal study supported by the Michael J. Fox foundation. PPMI recruited 30-
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80 years old HC individuals at multiple centers with similar structural MRI protocols who were free of neuropsychiatric disorders with preserved activities of daily living. PPMI allows sharing
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of MRI images and demographic information with qualified researchers and investigators through LONI IDA (Image & Data Archive powered by the Laboratory of Neuro Imaging, https://ida.loni.usc.edu). All PPMI protocols were approved by the institutional review board of each participating study center and all PPMI participants provided written informed consent. To
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match the demographic and imaging features with the veterans with GWI recruited in our clinic, HC participants were selected from PPMI if: 1) participants were between 35 and 70 years old; 2) MRI was collected on 3-Tesla scanners; and 3) volume voxel sizes ranged between 0.7 and 1.3 mm3. Using these criteria, MRI scans of 59 HC participants were selected from PPMI (Table 2). 7
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Information GWI HC Number 111 59 Age 49 ± 6 51 ± 9 Sex (men:women) 98:13 46:13 Education (years) * 14 ± 2 15 ± 3 Ethnicity (W:B:L:O)[1]* 91:6:3:5 45:4:1:0 Handedness (right:left:ambidextrous)* 96:8:2 47:8:2 Combat Expose 1990-91 Persian Gulf War none Duration in Combat (months)* 6 ± 2.6 — Symptoms meet CDC CMI 111 — Symptoms meet Kansas GWI 111 — Symptoms meet Kansas Exclusions 55 — TBI (none:mild:moderate)[2] 52:49:10 none Current PTSD (yes:no)[3]* 71:32 none Current Depression (MDD:DPD:none)[4]* 32:35:36 none Alcohol abuse (yes:no)[5]* 58:50 — MRI data Source WRIISC-CA PPMI Estimated total intracranial volume (milliliter) 1545.6 1545.3 [1] Ethnicity: W=White/Caucasian, B=Black/African American, L=Latino/Hispanic, O=Other races including American Indian and Hawaiian Pacific [2] TBI: history of TBI, defined from self-reports and neuropsychiatric interview. [3] Current PTSD: determined from DSM-IV-TR Axis I Disorders. [4] Current Depression: MDD=Major Depressive Disorder, DPD=Depressive Disorder. Both are determined from DSM-IV-TR Axis I Disorders. [5] Alcohol abuse: clinician’s reports for substances abuse. * Missing information: 9 educational information, 8 Ethnic information, 5 Handedness information, 10 Combat duration information, 8 clinical diagnoses for PTSD and depression, and 3 alcohol information were missing. — information not obtained. No significant group difference was observed in terms of age, sex, years of education, handedness, and eTIV.
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Table 2. Demographic and clinical characteristics of participants included in analyses.
MR Image Acquisition and Volume Estimation T1-weighted MRI (T1WI) scans of all participants were acquired from 3-Tesla MRI scanners by three different vendors (Supplementary Table S1). The acquired 3-dimensional T1WI data were
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processed with cortical reconstruction, noise and distortion correction, volume segmentation, intensity normalization, and parcellations using the current stable release FreeSurfer version 6.0.0 software package (http://surfer.nmr.mgh.harvard.edu/) (Fischl et al., 2002; Fischl et al., 2004). A Freesurfer standard parcellation atlas (the Desikan-Killiany atlas) with anatomically labeled regions of interest (ROIs) was then spatially warped from the Talairach template onto 8
each individual T1WI. Quantitative volume measures in 68 left and right cortical regions, 19 left and right subcortical regions, and three brainstem subregions, as well as the estimated total intracranial volume (eTIV) were extracted from all parcellated brain regions of each T1WI (see supplementary Figure S1 and supplementary Table S2). Note, none of these volume measures were affected by the FreeSurfer version 6 -related measurement bug (http://freesurfer.net/fswiki/BrainVolStatsFixed). The resulting segmentation and parcellation
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maps of each individual T1WI were checked for accuracy by a neuroanatomical expert (Y.Z.),
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blinded to individual’s information. Because visually incorrect parcellation was identified in
bilateral temporal cortices of only one GWI individual, volume outcomes measures in all cortical
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regions of this case were excluded from further analyses. Although FreeSurfer processing and
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analyses are considered generally reliable across different data collection sites regardless of MRI systems used (Jack et al., 2008; Jovicich et al., 2013), we still examined FreeSurfer volume
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outcomes of this data from multiple vendors and scan protocols, to determine whether the variations in scanners and voxel sizes affected the FreeSurfer volume measures, and in which
Statistics
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case the data would require statistical adjustments.
To account for the different T1WI acquisitions in the multicenter HC data, we performed
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the statistical analyses based on parcellated ROI volumes instead of voxel-based morphometry. All statistics were performed in SPSS (IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY: IBM Corp.). Prior to testing for any potential GWI-related effects, we performed a multivariate analysis of covariance (MANCOVA) for each confounding factor to test which variables were significantly associated with brain volume, thus requiring consideration in the statistical design. In this MANCOVA model, volumes of 87 cortical and subcortical regions (19 9
subcortical regions and 68 cortical regions, separately) were used as multiple dependent variables together with each confounding factor (e.g., age, sex, eTIV, etc.). Supplementary Table S3 summarized the relationship between each confounding factors and ROI measures. For both GWI and HC cohorts, eTIV, age and voxel size were significantly associated with ROI volume measures. In particular, voxel size that result from different T1WI parameters, rather than scanner differences, was the major factor that affect Freesurfer measurement (Supplementary
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Figure 2). On the other hand, in the GWI cohort, Kansas exclusion criteria, TBI severity, PTSD
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or depression status, alcohol abuse did not show significant correlation with regional brain
volumes, therefore these factors were not included in the Pearson’s correlation models. Because
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eTIV yielded an extremely strong association with regional volume measures, we expressed each
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regional brain volume as a proportion that normalized by eTIV, in order to better correct this confounding effect on an individual basis. For other factors that also significantly affect regional
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FreeSurfer volume measures, we regressed out the effects of age, gender, and voxel size by taking the residuals from the following two regression models below.
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1) Group differences in volumes of multiple regions (e.g., the cortical or subcortical regions, or total regions) between GWI and HC were tested based on MANCOVA models in which the normalized volume in multiple regions was the dependent variable, group was the
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main effect, and age, sex, and voxel sizes were the covariates. The MANCOVA models were performed initially to rule out the non-significant dependent variables and covariates from subsequent ANCOVA tests. For the MANCOVA analyses, the significance threshold was set to family-wise error rate of P-corr < 0.05. 2) Group differences in volume of a single region were tested using ANCOVA, in which the normalized volume was the dependent variable, group was the main effect, and age, sex, and 10
voxel sizes were the covariates. For ANCOVA analyses of group differences (test No.=14), significance was set to a false discovery rate adjusted critical p < 0.0268 based on Benjamini– Hochberg procedure (Benjamini & Hochberg, 1995). Effect sizes (Cohen's d) for group differences were determined using the d-value standardized coefficient (Rosnow, Rosenthal, & Rubin, 2000) from the following formula:
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d = 2 ∗ 𝑇𝑣𝑎𝑙𝑢𝑒/√𝑑𝑒𝑔𝑟𝑒𝑒𝑠 𝑜𝑓 𝐹𝑟𝑒𝑒𝑑𝑜𝑚
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3) Pearson’s correlation coefficients between normalized volumes and symptom severity
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(intensity and frequency) were assessed in a subset of 64 veterans with GWI who had completed the CFS Symptom Inventory. To avoid overfitting in subsequent linear regressions, we did not
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include age and voxel size in the correlation models. Instead, partial correlation models by adding age and voxel size as controlling covariates were performed separately to test if the
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observed significant correlations were altered after controlling for these covariates. For correlation analyses (test No.=128), significance was set to a false discovery rate adjusted critical
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Results
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p < 0.0252.
The group characteristics are summarized in Table 2. No significant differences between
groups were seen in terms of age, sex, years of education, handedness, and global head size as measured by eTIV. The Mean ± Std. volumes (mm3) in each group of all FreeSurfer parcellations can be found in supplementary Table S2.
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Figure 1 shows the group differences in volumes of multiple left/right/total cortical and subcortical regions. Consistent with a previous report (Christova et al., 2017), the MANCOVA analyses demonstrated that veterans with GWI had smaller left, right, or total subcortical volumes relative to HC. In contrast, there were no significant group differences in volumes in the
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left, right, or the total cortical regions.
Figure 1. Group differences in overall cortical and subcortical volumes. Scatter and line plots of eTIV normalized volumes (Z-Residuals=standardized residuals when age, sex, and voxel-size effects were regressed out).
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In addition, there were no significant interactions with age, sex, or laterality for the
observed group differences in subcortical volumes (see Supplementary Table S4). The subsequent ANCOVA analyses of the group differences in individual subcortical volumes were performed on regional volumes in both left and right hemispheres.
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Mean volumes and ANCOVA results of group differences for each subcortical region and brainstem subregion are summarized in Table 3. The group differences in volumes of each subcortical region are shown in Figure 2. Three out of 10 subcortical regions showed significant group differences: 1) the brainstem showed the most prominent volume loss (p=0.0004, Cohen’s d=-0.57) in the GWI group relative to HC; 2) the ventral diencephalon, which anatomically overlaps with the midbrain, also showed volume decreases (p=0.008, Cohen’s d =-0.42) in the
[1] *
GWI vs. HC Cohen’s d effect size p-values
0.7308 (0.0727) 0.2022 (0.0250) 0.1379 (0.0115) 0.0527 (0.0039) 0.0941 (0.0079) 0.0456 (0.0052) 0.0639 (0.0066) 0.0254 (0.0027) 0.0549 (0.0047) 0.0223 (0.0025)
0.7325 (0.0729) 0.2002 (0.0330) 0.1442 (0.0144) 0.0535 (0.0052) 0.0966 (0.0098) 0.0441 (0.0059) 0.0621 (0.0081) 0.0262 (0.0032) 0.0539 (0.0058) 0.0226 (0.0025)
-0.15 -0.07 -0.57** -0.42** -0.38** 0.20 0.20 -0.22 0.05 -0.23
0.32 0.63 0.0004 0.008 0.015 0.22 0.20 0.16 0.76 0.14
0.0413 (0.0045) 0.1025 (0.0117) 0.0308 (0.0038) 0.1765 (0.0188)
-0.54** -0.27 -0.99** -0.53**
0.001 0.08 0.0000 0.001
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HC Normalized volume [1] Mean (SD) (%)
0.0359 (0.0028) 0.1007 (0.0009) 0.0271 (0.0032) 0.1691 (0.0130)
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Subcortical regions Cerebellar Cortex Cerebellar White Matter Brainstem Ventral Diencephalon Thalamus Caudate Putamen Pallidum Hippocampus Amygdala Brainstem subregions Midbrain Pons Medulla Total Brainstem
GWI Normalized volume [1] Mean (SD) (%)
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Group
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(p=0.015, Cohen’s d =-0.38) in the veterans with GWI relative to HC.
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GWI cohort relative to HC; and 3) the thalamus showed a marginally significant volume loss
Normalized volume = Regional volume (mm3) / eTIV (mm3) × 100. 0.0268 ≤ uncorrected p <0.05, ** False discovery rate (FDR)-adjusted p< 0.0268
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Table 3. Summary of the mean (SD) volumes and ANCOVA results of group differences in each individual subcortical region, and the brainstem subregions.
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Figure 2. Group differences in volumes of subcortical regions. A, anatomical distribution of regions with group differences, colors indicate the Cohen’s d in each displayed region. B, scatter plots of eTIV normalized volumes (Z-Residuals=standardized residuals when age, sex, and voxel-size effects were regressed out).
The group differences in volumes of the three brainstem subregions are shown in Figure
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3. Compared to HC, GWI patients had significantly smaller midbrain volumes (p=0.001, Cohen’s d=-0.54), a non-significantly smaller pons (p=0.08, Cohen’s d=-0.27), and significantly
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smaller medulla volumes (p<0.001, Cohen’s d=-0.99).
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Figure 3. Group differences in volumes of the three brainstem subregions. A, color labels the Cohen’s d in each brainstem subregion. B, scatter and line plots of eTIV normalized volumes (ZResiduals=standardized residuals when age, sex, and voxel-size effects were regressed out).
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Table 4 shows Pearson’s correlations between brainstem volumes (including the eTIV normalized total brainstem, midbrain, pons and medulla) and each GWI symptom intensity and
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frequency in the GWI subsample (N = 64) without adjusting for covariates, such as age, sex, and voxel sizes. Figure 4 depicts distributions of brainstem volumes along none/mild, moderate and severe intensity levels in four most robust relationships: total brainstem volume and fatigue, medulla volume and shortness of breath, midbrain volume and memory loss, midbrain volume
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and depression. Smaller total brainstem volumes were significantly correlated with increased fatigue intensity (r=-0.36, p=0.003) and frequency (r=-0.30, p=0.017), and were also correlated with increased depression intensity (r=-0.33, p=0.008). For brainstem subregions, specifically, atrophy in midbrain correlated significantly with increased intensity (r=-0.33, p=0.007) and frequency (r=-0.29, p=0.021) of memory impairment, also the depression intensity (r=-0.37, 15
p=0.003); was significantly associated with increased fatigue intensity (r=-0.35, p=0.005) and frequency (r=-0.31, p=0.014); in addition, atrophy in the medulla was substantially correlated with increased severity (r=-0.38, p=0.002) and frequency (r=-0.35, p=0.005) of respiratory difficulties. There was also a strong correlation between medulla atrophy and increased abdominal pain (r=-0.30, p=0.015). After controlling for covariates, the correlations between
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brainstem (total and each subregion) volumes and symptomatic intensities remained significant. Table 4. Pearson’s correction coefficient (r) and significance (p) between brainstem volumes and GWI symptom intensities and frequencies in 64 veterans with GWI. Stat Total Brainstem Midbrain Volume Pons Volume Medulla Volume Volume[1] Intensity Frequency Intensity Frequency Intensity Frequency Intensity Frequency Fatigue Fatigue r -0.36** -0.30** -0.26* -0.15 -0.35** -0.31** -0.20 -0.16 (p) (0.003) (0.017) (0.040) (n.s.) (0.005) (0.014) (n.s.) (n.s.) Sleep Unrefreshing r -0.13 -0.00 -0.09 0.01 -0.09 0.02 -0.18 -0.07 Sleep (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Sleeping r -0.12 -0.06 -0.14 -0.06 -0.12 -0.04 -0.03 -0.04 problems (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Pain Muscle pains r 0.04 0.05 -0.01 0.03 0.07 0.09 0.01 -0.08 (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Joint pains r -0.00 -0.09 0.02 -0.07 0.06 -0.03 -0.16 -0.24 (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Cognition Memory r -0.27* -0.24 -033** -0.29** -0.23 -0.22 -0.07 -0.06 problem (p) (0.036) (n.s.) (0.007) (0.021) (n.s.) (n.s.) (n.s.) (n.s.) Concentration r -0.18 -0.15 -0.17 -0.14 -0.16 -0.12 -0.10 -0.11 problem (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Mood Depression r -0.33** -0.20 -0.37** -0.27* -0.28* -0.15 -0.20 -0.14 (p) (0.008) (n.s.) (0.003) (0.031) (0.028) (n.s.) (n.s.) (n.s.) Neurologic Headaches r -0.00 -0.02 -0.07 -0.04 0.04 0.01 -0.05 -0.05 (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Sensitive to r 0.09 0.07 -0.04 -0.00 0.19 0.15 -0.21 -0.18 light (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Feeling feverish r -0.15 -0.22 -0.08 -0.13 -0.13 -0.18 -0.16 -0.23 or Chills (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) G.I. Diarrhea r -0.01 -0.02 0.12 0.10 0.01 -0.03 -0.18 -0.10 (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Nausea r -0.11 -0.08 -0.15 -0.11 -0.08 -0.05 -0.10 -0.09 (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) Abdominal Pain r -0.17 -0.19 -0.08 -0.13 -0.11 -0.16 -0.30** -0.20 (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (0.015) (n.s.) Respiratory Shortness of r -0.14 -0.13 -0.22 -0.13 -0.01 -0.02 -0.38** -0.35** breath (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (0.002) (0.005) Sinus or nasal r -0.01 -0.03 -0.07 -0.05 -0.04 -0.02 -0.09 -0.01 problems (p) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) (n.s.) [1] Total brainstem volume = a summarized volume of the three brainstem subregions, and normalized by the eTIV.
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Symptom Symptoms Domains
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Intensity = self-reported symptom intensity in the past 6 months from the CSF inventory: 0=none, 1=mild, 2=moderate, 3=severe. Frequency = self-reported symptom frequency in the past 6 months from the CSF inventory: 0=never, 1=little of the time, 2=some of the time, 3=most of the time, 4=all of the time * 0.0252 ≤ uncorrected p <0.05, ** False discovery rate (FDR)-adjusted p< 0.0252, n.s. p>0.05
Figure 4. Examples of significant correlations between normalized brainstem volume and intensity levels of the GWI s symptoms, in relations between: total brainstem volume and fatigue, medulla volume and shortness of breath, midbrain volume and memory loss, midbrain volume and depression.
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Discussion Using a cohort of 111 veterans with GWI, we identified a significant reduction in subcortical brain volumes relative to 59 controls. The most atrophy was observed in the brainstem, ventral diencephalon, and thalamus. The study further demonstrated that reduced brainstem volumes are associated with chronic fatigue and depressive mood, which are core
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symptoms of GWI.
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Brainstem atrophy in GWI
The finding that GWI is associated with volume loss in the brainstem, as evidenced by
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our Freesurfer volumetric approach in a large sample, mirrors previous observations in veterans
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with GWI (Christova et al., 2017) and veterans with GWI who also demonstrated exertional tachycardia (Rayhan et al., 2013). Previous functional imaging studies have provided evidence
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that GWI is characterized by reduced brainstem metabolism measured by N-acetylaspartate (NAA) from MR spectroscopy (Haley, Marshall, et al., 2000; Weiner et al., 2011). The brainstem contains major autonomic and neurotransmitter pathways and nuclei that regulate the sympathetic and parasympathetic nervous systems and play important roles in sensation, motion,
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cardiovascular system control, respiratory control, pain sensitivity control, alertness, awareness, and consciousness (Simic et al., 2017). Several studies (Haley et al., 2004; Rayhan et al., 2013) have suggested that GWI symptoms, such as chronic diarrhea, dizziness, and fatigue, as well as changes in cardiovascular indices are linked to autonomic dysregulation. Furthermore, potential exposure to neurotoxic substances in GWI (Brimfield, 2012) is also thought to produce an accumulation of certain neurotransmitters (e.g., acetylcholine released partly from the 18
brainstem), and lead to neuroinflammation. Consequently, evidence of brainstem structural deficiencies alludes to the presence of autonomic dysfunctions and dysregulated neurotransmission that may produce GWI symptoms. Substructures inside the brainstem, such as the red nucleus, substantia nigra, periaqueductal gray area, locus coeruleus, reticular formation, and white matter fibers that
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interconnect these nuclei, are differentially implicated in autonomic control (Al-Khazraji & Shoemaker, 2018). These structures are difficult to delineate and quantitatively measure on
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conventional MRI acquisitions because of the limited contrast and resolution in small structures.
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Although this study showed that brainstem atrophy in GWI was most severe in the medulla, followed by the midbrain, and lastly a trend in the pons, the current MRI scan protocol does not
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afford us the necessary resolution to explore substructures in these important regions. Analytic
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pathways are underway.
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techniques that aim to distinguish and extract these functionally relevant brainstem nuclei and
Atrophy of other brain regions in GWI
Apart from brainstem atrophy, (Christova et al., 2017). also reported atrophy of the
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cerebellum, amygdala, and diencephalon in veterans with GWI. Previous studies, mostly employing functional imaging, have also reported that GWI veterans showed abnormal function in the basal ganglia, including putamen and globus pallidus (Haley, Fleckenstein, et al., 2000; Weiner et al., 2011), caudate nuclei (Calley et al., 2010), thalamus (Haley et al., 2009; Liu et al., 2011), and hippocampus (Li et al., 2011). In line with the previous studies, our study found that brain atrophy of GWI was notable in selective regions including the brainstem, midbrain ventral 19
diencephalon, and the thalamus. In particular, this selective atrophic pattern was greater in the brainstem than in the ventral diencephalon and the thalamus, suggesting that GWI-related atrophy exhibits a roughly inferior-to-superior gradient of regional vulnerability. Furthermore, this pattern resembles a degeneration pattern of neurological, cognitive, and mood dysfunctions (Grinberg, Rueb, & Heinsen, 2011), and therefore may contribute to our understanding of the
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neuropathology underlying GWI. It is noteworthy that we did not observe any abnormal cerebellar atrophy in GWI as
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reported by two previous studies (Christova et al., 2017; Rayhan et al., 2013). Given our sample
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size and careful sample selection, it unlikely that cerebellar atrophy is the most characteristic signs of GWI. This discrepancy may reflect effects of confounding variables, such as alcohol
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consumption, that should be considered in future investigations.
Brainstem structural associations with GWI symptoms
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A novel finding of this study is the association between brainstem atrophy and severity of chronic fatigue and depressive mood. This finding suggests that damaged neural structures in the brainstem are related to chronic symptom complaints in GWI. Moreover, linking clinical
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symptoms with alterations in these key brain structures provides important clues concerning potential disease modifying therapies. We found a robust correlation between reduced volume of the total brainstem
(particularly in pons) and increased fatigue severity (i.e. the intensity and frequency). Regardless of etiology, early conventional (MRI) studies (Dickinson, 1997) demonstrated that small, 20
discrete, and patchy brainstem and subcortical lesions can often be seen in individuals with CFS. More recent research has confirmed that individuals with CFS demonstrate regional, but not global atrophy, particularly in the brainstem, midbrain, and basal ganglia (Barnden et al., 2018; Finkelmeyer et al., 2018; Rocca et al., 2014). Our finding is also supported by previous observations that brainstem white matter atrophy is significantly correlated with the duration of
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fatigue in individuals with CFS (Barnden et al., 2011). GWI symptoms commonly include neurological complaints about memory,
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concentration, and dysregulated mood. Our finding of a significant correlation between
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brainstem atrophy with a memory loss and depression severity suggests that memory and mood dysfunctions are results from brainstem structural damages. Although the brain areas, such as
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amygdala, the hippocampus, the cerebellum, and the prefrontal cortex have been considered functioning the memory and emotions, the brainstem is also important because it modulates the
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memory and mood processes by communicating cerebral neurons via neurotransmitters such as epinephrine, dopamine, serotonin, glutamate, and acetylcholine (Myhrer, 2003). Several previous
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reports could be evidenced: a midbrain atrophy was identified in patients with dementia; abnormal signals in brainstem raphe nuclei (Supprian et al., 2004), and an overall decrease in brainstem volume (Soriano-Mas et al., 2011) were found in patients with depression. However,
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further investigations of the relationship between GW exposures, brainstem dysfunction/damage, and related neurobehavioral impairments are needed. The observation of the correlation between increased severity of shortness of breath and
medullary volume loss is particularly robust. The neuroanatomical correlates of respiratory dysregulation and dysfunction are in line with the literature, as the medulla is known to be one of the key structures regulating respiratory and other major autonomic functions (Harper, Woo, & 21
Alger, 2000; Pilowsky, 2014). Exposure to airborne toxins during the Gulf War have frequently led to direct pulmonary symptoms such as breathing difficulties. The finding of dyspnea related medulla damages suggests these respiratory difficulties may stem from a neurotoxic response in the brain stem in veterans with GWI. The observation that medullar atrophy correlates with abdominal pain is also interesting and is supported by evidence suggesting the brainstem is responsible for receiving and relaying gastrointestinal sensations (Travagli, Hermann, Browning,
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& Rogers, 2006). These findings merit further validation studies using improved imaging
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resolution and advanced analytic methods.
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Limitations
This study has some limitations that should be considered: 1) Although comparing
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veterans with GWI with veteran controls would be ideal, we were unable to obtain a sufficient number of symptom-free veterans and instead used a well-described civilian cohort as the control
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group. 2) It necessary to account for inter-scan heterogeneities and confounds in analytical models where multi-center data are pooled. Although in this study we used regression models to account voxel sizes, which we detected as a major confounding factor, we were unable to
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establish inter-scan harmonization approaches in absent of a group of control participants who were scanned using every scanner and T1WI protocol. 3) We did not perform the analyses at voxel-level, because the whole-brain template approaches, such as VBM, are potentially susceptible to image registration errors that may introduce spurious results. Technical improvements are needed to improve the accuracy of voxel-based approaches. Future directions 22
Our findings, together with previous GWI studies, raise the possibility that neuronal abnormalities in deep brain structures, especially the brainstem, may be key to a better understanding of this complex disorder. In this context, multi-modal imaging markers that allow detailed assessment of brainstem structures – such as functional nuclei and white matter pathways – may prove particularly useful. We recently reported that increased pain levels are associated with disrupted fibers which are connected through brainstem pain centers such as the
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periaqueductal gray, locus coeruleus and nucleus raphe magnus, and suggest the microstructural
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measurement in brainstem fibers could be an indicator for pain and pain regularization in
veterans with chronic pain (Zhang et al., 2019). Future studies using multimodal imaging
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analyses of brainstem pain-centers and pain-pathways in GWI-related pain will shed more light
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on this possibility.
Exposure to various neurotoxicants has been proposed as a contributor to GWI. Previous
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MRI analyses have been used to examine structural damage in relation to specific exposures such as suspected sarin/cyclosarin (Chao, Abadjian, Hlavin, Meyerhoff, & Weiner, 2011; Chao,
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Kriger, Buckley, Ng, & Mueller, 2014; Chao, Rothlind, Cardenas, Meyerhoff, & Weiner, 2010; Chao & Zhang, 2018; Chao, Zhang, & Buckley, 2015; Heaton et al., 2007), Khamisiyah smoke plume (Chao, Raymond, Leo, & Abadjian, 2017), and chemical alarms (Chao, 2016) and found
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associated atrophy in whole-brain tissues, cortical lobes, and the hippocampus. Future imaging studies should also consider the effects of exposures to other substances implicated in GWI, such as exposures to chemical weapons (nerve agents, sarin, etc.), burn-pits and smoke oils, side effects of taking cholinergic agents (e.g., pyridostigmine bromide, PB), with respect to brainstem dysfunction/damage.
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Conclusion This study demonstrated atrophy of deep brain regions – including the brainstem, ventral diencephalon, and thalamus – in veterans with GWI and detected significant correlations between brainstem atrophy and symptom severities of chronic fatigue and depressed mood among individuals with GWI. These findings build upon past studies by refining the specific
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brain regions implicated in GWI, and encourage further investigations of GWI-related symptoms
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and neurotoxic exposures in relation to abnormalities in brainstem structures and pathways.
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CRediT Author Statement
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Yu Zhang: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Visualization
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Timothy Avery: Validation, Investigation, Resources, Writing - Review & Editing Andrei A. Vakhtin: Software, Validation, Investigation, Writing - Review & Editing
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Danielle C. Mathersul: Investigation, Writing - Review & Editing Eric Tranvinh: Investigation, Resources, Writing - Review & Editing Max Wintermark: Investigation, Resources, Writing - Review & Editing Payam Massaband: Resources, Writing - Review & Editing
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J. Wesson. Ashford: Conceptualization, Writing - Review & Editing, Supervision, Funding acquisition
Peter J. Bayley: Conceptualization, Supervision, Writing - Review & Editing Ansgar J. Furst: Conceptualization, Methodology, Software, Data Curation, Writing - Review & Editing, Supervision, Project administration 24
Funding None of the authors have commercial or financial involvements that might present a conflict of interest in connection with this manuscript.
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This study was supported by the WRIISC, a National VA Post-Deployment Health Resource and the Department of Veterans Affairs, Office of Academic Affiliations WRIISC
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Fellowship program.
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Declaration of interests
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Acknowledgement
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☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
The authors would like to thank all veterans for volunteering to participate in this project. Without their generous support this research would not have been possible.
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Further, the authors thank Ms. Stacy Moeder for administrating the California War
Related Illness and Injury Study Center (WRIISC-CA) research programs, Mr. Dr. Miguel T. Robinson and Mr. Gerald E. Robinson for managing the WRIISC-CA database of veterans’ selfreport questionnaires.
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A part of the data used in this article was obtained from the Parkinson’s Progression Markers Initiative (PPMI) database. PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson's Research and funding partners, including a consortium of industry players, non-profit organizations and private individuals. Please visit
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PPMI website (http://www.ppmi-info.org/) for details regarding the PPMI sponsors.
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