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Improved Turnaround Times | Median time to first decision: 12 days

Research ArticleNEUROPSYCHIATRIC IMAGING
Open Access

DTI-Derived Evaluation of Glymphatic System Function in Veterans with Chronic Multisymptom Illness

Yu Zhang, Matthew Moore, Yashar Rahimpour, J. David Clark, Peter J. Bayley, J. Wesson Ashford and Ansgar J. Furst
American Journal of Neuroradiology December 2025, DOI: https://doi.org/10.3174/ajnr.A8901
Yu Zhang
aFrom the War Related Illness & Injury Study Center (WRIISC) (Y.Z., M.M., Y.R., P.J.B., J.W.A., A.J.F.), VA Palo Alto Health Care System, Palo Alto, California
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Matthew Moore
aFrom the War Related Illness & Injury Study Center (WRIISC) (Y.Z., M.M., Y.R., P.J.B., J.W.A., A.J.F.), VA Palo Alto Health Care System, Palo Alto, California
bDepartment of Psychiatry and Behavioral Sciences (M.M., Y.R., P.J.B., J.W.A., A.J.F.), Stanford University School of Medicine, Stanford, California
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Yashar Rahimpour
aFrom the War Related Illness & Injury Study Center (WRIISC) (Y.Z., M.M., Y.R., P.J.B., J.W.A., A.J.F.), VA Palo Alto Health Care System, Palo Alto, California
bDepartment of Psychiatry and Behavioral Sciences (M.M., Y.R., P.J.B., J.W.A., A.J.F.), Stanford University School of Medicine, Stanford, California
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J. David Clark
cPain Clinic (J.D.C.), VA Palo Alto Health Care System, Palo Alto, California
dDepartment of Anesthesiology, Perioperative and Pain Medicine (J.D.C.), Stanford University School of Medicine, Stanford, California
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Peter J. Bayley
aFrom the War Related Illness & Injury Study Center (WRIISC) (Y.Z., M.M., Y.R., P.J.B., J.W.A., A.J.F.), VA Palo Alto Health Care System, Palo Alto, California
bDepartment of Psychiatry and Behavioral Sciences (M.M., Y.R., P.J.B., J.W.A., A.J.F.), Stanford University School of Medicine, Stanford, California
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J. Wesson Ashford
aFrom the War Related Illness & Injury Study Center (WRIISC) (Y.Z., M.M., Y.R., P.J.B., J.W.A., A.J.F.), VA Palo Alto Health Care System, Palo Alto, California
bDepartment of Psychiatry and Behavioral Sciences (M.M., Y.R., P.J.B., J.W.A., A.J.F.), Stanford University School of Medicine, Stanford, California
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Ansgar J. Furst
aFrom the War Related Illness & Injury Study Center (WRIISC) (Y.Z., M.M., Y.R., P.J.B., J.W.A., A.J.F.), VA Palo Alto Health Care System, Palo Alto, California
bDepartment of Psychiatry and Behavioral Sciences (M.M., Y.R., P.J.B., J.W.A., A.J.F.), Stanford University School of Medicine, Stanford, California
eDepartment of Neurology and Neurological Sciences (A.J.F.), Stanford University School of Medicine, Stanford, California
fPolytrauma System of Care (A.J.F.), VA Palo Alto Health Care System, Palo Alto, California
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Abstract

BACKGROUND AND PURPOSE: Chronic multisymptom illness (CMI) includes symptoms of fatigue, pain, and sleep difficulties, as well as neurologic, respiratory, and gastrointestinal problems and is particularly common in veterans from the 1990–1991 Gulf War and the Afghanistan and Iraq Wars. Glymphatic system function may play an important role in the etiopathology of CMI but has not been addressed. DTI-derived analysis along the perivascular space provides a promising proxy for glymphatic system function by evaluating the status of perivascular space fluid flow. The objective of this study was to compare this DTI-derived glymphatic index in veterans with CMI and healthy controls, and to reveal possible correlations between this index and the severity of CMI symptoms.

MATERIALS AND METHODS: DTI-derived indices were extracted from imaging data of 203 veterans who met clinical diagnostic criteria for CMI, and 224 age-matched healthy control subjects from multiple public research databases. Severity of CMI, sleep difficulty, pain intensity, and the degree of chronic fatigue were based on self-report measures. MRI scanner and site variations were harmonized. Statistical analyses were performed adjusting for demographic confounding factors.

RESULTS: Both healthy controls and veterans showed significantly reduced glymphatic indices associated with increased age. Compared with controls, veterans showed bilaterally lower indices (Cohen d = −0.47; P < .001) after adjusting for age, sex, and education. Across the entire sample of veterans, negative correlations were observed between glymphatic indices and pain intensities (r = −0.17; P = .01), sleep disturbances (r = −0.17; P = 0.02), degree of fatigue (r = −0.20; P = 0.006), severity of CMI (r = −0.17; P = 0.02), and the indices were positively correlated with medullar volumes (r = −0.19; P = .007). Note, these results showing significant outcomes for a group of patients do not guarantee the same outcome for individual patients.

CONCLUSIONS: This study suggests that impaired glymphatic functions are strongly associated with CMI. These findings improve our understanding of the pathologic mechanism underlying CMI and point to DTI-based metrics as a potential biomarker for disease severity in this condition.

ABBREVIATIONS:

ALPS
analysis along the perivascular space
BPI-sum
Brief Pain Inventory-summary
CGRP
calcitonin gene-related peptide
CFS
chronic fatigue syndrome
CMI
chronic multisymptom illness
COVID
coronavirus disease
GWI
Gulf War Illness
HC
healthy control
OSA
obstructive sleep apnea
PSQI-glob
Pittsburgh Sleep Quality Index-global
PTSD
post-traumatic stress disorder
PVS
perivascular space
REM
rapid eye movement
TBI
traumatic brain injury

SUMMARY

PREVIOUS LITERATURE:

Previous literature suggested many neurologic disorders or health conditions can be explained by impaired clearance of toxins or waste products by the glymphatic system. However, the connection between the glymphatic system and CMI, a complex condition characterized by multiple persistent symptoms in veterans who served in modern wars, is unknown. DTI-ALPS is a noninvasive method used to indirectly assess the glymphatic system function. This method has shown clinical value in examining the glymphatic system in various medical conditions.

KEY FINDINGS:

Compared with healthy controls, veterans with CMI showed significantly lower DTI-ALPS indices. In veterans, a strong relationship was found between reduced DTI-ALPS indices and increased severity of chronic symptoms including sleep difficulty, pain, chronic fatigue, and CMI.

KNOWLEDGE ADVANCEMENT:

This study suggests a connection between impaired glymphatic system function and CMI in veterans. These findings will help in understanding potential causes and the pathophysiological mechanism in this combat-related illness and improve clinical diagnosis and management.

Chronic multisymptom illness (CMI) refers to a medically unexplained illness characterized by chronic symptoms that can include fatigue, muscle and joint pain, sleep problems, memory and concentration problems, headaches, dizziness, indigestion, insomnia, respiratory disorders, and skin problems. Approximately 30% of veterans who deployed to the 1990–1991 Persian Gulf War have developed CMI, and in these veterans, the condition is referred to as Gulf War Illness (GWI). Conditions consistent with CMI have also been described in veterans deployed to Afghanistan and Iraq conflicts. The etiology and pathology of CMI remain poorly understood. Several studies have suggested that CMI may be best explained as a disorder involving the central nervous system.1 The identification of potential brain abnormalities is therefore of great interest to facilitate the understanding of the pathogenic mechanisms for postcombat CMI and GWI.

The glymphatic system refers to a waste clearance system2 that utilizes CSF flow in the perivascular space (PVS), facilitated by aquaporin-4 water channels, to drain metabolic wastes and toxins from the brain parenchyma to extracranial lymphatic cleansing sites. Early studies imaging the glymphatic system function focused on its physiologic mechanism in the healthy brain. Many animal and human studies have shown that glymphatic clearance is mainly active during sleep,3 and is impaired by sleep deprivation.4 In animal models of neurologic disorders,3,5 impaired glymphatic clearance is thought to involve decreased aquaporin-4 water channels in the interstitial compartment, leading to a reduction of CSF movement and decreasing the efficacy of glymphatic clearance. Impairment of this waste clearance can directly result in the accumulation of toxic substances and metabolites, leading to various neurodegenerative disorders. Impaired glymphatic system clearance can also be linked to traumatic brain injury (TBI)6 and other health conditions such as chronic pain and post-coronavirus disease (COVID) fatigue because these conditions are tied to poor sleep. Postcombat CMI, as a symptom constellation, most commonly includes chronic fatigue, sleep problems, and musculoskeletal pain, and is often comorbid with neuropsychological disorders and TBI. Its biologic mechanisms, especially its connection to glymphatic function, remain unknown.

The initial method of assessing glymphatic pathway function by using dynamic contrast-enhanced MRI in humans has limited utility due to the need for invasive contrast agents. A more recent approach, by using the DTI-analysis along the perivascular space (ALPS) index7 provides a noninvasive evaluation of the glymphatic system function by indicating the directional water diffusion along the PVS in periventricular areas. Experiments have shown the DTI-ALPS to be a valid measure of glymphatic function, because it shows a significant correlation with glymphatic function measured on MRI after intrathecal administration of gadolinium.8 Although the DTI-ALPS tends to indicate water diffusion rather than CSF flow, it is considered a promising putative index in evaluating the glymphatic system function. A low ALPS index, which is suggestive of impaired glymphatic function in the human brain, has been reported in aging and various neurologic disorders,9 such as cognitive impairment and Alzheimer disease, rapid eye movement (REM) sleep behavior disorder, Parkinson disease, and small vessel disease. Reduced ALPS indices are also associated with sleep problems,10 headache, and pain.11 DTI-ALPS, as a noninvasive, low-risk glymphatic measure, has its merits in evaluating clinical patients. According to a review article,12 Table 1 compares the pros and cons of the existing MRI methods in evaluating the glymphatic system of the human brain.

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Table 1:

Imaging methods for evaluation of glymphatic system

The etiopathology of CMI remains unknown. Investigating the glymphatic system’s role in CMI will help identify the underlying cause and pathogenic process in this poorly understood illness and improve clinical diagnosis and treatment. To our knowledge, no previous studies have assessed glymphatic function in CMI. In this study, we used the DTI-ALPS method to evaluate the glymphatic system in a large sample of veterans who deployed to the Persian Gulf, Afghanistan, and Iraq Wars. We aimed to investigate changes in glymphatic function in these veterans who suffered from CMI compared with healthy control (HC) data from public data repositories. Furthermore, we aimed to explore the associations between glymphatic function and the severity of CMI symptoms, including sleep, pain, and fatigue.

MATERIALS AND METHODS

Study Samples

Veteran data were collected at the War Related Illness and Injury Study Center at the Veterans Affairs Hospital in Palo Alto, California. The study protocol was approved by the Stanford University institutional review board. All veterans provided written informed consent to participate in the study and shared their data in accordance with the Declaration of Helsinki. All 203 veterans met the Centers for Disease Control and Prevention case definition criteria of CMI,13 and had been deployed to either Operation Desert Shield/Desert Storm between August 1990 and May 1991 in the Persian Gulf War, or Operation Enduring Freedom/Operation Iraqi Freedom in the Afghanistan and Iraq conflicts from September 11, 2001 to the present.

For comparison with veteran data, we collected a total of 224 HC imaging data sets from 3 publicly available study cohorts: 1) 103 from the National Institute of Mental Health Data Archive (https://nda.nih.gov); 2) 49 from the Image and Data Archive (https://ida.loni.usc.edu/) of the Parkinson Progression Marker Initiative cohort; and 3) 72 from the Image and Data Archive (https://ida.loni.usc.edu) of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. All 3 study cohorts were reviewed and approved by institutional review boards at each participating site. All participants were enrolled after obtaining informed consent. The Supplemental Data provide details of the inclusion and exclusion criteria across the cohorts.

This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. The STROBE checklist for cross-sectional studies is available in the Supplemental Data.

Clinical Assessments

Table 2 summarizes demographic and clinical information obtained from veteran and HC data. For veterans, diagnoses of post-traumatic stress disorder (PTSD) and depression were made according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) Axis I Disorders.14 TBI was diagnosed following the Department of Veterans Affairs and the Department of Defense Clinical Practice Guidelines for Management of Concussion/Mild TBI.15 Scoring of CMI symptoms was accomplished by using veterans’ self-report questionnaires (see detailed descriptions in the Supplemental Data). Specifically, sleep was assessed by using the Pittsburgh Sleep Quality Index global score (PSQI-glob).16 Chronic pain was assessed by using the Brief Pain Inventory (BPI-sum)17 short form items 3–6. Chronic fatigue was measured by the degree to which fatigue limited daily activities over the past 6 months. A score reflecting CMI severity was calculated from the summarized intensity scores of 19 symptom items lasting at least 6 months, from the Chronic Fatigue Syndrome (CFS) Symptom Inventory18 by the Centers for Disease Control and Prevention.

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Table 2:

Demographic and clinical information

MRI Acquisition, Processing

Acquisition parameters for all cohorts are provided in the Supplemental Data. The T1WI and DTI data were processed through a previously described pipeline.19 DTI data underwent skull-stripping, denoising, and correction for both eddy-current and geometric distortions by using FMRIB’s Diffusion Toolbox within FSL v5.0 (http://www.fmrib.ox.ac.uk/fsl). Following preprocessing, each individual image was registered to the standardized T1WI and DTI templates in the Montreal Neurological Institute space by using Advanced Normalization Tools (https://www.nitrc.org/projects/ants). The registration parameters were recorded to enable reverse registration of ROI from the Montreal Neurological Institute’s standardized space onto the subject’s native space.

As we previously noted, the brainstem appears to be the most vulnerable region in CMI.20 We also conducted brainstem volumetric measures by using a brainstem segmentation tool in FreeSurfer v6.0 (https://surfer.nmr.mgh.harvard.edu/fswiki/BrainstemSubstructures).

Calculation of ALPS Index

Figure 1 illustrates the concept of DTI-ALPS7 and the calculation of the ALPS index. We used an atlas- and registration-based approach to define ROIs. The approach involved several procedures: 1) diffusivity maps along the x-axis (Dxx), y-axis (Dyy), z-axis (Dzz), and the fractional anisotropy map were reconstructed in each individual’s native space; 2) the ROIs with a size of 5 mm3 labeled in the Montreal Neurological Institute’s standardized space were registered to each individual’s space; 3) a mask with fractional anisotropy > 0.2 was applied to the ROIs in individual space to exclude nonfiber voxels; 4) mean values of Dxx, Dyy, and Dzz were calculated in each ROI, and the ALPS index was computed by using the equation in Fig 1.

FIG 1.
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FIG 1.

Illustration of the ALPS index. Upper panel: the periventricular PVSs appear as bright signals along the x-axis on T2WI. The locations of ROIs are shown on projection (blue), association (green) and subcortical (red) fibers. Lower panel: directions of dominant diffusivity (z-axis for projection fibers, y-axis for association fibers), x-axis for subcortical fibers and perivascular CSF flow (gray); and calculation of the ALPS index.

Statistics

Because MRI data were collected from multiple MR centers by using various MR scanners and parameters (Supplemental Data), we used the ComBat tool in R (Version 4.2.2) (https://www.R-project.org) to harmonize the ALPS indices. The ComBat approach is based on an empirical Bayes framework for adjusting data for batch effects,21 previously demonstrated in genomics, and adapted for use in a variety of brain imaging modalities. The ComBat model helps to remove the effects of nonbiological variables such as different scanners from biologic variables of interest, improving the reliability and validity of results. In the ComBat model, 11 scanner/parameter batches (listed in the Supplemental Data) were treated as nonbiologic variables. Diagnostic features, age, sex, and education were included as biologic variables that need to be maintained. Postharmonization effects in all healthy subjects with a single diagnostic feature are shown in the Supplemental Data. Compared with the original data, the scanner differences have been successfully removed.

All analyses were performed in SPSS Statistics for Windows (v.24, IBM). Statistics involving the HCs were performed by using harmonized ALPS indices, while original ALPS indices were used for statistical tests in veterans because the data were obtained from the same scanner and the same protocol. Group differences were tested by using a linear regression model with ALPS indices as the outcome variable, and groups (veterans with CMI versus HC, veterans with minimal symptoms versus symptomatic veterans) as the independent variable. Demographic confounders, including age, sex, and education, which may potentially affect the group difference, were included as covariates in the regression model. We did not include handedness and total intracranial volume because they are balanced between groups, thus had no confounding effect on group differences. The relationships between ALPS indices and clinical scores (ie, continuous variables including PSQI-glob and its component scores, BPI-sum and subitem scores, degree of fatigue, CMI severity, and brainstem volumes) were tested by using Pearson correlation. For all tests, a critical statistical significance was set at P ≤ .02, according to the Benjamini-Hochberg false discovery rate.

RESULTS

Demographic Differences

Table 2 shows detailed demographic differences between groups. Veterans were significantly younger (Kruskal-Wallis: P < .001) than HCs. There was a greater proportion of men in the veterans group compared with HCs (χ2: P < .001). The educational levels of veterans were significantly lower than those of HCs (χ2: P < .001). Differences in handedness and race were not significant.

Age, Sex, and ALPS Index

Figure 2A illustrates the relationship between age and harmonized ALPS in HCs and veterans with CMI, in men and women, respectively. In both HC group and veterans, there was a negative correlation between harmonized ALPS and age (P = .001 and P = .02, respectively). The age-related reduction in ALPS was more pronounced in male HCs (P < .001) and secondarily strong in male veterans (P = .02) but not significant in female HCs or veterans. Figure 2B shows that after adjusting for age and education, both male and female veterans had significantly lower ALPS than their paired HCs (P = .002 and P < .001, respectively). Female HCs had higher harmonized ALPS than male HCs (P < .001). However, there was no difference in ALPS between male and female veterans.

FIG 2.
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FIG 2.

Age, sex, ALPS associations, and group differences. A. Scatterplot of correlations between age and harmonized ALPS in male and female HCs, male and female veterans with CMI, respectively. B. Harmonized ALPS differences between HCs and veterans with CMI, separated by male and female. n.s. indicates not significant.

Group Differences in ALPS Index

Figure 3 shows the differences in harmonized ALPS indices between the HC and veteran group. After regressing out age, sex, and education, veterans exhibited significantly lower ALPS in the left, right, and bilateral periventricular areas (P < .001) compared with HCs.

FIG 3.
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FIG 3.

Group differences in harmonized ALPS indices between HCs and veterans, after regressing out age, sex, and education effects. NIMH indicates National Institute of Mental Health; PPMI, Parkinson Progression Marker Initiative; ADNI, Alzheimer’s Disease Neuroimaging Initiative; WRIISC, War Related Illness & Injury Study Center.

Figure 4 shows the mean ALPS in 3 subgroups: 1) the total HC group, assumed to be free from CMI and other comorbidities seen in veterans; 2) veterans without above-threshold symptoms or comorbid conditions (ie, PTSD, depression, and TBI); and 3) veterans with symptoms or comorbidities. Compared with the combined HC and asymptomatic veterans, veterans with symptoms/health conditions or comorbidities consistently showed lower ALPS scores (P < .001). Comparisons within veterans showed that veterans with above-threshold fatigue (P = .008), sleep difficulties (P = .004), pain (P = .03), and CMI (P = .05) had lower ALPS indices than veterans with minor symptoms (ie, the asymptomatic group). There was no significant difference in ALPS indices between veterans with and without comorbidities. Further details of the comparison results are provided in the Supplemental Data.

FIG 4.
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FIG 4.

Group differences in harmonized ALPS indices between HC and veteran subgroups of patients (see footnotes in Supplemental Data for symptomatic cutoffs).

ALPS Index and Symptom Severities in Veterans with CMI

Figure 5 shows scatter plots of pronounced Pearson correlations between original ALPS and symptomatic scores, subset scores, and brainstem volumetry. More detailed correlation results can be found in the Supplemental Data. In all veterans, reduced ALPS indices were correlated with increased CMI severity (P = .02) and a greater degree of chronic fatigue (P = .006). The ALPS were also negatively correlated with “worst pain during past week,” a subscore in the BPI-sum scores (P = .01), and “sleep disturbance,” a subcomponent in the PSQI-glob scores (P = .02). Additionally, ALPS was significantly associated with all breathing-related sleep difficulties (PSQI questions 5d, 5e, 10a, and 10b; see footnotes in the Supplemental Data for the definitions of these scores).

FIG 5.
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FIG 5.

Pearson correlation between original ALPS and symptomatic severities (ie, sleep disturbance, CMI severity, degree of fatigue over the past 6 months, and worst pain intensity in the past week).

ALPS Indices and Brainstem Volume in Veterans with CMI

The Supplemental Data show that the only significant correlation among all models was a positive correlation between ALPS and medulla volume (P = .007).

DISCUSSION

This is the first study to analyze the glymphatic function in a large cohort of veterans with CMI. The main finding of this study was that veterans with CMI had substantially impaired glymphatic function compared with healthy controls. This effect remained significant even after controlling for age, sex, education, and MRI scanner/parameter variations. In veterans, a strong relationship between reduced ALPS indices and increased severity of chronic symptoms including sleep difficulty, pain, chronic fatigue, and CMI was found. In the discussion below, we propose a potential mechanistic link between glymphatic dysfunction, exposure to neurotoxic substances, and chronic symptoms of CMI. Note, these results showing significant outcomes for a group of patients do not guarantee the same outcome for individual patients.

Glymphatic System and Neurotoxic Exposures

The commonly known condition of GWI was described by the Centers for Disease Control and Prevention13 as an unexplained CMI that persistently presents in approximately 30%–44% of the nearly 700,000 US military personnel who served in the 1990–1991 Persian Gulf War. Postdeployment CMI appears to be more common in modern wars than previously appreciated, with some studies reporting CMI22 and brainstem alterations20 in veterans who served in Afghanistan and/or Iraq Wars. Veterans who deployed to Gulf War and Afghanistan/Iraq Wars had similar environmental exposures, including sand dust, oil well fires, burn pits, chemical weapons (eg, sarin gas), chemical nerve agent antidotes (pyridostigmine bromide or nerve agent pyridostigmine pretreatment), pesticide uses, vaccines not previously used by the military, depleted uranium munitions, as well as hearing chemical alarms or wearing mission-oriented protective posture gear.23 GWI is reported to be strongly associated with using pyridostigmine bromide pills and wearing mission-oriented protective posture gear.24 Furthermore, veterans’ exposures to Scud missiles, and to chemical and biologic warfare agents were associated with major CMI symptoms.25 Although exposures to these toxic substances have been considered as a possible cause for acute symptoms during the wars26 and an initial contributing factor for CMI, they are unlikely to explain the persistent chronic conditions that occur, usually months after returning from deployment. Studies in animal models of GWI suggest that war-related stress, head trauma, and repeated pyridostigmine bromide administration are associated with disruption of the BBB.27 Thus, soldiers with impaired glymphatic clearance may have experienced long-term, aberrant accumulation of neurotoxic substances, potentially leading to multiple chronic symptoms, but further research is required to establish this link.

DTI-ALPS and Aging

Animal and human studies have shown that glymphatic activity decreases in elderly populations.28 The reduced clearance of metabolic waste such as amyloid-β and τ aggregates may contribute to aging and cognitive impairment.5 In this study, the observation of a strong association between age and ALPS in the HC group is consistent with the notion that aging adversely affects glymphatic function. On the other hand, sleep problems often worsen with normal aging, as well as in CMI and other conditions commonly seen in postdeployment veterans such as PTSD, leading to disrupted non-REM sleep stage and a decline of glymphatic clearance.29 In this study, we observed a weaker effect of age-related glymphatic dysfunction among the CMI group, in comparison with the strong effect in HCs. This observation is not surprising, because most of the veterans were in young- and mid-adulthood where aging has less impact on brain health. Therefore, the glymphatic impairment seen at this age range is more likely due to CMI pathology than aging.

We observed a higher ALPS in female HCs than in male HCs. This finding is in line with previously reported sex differences in glymphatic function measured by ALPS.30 Although a previous study suggested that this sex effect may be confounded by head size,31 our data clearly showed that sex differences in ALPS remained significant after adjusting for total intracranial volume. As the number of women in the armed forces continues to rise, studying sex differences in glymphatic function may offer important insights for subsequent women veterans’ health.

DTI-ALPS and Sleep

Most glymphatic clearance occurs during sleep. During sleep, the efficiency of amyloid-beta clearance has been found to double, and conversely, sleep deprivation leads to a reduction in the clearance of metabolic waste.5 Using DTI-ALPS, studies have implicated glymphatic dysfunction in idiopathic REM sleep behavior disorder,32 obstructive sleep apnea (OSA),33 and non-REM sleep,10 suggesting that poor sleep quality is highly relevant to glymphatic dysfunctions. Sleep disturbances are very common in veterans from modern wars and postcombat CMI. Our data showed that about 96% of veterans with CMI had insomnia. Although a significant correlation between ALPS and PSQI-glob was not found in this study, we observed that ALPS was substantially lower in veterans with insomnia compared with veterans with minor sleep problems (Fig 4). Furthermore, we also observed that sleep disturbance, one of the PSQI components (particularly items related to snoring and breathing difficulties), correlated with glymphatic dysfunction. These results are consistent with a published study10 of elderly civilians that found ALPS was associated with non-REM sleep and apnea-hypopnea but not PSQI. Other evidence includes sleep-disordered breathing-related glymphatic circulation issues34 and decreased glymphatic clearance by OSA.35 There is a broad consensus that sleep plays an important role in maintaining normal glymphatic system function, and sleep disturbances conversely result in impaired glymphatic functions and accumulation of neural wastes and toxins.36

DTI-ALPS and Pain

The glymphatic system is known to be regulated by sleep and norepinephrine. Glymphatic clearance is optimal under conditions of good sleep and blockade of adrenergic signaling.37 Conversely, norepinephrine, an arousal-mediation neurotransmitter, promotes wakefulness and reduces glymphatic system function by decreasing the size of the interstitial space.38 Other studies on persistent pain conditions and headaches suggest that impaired glymphatic drainage can lead to excessive accumulation of calcitonin gene-related peptide (CGRP), a mediator that aggravates headaches and chronic migraines.39 Our findings of a correspondence between higher chronic pain intensity and poorer glymphatic clearance are consistent with these reports. Furthermore, animal studies also suggest that impaired glymphatic function is a potential driver of pain chronification.40 In chronic pain, the norepinephrine levels in the brain increase due to elevated locus coeruleus–derived noradrenergic tone,3 which could result in inactivated CSF drainage.

DTI-ALPS and Fatigue

Less is known about whether the glymphatic function is impaired in chronic fatigue. A recent study has proposed that the “post-COVID-19 fatigue syndrome” may be caused by congestion of the glymphatic system with subsequent toxic buildup within the central nervous system.41 Interestingly, similarities in symptomatology between COVID-19 and GWI have been noted by several authors.42 In our study, we observed a strong relationship between the degree of chronic fatigue and impaired glymphatic system function. Postcombat physical exhaustion could in part stem from glymphatic dysfunction due to sleep disturbances, disrupted neurologic restoration and performance induced by the accumulation of toxic and metabolic wastes.

DTI-ALPS and CMI

The CFS score represents a wide spectrum of symptoms, including, but not limited to, fatigue, headaches, joint and muscle pain sleep problems, memory and mood issues, sensitivity in lymph node, respiratory, gastrointestinal, and sensory problems. In this study, we used a summarized CFS severity score to indicate veterans’ CMI severity. As discussed above, glymphatic impairment may be directly related to impaired clearance of neurotoxic substances (that veterans might have been exposed to), CGRP, and metabolic wastes, and is also indirectly related to sleep disturbances that lead to decreased glymphatic function. Stress, particularly chronic stress, can affect glymphatic clearance as it reduces glymphatic activity during sleep6 and impairs the function of aquaporin-4.43 These negative effects on the glymphatic system by stress could partially explain the glymphatic failure in CMI, because chronic PTSD is commonly comorbid with CMI. In addition, reduced glymphatic drainage of metabolic waste such as amyloid-β and τ has been considered relevant to memory and cognitive impairments,44 and postcombat TBI.6 Some other concerns include glymphatic impairment as an immune dysfunction causing toxic buildup in CSF45; low-grade inflammation within CSF might facilitate diffuse stagnation of flow in the interstitial and perivascular spaces.46

Considering the above, it is conceivable that disturbances in the glymphatic drainage system may contribute to the pathogenesis of CMI. In this study, we did not observe significant differences in ALPS between veterans with and without comorbidities (such as TBI, PTSD, and depression) However, a significant reduction in ALPS was found in veterans with comorbid conditions compared with HCs (Fig 4). These findings suggest that glymphatic dysfunction may not be associated with a single symptom or condition but rather with systemic changes.

DTI-ALPS and Brain Volume Measures

Given that the autonomic nervous system is under the control of the brainstem, disruption or shrinkage of the brainstem, particularly the medulla and midbrain, is considered a key feature of GWI.20 In this study, we found significant associations between lower DTI-ALPS and smaller volumes in the medulla, which we previously identified as a particularly vulnerable structure in GWI with respect to GWI-induced sleep and pain problems.20 Further investigation by using brainstem imaging in CMI/GWI is warranted.

Limitations

There are important considerations when interpreting DTI-ALPS. Table 1 provides a comparison between DTI-ALPS and neuroimaging of CSF dynamics. Intrathecal injection of CSF tracer is still the standard for measuring glymphatic clearance47 but is not practical due to its invasiveness. Compared with methods that examine the dynamic flow of CSF-interstitial fluid exchange, the DTI-ALPS index was proposed to measure water movement along the directions of the PVS and applied in regions of interest in periventricular regions parallel to PVS. The distinct spatial and temporal differences between DTI and CSF indicate that the DTI-ALPS index should not be viewed as a direct marker of glymphatic clearance.48 However, DTI-ALPS has been shown to correlate with other measures of CSF dynamics,49 as well as noninvasive methods (eg, measuring PVS burden)50 that are proposed to reflect the status of the glymphatic system. An ongoing study of the same veteran population from our group has found that DTI-ALPS correlates with a greater PVS burden, especially with the number of enlarged cerebral PVSs. Therefore, as we described in the introduction, the merit of using DTI-ALPS in clinical application should not be ignored. There are technical problems affecting DTI-ALPS. The accuracy of DTI-ALPS may be affected not only by local tissue complexity, but also by head orientation during MRI scans. As we illustrate in the Supplemental Data, an over-tilted brain orientation or crossing-fiber tissue, can change the principal diffusivity in the fiber ROIs, leading to incorrect ALPS calculations. These challenges should be considered in future DTI-ALPS analyses. There are limitation on acquiring DTI during sleep. Some imaging studies of the CSF dynamics in anesthetized rodent brains suggest that the glymphatic system is active during sleep and inactive during wakefulness.4 However, more recent evidence casts doubt on the validity of this strict dichotomy51 and rather points to the more likely scenario of a gradient ranging between more and less active. Interestingly, to our knowledge there has not been any study evaluating the within-subject sleep/wake correlation of DTI-ALPS. In contrast, many studies measuring DTI-ALPS in the human brain during wakefulness have shown strong associations between glymphatic function and the accumulation of brain wastes (eg, amyloid-β). Therefore DTI-ALPS appears to be a reliable proxy for glymphatic function even during wakefulness. Furthermore, we did not test medication effects in this study. In fact, little is currently known about pharmacologic interactions with the glymphatic system other than sleep medication. Regarding the complexity of confounding factors, because of insufficient information from the healthy cohorts, we were unable to include some clinical variables (eg, alcohol use, diabetes mellitus, etc) as confounding factors. The absence of potential confounders may amplify group differences when compared with controls. Additionally, we could not include some white matter variables (eg, Fazekas scale, fractional anisotropy, and mean diffusivity) as confounders, because they exhibited collinearity, making it difficult to interpret the regression model’s results. Future research projects will be needed to reduce confounders by recruiting participants with less demographic heterogeneity and consistent clinical information and imaging protocols.

CONCLUSIONS

This is, to our knowledge, the first study reporting substantially reduced DTI-ALPS in veterans with CMI. Note, these results showing significant outcomes for a group of patients do not guarantee the same outcome for individual patients. Further studies are needed to replicate these findings and to determine whether there is a causal relationship between disrupted CSF waste clearance and CMI symptoms or whether other pathologic factors can account for both. Sex differences in glymphatic function are another topic for future study, especially with the increasing number of women veterans in modern wars. Current standard practices for CMI treatment require subjective indicators to monitor symptom progression and treatment effects, and MRI offers limited benefits beyond aiding diagnosis. Given the potential role of glymphatic clearing in veterans with CMI/GWI, measuring glymphatic function by using DTI-ALPS may serve as an objective indicator of these complex postcombat conditions, which could benefit the optimization of treatment strategies and monitoring patient progress in CMI/GWI management.

Acknowledgment

The authors want to sincerely thank all veterans for volunteering to participate in this project. Without their generous support this research would not have been possible. The authors also thank Ms. Stacy Moeder for administrating the California War Related Illness and Injury Study Center research programs, Dr. Miguel T. Robinson for managing the database of veterans’ self-report questionnaires, Mr. Alan Kim for assistance with the literature search.

Healthy control data used in this article were obtained from the National Institute of Mental Health volunteer data set, the Parkinson Progression Markers Initiative control data set, and the Alzheimer’s Disease Neuroimaging Initiative control data set. We express our profound gratitude to all participants to these study cohorts, principal investigators, and administerial committees.

Footnotes

  • This study is supported by the VA Clinical Science Research and Development (CSR&D) grant entitled “The role of the brainstem in GWVI pathology” (1 I01 CX002182-01). Additional support comes from California War Related Illness and Injury Study Center (CA WRIISC) and the VA, Office of Academic Affiliations, CA WRIISC Fellowship program. This material is the result of work supported with resources and the use of facilities at the Veterans Affairs Palo Alto Health Care System.

  • Disclosure forms provided by the authors are available with the full text and PDF of this article at www.ajnr.org.

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  • Received March 16, 2025.
  • Accepted after revision June 16, 2025.
  • © 2025 by American Journal of Neuroradiology
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Yu Zhang, Matthew Moore, Yashar Rahimpour, J. David Clark, Peter J. Bayley, J. Wesson Ashford, Ansgar J. Furst
DTI-Derived Evaluation of Glymphatic System Function in Veterans with Chronic Multisymptom Illness
American Journal of Neuroradiology Dec 2025, DOI: 10.3174/ajnr.A8901

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DTI-ALPS in Veterans with CMI
Yu Zhang, Matthew Moore, Yashar Rahimpour, J. David Clark, Peter J. Bayley, J. Wesson Ashford, Ansgar J. Furst
American Journal of Neuroradiology Dec 2025, DOI: 10.3174/ajnr.A8901
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