Evaluation of glymphatic system dysfunction in patients with insomnia via diffusion tensor image analysis along the perivascular space

Introduction

Insomnia disorder (ID) is defined as difficulty falling asleep or waking up early and falling back to sleep when there is sufficient opportunity for sleep and a suitable environment (1). Insomnia is the most prevalent sleep disorder and the second most prevalent neuropsychiatric illness (2). The prevalence of ID in the adult population exceeds 10% (3). Xie et al. (4) found that the restorative function of sleep results in a significant increase in the convective exchange of cerebrospinal and interstitial fluids (ISFs), enhancing the removal of potentially neurotoxic waste products that accumulate in the central nervous system during wakefulness. The glymphatic system lacks real lymphatic channels and includes the input of perivascular interstitial cerebrospinal fluid (CSF) that is driven by pulsations in the arterial walls, flowing in the same direction as blood flow (5). Using animal experiments, Taoka et al. (6) found that gadolinium diamine (a contrast agent) could quickly transferred from the bloodstream to the CSF via the rat’s cerebral plexus through dynamic magnetic resonance imaging (MRI). They also found that the soluble form of the gadolinium contrast agent may be transferred through the glymphatic system and other mechanisms via the CSF or ISF translocation, suggesting that the glymphatic system may be involved in the effects of sleep and anesthesia in mice. Through use of intravenous contrast agent injections, Lee et al. (7) observed how sleep affects the glymphatic system’s ability to remove gadolinium contrast agent from human bodies. They discovered that the glymphatic system was able to remove more gadolinium contrast agent after sleep than during waking hours. Another study on mice reported that chronic sleep fragmentation impaired cognition and prevented metabolites from being removed from the brains of young wild-type mice (8). In other research, MRI-informed biophysics was used to measure the solute transport in the human brain during sleep and sleep deprivation, and it was found that tracer clearance decreased during sleep deprivation (9). Sleep plays an important role in the clearance function of the glymphatic system. When the glymphatic system is blocked, this leads to impaired waste elimination, and the accumulation of some wastes can result in the emergence of diseases such as Alzheimer’s disease (10-12). This system is also associated with pediatric idiopathic intracranial hypertension (13) and major depression (14).

The diffusion tensor image analysis along the perivascular space (DTI-ALPS) is an emerging noninvasive method for assessing glymphatic system function. It is a DTI-based technique that measures diffusion coefficients to assess the flow of water molecules in the perivascular space. The ALPS index is an important parameter derived based on this technique. The ALPS index has shown good interscanner, interevaluator, and retest reproducibility, making it a reliable candidate biomarker for assessing the glymphatic system’s clearance function in neurological disorders.

Previous studies (15-18) have examined the relationship between insomnia and the glymphatic system, but no in-depth investigations into the sleep parameters associated with this system have been conducted. This study aimed to clarify the association between insomnia and glymphatic system dysfunction as well as the relationship between sleep quality indicators and glymphatic system function. The goal was to identify a validated assessment index for assessing glymphatic system status in insomnia, characterize the effects of insomnia on the glymphatic system, and further inform diagnostic and therapeutic approaches. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-1447/rc).

Methods

Participants

This prospective study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and approved by the ethics committee of the Second Affiliated Hospital of Nanchang University {approval No. I-Medical Research and Ethical Review [2023] No. (67)}. Informed consent was provided by all individual participants. A prospective sample of 25 patients with insomnia and 37 healthy volunteers was collected between September 2022 and January 2024 (Figure 1). Each participant underwent MRI, keeping their heads still during the scans. All patients with insomnia were subjected to a polysomnogram (PSG) examination and tested on the Pittsburgh Sleep Quality Index (PSQI) Rating Scale in a quiet and comfortable environment. The inclusion criteria for patients with insomnia were as follows: (I) satisfying the International Classification of Sleep Disorders, Third Edition (ICSD-3) diagnostic criteria (1); (II) a PSQI score ≥8; and (III) absence of other sleep disorders, multiple sclerosis, Alzheimer’s disease, Parkinson’s disease, history of craniocerebral trauma, immune demyelinating lesions, metabolic disorder, toxicity, infections, etc. Meanwhile, the inclusion criteria for healthy controls were as follows: (I) no illnesses linked to sleep problems and (II) no organic lesions in the cranium. The exclusion criteria for the insomnia and control groups were as follows: (I) motion artifacts interfering with viewing and (II) claustrophobia or other contraindications to MRI. The basic clinical information of all participants is shown in Table 1.

Figure 1 Flow diagram of the study sample. DTI, diffusion tensor imaging; PSQI, Pittsburgh Sleep Quality Index; MRI, magnetic resonance imaging.

Scanning procedures and image analysis

All scans were performed on a 3.0-T MRI scanner (SIGNA Architect; GE Healthcare, Chicago, IL, USA) with a 48-channel head coil. All participants underwent routine MRI [T1-weighted imaging, T2-weighted imaging, T2 fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted imaging] and DTI scans at night. The DTI parameters were as follows: direction of diffusions =64, repetition time/echo time (TR/TE) =8,441/95.5 ms, field of view (FOV) =240×240 mm², slice thickness =3 mm, reconstruction matrix size =256×256, b value =0 and 1,000 s/mm², and number of excitations =1.

All images were processed by two radiologists (one junior with 3 years’ experience and one senior with 15 years’ experience) in a double-blind method of coprocessing according to uniform criteria, with and the average of the two measurements being recorded. Enlarged perivascular spaces (ePVSs) are perivascular spaces with a diameter greater than 2 mm (19). The ePVSs in the centrum semiovale, basal ganglia, corona radiata, and hippocampus were counted using T2-weighted imaging sequences as the primary sequences. The artificial intelligence engines developed by United Imaging Healthcare (Shanghai, China) have excellent performance in image segmentation (20-22). In our study, the number of ePVSs was first obtained with this artificial intelligence and then further corrected manually with T2 FLAIR sequences. DTI was postprocessed using DSI Studio postprocessing software (May 2021 version; http://dsi-studio.labsolver.org). The software was first used to correct the DTI images for head movement, and then regions of interest (ROIs) around 5 mm² in size were outlined in the original diffusion color-coded anisotropy score image at the level where the lateral ventricle was located. Four ROIs were outlined in the bilateral projection fibers and in the bilateral association fibers to measure the projection fiber X and Y directions and the association fiber X and Z directions (Figure 2). These were used to calculate the ALPS index. Finally, the ALPS index was calculated using the following formula (23):

ALPSindex = mean(Dxproj,Dxassoc)/mean(Dyproj,Dzassoc)[1]

Figure 2 Postprocessed images. (A) DTI-ALPS post-processing schematics. (B) The recognition of ePVSs by the united imaging artificial intelligence. DTI-ALPS, diffusion tensor image analysis along the perivascular space; ePVS, enlarged perivascular space.

The ALPS index is the ratio of the average of the x-axis diffusivity in the projection region (Dxproj) and the x-axis diffusivity in the association region (Dxassoc) to the average of the y-axis diffusivity in the projection region (Dyproj) and the z-axis diffusivity in the association region (Dzassoc).

Statistical analysis

SPSS 26.0 statistical software (IBM Corp., Armonk, NY, USA) was used for analysis. Count data are expressed as frequencies, and comparisons were made using the χ² test; measurement data were tested for normality via the Shapiro-Wilk test. The normally distributed variables are expressed as the mean ± standard deviation and analyzed via the t-test. Analysis of covariance (ANCOVA) was applied to the dependent variable with a confounding factor, which was age. Data that did not conform to a normal distribution are expressed the median and the first and third quartile (P₂₅, P₇₅) and were analyzed via nonparametric tests. Pearson or Spearman correlation analysis was conducted to examine the relationship between sleep indicators and ALPS index. The P values for intergroup comparisons of the ALPS index were corrected using the false discovery rate (FDR) test. P<0.05 (two-sided) was considered statistically significant.

Results

Comparison of the number of ePVSs between the insomnia group and the control group

The number of ePVSs in the four regions and in the brain regions was not statistically different between the groups (Table 2).

Comparison of DTI-related indicators in the insomnia and control groups

The left and right ALPS indexes were not statistically different in any of groups (Table 3). The left ALPS index, the right ALPS index, and the average of ALPS-index significantly differed between the insomnia group and the healthy controls (Table 4 and Figure 3).

Figure 3 Comparison of the ALPS index between the insomnia and control groups. (A) Comparison of left ALPS index between the insomnia and healthy control groups. (B) Comparison of the right ALPS index between the insomnia and healthy control groups. (C) Comparison of average ALPS index between the insomnia and healthy control groups. *, P<0.05. ALPS, analysis along the perivascular space.

Correlation between DTI-related indicators and PSG-related indicators

The average ALPS index was positively correlated with the proportion of rapid-eye-movement (REM) (r=0.478; P_FDR=0.05) and negatively correlated with the proportion of N1 stage (r=−0.541; P_FDR=0.03). The other PSG-related indices were not significantly correlated with the DTI-ALPS indices (Table 5, Figure 4).

Figure 4 Scatterplot of correlation between PSG-related indicators and the ALPS index. ALPS, analysis along the perivascular space; PSG, polysomnography.

Discussion

In this study, the association between insomnia and glymphatic system dysfunction was explored using the DTI-ALPS technique and an artificial intelligence tool. The principal findings were as follows: (I) the mean values of left ALPS index, right ALPS index, and ALPS-index were significantly lower in the insomnia group than in the control group. (II) The left ALPS index correlated with the proportion of R phase. The right ALPS index correlated with the number of awakening transitions and the proportion of the N1 phase.

Change in ePVS in ID

Perivascular spaces (24) are spaces or potential spaces around small arteries, capillaries, and small veins in the brain, which constitute an important part of the glymphatic system. The enlargement of perivascular spaces reflects impaired waste clearance, resulting from a pathological cascade reaction involving perivascular inflammation and vascular reactive damage, leading to reduced clearance of ISF waste solutes (25).

This study was able to observe a higher number of ePVSs among individuals in the insomnia group compared to those in the healthy control group. However, there was no statistical difference, possibly due to the small sample size. Further exploration with a larger sample size is needed. It is possible that the insomnia group’s disruption of glymphatic circulation hinders the clearance of brain waste, leading to its accumulation. This accumulation may stimulate vascular inflammation and damage, exacerbating the burden of perivascular spaces, resulting in compensatory expansion and progressive fluid dynamic damage. This study only analyzed the quantity of ePVSs and did not conduct further analysis on related parameters of ePVS burden. However, previous studies have examined this. A study in preschool children (26) indicated a unique association between ePVSs and nocturnal awakenings. Other research (27) suggests that the load of perivascular spaces in the centrum semiovale mediates a 5% association between sleep parameters and brain changes, with sleep disorders correlating with increased perivascular space burden. Furthermore, poor sleep efficiency is independently associated with increased perivascular spaces in the basal ganglia region (28). In a study on older adults (29), it was found that older adults with better sleep quality and efficiency had larger volumes of perivascular spaces in the basal ganglia; however, sleep metrics were not correlated with perivascular space volume in the semioval center. Additionally, body mass index was found to affect perivascular space volume in middle-aged and older adult participants, with the impact of sleep on perivascular space volume varying across different age groups and ethnicities. Wang et al. (16) reported that patients with chronic insomnia and cognitive dysfunction had significantly increased ePVSs in the frontal cortex, centrum semiovale, and basal ganglia regions compared to cognitively normal patients with insomnia and healthy volunteers. Moreover, levels of Aβ, t-tau, and p-tau proteins in the CSF were elevated in patients with chronic insomnia and cognitive dysfunction, indicating a link between abnormal protein deposition caused by glymphatic dysfunction and cognitive impairment, which may pose a risk for further progression to dementia. Other literature (15) suggests that shallow sleep, such as longer N1 sleep and shorter slow-wave sleep, is associated with a higher burden of ePVSs, indicating a relationship between sleep structure and glymphatic system clearance function. These studies demonstrate that insomnia disrupts the brain clearance and increases the accumulation of waste products in the brain.

DTI-ALPS scores in patients with insomnia for assessing glymphatic dysfunction of ID

With the development of MRI technology, an increasing number of imaging studies are using new techniques to broaden our understanding of glymphatic circulation system. DTI-ALPS (30) is a method based on DTI measurements of diffusion rates to evaluate the movement of water molecules in the space around blood vessels. It is based on the assumption that the direction of the perivascular space around blood vessels is the same as that of the medullary veins at the level of the lateral ventricle, with the medullary veins perpendicular to the ventricular wall, defining this left-right direction as the x-axis. In the plane of this area, adjacent projection fibers run in the head-to-foot direction, while association fibers run in the anterior-posterior direction; these directions are orthogonal to the direction of the perivascular space, defined as the y-axis and z-axis, respectively. When histological changes occur along the perivascular space, both the diffusion rates of projection fibers and association fibers are affected. A low ALPS index indicates a decrease in the water diffusion rate in the perivascular space, suggesting dysfunction in the glymphatic system (31). Therefore, DTI-ALPS can achieve noninvasive indirect evaluation of glymphatic circulation function that is consistent with the assessment of glymphatic circulation function accomplished via direct intrathecal tracing methods (32,33).

In this study, there was no difference in the ALPS index between the left and right sides in the insomnia group, control group, or the entire cohort, indicating that there is no difference in the glymphatic circulation function of the bilateral cerebral hemispheres. The left ALPS index, right ALPS index, and their average showed differences between the insomnia group and the control group, with all values being higher in the control group than in the insomnia group (P<0.001). This indicates that water diffusion in the perivascular spaces was restricted in those with insomnia and that the bilateral glymphatic circulation function was impaired, which is consistent with previous research (17,18). It has been reported that the left ALPS index of patients with chronic insomnia is lower than that of good sleepers, suggesting the glymphatic circulation dysfunction, which is in line with our study. A study on sleep interruption demonstrated a significant decrease in ALPS index in young people with sleep interruption, possibly due to glymphatic system dysfunction, and further reported a significant association between ALPS index and sleep quality, sleep latency, and the use of sleep medication (34). Many studies have indicated that glymphatic circulation disorders may lead to abnormal protein aggregation and cognitive dysfunction, and thus it can be surmised that patients with insomnia are at greater risk of disease progression that leads to cognitive impairment and dementia (35-37). Therefore, early detection of glymphatic circulation abnormalities in insomnia and early intervention can reduce the risk of further disease progression. The ALPS index can serve as a rapid and convenient tool for the clinical assessment of glymphatic circulation in insomnia. This can guide early targeted treatment in clinical practice and reduce the risk of disease progression to dementia.

Glymphatic circulation disorders and sleep quality

Sleep can be defined and classified based on physiological and behavioral criteria into non-REM (NREM) sleep stages (N1, N2, and N3) and REM sleep (38). Patients with insomnia exhibit disrupted sleep architecture, decreased sleep efficiency, reduced proportion of REM sleep within the sleep cycle, and an increased proportion of N1 stage sleep within the sleep cycle (39). In our study, the ALPS index was not significantly different the right and left hemispheres of the brain, so we directly correlated the mean ALPS index with PSG parameters to investigate the relationship between whole-brain glymphatic circulation and clinical sleep evaluation parameters. We found that the ALPS index was positively correlated with the percentage of the R phase in the sleep cycle. The smaller the percentage of R phase was in patients with insomnia, the lower the average value of the ALPS index and the more severely impaired the function of the whole brain glymphatic circulation. Meanwhile, our results indicated that the mean value of ALPS index was negatively correlated with the percentage of N1 period in the sleep cycle, while the patients with insomnia had difficulty in falling asleep and waking early. The larger the percentage of N1 was in the sleep cycle, the lower the ALPS index and the worse the function of the whole-brain glymphatic circulation. All these findings suggest that poor sleep quality has a negative impact on whole-brain glymphatic circulatory activity.

Evidence from previous human and animal studies suggests that CSF transport is most active during slow-wave sleep and inhibited during wakefulness (40,41). These findings suggest that poor sleep quality and disrupted sleep architecture in those with insomnia hinder glymphatic circulation activity. Additionally, a higher PSQI score indicates a greater severity of insomnia, but we found no correlation between PSQI score and ALPS index, which is consistent with previous research (18,35), suggesting there is connection between ALPS index and the severity of insomnia.

This study involved certain limitations that should be acknowledged. First, we employed a small sample size, groups were not age-matched, and no additional subgroups of patients with insomniac were defined, which precluded the examination of glymphatic circulation status in individuals with different types of insomnia. Follow-up studies will include an expanded sample size, sample matching, control for additional confounders, and subgroup analyses. Second, our study focused mainly on the quantitative changes of ePVSs and did not analyze other parameters related to ePVS load, such as the volume of the ePVSs, which limited the interpretation of the mechanism of ePVS in the glymphatic system activity of insomnia. In future studies, consideration should be given to evaluating other parameters of the ePVS, such as volume, morphology, and distribution, in order to more fully characterize the relationship between insomnia and glymphatic system dysfunction. Third, this study failed to provide measurements of molecular biomarkers (e.g., tau protein and Aβ levels) associated with glymphatic system dysfunction, which limits the understanding of the molecular mechanisms underlying the relationship between insomnia and glymphatic system dysfunction. Future studies should include measurements of molecular biomarkers, such as tau protein and Aβ levels in the CSF, to examine the molecular mechanisms underlying the relationship between insomnia and glymphatic system dysfunction in greater depth and to validate the correlation between the ALPS index and these biomarkers. Finally, the DTI-ALPS method can only evaluate the white matter outside the lateral ventricles in an image section that includes the body of the lateral ventricles (42). Future research is anticipated to introduce new noninvasive methods for assessing glymphatic circulation, enabling qualitative and quantitative analyses of this circulatory dysfunction.

Conclusions

The quality of sleep is directly correlated with glymphatic circulation in cases of insomnia. Our findings show that a reduced ALPS index and increased number of ePVSs in the corona radiata in patients with insomnia indicate dysfunction in glymphatic circulation. The mean value of the ALPS index can serve as an imaging marker for assessing glymphatic circulation disorder in patients with insomnia, which may assist in evaluating their condition and selecting therapeutic targets to prevent the progression of the disease.

Acknowledgments

We thank the individuals with insomnia and the healthy controls for participating in this study.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-1447/rc

Funding: This study was supported by the Natural Science Foundation of Jiangxi Province (No. 20232BAB206132).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-1447/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This prospective study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by the ethics committee of the Second Affiliated Hospital of Nanchang University {approval No. I-Medical Research and Ethical Review [2023] No. (67)}. Informed consent was provided by all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. American Academy of Sleep Medicine. International classification of sleep disorders, 3rd ed. Darien, IL: American Academy of Sleep Medicine, 2014.
2. Van Someren EJW. Brain mechanisms of insomnia: new perspectives on causes and consequences. Physiol Rev 2021;101:995-1046. [Crossref] [PubMed]
3. Baranwal N, Yu PK, Siegel NS. Sleep physiology, pathophysiology, and sleep hygiene. Prog Cardiovasc Dis 2023;77:59-69. [Crossref] [PubMed]
4. Xie L, Kang H, Xu Q, Chen MJ, Liao Y, Thiyagarajan M, O'Donnell J, Christensen DJ, Nicholson C, Iliff JJ, Takano T, Deane R, Nedergaard M. Sleep drives metabolite clearance from the adult brain. Science 2013;342:373-7. [Crossref] [PubMed]
5. Benveniste H, Liu X, Koundal S, Sanggaard S, Lee H, Wardlaw J. The Glymphatic System and Waste Clearance with Brain Aging: A Review. Gerontology 2019;65:106-19. [Crossref] [PubMed]
6. Taoka T, Jost G, Frenzel T, Naganawa S, Pietsch H. Impact of the Glymphatic System on the Kinetic and Distribution of Gadodiamide in the Rat Brain: Observations by Dynamic MRI and Effect of Circadian Rhythm on Tissue Gadolinium Concentrations. Invest Radiol 2018;53:529-34. [Crossref] [PubMed]
7. Lee S, Yoo RE, Choi SH, Oh SH, Ji S, Lee J, Huh KY, Lee JY, Hwang I, Kang KM, Yun TJ, Kim JH, Sohn CH. Contrast-enhanced MRI T1 Mapping for Quantitative Evaluation of Putative Dynamic Glymphatic Activity in the Human Brain in Sleep-Wake States. Radiology 2021;300:661-8. [Crossref] [PubMed]
8. Deng S, Hu Y, Chen S, Xue Y, Yao D, Sun Q, Nedergaard M, Wang W, Ding F. Chronic sleep fragmentation impairs brain interstitial clearance in young wildtype mice. J Cereb Blood Flow Metab 2024;44:1515-31. [Crossref] [PubMed]
9. Vinje V, Zapf B, Ringstad G, Eide PK, Rognes ME, Mardal KA. Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation. Fluids Barriers CNS 2023;20:62. [Crossref] [PubMed]
10. Harrison IF, Ismail O, Machhada A, Colgan N, Ohene Y, Nahavandi P, Ahmed Z, Fisher A, Meftah S, Murray TK, Ottersen OP, Nagelhus EA, O'Neill MJ, Wells JA, Lythgoe MF. Impaired glymphatic function and clearance of tau in an Alzheimer's disease model. Brain 2020;143:2576-93. [Crossref] [PubMed]
11. Huang SY, Zhang YR, Guo Y, Du J, Ren P, Wu BS, Feng JF, Cheng W, Yu JT. Glymphatic system dysfunction predicts amyloid deposition, neurodegeneration, and clinical progression in Alzheimer's disease. Alzheimers Dement 2024;20:3251-69. [Crossref] [PubMed]
12. Zhang X, Wang Y, Jiao B, Wang Z, Shi J, Zhang Y, Bai X, Li Z, Li S, Bai R, Sui B. Glymphatic system impairment in Alzheimer's disease: associations with perivascular space volume and cognitive function. Eur Radiol 2024;34:1314-23. [Crossref] [PubMed]
13. Cohen I, Hoffmann C, Barash Y, Lekach R, Ben-Zeev B, Zohar-Dayan E, Shrot S. Assessment of glymphatic dysfunction in pediatric idiopathic intracranial hypertension: insights from quantitative diffusivity and perivascular spaces analysis-a case-control study. Quant Imaging Med Surg 2024;14:653-61. [Crossref] [PubMed]
14. Yang C, Tian S, Du W, Liu M, Hu R, Gao B, Pan T, Song Q, Liu T, Wang W, Zhang H, Miao Y. Glymphatic function assessment with diffusion tensor imaging along the perivascular space in patients with major depressive disorder and its relation to cerebral white-matter alteration. Quant Imaging Med Surg 2024;14:6397-412. [Crossref] [PubMed]
15. Baril AA, Pinheiro AA, Himali JJ, Beiser A, Sanchez E, Pase MP, Seshadri S, Demissie S, Romero JR. Lighter sleep is associated with higher enlarged perivascular spaces burden in middle-aged and elderly individuals. Sleep Med 2022;100:558-64. [Crossref] [PubMed]
16. Wang XX, Cao QC, Teng JF, Wang RF, Yang ZT, Wang MG, Cao ZH. MRI-visible enlarged perivascular spaces: imaging marker to predict cognitive impairment in older chronic insomnia patients. Eur Radiol 2022;32:5446-57. [Crossref] [PubMed]
17. Zheng X, Zhang Z, Liang X, Huang S, Wu L, Zhou F. Application of DTI-ALPS index for the analysis of the function of central glymphatic system in patients with chronic insomnia. Radiologic Practice 2023;38:1508-12.
18. Jin Y, Zhang W, Yu M, Li J, Du Y, Wang W, Chen G, Ding X, Ding J. Glymphatic system dysfunction in middle-aged and elderly chronic insomnia patients with cognitive impairment evidenced by diffusion tensor imaging along the perivascular space (DTI-ALPS). Sleep Med 2024;115:145-51. [Crossref] [PubMed]
19. Mathias J, Koessler L, Brissart H, Foscolo S, Schmitt E, Bracard S, Braun M, Kremer S. Giant cystic widening of Virchow-Robin spaces: an anatomofunctional study. AJNR Am J Neuroradiol 2007;28:1523-5. [Crossref] [PubMed]
20. Dong H, Yin L, Chen L, Wang Q, Pan X, Li Y, Ye X, Zeng M. Establishment and validation of a radiological-radiomics model for predicting high-grade patterns of lung adenocarcinoma less than or equal to 3 cm. Front Oncol 2022;12:964322. [Crossref] [PubMed]
21. Qin BE, Cheng C, Luo C, Liu J, Xu XF, Tong J, Yuan D, Chen Y, Peng FH, Jiang Y. The effect on brain volume in HIV-negative and non-transplant cryptococcal meningitis. Med Mycol 2022;60:myac068. [Crossref] [PubMed]
22. Wu J, Xia Y, Wang X, Wei Y, Liu A, Innanje A, Zheng M, Chen L, Shi J, Wang L, Zhan Y, Zhou XS, Xue Z, Shi F, Shen D. uRP: An integrated research platform for one-stop analysis of medical images. Front Radiol 2023;3:1153784. [Crossref] [PubMed]
23. Liu H, Yang S, He W, Liu X, Sun S, Wang S, Wang Y, Zhou X, Tang T, Xia J, Liu Y, Huang Q. Associations Among Diffusion Tensor Image Along the Perivascular Space (DTI-ALPS), Enlarged Perivascular Space (ePVS), and Cognitive Functions in Asymptomatic Patients With Carotid Plaque. Front Neurol 2021;12:789918. [Crossref] [PubMed]
24. Dredla BK, Del Brutto OH, Castillo PR. Sleep and Perivascular Spaces. Curr Neurol Neurosci Rep 2023;23:607-15. [Crossref] [PubMed]
25. Brown R, Benveniste H, Black SE, Charpak S, Dichgans M, Joutel A, Nedergaard M, Smith KJ, Zlokovic BV, Wardlaw JM. Understanding the role of the perivascular space in cerebral small vessel disease. Cardiovasc Res 2018;114:1462-73. [Crossref] [PubMed]
26. Garic D, McKinstry RC, Rutsohn J, Slomowitz R, Wolff J, MacIntyre LC, Weisenfeld LAH, Kim SH, Pandey J, St John T, Estes AM, Schultz RT, Hazlett HC, Dager SR, Botteron KN, Styner M, Piven J, Shen MD. Enlarged Perivascular Spaces in Infancy and Autism Diagnosis, Cerebrospinal Fluid Volume, and Later Sleep Problems. JAMA Netw Open 2023;6:e2348341. [Crossref] [PubMed]
27. Aribisala BS, Valdés Hernández MDC, Okely JA, Cox SR, Ballerini L, Dickie DA, Wiseman SJ, Riha RL, Muñoz Maniega S, Radakovic R, Taylor A, Pattie A, Corley J, Redmond P, Bastin ME, Deary I, Wardlaw JM. Sleep quality, perivascular spaces and brain health markers in ageing - A longitudinal study in the Lothian Birth Cohort 1936. Sleep Med 2023;106:123-31. [Crossref] [PubMed]
28. Del Brutto OH, Mera RM, Del Brutto VJ, Castillo PR. Enlarged basal ganglia perivascular spaces and sleep parameters. A population-based study. Clin Neurol Neurosurg 2019;182:53-7. [Crossref] [PubMed]
29. Shih NC, Barisano G, Lincoln KD, Mack WJ, Sepehrband F, Choupan J. Effects of sleep on brain perivascular space in a cognitively healthy population. Sleep Med 2023;111:170-9. [Crossref] [PubMed]
30. Liu X, Barisano G, Shao X, Jann K, Ringman JM, Lu H, et al. Cross-Vendor Test-Retest Validation of Diffusion Tensor Image Analysis along the Perivascular Space (DTI-ALPS) for Evaluating Glymphatic System Function. Aging Dis 2024;15:1885-98. [PubMed]
31. Park KM, Kim KT, Lee DA, Motamedi GK, Cho YW. Glymphatic system dysfunction in restless legs syndrome: evidenced by diffusion tensor imaging along the perivascular space. Sleep 2023;46:zsad239. [Crossref] [PubMed]
32. Zhang W, Zhou Y, Wang J, Gong X, Chen Z, Zhang X, Cai J, Chen S, Fang L, Sun J, Lou M. Glymphatic clearance function in patients with cerebral small vessel disease. Neuroimage 2021;238:118257. [Crossref] [PubMed]
33. Lee MK, Cho SJ, Bae YJ, Kim JM. MRI-Based Demonstration of the Normal Glymphatic System in a Human Population: A Systematic Review. Front Neurol 2022;13:827398. [Crossref] [PubMed]
34. Saito Y, Hayakawa Y, Kamagata K, Kikuta J, Mita T, Andica C, et al. Glymphatic system impairment in sleep disruption: diffusion tensor image analysis along the perivascular space (DTI-ALPS). Jpn J Radiol 2023;41:1335-43. [Crossref] [PubMed]
35. Siow TY, Toh CH, Hsu JL, Liu GH, Lee SH, Chen NH, Fu CJ, Castillo M, Fang JT. Association of Sleep, Neuropsychological Performance, and Gray Matter Volume With Glymphatic Function in Community-Dwelling Older Adults. Neurology 2022;98:e829-38. [Crossref] [PubMed]
36. Hsu JL, Wei YC, Toh CH, Hsiao IT, Lin KJ, Yen TC, Liao MF, Ro LS. Magnetic Resonance Images Implicate That Glymphatic Alterations Mediate Cognitive Dysfunction in Alzheimer Disease. Ann Neurol 2023;93:164-74. [Crossref] [PubMed]
37. Liang T, Chang F, Huang Z, Peng D, Zhou X, Liu W. Evaluation of glymphatic system activity by diffusion tensor image analysis along the perivascular space (DTI-ALPS) in dementia patients. Br J Radiol 2023;96:20220315. [Crossref] [PubMed]
38. Bernard C, Frauscher B, Gelinas J, Timofeev I. Sleep, oscillations, and epilepsy. Epilepsia 2023;64:S3-S12. [Crossref] [PubMed]
39. Feige B, Baumgartner B, Meyer D, Riemann D. The Relationship Between PSG and Morning/Evening Emotional Parameters in Patients With Insomnia Disorder and Good Sleepers. Front Psychol 2018;9:2712. [Crossref] [PubMed]
40. Olsson M, Ärlig J, Hedner J, Blennow K, Zetterberg H. Sleep deprivation and cerebrospinal fluid biomarkers for Alzheimer's disease. Sleep 2018; [Crossref] [PubMed]
41. Klostranec JM, Vucevic D, Bhatia KD, Kortman HGJ, Krings T, Murphy KP, terBrugge KG, Mikulis DJ. Current Concepts in Intracranial Interstitial Fluid Transport and the Glymphatic System: Part II-Imaging Techniques and Clinical Applications. Radiology 2021;301:516-32. [Crossref] [PubMed]
42. Taoka T, Ito R, Nakamichi R, Nakane T, Kawai H, Naganawa S. Diffusion Tensor Image Analysis ALong the Perivascular Space (DTI-ALPS): Revisiting the Meaning and Significance of the Method. Magn Reson Med Sci 2024;23:268-90. [Crossref] [PubMed]