
==== Front
NPJ Parkinsons Dis
NPJ Parkinsons Dis
NPJ Parkinson's Disease
2373-8057
Nature Publishing Group UK London

796
10.1038/s41531-024-00796-x
Article
Intravenous arachnoid granulation hypertrophy in patients with Parkinson disease
Leguizamon Melanie 1
http://orcid.org/0000-0002-4386-8999
McKnight Colin D. 2
Ponzo Tristan 1
Elenberger Jason 1
Eisma Jarrod J. 1
Song Alexander K. 1
Trujillo Paula 1
Considine Ciaran M. 1
Donahue Manus J. 134
http://orcid.org/0000-0002-9853-4902
Claassen Daniel O. 1
Hett Kilian kilian.hett.1@vumc.org

1
1 grid.412807.8 0000 0004 1936 9916 Vanderbilt Medical Center, Department of Neurology, Nashville, TN USA
2 grid.412807.8 0000 0004 1936 9916 Vanderbilt Medical Center, Department of Radiology and Radiological Sciences, Nashville, TN USA
3 grid.412807.8 0000 0004 1936 9916 Vanderbilt Medical Center, Department of Psychiatry and Behavioral Sciences, Nashville, TN USA
4 https://ror.org/02vm5rt34 grid.152326.1 0000 0001 2264 7217 Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, TN USA
20 9 2024
20 9 2024
2024
10 1778 4 2024
11 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Intravenous arachnoid granulations (AGs) are protrusions of the arachnoid membrane into the venous lumen and function as contributors to the cerebrospinal fluid (CSF) flow circuit. Patients with Parkinson disease (PD) often present with accumulation of alpha synuclein. Previous works have provided evidence for neurofluid circulation dysfunction in neurodegenerative diseases associated with changes in CSF egress, which may have implications regarding AG morphology. The present study aims to investigate group differences in AG volumetrics between healthy and PD participants, as well as relationships between AG characteristics and clinical assessments. Generalized linear models revealed significant increases in AG volumetrics and number in PD compared to healthy controls. Partial Spearman-rank correlation analyses demonstrated significant relationships between AG metrics and motor and cognitive assessments. Finally, AG volumetrics were positively correlated with objective actigraphy measures of sleep dysfunction, but not self-report sleep symptoms.

Subject terms

Parkinson's disease
Neurological manifestations
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Cerebrospinal fluid (CSF) plays an integral role in cerebral nutrient delivery1 and waste clearance2,3. Most CSF is produced in the atrium of the lateral ventricles by the choroid plexus complexes4 before flowing through the cerebral aqueduct and entering the subarachnoid space. Subsequent passage of CSF into the perivascular routes and interstitial spaces has been observed5, which supports interstitial fluid (ISF) flow as part of the hypothesized glymphatic system2. Recent models show evidence of CSF egress from the central nervous system via different pathways6, including along the cranial nerves7 and the spinal nerves into lymphatics8, as well as along other peri-sinus structures to the dural and extracranial lymphatics9. Among these peri-sinus structures, arachnoid granulations (AGs) contribute fundamentally to CSF resorption into the blood circulation and CSF homeostasis10.

AGs present as a hypertrophy of normal arachnoid villi and are projections of the subarachnoid space into both the lacuna lateralis and the dural venous sinuses11. The focus of this study is primarily concerned with intravenous AGs protruding into the superior sagittal sinus12, also defined as type I AGs13. While AGs are not present at birth, arachnoid villi are found in all humans and even smaller mammalian brains before maturation as microscopic structures. However, current imaging techniques do not provide acceptable contrast and spatial resolution for in vivo visualization of arachnoid villi. By 18 months of age, AGs are visible on gross inspection in larger mammals and humans, soon forming identifiable nodules in the sinus lumen and continuing to grow in size and number14. In addition to being larger in size, intravenous AGs are also distinct from arachnoid villi as they demonstrate endothelial-lined tubules that extend from the subarachnoid space to the venous system15. A previous study indicated that intravenous AG diameter, stalk diameter, and number are all positively correlated with age, while the percentage of meningothelial covering of AG is inversely correlated with age, along with significant changes in the internal milieu of AGs over time16. Together, these findings underscore the complex changes in AG morphology across the lifespan.

Past research based on animal models has suggested that advancing age results in a decline in the efficient clearance of interstitial solutes from the brain parenchyma via CSF-ISF exchange along perivascular routes17. Animal models have also provided evidence that ISF circulation is increased during sleep18 and clearance efficiency is decreased when sleep is reduced or dysfunctional19,20. Yet, the present literature lacks an understanding of the relationship between sleep and the CSF circuit in human models, including in Parkinson disease (PD), a neurodegenerative disorder with a high prevalence of sleep dysfunction21. PD is the second most common neurodegenerative disease of aging populations, presenting with cognitive, functional, and motor impairments22. Given that glymphatic clearance is likely upregulated during sleep23 and sleep deprivation is known to exacerbate cognitive and behavioral symptoms24, there is likely a bidirectional relationship between sleep impairments and symptomatology in PD. Further, previous work has uncovered significant relationships between sleep disorders and reduced CSF alpha-synuclein within patients with PD25. As aging negatively impacts sleep patterns, the subsequent increase in sleep dysfunction, observed in PD, may contribute to dysregulation of CSF movement and decreased CSF clearance efficiency26. Understanding the link between sleep dysfunction and impairments in CSF outflow, including via AGs, is therefore relevant to elucidating whether these changes may be a cause or a consequence of advancing age and disease progression.

Here, an investigation of morphological changes of intravenous AGs was conducted using a combination of high-resolution 3D T2-weighted MRI and deep-learning segmentation to test the hypothesis that AGs hypertrophy in the setting of PD. To address our first hypothesis of group differences in AG volumetrics, different measures of AG volume and count were compared between patients with PD and those of a similarly-aged healthy control cohort. Second, using correlation analyses, we tested hypotheses regarding AG hypertrophy and greater motor and cognitive symptom presentation in PD. Finally, as an exploratory analysis, correlation analyses were again conducted to investigate the hypothesis that AG volumes are increased in the setting of higher levels of sleep dysfunction in patients with PD.

Results

Demographics

In total, 74 participants completed the study. Thirty-two participants were diagnosed with PD (average years of symptoms = 6.2, average years since diagnosis = 4.4). Patients were on average 65.7 years old with a standard deviation of 8.5 years. Within the PD cohort, 12 participants were female and 20 were male. Moreover, 42 similarly-aged healthy controls were included in this study, with an average age of 67.4 years old and a standard deviation of 9.9 years. In the healthy control cohort, 22 participants were female and 20 were male. Details of the clinical and sleep assessments of the PD cohort can be found in Table 1. The PD cohort was composed of individuals with largely preserved cognitive function, with an average MoCA score of 24.4. We obtained baseline sleep data from 14 participants with PD who wore an actigraphy device ranging from 2 to 82 days (average = 23.5 days).Table 1 Description of the clinical presentation and sleep assessments conducted on the cohort of patients with Parkinson disease (PD)

	Mean [min–max]	
Disease duration (n = 32)		
Years since diagnosis	4.4 [0.1–18]	
Years since onset	6.2 [0.4–19]	
Clinical assessments (n = 32)	
MoCA	24.4 [18–30]	
SDMT	41.6 [24–57]	
MiniBEST	21 [3–28]	
UPDRS (Part III)	31.3 [8–61]	
Self-reported sleep assessments (n = 32)	
PROMIS SD	20.3 [9–31]	
PROMIS SRI	15.8 [8–28]	
Actigraphy (n = 14)	
Sleep efficiency (%)	94.8 [91.6–97.1]	
Wake after sleep onset (min/day)	26.1 [9.6–30.7]	
Awakenings (number)	8.16 [5.0–12.8]	
Clinical assessments were conducted off medication.

Group differences

We observed a significant increase of 73.3 mm3 in total AG volume (Fig. 1) in baseline PD scans compared to age and sex-matched healthy control scans (pFDR = 0.004). In addition, data revealed significantly increased AG number (difference = 2.3, pFDR = 0.019), maximum AG volume (difference = 24.4 mm3, pFDR = 0.004), and mean AG volume (difference = 3.3 mm3, pFDR = 0.004) in PD compared to age-matched controls (pFDR = 0.004).Fig. 1 Group differences in arachnoid granulation (AG) total volume (mm3).

A Significantly increased total AG volume in baseline Parkinson disease scans compared to healthy controls. B Significantly increased AG number in Parkinson disease compared to healthy controls. C Significantly increased maximum AG volume in Parkinson disease compared to healthy controls. D Significantly increased mean AG volume in Parkinson disease compared to healthy controls. Violin plots are shown with conventional boxplots and individual data points overlaid. Corrected p-values are shown.

Correlation with clinical assessment

Partial Spearman correlations adjusted for age (Table 2) revealed a significant positive relationship between total AG volume in patients with PD and MiniBEST (r = 0.46, pFDR = 0.02), but no significant relationships with MoCA (r = 0.19, pFDR = 0.57), SDMT (r = 0.35, pFDR = 0.20) or UPDRS total score (r = −0.02, pFDR = 0.98). We also found significant relationships between AG number in patients with PD with MiniBEST (r = 0.46, pFDR < 0.05) and with SDMT (r = 0.50, praw = 0.01) before correction for false discovery rate. Data suggest inconclusive relationships between maximum AG volume in patients with PD and MoCA, SDMT, MiniBEST, and UPDRS-III total scores (Table 2).Table 2 Results of partial Spearman’s rank correlations, adjusted for age, between AG measures and clinical assessments (MoCA, MiniBEST, UPDRS) in participants with PD

	Total volume	Maximum volume	Mean volume	Number	
	r	praw	pFDR	r	praw	pFDR	r	praw	pFDR	r	praw	pFDR	
Cognitive	
MoCA	0.19	0.30	0.57	0.15	0.42	0.57	0.09	0.61	0.61	0.31	0.09	0.37	
SDMT	0.35	0.10	0.20	0.17	0.44	0.51	0.14	0.51	0.51	0.50	0.01	0.06	
Motor	
UPDRS-III	−0.02	0.93	0.98	0.07	0.76	0.98	−0.01	0.98	0.98	−0.01	0.98	0.98	
MiniBEST	0.46	0.02	<0.05	0.31	0.15	0.59	0.21	0.33	0.81	0.64	<0.01	<0.01	
Significant relationships are underlined and bolded. Underlined refers to significant raw p-values that did not survive false discovery rate (FDR) correction.

MoCA Montreal Cognitive Assessment, SDMT Symbol Digit Modalities Test, UPDRS-III Universal Parkinson Disease Rating Scale part III, MiniBEST Mini Balance Evaluation Systems Test.

Correlation with brain and CSF volumetrics

Data indicate no significant relationships between AG metrics and total intracranial volume, gray matter volume, or white matter volume (Supplementary Table 1). Similarly, data suggest no significant relationships between total, maximum, and mean AG volume and AG number with CSF volumetrics, including total CSF volume, internal CSF volume, and external CSF volume (Supplementary Table 2).

Correlation with sleep assessments

Table 3 shows details of correlation analyses of sleep assessment data. Findings suggest non-significant relationships between AG measures and self-reported sleep assessments, with neither the SD or SRI sub-scores of the PROMIS measure being significantly related to AG measures, despite showing trends of increased sleep dysfunction (i.e., higher scores on SD and SRI tests) associated with larger AG volumetrics. Despite not reaching significance, these trends in subjective sleep reports were consistent with findings from actigraphy indicating that increases in AG volume and number in PD are related to sleep dysfunction.Table 3 Results of partial Spearman’s rank correlations, adjusted for age, between AG measures and sleep assessments (self-reported as well as actigraphy data), in participants with PD

	Total volume	Maximum volume	Mean volume	Number	
	r	praw	pFDR	r	praw	pFDR	r	praw	pFDR	r	praw	pFDR	
Self-reported assessments	
SD	0.35	0.24	0.43	0.38	0.19	0.43	0.21	0.50	0.50	0.30	0.32	0.43	
SRI	0.43	0.09	0.21	0.41	0.16	0.21	0.42	0.15	0.21	0.33	0.27	0.27	
Actigraphy measures	
SE	−0.88	<0.01	<0.01	−0.72	<0.01	0.01	−0.55	0.05	0.05	−0.69	<0.01	0.01	
WASO	0.54	0.06	0.22	0.34	0.25	0.27	0.45	0.12	0.25	0.33	0.27	0.27	
Awake	0.68	0.01	0.04	0.46	0.11	0.15	0.49	0.09	0.15	0.36	0.23	0.23	
Significant relationships are underlined and bolded.

SD sleep disturbance, SRI sleep-related impairment, SE sleep efficiency, WASO wake after sleep onset, Awake and number of awakenings.

All individuals in the PD cohort were instructed to wear an actigraphy device for three months after MRI acquisition as part of a larger interventional study; however, incomplete compliance across participants required excluding a subset of participants from analysis. We selected a subset of the enrolled PD cohort (n = 14) who wore the actigraph for up to 82 days (range 2 to 82 days; Md = 28; M = 23.5, SD = 23.4) following MRI acquisition but prior to introduction of the intervention. This data was used as an estimate of baseline sleep parameters for exploratory analysis investigating the relationship between sleep behavior and AG volumetrics. We noted significant relationships between sleep efficiency and total AG volume (r = −0.88, pFDR < 0.01), as well as with maximum AG volume (r = −0.72, pFDR = 0.01) and AG number (r = −0.69, pFDR = 0.01). We also found a significant relationship between number of awakenings and total AG volume (r = 0.68, pFDR = 0.04). Further, total CSF volume was significantly related to SRI (r = −0.75, pFDR < 0.01), sleep efficiency (r = 0.81, pFDR < 0.01), wake after sleep onset (r = −0.84, pFDR < 0.01), and number of awakenings (r = −0.82, pFDR < 0.01) in this subset of participants. Internal and external CSF volume were not significantly related to any sleep assessments (Table 4).Table 4 Results of partial Spearman’s rank correlations, adjusted for age, between CSF volumetrics and sleep assessments (self-reported as well as actigraphy data), in participants with PD

	CSF volume	Internal CSF volume	External CSF volume	
	r	praw	pFDR	r	praw	pFDR	r	praw	pFDR	
Self-reported assessments	
SD	−0.17	0.57	0.57	0.33	0.28	0.44	−0.31	0.30	0.44	
SRI	−0.75	<0.01	<0.01	−0.42	0.15	0.17	−0.41	0.17	0.17	
Actigraphy measures	
SE	0.81	<0.01	<0.01	0.35	0.23	0.23	0.46	0.11	0.17	
WASO	−0.84	<0.01	<0.01	−0.38	0.20	0.20	−0.50	0.08	0.13	
Awake	−0.82	<0.01	<0.01	−0.39	0.19	0.19	−0.50	0.08	0.12	
Significant relationships are underlined and bolded.

SD sleep disturbance, SRI sleep-related impairment, SE sleep efficiency, WASO wake after sleep onset, Awake and number of awakenings.

Discussion

In the present study, we investigated arachnoid granulation hypertrophy in patients with PD compared to healthy controls using a high-resolution 3D T2-weighted MRI combined with a deep-learning segmentation model. We conducted correlation analyses to test hypotheses pertaining to the relationship between AG enlargement in PD with cognitive and motor impairments, and as an exploratory analysis, we investigated the relationship between AG enlargement and multiple measures of sleep disruption. The observed data provide evidence of enlarged AGs in the PD population, which correlate with multiple clinical metrics and sleep measures. These findings provide potential preliminary evidence that AG morphology relates to sleep dysfunctions in PD pathophysiology.

Intravenous arachnoid granulations have long been observed in the superior sagittal sinus12. Various studies, including both post-mortem10 and in vivo13, have evaluated AGs within healthy adults and have demonstrated that AGs increase with age in healthy individuals27. A previous study has hypothesized that AG hypertrophy results from increased CSF pressure in the subarachnoid space14 and/or decreased CSF outflow via the meningeal and cranial nerves17. This consequently results in increased CSF pressure and a need for another pathway to rid the brain of metabolic waste, causing AGs to hypertrophy to compensate for decreased egress efficiency and aid in further CSF bulk outflow. Other etiologies, such as incidental findings of giant AG (i.e., large enough to fill the dural sinus lumen and cause local dilation), have been reported in patients presenting with venous hypertension and headaches, potentially due to the abnormal growth of AG filling dural sinuses, and consequently altering venous outflow dynamics28. In the present study, we observed significantly increased AG volume in patients with PD compared to healthy controls. This increase may serve as a compensatory mechanism for impaired CSF clearance via the recently proposed glymphatic pathway; however, independent measures of perivascular and interstitial flow are required to rigorously evaluate this possibility.

AGs were originally described as a main site for CSF egress29. However, studies using intrathecal injection of gadolinium-based contrast agents challenged this idea30,31, as the contrast agent reached maximum concentration in the plasma before peak concentration was detected in peri-sinus structures. This implicated other efflux routes as primary sites for CSF-mediated metabolic waste clearance. Although the results of these studies improve the present understanding of CSF egress, there are some limitations that must be addressed. First, gadolinium-based contrast is known to cross the blood-CSF barrier and subsequently enter the brain parenchyma32. In addition, it may not be valid to equate exogenous contrast movement with fluid and waste movement, as the molecular properties of gadolinium-based contrast agents may influence egress patterns along such small conduits.

Total, maximum, and mean AG volume, as well as AG number, were not significantly correlated with brain or CSF measures, indicating that increases in AG volumetrics and count in patients with PD likely occur independently of global brain atrophy. Past literature has described overall loss of gray matter33 and ventricular expansion34 in PD compared to controls, but few studies have evaluated global CSF volumetric changes as they relate to white and gray matter changes in the PD population. Across the healthy lifespan, however, intracranial CSF volume has been shown to increase linearly due to brain volume reduction35, but whether these changes are replicated in PD remains unknown. Further, the present literature lacks an understanding of how AG volume relates to CSF volume and brain matter volume. Histologic evidence of the composition of AG reports some soft tissue elements with CSF flow turbulence36, as well as CSF-incongruent fluid37. As the morphology of AG is not completely understood, these reports reveal a gap in the interpretation of AG structure and function in relation to CSF and brain volumes. Thus, AG hypertrophy in PD compared to healthy control participants may reflect a unique difference between these cohorts not otherwise explained by potential changes in CSF or brain volume.

Total AG volume and AG number were significantly correlated with MiniBEST total scores in patients with PD, despite non-significant relationships with UPDRS-III scores. This could in part reflect the fact that the UPDRS-III reflects global motor dysfunction, with less sensitivity for motor symptoms in early PD38 (mean score for our sample was consistent with Hoen and Yahr39 stage 2.5, i.e., “mild bilateral involvement with recovery on retropulsion test”), whereas the MiniBEST is considered the strongest individual predictor of falls in patients with PD40. Therefore, it is possible that the MiniBEST provides a more sensitive measure of gait and dynamic instability than the UPDRS-III, as it addresses specific impairments, suggesting that changes in AG volumetrics are correlated with the severity of specific balance and postural deficits or early motor symptomatology in PD, instead of global disease severity. Similarly, total AG volume (before correction for false discovery rate) and AG number were significantly related to scores on the SDMT in patients with PD, but not MoCA, a screening assessment of global cognitive status. Since global cognitive status was largely preserved in our sample, it is likely that low variability in the MoCA and higher sensitivity of the SDMT to frontal-subcortical network dysfunction associated with PD pathology accounted for these findings.

It should first be noted that this study did not measure perivascular or interstitial flow, which are the fundamental components of the proposed glymphatic system. However, results of the experiments conducted in this study may be considered alongside the growing literature investigating changes of glymphatic-related markers in PD. Previous studies have implicated the role of the CSF circuits in brain clearance41 and have suggested glymphatic system dysfunction in patients with PD42–44. By utilizing diffusion tensor imaging (DTI-ALPS)45–47, a metric of water diffusion at the level of the medullary veins and orthogonal to primary fiber tracks, past research has reported a decrease in DTI-ALPS in patients with PD compared to healthy controls48. In addition, recent work suggested that fluid movement within the posterior aspects of the suprasellar cistern is reduced in 32 individuals with PD relative to 27 healthy controls49. Further investigations revealed that a lower DTI-ALPS score was positively correlated with disease duration in PD50, which may reflect downstream dysfunction. In this study, the finding of increased AG volumetrics in PD compared to controls may highlight the role of AGs as a compensatory response to decreased perivascular fluid movement in patients with PD.

Past studies have suggested water diffusion parallel to the medullary veins to be significantly lower in individuals with sleep disruption compared to healthy controls51. In those experiencing chronic sleep dysfunction, recent work has also shown enlargement of the perivascular spaces, which are critical to glymphatic function52. The increased volume of perivascular spaces and peri-sinus structures, such as AG, may not only be a marker of aging and neurodegenerative disease, but also of chronic sleep dysfunction. Taken together, these studies lend further support for reduced CSF clearance in both neurodegenerative states and in sleep dysfunctions, which could contribute to PD pathology progression. As reported in our study, total AG volume significantly relates to actigraphy-based sleep efficiency, an objective measure of sleep disturbance. In addition, mean and maximum AG volume, as well as AG number, significantly relate to actigraphy-based sleep efficiency before correction for false discovery rate. However, this finding was shown in a subsample of 14 participants, and subjective sleep reports (i.e., PROMIS SD and SRI sub-scores) were not significantly correlated with any changes in AG metrics, despite replicating the trends shown with actigraphy. Thus, our findings are suggestive of a relationship, but confirmation and clarification of causal relationship requires further empirical investigation.

Overall, the data indicate significant increases in total, average, and maximum AG volume, as well as AG number, in a cohort of patients with PD who present with mildly impaired-to-normal range cognition. This suggests that pathological changes in AG may develop in PD even in the absence of substantial neurocognitive decline. However, our findings do support relationships between AG volumetry and various assessment measures more sensitive to neurobehavioral dysfunction in PD (i.e., MiniBEST, SDMT, actigraphy-based sleep quality), suggesting pathological AG changes may be associated with prodromal or mild clinical decline in PD.

While results indicate significant relationships between AG hypertrophy and sleep difficulties, future studies are needed to determine the causal mechanism of increased AG volume in PD and its impact on motor and cognitive impairment and sleep. Potential explanations include AG hypertrophy due to impaired downstream clearance of CSF and/or AG enlargement as a compensatory response to handle increased CSF-mediated waste clearance of protein aggregation in PD pathology. In addition, as glymphatic clearance may occur at higher rates during sleep53, chronic sleep dysfunction may contribute to less efficient CSF clearance.

Finally, while our work supports significant differences in AG volumetry between PD and healthy controls, future work could expand upon the scope of our findings to compare AGs in PD to other disease populations. This would allow for exploration of AG morphostructural and functional differences between various neurodegenerative disorders to better understand how they contribute to brain waste clearance and clinical impairments.

The deep-learning algorithm provided a method to automatically segment AG structures in vivo using high-resolution 3D T2-weighted MRI. However, manual correction was necessary to ensure a high level of accuracy and precision in delineating AGs. Moreover, despite the use of high-resolution 3D T2-weighted MRI, the current MRI resolution could only provide visualization of AG volumes above 2 mm3. Therefore, smaller AGs could not be assessed, resulting in a limitation of accurately counting the number of AG protruding the lumen of the superior sinus. To address this limitation, we investigated additional metrics, including total, mean, and maximum volume (i.e., larger AG size within an individual). Nevertheless, there are other anatomical variables, such as pedicle diameter, AG sphericity, and percent meningothelial coverage, amongst others, that we did not evaluate with the current method and may contribute to differences in AG morphology.

In addition, in this study, we conducted volumetrics analysis of intravenous AG structures. Although volume provides a comprehensive measure of structural changes, it does not allow us to assess functional changes. Findings presented in this study motivate future structural imaging analysis of other anatomical variables and functional imaging analysis of AGs to understand the impact of increased AG volume in patients with neurodegenerative proteinopathy. Finally, in our cross-sectional study design, while we are able to evaluate correlation between AG hypertrophy and clinical presentation, we are unable to infer causality. Future work should aim to investigate AG morphological changes longitudinally to uncover causal relationships between AG enlargement, PD pathology, and sleep dysfunction.

Furthermore, total AG volume was only significantly correlated with objective measures of sleep from actigraphy but not subjective measures of sleep using self-report PROMIS questionnaires. Additional investigation of whether AG volumetrics may relate to various sleep measures in a larger sample and in a controlled setting is necessary to validate the relationships detected in the current study. The present observational study using actigraphy was conducted in a small sample and extracted from a group of individuals who happened to wear an actigraph pre-intervention; as such, the study our data was taken from had not been designed for the purpose of investigating sleep quality in individuals with PD at baseline. While both subjective and objective measures of sleep quality can inform a multimodal assessment of sleep in our population, other, more robust measures, such as polysomnography, could be employed in future studies to better measure sleep disruption and build upon our results. Our finding of sleep dysfunction in PD relating to AG hypertrophy motivates further study in a research setting designed and aimed to specifically investigate the relationship between the CSF flow circuit and sleep dysfunction in humans, in both healthy and disease states.

We observed increased volume of intravenous arachnoid granulations in patients with Parkinson disease. Arachnoid granulation hypertrophy was also significantly related to motor impairment (on MiniBEST) and poorer cognitive functioning (on SDMT). In our sample, data revealed a significant relationship between AG volume and actigraphy-based sleep dysfunction, potentially suggesting a critical role of sleep in neurofluid regulation. Follow-up studies should investigate the causal role of arachnoid granulations in pathology progression, as well as how impaired sleep may influence this relationship.

Methods

Demographics

All participants provided informed, written consent in accordance with the Vanderbilt University Medical Center Institutional Review Board (IRB) and consistent with the Declaration of Helsinki and its amendments (IRB Study #191206). PD and healthy control participants completed a 3-Tesla MRI between January 2020 and September 2023 at the Vanderbilt University Medical Center (Table 5).Table 5 Description of the demographics and brain (total intracranial volume, gray matter volume, and white matter volume) and cerebrospinal fluid (CSF) volumetrics (total, internal, and external) in patients with Parkinson disease (PD) and healthy controls

	Parkinson disease	Healthy	p	
Demographics	
Number of participants	32	42	–	
Age (year) [min–max]	67.4 [56–79]	64.4 [50–86]	0.09	
Sex (Female/Male)	12/20	22/20	0.17	
Brain volume	
Intracranial (cm3)	1434.1 [1167.3–1750.0]	1389.7 [1060.6–1639.6]	0.28	
Gray matter (cm3)	720.0 [582.4–895.0]	706.5 [532.8–845.9]	0.61	
White matter (cm3)	430.9 [323.0–588.8]	422.7 [286.4–561.5]	0.55	
CSF volume	
Total (cm3)	264.2 [177.3–403.4]	242.5 [144.8–389.5]	0.05	
Internala (cm3)	42.9 [15.8–95.6]	37.8 [16.6–72.8]	0.70	
Externalb (cm3)	219.6 [160.7–331.3]	203.4 [123.3–350.2]	0.07	
aInternal CSF volume comprises lateral, third, and fourth ventricles.

bExternal CSF volume comprises all subarachnoid space and other CSF compartment surrounding brain space.

Participants with PD were recruited as volunteers from the community seen in neurology clinics and were included if they were aged 55–80 and had PD as defined by the UK Brain Bank Criteria2, which requires a diagnosis of Parkinsonian Syndrome (bradykinesia and muscular rigidity, 4–6 Hz rest tremor, and/or postural instability not caused by primary visual, vestibular, cerebellar or proprioceptive dysfunction) and at least three supportive prospective criteria for PD. Participants with any contraindication or inability to tolerate brain MRI, or with a history or signs of cerebrovascular disease, clinically significant neurological disorder (e.g. motor neuron disease, normal pressure hydrocephalus, brain tumor, etc.), severe or repeated head injury, encephalitis, untreated obstructive sleep apnea, or a Clinical Dementia Rating scale score ≥1 were excluded from the current study. Healthy controls were recruited from the Vanderbilt Glymphatic Imaging Project dataset (VGIP), in which all participants (age = 18–83 years) were scanned between January 2020 and September 2021 at Vanderbilt University Medical Center. Inclusion criteria involved compatibility with 3 Tesla MRI. Healthy participants were excluded if they presented with a history of cerebrovascular disease, anemia, psychiatric, or neurological disorder including but not limited to prior overt stroke, sickle cell anemia, schizophrenia, bipolar disorder, Alzheimer’s disease, PD, or multiple sclerosis. The presence of non-specific white matter lesions was not an exclusion criterion, as these lesions are prevalent with normal aging, and we sought our cohort to be representative. Clinical history was reviewed by a board-certified Neurologist (DOC; experience = 15 years) and anatomical imaging and angiography by a board-certified neuroradiologist (CDM; experience = 13 years).

Clinical assessments

Cognitive and motor impairments were assessed in patients with PD (Table 1) using the Montreal Cognitive Assessment (MoCA), Symbol Digit Modality Test (SDMT)54, Mini Balance Evaluation Systems Test (MiniBEST)55, and Universal Parkinson Disease Rating Scale – part III (UPDRS)56. The MoCA is a screening assessment of cognitive function. The SDMT is a cognitive test that assesses processing speed and graphomotor speed. The UPDRS – part III is the motor examination portion used to measure disease severity in PD. Finally, the MiniBEST is a shortened version of the BEST assessment that measures dynamic balance, gait, postural reactivity, and mobility. All clinical assessments were performed on the day of MRI with the patient off medication for a period of 16 h prior to the examination.

Sleep assessments

Sleep assessment was accomplished through multiple methods to more comprehensively characterize the clinical domain. Clinical actigraphy units (ActiGraph wGT3X-BT)57 were worn by patients following neuroimaging and clinical assessment visit, to characterize sleep-wake behavior (see Correlation with sleep assessments section for details on timing and length of assessment window). Resultant variables included in this study were sleep efficiency (SE; total estimated sleep time divided by total estimated sleep opportunity period), wake after sleep onset (WASO; total minutes awake during estimated sleep opportunity period), and number of awakenings (the number of times individuals would wake up throughout the night after falling asleep), all pre-processed and scored using ActiLife software (version 6.13.15). Assessment of sleep-related symptom burden was accomplished using the Patient-Reported Outcomes Measurement Information System (PROMIS)58. Sleep-wake-related PROMIS short forms for two constructs were employed: sleep disturbance (SD) and sleep-related impairment (SRI). SD assesses perception of sleep quality, sleep depth, and restoration associated with sleep, while SRI focuses on perceptions of alertness, sleepiness, and tiredness during usual waking hours. PROMIS sleep questionnaires were administered on the day of MRI scan, following standard instructions requesting the participant to reflect on the last week of symptoms. In this study, raw total scores were used for analyses.

MRI acquisition

All participants underwent the same acquisition protocol. Participants were scanned using 3-Tesla MRI (Philips Medical Systems, Best, The Netherlands) with body coil radiofrequency transmission and phased array 32-channel reception. The scan protocol included standard non-contrasted anatomical imaging to ensure inclusion criteria were met along with a 3D T1-weighted magnetization-prepared-rapid-gradient-echo (MPRAGE) scan with field-of-view = 150 × 256 × 180 mm, repetition time = 3.7 ms, echo time = 8.1 ms, flip angle = 8°, spatial resolution = 1 × 1 × 1 mm and a 3D T2-weighted (sagittal acquisition) volumetric isotropic turbo-spin-echo acquisition (VISTA) sequence with field-of-view = 250 × 250 × 188.8 mm, repetition time = 2500 ms, echo time = 331 ms, flip angle = 90°, spatial resolution = 0.78 × 0.78 × 0.78 mm (see Fig. 2).Fig. 2 Case examples of AG hypertrophy.

Panels A and B represent the maximum intensity projection (top) and coronal slices (bottom) of T2-weighted scans, highlighting regions with high concentration of fluids, such as lateral ventricles and subarachnoid space, overlaid with 3D rending of the superior sinus (red), and intravenous arachnoid granulations (blue) from two participants in the axial, sagittal, and coronal planes. Panel A illustrates a participant with increased average AG volume (1, 2, and 3). Panel B illustrates a participant with normal average AG volume (1) and (2), but an increase of maximum AG volume (3). Both participants (A, B) had the same number of detected AGs.

Image processing

Quantification of total intracranial volume (TICV), brain tissue volumes (total volume, gray matter, and white matter) as well as CSF volumes, including internal CSF (i.e., lateral, third, and fourth ventricle), external CSF (i.e., subarachnoid space and basal cisterns59), and total CSF, was obtained by feeding the T1-weighted MRI to AssemblyNet, an ensemblist deep-learning algorithm estimating brain structure volumes from structural MRI60. AGs were measured using the following metrics: total volume (sum of AG individual volumes), number of AG detected, mean volume (total AG volume divided by number of AG), and maximum volume (represents the volume of the largest AG detected in each participant).

Arachnoid granulations were measured using a novel non-invasive method combining high-resolution T2-weighted MRIs and deep-learning61. T2-weighted MRIs were corrected for field inhomogeneity and aligned to the MNI ICBM 152 symmetric template using affine registration62. Following pre-processing, a deep-learning model was trained and validated using 82 manually delineated scans from healthy controls and participants with various neurological conditions (i.e., Alzheimer disease, Parkinson disease, and Huntington disease) under the supervision of a board certified neuroradiologist. This software is freely available for research purposes (https://github.com/Center-of-Imaging-Biomarker-Development/spesis). Quality assessment of automatic segmentation was performed, and manual correction of segmentation was applied blinded from the neurological conditions of the patients.

Distribution of age and sex ratio differences between healthy controls and patients with PD were assessed using Kruskal–Wallis test and Chi-squared test, respectively. A group-wise analysis was conducted to investigate the primary hypothesis of differences in AG metrics between the two investigated cohorts and was performed using a generalized linear model. AG metrics (i.e., number, total, mean, and maximum AG volume) were used as dependent variables; pathological group (i.e., healthy control and PD) was defined as the independent variable; and age, sex, and intracranial volume (ICV) were used as covariates. As a secondary analysis, the relationship between AG features and clinical (i.e., cognitive and motor) assessments was determined using partial Spearman-rank correlation63, adjusting relationship for age. The exploratory analysis of the relationship between AG features and sleep dysfunction, assessed using self-reported and actigraphy measures, was also conducted using the partial Spearman-rank correlation method, adjusted for age. All p-values are reported as uncorrected (noted praw) and corrected for false discovery rate (noted pFDR) using the Benjamini–Hochberg procedure48.

Supplementary information

Supplementary Materials

Supplementary information

The online version contains supplementary material available at 10.1038/s41531-024-00796-x.

Acknowledgements

This work was supported in part by the U.S. Department of Defense under Grant W81XWH-19-1-0812 and by the National Institute of Health (NIH) under Grants R01AG062574, R01AT11456, K24AG064114, and the Huntington Disease Society of America Human Biology Project fellowship.

Author contributions

M.L., C.D.M., D.O.C., M.J.D., and K.H. contributed to study design and interpretation of data. J.J.E., M.J.D., A.K.S., J.E., and T.P. contributed to data acquisitions. C.D.M. and P.T. contributed to the review of the MRI data. M.L., C.D.M., M.J.D., and K.H. contributed to analysis and development of new methods. All authors contributed to writing the manuscript. All authors read and approved the final manuscript.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Code availability

The underlying code for this study is available in GitHub and can be accessed via this link: https://github.com/Center-of-Imaging-Biomarker-Development/spesis.

Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. Veening JG Barendregt HP The regulation of brain states by neuroactive substances distributed via the cerebrospinal fluid; a review Fluids Barriers CNS 2010 7 1 10.1186/1743-8454-7-1
Veening, J. G. & Barendregt, H. P. The regulation of brain states by neuroactive substances distributed via the cerebrospinal fluid; a review. Fluids Barriers CNS 7, 1 (2010).
2. Iliff JJ A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid β Sci. Transl. Med. 2012 4 147ra111 10.1126/scitranslmed.3003748 22896675
Iliff, J. J. et al. A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid β. Sci. Transl. Med. 4, 147ra111 (2012).22896675
3. Benveniste H The glymphatic system and waste clearance with brain aging: a review Gerontology 2019 65 106 119 10.1159/000490349 29996134
Benveniste, H. et al. The glymphatic system and waste clearance with brain aging: a review. Gerontology 65, 106–119 (2019).29996134
4. Scelsi CL The lateral ventricles: a detailed review of anatomy, development, and anatomic variations AJNR Am. J. Neuroradiol. 2020 41 566 572 10.3174/ajnr.A6456 32079598
Scelsi, C. L. et al. The lateral ventricles: a detailed review of anatomy, development, and anatomic variations. AJNR Am. J. Neuroradiol. 41, 566–572 (2020).32079598
5. Salehpour F Khademi M Bragin DE DiDuro JO Photobiomodulation therapy and the glymphatic system: promising applications for augmenting the brain lymphatic drainage system IJMS 2022 23 2975 10.3390/ijms23062975 35328396
Salehpour, F., Khademi, M., Bragin, D. E. & DiDuro, J. O. Photobiomodulation therapy and the glymphatic system: promising applications for augmenting the brain lymphatic drainage system. IJMS 23, 2975 (2022).35328396
6. Khasawneh A Garling R Harris C Cerebrospinal fluid circulation: What do we know and how do we know it? Brain Circ. 2018 4 14 10.4103/bc.BC_3_18 30276331
Khasawneh, A., Garling, R. & Harris, C. Cerebrospinal fluid circulation: What do we know and how do we know it? Brain Circ. 4, 14 (2018).30276331
7. Norwood JN Anatomical basis and physiological role of cerebrospinal fluid transport through the murine cribriform plate eLife 2019 8 e44278 10.7554/eLife.44278 31063132
Norwood, J. N. et al. Anatomical basis and physiological role of cerebrospinal fluid transport through the murine cribriform plate. eLife 8, e44278 (2019).31063132
8. Proulx ST Cerebrospinal fluid outflow: a review of the historical and contemporary evidence for arachnoid villi, perineural routes, and dural lymphatics Cell Mol. Life Sci. 2021 78 2429 2457 10.1007/s00018-020-03706-5 33427948
Proulx, S. T. Cerebrospinal fluid outflow: a review of the historical and contemporary evidence for arachnoid villi, perineural routes, and dural lymphatics. Cell Mol. Life Sci. 78, 2429–2457 (2021).33427948
9. Brady M Cerebrospinal fluid drainage kinetics across the cribriform plate are reduced with aging Fluids Barriers CNS 2020 17 71 10.1186/s12987-020-00233-0 33256800
Brady, M. et al. Cerebrospinal fluid drainage kinetics across the cribriform plate are reduced with aging. Fluids Barriers CNS 17, 71 (2020).33256800
10. Grzybowski DM Herderick EE Kapoor KG Holman DW Katz SE Human arachnoid granulations Part I: a technique for quantifying area and distribution on the superior surface of the cerebral cortex Fluids Barriers CNS 2007 4 6 10.1186/1743-8454-4-6
Grzybowski, D. M., Herderick, E. E., Kapoor, K. G., Holman, D. W. & Katz, S. E. Human arachnoid granulations Part I: a technique for quantifying area and distribution on the superior surface of the cerebral cortex. Fluids Barriers CNS 4, 6 (2007).
11. Trolard D Les Lacunes Veineuses de la dura-mere J. L’anatomie 1892 38 28 56
Trolard, D. Les Lacunes Veineuses de la dura-mere. J. L’anatomie 38, 28–56 (1892).
12. Brunori A Vagnozzi R Giuffrè R Antonio Pacchioni (1665–1726): early studies of the dura mater J. Neurosurg. 1993 78 515 518 10.3171/jns.1993.78.3.0515 8442786
Brunori, A., Vagnozzi, R. & Giuffrè, R. Antonio Pacchioni (1665–1726): early studies of the dura mater. J. Neurosurg. 78, 515–518 (1993).8442786
13. Shah T Arachnoid granulations are lymphatic conduits that communicate with bone marrow and dura-arachnoid stroma J. Exp. Med. 2023 220 e20220618 10.1084/jem.20220618 36469302
Shah, T. et al. Arachnoid granulations are lymphatic conduits that communicate with bone marrow and dura-arachnoid stroma. J. Exp. Med. 220, e20220618 (2023).36469302
14. le Gros Clark WE On the pacchionian bodies J. Anat. 1920 55 40 48 17103914
le Gros Clark, W. E. On the pacchionian bodies. J. Anat. 55, 40–48 (1920).17103914
15. Grossman CB Potts DG Arachnoid granulations: radiology and anatomy Radiology 1974 113 95 100 10.1148/113.1.95 4608236
Grossman, C. B. & Potts, D. G. Arachnoid granulations: radiology and anatomy. Radiology 113, 95–100 (1974).4608236
16. Radoš M Živko M Periša A Orešković D Klarica M No arachnoid granulations—no problems: number, size, and distribution of arachnoid granulations from birth to 80 years of age Front Aging Neurosci. 2021 13 698865 10.3389/fnagi.2021.698865 34276348
Radoš, M., Živko, M., Periša, A., Orešković, D. & Klarica, M. No arachnoid granulations—no problems: number, size, and distribution of arachnoid granulations from birth to 80 years of age. Front Aging Neurosci. 13, 698865 (2021).34276348
17. Kress BT Impairment of paravascular clearance pathways in the aging brain: paravascular clearance Ann. Neurol. 2014 76 845 861 10.1002/ana.24271 25204284
Kress, B. T. et al. Impairment of paravascular clearance pathways in the aging brain: paravascular clearance. Ann. Neurol. 76, 845–861 (2014).25204284
18. Hablitz LM Circadian control of brain glymphatic and lymphatic fluid flow Nat. Commun. 2020 11 4411 10.1038/s41467-020-18115-2 32879313
Hablitz, L. M. et al. Circadian control of brain glymphatic and lymphatic fluid flow. Nat. Commun. 11, 4411 (2020).32879313
19. Xie L Sleep drives metabolite clearance from the adult brain Science 2013 342 373 377 10.1126/science.1241224 24136970
Xie, L. et al. Sleep drives metabolite clearance from the adult brain. Science 342, 373–377 (2013).24136970
20. Rasmussen MK Mestre H Nedergaard M The glymphatic pathway in neurological disorders Lancet Neurol. 2018 17 1016 1024 10.1016/S1474-4422(18)30318-1 30353860
Rasmussen, M. K., Mestre, H. & Nedergaard, M. The glymphatic pathway in neurological disorders. Lancet Neurol. 17, 1016–1024 (2018).30353860
21. Lees AJ Blackburn NA Campbell VL The nighttime problems of Parkinsonʼs disease Clin. Neuropharmacol. 1988 11 512 519 10.1097/00002826-198812000-00004 3233589
Lees, A. J., Blackburn, N. A. & Campbell, V. L. The nighttime problems of Parkinsonʼs disease. Clin. Neuropharmacol. 11, 512–519 (1988).3233589
22. Mhyre T. R., Boyd J. T., Hamill R. W. & Maguire-Zeiss K. A. In Protein Aggregation and Fibrillogenesis in Cerebral and Systemic Amyloid Disease (ed. Harris, J. R.) Vol. 65, 389–455 (Springer Netherlands, 2012).
23. Chong PLH Garic D Shen MD Lundgaard I Schwichtenberg AJ Sleep, cerebrospinal fluid, and the glymphatic system: a systematic review Sleep. Med. Rev. 2022 61 101572 10.1016/j.smrv.2021.101572 34902819
Chong, P. L. H., Garic, D., Shen, M. D., Lundgaard, I. & Schwichtenberg, A. J. Sleep, cerebrospinal fluid, and the glymphatic system: a systematic review. Sleep. Med. Rev. 61, 101572 (2022).34902819
24. Mahmood Z REM sleep behavior disorder in Parkinson’s disease: effects on cognitive, psychiatric, and functional outcomes J. Int. Neuropsychol. Soc. 2020 26 894 905 10.1017/S1355617720000430 32375913
Mahmood, Z. et al. REM sleep behavior disorder in Parkinson’s disease: effects on cognitive, psychiatric, and functional outcomes. J. Int. Neuropsychol. Soc. 26, 894–905 (2020).32375913
25. Wang XT Associations of sleep disorders with cerebrospinal fluid α-synuclein in prodromal and early Parkinson’s disease J. Neurol. 2022 269 2469 2478 10.1007/s00415-021-10812-2 34605986
Wang, X. T. et al. Associations of sleep disorders with cerebrospinal fluid α-synuclein in prodromal and early Parkinson’s disease. J. Neurol. 269, 2469–2478 (2022).34605986
26. Voumvourakis KI The dynamic relationship between the glymphatic system, aging, memory, and sleep Biomedicines 2023 11 2092 10.3390/biomedicines11082092 37626589
Voumvourakis, K. I. et al. The dynamic relationship between the glymphatic system, aging, memory, and sleep. Biomedicines 11, 2092 (2023).37626589
27. Kaplanoglu V Evaluation of arachnoid granulations in cranial dural sinuses with contrast-enhanced 3-dimensional T1-weighted magnetic resonance imaging Eurasia. J. Med. 2023 55 95 99 10.5152/eurasianjmed.2023.22104
Kaplanoglu, V. et al. Evaluation of arachnoid granulations in cranial dural sinuses with contrast-enhanced 3-dimensional T1-weighted magnetic resonance imaging. Eurasia. J. Med. 55, 95–99 (2023).
28. Kan P Stevens EA Couldwell WT Incidental giant arachnoid granulation AJNR Am. J. Neuroradiol. 2006 27 1491 1492 16908565
Kan, P., Stevens, E. A. & Couldwell, W. T. Incidental giant arachnoid granulation. AJNR Am. J. Neuroradiol. 27, 1491–1492 (2006).16908565
29. Upton ML Weller RO The morphology of cerebrospinal fluid drainage pathways in human arachnoid granulations J. Neurosurg. 1985 63 867 875 10.3171/jns.1985.63.6.0867 4056901
Upton, M. L. & Weller, R. O. The morphology of cerebrospinal fluid drainage pathways in human arachnoid granulations. J. Neurosurg. 63, 867–875 (1985).4056901
30. Melin E Ringstad G Valnes LM Eide PK Human parasagittal dura is a potential neuroimmune interface Commun. Biol. 2023 6 260 10.1038/s42003-023-04634-3 36906686
Melin, E., Ringstad, G., Valnes, L. M. & Eide, P. K. Human parasagittal dura is a potential neuroimmune interface. Commun. Biol. 6, 260 (2023).36906686
31. Rustenhoven J Functional characterization of the dural sinuses as a neuroimmune interface Cell 2021 184 1000 1016.e27 10.1016/j.cell.2020.12.040 33508229
Rustenhoven, J. et al. Functional characterization of the dural sinuses as a neuroimmune interface. Cell 184, 1000–1016.e27 (2021).33508229
32. Kanal E Intracranial gadolinium retention: “nothing more to see here… move along….” Radiology 2020 294 386 387 10.1148/radiol.2019192315 31770080
Kanal, E. Intracranial gadolinium retention: “nothing more to see here… move along….”. Radiology 294, 386–387 (2020).31770080
33. Biundo R Brain volume changes in Parkinson’s disease and their relationship with cognitive and behavioural abnormalities J. Neurol. Sci. 2011 310 64 69 10.1016/j.jns.2011.08.001 21862438
Biundo, R. et al. Brain volume changes in Parkinson’s disease and their relationship with cognitive and behavioural abnormalities. J. Neurol. Sci. 310, 64–69 (2011).21862438
34. Mak E Longitudinal whole-brain atrophy and ventricular enlargement in nondemented Parkinson’s disease Neurobiol. Aging 2017 55 78 90 10.1016/j.neurobiolaging.2017.03.012 28431288
Mak, E. et al. Longitudinal whole-brain atrophy and ventricular enlargement in nondemented Parkinson’s disease. Neurobiol. Aging 55, 78–90 (2017).28431288
35. Yamada S Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation Eur. Radio. 2023 33 7099 7112 10.1007/s00330-023-09632-x
Yamada, S. et al. Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation. Eur. Radio. 33, 7099–7112 (2023).
36. Mehta RI Mangla R Mehta RI Giant arachnoid granulations: diagnostic workup and characterization in three symptomatic adults IJMS 2023 24 11410 10.3390/ijms241411410 37511166
Mehta, R. I., Mangla, R. & Mehta, R. I. Giant arachnoid granulations: diagnostic workup and characterization in three symptomatic adults. IJMS 24, 11410 (2023).37511166
37. Trimble CR Harnsberger HR Castillo M Brant-Zawadzki M Osborn AG “Giant” arachnoid granulations just like CSF?: NOT!! AJNR Am. J. Neuroradiol. 2010 31 1724 1728 10.3174/ajnr.A2157 20581064
Trimble, C. R., Harnsberger, H. R., Castillo, M., Brant-Zawadzki, M. & Osborn, A. G. “Giant” arachnoid granulations just like CSF?: NOT!! AJNR Am. J. Neuroradiol. 31, 1724–1728 (2010).20581064
38. Regnault A Does the MDS-UPDRS provide the precision to assess progression in early Parkinson’s disease? Learnings from the Parkinson’s progression marker initiative cohort J. Neurol. 2019 266 1927 1936 10.1007/s00415-019-09348-3 31073716
Regnault, A. et al. Does the MDS-UPDRS provide the precision to assess progression in early Parkinson’s disease? Learnings from the Parkinson’s progression marker initiative cohort. J. Neurol. 266, 1927–1936 (2019).31073716
39. Shulman LM The clinically important difference on the unified Parkinson’s disease rating scale Arch. Neurol. 2010 67 64 70 10.1001/archneurol.2009.295 20065131
Shulman, L. M. et al. The clinically important difference on the unified Parkinson’s disease rating scale. Arch. Neurol. 67, 64–70 (2010).20065131
40. Lopes LKR Scianni AA Lima LO De Carvalho Lana R Rodrigues-De-Paula F The Mini-BESTest is an independent predictor of falls in Parkinson disease Braz. J. Phys. Ther. 2020 24 433 440 10.1016/j.bjpt.2019.07.006 31377123
Lopes, L. K. R., Scianni, A. A., Lima, L. O., De Carvalho Lana, R. & Rodrigues-De-Paula, F. The Mini-BESTest is an independent predictor of falls in Parkinson disease. Braz. J. Phys. Ther. 24, 433–440 (2020).31377123
41. McKnight CD Rouleau RM Donahue MJ Claassen DO The regulation of cerebral spinal fluid flow and its relevance to the glymphatic system Curr. Neurol. Neurosci. Rep. 2020 20 58 10.1007/s11910-020-01077-9 33074399
McKnight, C. D., Rouleau, R. M., Donahue, M. J. & Claassen, D. O. The regulation of cerebral spinal fluid flow and its relevance to the glymphatic system. Curr. Neurol. Neurosci. Rep. 20, 58 (2020).33074399
42. Massey A Glymphatic system dysfunction and sleep disturbance may contribute to the pathogenesis and progression of Parkinson’s disease IJMS 2022 23 12928 10.3390/ijms232112928 36361716
Massey, A. et al. Glymphatic system dysfunction and sleep disturbance may contribute to the pathogenesis and progression of Parkinson’s disease. IJMS 23, 12928 (2022).36361716
43. Si X Neuroimaging evidence of glymphatic system dysfunction in possible REM sleep behavior disorder and Parkinson’s disease NPJ Parkinsons Dis. 2022 8 54 10.1038/s41531-022-00316-9 35487930
Si, X. et al. Neuroimaging evidence of glymphatic system dysfunction in possible REM sleep behavior disorder and Parkinson’s disease. NPJ Parkinsons Dis. 8, 54 (2022).35487930
44. He P The association of the glymphatic function with Parkinson’s disease symptoms: neuroimaging evidence from longitudinal and cross‐sectional studies Ann. Neurol. 2023 94 672 683 10.1002/ana.26729 37377170
He, P. et al. The association of the glymphatic function with Parkinson’s disease symptoms: neuroimaging evidence from longitudinal and cross‐sectional studies. Ann. Neurol. 94, 672–683 (2023).37377170
45. Taoka T Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer’s disease cases JPN J. Radio. 2017 35 172 178 10.1007/s11604-017-0617-z
Taoka, T. et al. Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer’s disease cases. JPN J. Radio. 35, 172–178 (2017).
46. Bae YJ Glymphatic function assessment in Parkinson’s disease using diffusion tensor image analysis along the perivascular space Parkinsonism Relat. Disord. 2023 114 105767 10.1016/j.parkreldis.2023.105767 37523953
Bae, Y. J. et al. Glymphatic function assessment in Parkinson’s disease using diffusion tensor image analysis along the perivascular space. Parkinsonism Relat. Disord. 114, 105767 (2023).37523953
47. Shen T Diffusion along perivascular spaces as marker for impairment of glymphatic system in Parkinson’s disease NPJ Parkinsons Dis. 2022 8 174 10.1038/s41531-022-00437-1 36543809
Shen, T. et al. Diffusion along perivascular spaces as marker for impairment of glymphatic system in Parkinson’s disease. NPJ Parkinsons Dis. 8, 174 (2022).36543809
48. Benjamini Y Hochberg Y Controlling the false discovery rate: a practical and powerful approach to multiple testing J. R. Stat. Soc.: Ser. B 1995 57 289 300 10.1111/j.2517-6161.1995.tb02031.x
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc.: Ser. B 57, 289–300 (1995).
49. Mays, G. P. et al. DWI with Dynamic B-value Cycling Reveals Evidence of Reduced Suprasellar Cistern Neurofluid Motion in Parkinson’s Disease (2023).
50. Ruan X Diffusion tensor imaging analysis along the perivascular space index in primary Parkinson’s disease patients with and without freezing of gait Neuroscience 2022 506 51 57 10.1016/j.neuroscience.2022.10.013 36341724
Ruan, X. et al. Diffusion tensor imaging analysis along the perivascular space index in primary Parkinson’s disease patients with and without freezing of gait. Neuroscience 506, 51–57 (2022).36341724
51. Saito, Y. et al. Glymphatic system impairment in sleep disruption: diffusion tensor image analysis along the perivascular space (DTI-ALPS). Jpn J. Radiol. (2023).
52. Adirim ZL Heyn C Murray BJ Sleep and perivascular disruption: an illustrative case of severe sleep apnea, an enlarged Virchow-Robin space, and contralateral asymmetric periodic limb movements Sleep. Med. 2023 112 191 193 10.1016/j.sleep.2023.10.026
Adirim, Z. L., Heyn, C. & Murray, B. J. Sleep and perivascular disruption: an illustrative case of severe sleep apnea, an enlarged Virchow-Robin space, and contralateral asymmetric periodic limb movements. Sleep. Med. 112, 191–193 (2023).
53. Reddy OC Van Der Werf YD The sleeping brain: harnessing the power of the glymphatic system through lifestyle choices Brain Sci. 2020 10 868 10.3390/brainsci10110868 33212927
Reddy, O. C. & Van Der Werf, Y. D. The sleeping brain: harnessing the power of the glymphatic system through lifestyle choices. Brain Sci. 10, 868 (2020).33212927
54. Smith, A. Symbol Digit Modalities Test (SDMT). Manual (Revised). Western Psychological Services (1973).
55. Franchignoni F Horak F Godi M Nardone A Giordano A Using psychometric techniques to improve the Balance Evaluation Systems Test: the mini-BESTest J. Rehabil. Med. 2010 42 323 331 10.2340/16501977-0537 20461334
Franchignoni, F., Horak, F., Godi, M., Nardone, A. & Giordano, A. Using psychometric techniques to improve the Balance Evaluation Systems Test: the mini-BESTest. J. Rehabil. Med. 42, 323–331 (2010).20461334
56. Goetz CG Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results: MDS-UPDRS: clinimetric assessment Mov. Disord. 2008 23 2129 2170 10.1002/mds.22340 19025984
Goetz, C. G. et al. Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results: MDS-UPDRS: clinimetric assessment. Mov. Disord. 23, 2129–2170 (2008).19025984
57. Kripke DF Mullaney DJ Messin S Wyborney VG Wrist actigraphic measures of sleep and rhythms Electroencephalogr. Clin. Neurophysiol. 1978 44 674 676 10.1016/0013-4694(78)90133-5 77772
Kripke, D. F., Mullaney, D. J., Messin, S. & Wyborney, V. G. Wrist actigraphic measures of sleep and rhythms. Electroencephalogr. Clin. Neurophysiol. 44, 674–676 (1978).77772
58. Cella D The Patient-Reported Outcomes Measurement Information System (PROMIS): progress of an NIH roadmap cooperative group during its first two years Med. Care 2007 45 S3 S11 10.1097/01.mlr.0000258615.42478.55 17443116
Cella, D. et al. The Patient-Reported Outcomes Measurement Information System (PROMIS): progress of an NIH roadmap cooperative group during its first two years. Med. Care 45, S3–S11 (2007).17443116
59. Shafique S, Rayi A. Anatomy, Head and Neck, Subarachnoid Space. In: StatPearls. StatPearls Publishing; 2024.
60. Coupé P AssemblyNet: a large ensemble of CNNs for 3D whole brain MRI segmentation NeuroImage 2020 219 117026 10.1016/j.neuroimage.2020.117026 32522665
Coupé, P. et al. AssemblyNet: a large ensemble of CNNs for 3D whole brain MRI segmentation. NeuroImage 219, 117026 (2020).32522665
61. Hett K Deep learning segmentation of peri-sinus structures from structural magnetic resonance imaging: validation and normative ranges across the adult lifespan Fluids Barriers CNS 2024 21 15 10.1186/s12987-024-00516-w 38350930
Hett, K. et al. Deep learning segmentation of peri-sinus structures from structural magnetic resonance imaging: validation and normative ranges across the adult lifespan. Fluids Barriers CNS 21, 15 (2024).38350930
62. Fonov V Evans A McKinstry R Almli C Collins D Unbiased nonlinear average age-appropriate brain templates from birth to adulthood NeuroImage 2009 47 S102 10.1016/S1053-8119(09)70884-5
Fonov, V., Evans, A., McKinstry, R., Almli, C. & Collins, D. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage 47, S102 (2009).
63. Kendall MG Partial rank correlation Biometrika 1942 32 277 10.1093/biomet/32.3-4.277
Kendall, M. G. Partial rank correlation. Biometrika 32, 277 (1942).
