Glymphatic dysfunction evidenced by DTI-ALPS is related to obstructive sleep apnea intensity in newly diagnosed Parkinson’s disease

Glymphatic dysfunction evidenced by DTI-ALPS is related to obstructive sleep apnea intensity in newly diagnosed Parkinson’s disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Glymphatic dysfunction evidenced by DTI-ALPS is related to obstructive sleep apnea intensity in newly diagnosed Parkinson’s disease Jiri Nepozitek, Stanislav Mareček, Veronika Rottova, Simona Dostalova, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4673004/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jun, 2025 Read the published version in npj Parkinson's Disease → Version 1 posted 10 You are reading this latest preprint version Abstract Glymphatic dysfunction can contribute to Parkinson’s disease (PD). Obstructive sleep apnea (OSA) disturbs sleep, which is necessary for its proper function, and is frequent in PD. We investigated the glymphatic function in de novo PD and its relation to OSA. Fifty-four PD patients (mean age 58.9 ± 12.2 years) and 32 controls (mean age 59.4 ± 8.3 years) underwent polysomnography and 3T magnetic resonance imaging of the brain. Diffusion tensor imaging along the perivascular space (DTI-ALPS) was calculated using atlas-based automatic regions of interest selection. In PD ALPS-index negatively correlated with apnea-hypopnea index (rho=-0.41; p = 0.002), oxygen desaturation index (rho=-0.38; p = 0.006), sleep stage N1 (rho=-0.42; p = 0.002), and arousal index (rho=-0.24; p = 0.018), and positively correlated with sleep stage R (rho = 0.32; p = 0.023), while in controls no such correlations were observed. Glymphatic dysfunction is related to OSA severity in de novo PD but not in controls. We imply that OSA contributes to neurodegeneration via glymphatic impairment in PD. Health sciences/Neurology/Neurological disorders/Parkinson's disease Health sciences/Neurology/Neurological disorders/Neurodegenerative diseases/Parkinson's disease Health sciences/Neurology/Neurological disorders/Movement disorders/Parkinson's disease Health sciences/Neurology/Neurological disorders/Neurodegeneration Health sciences/Risk factors Figures Figure 1 Figure 2 Introduction The glymphatic system is a highly organized fluid transport pathway subserving cerebrospinal fluid (CSF) flow via perivascular pathways 1 , 2 . Its main importance is in providing brain clearance for waste products including neurotoxic protein aggregates implicated in the development of neurodegenerative process 3 – 5 . Glymphatic system dysfunction has been documented to play a role in a variety of neurological conditions, such as Alzheimer’s disease, ischemic strokes, traumatic brain injury, normal pressure hydrocephalus, Parkinson’s disease (PD), and multiple sclerosis 6 – 12 . The glymphatic system reaches its highest capacity during sleep 13 – 16 . Moreover, it has been proposed that the efficacy of glymphatic clearance relies on the depth of non-rapid-eye movement sleep (NREM), implying that the decrease of deep NREM sleep (NREM stage 3 – N3), the frequent interruptions of lighter sleep stages (NREM stages 1 and 2 – N1 and 2), and the shorter total sleep time (TST), all worsen glymphatic activity 15 – 17 . Thus, several disorders and conditions are suspected of suppressing glymphatic function during NREM. Indeed, recently, it was documented that the glymphatic system is impaired in patients with OSA 18 . In PD, the prevalence of OSA is repeatedly reported to be higher than in the general population 19 . The prevalence studies are reporting that OSA is observed in 20–70% of PD patients. OSA comorbidity in PD has proven to exacerbate both motor and non-motor symptoms. Multiple pathogenetic relationships have been suggested in the interplay between OSA and PD, including intermittent hypoxemia, sleep fragmentation, upper airway obstruction, inflammation, and finally, alteration of the glymphatic system homeostasis 19 . Surprisingly no studies have investigated the link between OSA and glymphatic dysfunction in PD to date. Imaging of the glymphatic system and assessment of its function is an emerging field 1 , 2 , 20 – 23 . One of the suitable tools for its evaluation in humans is magnetic resonance imaging (MRI) 2 . Diffusion tensor imaging (DTI) along the perivascular space (DTI-ALPS) has been suggested as a method for quantification of glymphatic system function 24 , 25 . It is based on evaluating the flow of water molecules in the direction of the perivascular spaces by measuring water diffusivity using DTI. This method relies on the assumption that the perivascular spaces along the medullary veins lie orthogonal to the projection and association fibers at the level of the lateral ventricle body 24 , 26 . The DTI-ALPS technique involves placing regions of interest (ROIs) near the top of the lateral ventricles, specifically in areas containing projection and association fibers. Since glymphatic flow along venous perivascular spaces is perpendicular to the lateral ventricles in these regions, we can estimate glymphatic dysfunction by evaluating diffusivity in the three axes. The diffusivity in the x-axis is perpendicular to the ventricles and major tracts and contains the glymphatic flow. Conversely, the y- and z-axes for the projection and association areas, respectively, are perpendicular to both the major tracts and glymphatic flow. By comparing the diffusivity in these areas using the ALPS-index equation (below), we can estimate glymphatic flow in the x-axis, excluding random diffusion values. ALPS-index = mean (D xx projection , D xx association ) / mean (D yy projection , D zz association ) In most studies that utilized the DTI-ALPS method, the ROI selection was performed manually. This introduces a possibility of human error and rater bias 27 . Several papers used an atlas-based automatic ROI selection method 28 , 29 , which does not share this limitation. To exclude undesirable voxels, some studies even utilized subsequent fractional anisotropy-based thresholding 30 . This study aimed to use the DTI-ALPS method to evaluate glymphatic system function in patients with de novo PD to investigate its relation to OSA severity and to parameters of sleep disruption, and to compare these associations with healthy controls. We hypothesize that 1) glymphatic dysfunction is correlated with OSA intensity and parameters of sleep fragmentation, and 2) these relations will be more pronounced in PD compared to the control group. Methods Study participants Fifty-four treatment-naive PD patients newly diagnosed at our Movement Disorders Center at the Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University, and General University Hospital in Prague were consecutively included in the study. The diagnosis was confirmed by a movement disorders specialist (PD) according to the Movement Disorders Society (MDS) clinical diagnostic criteria 31 . The exclusion criteria were treatment with antiparkinsonian medication before baseline examination, clinical, imaging, or laboratory signs of atypical parkinsonism, and normal findings on dopamine transporter single-photon emission computed tomography (DAT-SPECT) examination 32 . Thirty-two control subjects were recruited from the general community through advertisements. To be eligible for the study, controls had to be free of major neurologic disorders, active oncologic illness, and abuse of psychoactive substances. All study participants underwent a protocol consisting of a comprehensive medical history, a neurological examination including the Unified Parkinson's Disease Rating Scale (MDS-UPDRS), polysomnography, and brain MRI. The study was approved by the Ethics Committee of the General University Hospital in Prague, and participants signed written informed consent before entering the study in accordance with the Helsinki Declaration. Polysomnography Nocturnal polysomnography was performed using a digital polysomnography system (RemLogic, version 3.4.1, Embla Systems). It consisted of electrooculography, electroencephalography (F3-M2, C3-M2, O1-M2, F4-M1, C4-M1, O2-M1), surface electromyography (EMG) of the bilateral mentalis and tibialis anterior muscles, electrocardiography, nasal pressure, nasal and oral airflow, thoracic and abdominal respiratory effort, oxygen saturation, microphone, and digitally synchronized video monitoring, measured during the period from 10 p.m. to 6 a.m. according to the American Academy of Sleep Medicine (AASM) recommendation 33 . All features on PSG were analyzed visually. The sleep stages, respiratory events, limb movements, and arousals were scored according to the AASM Manual for the Scoring of Sleep and Associated Events version 2.2 2015 33 . Thus, we obtained the following sleep characteristics: total sleep time (TST), sleep latency (SL), sleep efficiency (SE), the ratio of wakefulness, sleep stage N1, N2, N3, and REM sleep (R) during sleep, arousal index, apnea-hypopnea index (AHI), oxygen desaturation index (ODI) and periodic limb movements index (PLMI). Imaging acquisition protocol MRI examination was carried out on a 3T scanner (Siemens Skyra, Siemens Healthcare, Erlangen, Germany) with a 32-channel head coil. The protocol included 1) a diffusion tensor MRI with repetition time (TR) = 10.5 s; echo time (TE) = 93 ms; total 72 slices with voxel resolution of 2 mm isotropic; 30 noncolinear directions with b-value of 1000 s/m2, one b = 0 s/m2 image in antero-posterior and one b = 0 s/m2 image in postero-anterior phase encoding direction, 2) an axial 3D T1-weighted Magnetization Prepared Rapid Gradient Echo (MPRAGE, repetition time (TR) 2,200 ms; echo time (TE) 2.4 ms; inversion time (TI) 900 ms; flip angle (FA) 8°; field of view (FOV) 230×197×176 mm; voxel resolution 1×1×1 mm3). DTI-ALPS calculation We measured the mean ALPS index of both hemispheres using an automatic atlas-based approach with subsequent color-coded fractional anisotropy thresholding. We chose this approach to avoid ROI selection bias and developed a color-coded fractional anisotropy thresholding to exclude voxels with a low inclination towards the desired axes. The analysis was performed in each subject’s native diffusion space and the DWI data were corrected for distortion, movement artifacts and eddy currents using FSL’s topup and eddy tools. We used the “JHU ICBM tracts maxprob thr25 1mm” atlas’s labels of superior longitudinal fasciculus (SLF) and corticospinal tract (CST) for the association and projection areas, respectively 34 (Fig. 1 a). We restricted these labels to areas at the top of the lateral ventricles and areas with a low probability of intruding into the cortex during transformations to each subject’s diffusion space with the resulting masks (Fig. 1 b). These restricted labels were then used as precursors to the final ROIs and were transformed from the MNI152 space to each subject’s native diffusion space. To achieve these transformations, each T1 image was first transformed into MNI152 space using FSL’s flirt and fnirt tools 35 . Subsequently, the original T1 image was coregistered to its corresponding diffusion image via flirt . The convertwarp function was then employed to generate a single transformation from MNI152 space to the subject’s native diffusion space. Each transformation was manually inspected for errors. Using FSL’s dtifit , we acquired each voxel’s principal eigenvector, defined by three volumes representing the x/y/z axes, and transformed these into absolute values (Fig. 1 c). As a result, we obtained three volumes representing the principal eigenvector’s inclination to each of the x/y/z/ axes, respectively, but we lost the precise direction of the principal eigenvector. We analyzed the ROIs for each hemisphere separately. For the projection ROIs, we subtracted the x- and y-volumes from the z-volume. The resulting image favored the “purest” diffusion in the z-axis (Fig. 1 d). An analogous approach was used to obtain the association area ROIs. The 87% highest-value voxels restricted to the CST and SLF masks were used as the projection and association area ROIs, respectively. Subsequently, we acquired the diffusivity images in the x/y/z axes using FSL’s dtifit . The diffusivity values used in the ALPS-index Eq. (1) were then extracted from these images using the projection and association area ROIs acquired in the previous steps. The average ALPS-index of both hemispheres was calculated for each subject. All resource-heavy computation steps were performed on the MetaCentrum distributed computing infrastructure. Using atlas regions of interest (a), restricting to the desired areas (b). Then, using color-coded fractional anisotropy thresholding by acquiring absolute values of the principal eigenvector (c) and subtracting the nuisance vectors from the desired one (d) and multiplying the result by fractional anisotropy. Statistical analysis The Shapiro-Wilk test was used to test the normality of the data distribution. Comparisons were conducted using the Mann–Whitney U test. Spearman's rank correlation coefficient (rho) was used for the correlation analysis. Statistical significance was defined as a two-tailed p -value less than 0.05. The Bonferroni method was applied to correct familywise error. All statistical analyses were performed using SPSS Software (IBM SPSS Statistics Version 26). Results Characteristics of study participants Compared to controls, PD patients had higher MDS UPDRS scores, lower stage R ratios, and lower arousal indexes. Other observed parameters, including age, ALPS index, AHI, and ODI, did not show significant differences in case-control analysis. Table 1 presents an overview of demographic and polysomnographic parameters and the ALPS-index. Table 1 Comparison of demographic, clinical and polysomnographic parameters and DTI-ALPS index. PD (N = 54) Controls (N = 32) Mean SD Mean SD p Age (years) 58.9 12.2 59.4 8.3 0.907 Gender (F/M) 17/37 10/22 1.000 Body Mass Index 27.5 3.7 27.6 4.0 0.908 MDS UPDRS III 30.2 14.0 3.19 4.3 < 0.0001* ALPS-Index 1.29 0.164 1.34 0.156 0.093 Total Sleep Time (min.) 334.4 255.2 332.1 72.3 0.235 Sleep Latency (min.) 25.9 37.3 13.9 13.0 0.190 Sleep Efficiency (%) 66.8 21.5 74.9 14.6 0.122 Wake (%) 30.0 20.4 22.7 13.8 0.160 Stage N1 (%) 7.9 5.1 8.6 4.0 0.129 Stage N2 (%) 33.1 12.6 35.3 9.9 0.457 Stage N3 (%) 16.7 9.8 17.4 7.1 0.621 Stage R (%) 12.2 7.9 16.0 7.0 0.021* Apnea-Hypopnea Index 16.0 19.9 18.5 15.8 0.076 Oxygen Desaturation Index 14.2 17.1 13.9 11.4 0.282 Periodic Limb Movements Index 8.9 19.5 10.6 23.0 0.590 Arousal Index 8.1 7.2 16.6 7.5 < 0.0001* Legend: F – females, M – males, MDS-UPDRS – Unified Parkinson's Disease Rating Scale, ALPS – analysis along the perivascular space, N1, 2, 3 – non-rapid eye movement sleep stage 1, 2, 3, R – rapid eye movement sleep, PD – Parkinson’s disease, N – number, SD – standard deviation. Results are presented as mean and standard deviation. Mann-Whitney U test was applied. *: Difference is significant at the 0.05 level. Bold : Significant after Bonferroni correction. Associations of DTI-ALPS with observed parameters The ALPS index was negatively correlated with age in both groups. In the PD group ALPS index was significantly negatively correlated with AHI, ODI, sleep stage N1 ratio, and arousal index, and significantly positively correlated with sleep stage R ratio (Fig. 2 ). This contrasted with the control group, where the ALPS-index was only correlated with age (Table 2 ). Table 2 Correlations between ALPS-index and observed parameters. Correlations PD Controls ALPS-index vs.: rho p rho p Age -0.36** 0.007 -0.56** 0.001 Body Mass Index -0.12 0.388 0.25 0.167 MDS UPDRS III -0.24 0.085 -0.08 0.672 Total Sleep Time 0.04 0.785 0.00 0.99 Sleep Latency 0.03 0.854 0.16 0.385 Sleep Efficiency 0.19 0.184 0.13 0.495 Wake ratio (%) -0.20 0.153 -0.18 0.335 Stage N1 ratio (%) -0.42** 0.002 0.21 0.239 Stage N2 ratio (%) 0.11 0.419 0.14 0.461 Stage N3 ratio (%) 0.11 0.423 -0.03 0.885 Stage R ratio (%) 0.31* 0.023 -0.13 0.49 Apnea-Hypopnea Index -0.41** 0.002 0.09 0.615 Oxygen Desaturation Index -0.38** 0.006 0.03 0.864 Periodic Limb Movements Index -0.21 0.144 -0.28 0.123 Arousal Index -0.42* 0.018 0.10 0.606 Legend: ALPS – analysis along the perivascular space, MDS-UPDRS – Unified Parkinson's Disease Rating Scale, N1, 2, 3 – non-rapid eye movement sleep stage 1, 2, 3, R – rapid eye movement sleep, PD – Parkinson’s disease. ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Bold : Significant after Bonferroni correction. The relationship of glymphatic function to gender was investigated using a comparison of ALPS-indices between females and males. In the PD group, males showed lower ALPS-indices compared to females (1.254 ± 0.163 vs.1.355 ± 0.148; p = 0.040), while in the control group, no differences in ALPS-indices were found when comparing gender. Among other relevant parameters in PD, AHI and ODI correlated with sleep efficiency (AHI: rho = -0.34; p = 0.012, ODI: rho = -0.34; p = 0.015), with wakefulness ratio (AHI: rho = 0.37; p = 0.007; ODI: rho = 0.37; p = 0.008), with sleep stage N1 ratio (AHI: rho = 0.31; p = 0.025, ODI: rho = -0.41; p = 0.003, with sleep stage N3 ratio (AHI: rho = -0.36; p = 0.008; ODI: rho = -0.34; p = 0.017), with sleep stage R (AHI: rho = -0.53; p < 0.0001, ODI: rho = -0.49; p < 0.0001). While the arousal index was correlated with ODI (rho = 0.40; p = 0.036), we could not find a correlation with AHI. Correlation analysis between ALPS-index and age (A), arousal index (B), sleep stage N1 ratio (C), sleep stage R ratio (D), apnea-hypopnea index (E), and oxygen desaturation index (F) in Parkinson’s disease. Legend: ALPS – analysis along the perivascular space, N1 – non-rapid eye movement sleep stage 1, R – rapid eye movement sleep. Discussion This study is the first to elucidate previously unexplored associations of glymphatic dysfunction in de novo PD with OSA parameters and other polysomnographic features. The major finding of this study was that in de novo PD patients, glymphatic system function was related to several parameters, particularly polysomnographic, which were absent in controls. The glymphatic function was negatively correlated with OSA severity, with sleep stage N1 ratio and arousal index, and positively correlated with sleep stage R ratio. Glymphatic system function was negatively correlated with age in both PD and controls. The ALPS-index, suggesting dysfunction of the glymphatic system, is presumably associated with the process of neurodegeneration and with neurodegenerative diseases, including PD 10 , 36 . In our study, glymphatic system dysfunction was related to OSA severity in PD patients. This finding is consistent with a recent report performed on a cohort of individuals with severe OSA 18 . OSA is frequent in PD, and it exacerbates both motor and non-motor symptoms of PD 19 . The influence of comorbid OSA on PD through dysfunction of the glymphatic system has only been hypothesized without direct evidence. This study documents the missing link between OSA and glymphatic system dysfunction in PD. PD patients in our study showed no difference in OSA intensity compared to controls of equal age. This suggests that OSA affects patients with PD more severely, already at the time of the diagnosis. It can be speculated that the neurodegenerative process in PD makes a substrate for the brain to become more vulnerable to OSA and its consequences. Concidering that glymphatic system is most active in sleep, particularly dependent on TST and deep sleep stages (N3) amounts, fragmented sleep is considered as a pathogenetic mechanism that leads to glymphatic dysfunction 15 – 17 . In this study, in PD, we found a negative association of glymphatic function with frequency of arousals, which is a direct parameter of sleep fragmentation, and further with the sleep stage N1 ratio, the shallowest sleep stage. We also consider this to be a manifestation of sleep disruption, as N1 is the initial sleep stage following a sleep interruption manifested as arousal, and its ratio increases proportionally when the ratio of deeper sleep stages decreases. In accordance with this, we found a positive association of glymphatic function with the proportion of sleep stage R, the normal proportion of which is conditioned by the complete uninterrupted course of the entire sleep cycle, so it can be considered a manifestation of maintained sleep continuity. Therefore, our results are in line with the assumption that glymphatic dysfunction has a relationship with sleep fragmentation in PD. It has been implied that the relationship between sleep fragmentation, glymphatic dysfunction, and the process of neurodegeneration is linked in potential bidirectional relationships that could accelerate pathophysiological processes over time 36 . Our results show that this “vicious triangle” starts already at the time of PD diagnosis; it can indeed be expected that the condition will progress during the PD progression and that the relationships between the mentioned parameters will be stronger in more advanced stages of the disease. Sleep fragmentation is a known consequence of OSA. In our study, we showed that most of the parameters that correlated with glymphatic dysfunction were the result of OSA since they correlated with AHI and ODI as well, in particular, decline in sleep efficiency, increase in sleep stage N1 ratio, and decrease of sleep stage R ratio. The finding that the arousal index was not related to AHI, although it correlated with DTI-ALPS, suggests that other mechanisms could mediate this association. Fragmentation of sleep is well documented in PD 37 with multiple causes besides OSA, for example, muscle cramps, periodic limb movements, increased muscle tension, and pathophysiology of the disease itself 38 . However, the arousal index was related to ODI, which points to the hypoxic events and therefore, still supports OSA as the probable cause. Besides sleep fragmentation, OSA leads to recurrent hypoxia. Hypoxia was suggested as a likely mechanism through which OSA increases the risk for cognitive impairment 39 . It was demonstrated that OSA is associated with declines in memory, attention, and executive functions in adults 40 . Further it was shown that glymphatic system dysfunction is correlated with ODI 18 , which is consistent with our findings demonstrating the same relationship. We conclude that OSA enters the “vicious triangle” pathogenesis with worsening sleep disruption and hypoxic burden. Therefore, we stress that OSA should be actively screened for at the moment of PD diagnosis. Surprisingly, the relationship of glymphatic dysfunction to OSA and other sleep parameters observed in PD was not expressed in the control group. The mere presence of early PD unmasked these relationships, which did not exceed the statistical significance threshold in the control group. It can be concluded that the neurodegenerative process creates a pathogenetic substrate of vulnerable brain tissue for the pathophysiological relationships and events described above. At this point, it is appropriate to emphasize that control subjects did not differ in the intensity of OSA or the proportions of NREM sleep stages, and the frequency of arousals was even higher in the control group. It seems that in control subjects, there could be a compensation mechanism that prevents the development of these relationships observed in de novo PD. Our results on the relation of OSA to glymphatic impairment in PD are consistent with a very recent study reporting the relation of glymphatic dysfunction to body mass index (BMI) in PD 41 . OSA is known to be tightly associated with obesity 42 . Therefore, it is reasonable to consider that OSA may have contributed to the mediation of the main finding of the mentioned study. Surprisingly, in our study, the ALPS-index did not correlate with BMI, and BMI was not significantly different between PD patients and controls. In our study, we did not find a significant change in glymphatic dysfunction in PD patients compared to controls; however, the apparent trend to lower ALPS-index values is in agreement with a recent study 43 . Unlike previous studies investigating glymphatic function in PD, in our study, the patients with PD were at the very threshold of diagnosis. That could be why their glymphatic function impairment may not have been so pronounced. The negative correlation of glymphatic function with age in both groups is consistent with the available literature 3 , 44 – 46 . The gradual decrease of glymphatic function during the aging of the brain is apparently a parallel process, most likely a result of changes in cerebrovascular function with age that lie in the background of the events discussed above. Our control group's results show that it does not avoid even healthy individuals. Conclusion We demonstrated that glymphatic system dysfunction is related to the severity of OSA and parameters of sleep fragmentation in newly diagnosed PD but not in controls. Thus, we documented for the first time the link between OSA and glymphatic system dysfunction in PD. It can be implied that OSA contributes to the neurodegenerative process via glymphatic impairment in PD. Therefore, we suggest that OSA should be actively screened for at the moment of PD diagnosis. Declarations Competing interests All authors declare no financial or non-financial competing interests. Author contributions JN designed the study concept, analyzed the polysomnography, performed the statistical analysis, interpreted the data and wrote the draft of the manuscript. SM analyzed the MRI and contributed to the critical edition of the manuscript. VR analyzed the MRI. SD analyzed the polysomnography and contributed to the critical edition of the manuscript. TK performed MRI preprocessing analysis. JK performed and evaluated the MRI. KS analyzed the polysomnography and contributed to the critical edition of the manuscript. PD performed clinical examinations, contributed to the data acquisition and to the critical edition of the manuscript. All authors read and approved the final manuscript. Acknowledgements The study was funded by National Institute for Neurological Research (Programme EXCELES, ID Project No. LX22NPO5107) - Funded by the European Union – Next Generation EU; Charles University: Cooperatio Program in Neuroscience; General University Hospital in Prague and Ministry of Health of the Czech Republic project MH CZ-DRO-VFN64165; Na Homolce Hospital project CZ-DRO-NHH00023884 and Czech Health Research Council grant NU21–04–00535. 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Jpn J Radiol 35, 172–178 (2017). https://doi.org/10.1007/s11604-017-0617-z Taoka, T. et al. Diffusion Tensor Image Analysis ALong the Perivascular Space (DTI-ALPS): Revisiting the Meaning and Significance of the Method. Magn Reson Med Sci (2024). https://doi.org/10.2463/mrms.rev.2023-0175 Gouveia-Freitas, K. & Bastos-Leite, A. J. Perivascular spaces and brain waste clearance systems: relevance for neurodegenerative and cerebrovascular pathology. Neuroradiology 63, 1581–1597 (2021). https://doi.org/10.1007/s00234-021-02718-7 Ringstad, G. Glymphatic imaging: a critical look at the DTI-ALPS index. Neuroradiology 66, 157–160 (2024). https://doi.org/10.1007/s00234-023-03270-2 Jiang, D. et al. Regional Glymphatic Abnormality in Behavioral Variant Frontotemporal Dementia. Ann Neurol 94, 442–456 (2023). https://doi.org/10.1002/ana.26710 Hsiao, W. C. et al. Association of Cognition and Brain Reserve in Aging and Glymphatic Function Using Diffusion Tensor Image-along the Perivascular Space (DTI-ALPS). Neuroscience 524, 11–20 (2023). https://doi.org/10.1016/j.neuroscience.2023.04.004 Wei, Y. C. et al. Vascular risk factors and astrocytic marker for the glymphatic system activity. Radiol Med 128, 1148–1161 (2023). https://doi.org/10.1007/s11547-023-01675-w Postuma, R. B. et al. MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord 30, 1591–1601 (2015). https://doi.org/10.1002/mds.26424 Dusek, P. et al. Clinical characteristics of newly diagnosed Parkinson's disease patients included in the longitudinal BIO-PD study. Cesk Slov Neurol N 83, 633–639 (2020). https://doi.org/10.48095/cccsnn2020633 Berry, R. B. et al. for the American Academy of Sleep Medicine, The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, Version 2.2. Darien, Illinois: American Academy of Sleep Medicine, 2015. (2015). Hua, K. et al. Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage 39, 336–347 (2008). https://doi.org/10.1016/j.neuroimage.2007.07.053 Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W. & Smith, S. M. Fsl. Neuroimage 62, 782–790 (2012). https://doi.org/10.1016/j.neuroimage.2011.09.015 Scott-Massey, A. et al. Glymphatic System Dysfunction and Sleep Disturbance May Contribute to the Pathogenesis and Progression of Parkinson's Disease. Int J Mol Sci 23 (2022). https://doi.org/10.3390/ijms232112928 Cai, G. E. et al. Sleep fragmentation as an important clinical characteristic of sleep disorders in Parkinson's disease: a preliminary study. Chin Med J (Engl) 132, 1788–1795 (2019). https://doi.org/10.1097/CM9.0000000000000329 Norlinah, M. I. et al. Sleep disturbances in Malaysian patients with Parkinson's disease using polysomnography and PDSS. Parkinsonism Relat Disord 15, 670–674 (2009). https://doi.org/10.1016/j.parkreldis.2009.02.012 Yaffe, K. et al. Sleep-disordered breathing, hypoxia, and risk of mild cognitive impairment and dementia in older women. JAMA 306, 613–619 (2011). https://doi.org/10.1001/jama.2011.1115 Jackson, M. L., Howard, M. E. & Barnes, M. Cognition and daytime functioning in sleep-related breathing disorders. Prog Brain Res 190, 53–68 (2011). https://doi.org/10.1016/B978-0-444-53817-8.00003-7 Tian, S. et al. Association between body mass index and glymphatic function using diffusion tensor image-along the perivascular space (DTI-ALPS) in patients with Parkinson's disease. Quant Imaging Med Surg 14, 2296–2308 (2024). https://doi.org/10.21037/qims-23-1032 Newman, A. B. et al. Progression and regression of sleep-disordered breathing with changes in weight: the Sleep Heart Health Study. Arch Intern Med 165, 2408–2413 (2005). https://doi.org/10.1001/archinte.165.20.2408 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). https://doi.org/10.1016/j.parkreldis.2023.105767 Benveniste, H. et al. The Glymphatic System and Waste Clearance with Brain Aging: A Review. Gerontology 65, 106–119 (2019). https://doi.org/10.1159/000490349 Zhou, Y. et al. Impairment of the Glymphatic Pathway and Putative Meningeal Lymphatic Vessels in the Aging Human. Ann Neurol 87, 357–369 (2020). https://doi.org/10.1002/ana.25670 Han, G. et al. Age- and time-of-day dependence of glymphatic function in the human brain measured via two diffusion MRI methods. Front Aging Neurosci 15, 1173221 (2023). https://doi.org/10.3389/fnagi.2023.1173221 Additional Declarations (Not answered) Cite Share Download PDF Status: Published Journal Publication published 11 Jun, 2025 Read the published version in npj Parkinson's Disease → Version 1 posted Editorial decision: revise 04 Mar, 2025 Review # 2 received at journal 21 Jan, 2025 Reviewer # 2 agreed at journal 21 Jan, 2025 Review # 1 received at journal 07 Sep, 2024 Reviewer # 1 agreed at journal 20 Aug, 2024 Reviewers invited by journal 12 Aug, 2024 Editor assigned by journal 04 Jul, 2024 Submission checks completed at journal 04 Jul, 2024 First submitted to journal 02 Jul, 2024 Unknown event 02 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Nepozitek","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3QMQrCMBSA4RcKukS6tih6BYur6FUaCo4uvUBCoV0KrnqLjo5PCnUpugoueoN06yBoKi4uKd0E8y/JkC95BMBk+sFIBICfLVfbOfR5ByI44AoodnlRkbydWMngjmQ/X9t2ntxkeWbpkJOq1g7WnyIpV6HLD0JsL1eWjtByHC3pAZI4Z5kSEZXX2dLxezBtJ0+W5URED3ma0Yb47QRZVigCFxy/CbYRFgehmxKxS8tAERa5XEO8TWHJKl6oHzveZF0sKHWCg/bHvOY+H76GJ7o3ACaf1dcdMplMpj/vBcO5UgAUbq2VAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3339-5286","institution":"First Faculty of Medicine, Charles University and General University Hospital in Prague","correspondingAuthor":true,"prefix":"","firstName":"Jiri","middleName":"","lastName":"Nepozitek","suffix":""},{"id":322741679,"identity":"231eb363-7a53-43f9-9353-67d7acc53af9","order_by":1,"name":"Stanislav Mareček","email":"","orcid":"https://orcid.org/0009-0004-0773-5373","institution":"First faculty of medicine Charles university in Prague","correspondingAuthor":false,"prefix":"","firstName":"Stanislav","middleName":"","lastName":"Mareček","suffix":""},{"id":322741680,"identity":"2899b19b-1857-4d42-85a4-12cc157759de","order_by":2,"name":"Veronika Rottova","email":"","orcid":"","institution":"First Faculty of Medicine, Charles University and General University Hospital in Prague","correspondingAuthor":false,"prefix":"","firstName":"Veronika","middleName":"","lastName":"Rottova","suffix":""},{"id":322741681,"identity":"b282ec85-c989-43ff-bc94-45239c3a252e","order_by":3,"name":"Simona Dostalova","email":"","orcid":"","institution":"First Faculty of Medicine, Charles University and General University Hospital in Prague","correspondingAuthor":false,"prefix":"","firstName":"Simona","middleName":"","lastName":"Dostalova","suffix":""},{"id":322741682,"identity":"0fe724b4-93a8-4b00-b480-dbc97cf834d2","order_by":4,"name":"Tomáš Krajča","email":"","orcid":"https://orcid.org/0000-0002-4746-1539","institution":"Czech Technical University in Prague, Faculty of Biomedical Engineering, Kladno, Czech Republic","correspondingAuthor":false,"prefix":"","firstName":"Tomáš","middleName":"","lastName":"Krajča","suffix":""},{"id":322741683,"identity":"4a13e9da-cbac-49db-b8fa-c9dced1c94df","order_by":5,"name":"Jiri Keller","email":"","orcid":"","institution":"Na Homolce Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiri","middleName":"","lastName":"Keller","suffix":""},{"id":322741684,"identity":"81f2305a-8ca1-467b-afa5-607caeb0b8b3","order_by":6,"name":"Karel Sonka","email":"","orcid":"","institution":"First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic","correspondingAuthor":false,"prefix":"","firstName":"Karel","middleName":"","lastName":"Sonka","suffix":""},{"id":322741685,"identity":"79fa07db-606f-494c-b4d0-e96cd09e2c60","order_by":7,"name":"Petr Dušek","email":"","orcid":"https://orcid.org/0000-0003-4877-9642","institution":"Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic","correspondingAuthor":false,"prefix":"","firstName":"Petr","middleName":"","lastName":"Dušek","suffix":""}],"badges":[],"createdAt":"2024-07-02 09:16:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4673004/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4673004/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41531-025-01018-8","type":"published","date":"2025-06-11T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66116669,"identity":"ac41fa59-fb0a-40e9-bfca-d12fa97ec323","added_by":"auto","created_at":"2024-10-08 00:46:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2750857,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePreprocessing steps of the automatic approach.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4673004/v1/c3ef6b2863ce997fa168306f.png"},{"id":66116668,"identity":"33cff2d4-3d3f-4056-9041-af246118e971","added_by":"auto","created_at":"2024-10-08 00:46:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177643,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation plots showing relation of ALPS index to observed parameters in de novo \u003c/strong\u003e\u003ca href=\"https://www.sciencedirect.com/topics/neuroscience/parkinsons-disease\" title=\"Learn more about Parkinson disease from ScienceDirect's AI-generated Topic Pages\"\u003e\u003cstrong\u003eParkinson’s disease\u003c/strong\u003e\u003c/a\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2240626fin.png","url":"https://assets-eu.researchsquare.com/files/rs-4673004/v1/ca7819c4ed7f1dc2efb54c5c.png"},{"id":84452969,"identity":"e5988c8d-6be7-46a4-9ea3-b01ed1d5ee7d","added_by":"auto","created_at":"2025-06-12 07:09:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4840838,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4673004/v1/b58bbfad-da7a-485f-a381-d1edc7c509e0.pdf"}],"financialInterests":"(Not answered)","formattedTitle":"Glymphatic dysfunction evidenced by DTI-ALPS is related to obstructive sleep apnea intensity in newly diagnosed Parkinson’s disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe glymphatic system is a highly organized fluid transport pathway subserving cerebrospinal fluid (CSF) flow via perivascular pathways \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Its main importance is in providing brain clearance for waste products including neurotoxic protein aggregates implicated in the development of neurodegenerative process \u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Glymphatic system dysfunction has been documented to play a role in a variety of neurological conditions, such as Alzheimer\u0026rsquo;s disease, ischemic strokes, traumatic brain injury, normal pressure hydrocephalus, Parkinson\u0026rsquo;s disease (PD), and multiple sclerosis \u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe glymphatic system reaches its highest capacity during sleep \u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Moreover, it has been proposed that the efficacy of glymphatic clearance relies on the depth of non-rapid-eye movement sleep (NREM), implying that the decrease of deep NREM sleep (NREM stage 3 \u0026ndash; N3), the frequent interruptions of lighter sleep stages (NREM stages 1 and 2 \u0026ndash; N1 and 2), and the shorter total sleep time (TST), all worsen glymphatic activity \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Thus, several disorders and conditions are suspected of suppressing glymphatic function during NREM. Indeed, recently, it was documented that the glymphatic system is impaired in patients with OSA \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn PD, the prevalence of OSA is repeatedly reported to be higher than in the general population \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The prevalence studies are reporting that OSA is observed in 20\u0026ndash;70% of PD patients. OSA comorbidity in PD has proven to exacerbate both motor and non-motor symptoms. Multiple pathogenetic relationships have been suggested in the interplay between OSA and PD, including intermittent hypoxemia, sleep fragmentation, upper airway obstruction, inflammation, and finally, alteration of the glymphatic system homeostasis \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Surprisingly no studies have investigated the link between OSA and glymphatic dysfunction in PD to date.\u003c/p\u003e \u003cp\u003eImaging of the glymphatic system and assessment of its function is an emerging field \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. One of the suitable tools for its evaluation in humans is magnetic resonance imaging (MRI) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Diffusion tensor imaging (DTI) along the perivascular space (DTI-ALPS) has been suggested as a method for quantification of glymphatic system function \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. It is based on evaluating the flow of water molecules in the direction of the perivascular spaces by measuring water diffusivity using DTI. This method relies on the assumption that the perivascular spaces along the medullary veins lie orthogonal to the projection and association fibers at the level of the lateral ventricle body \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe DTI-ALPS technique involves placing regions of interest (ROIs) near the top of the lateral ventricles, specifically in areas containing projection and association fibers. Since glymphatic flow along venous perivascular spaces is perpendicular to the lateral ventricles in these regions, we can estimate glymphatic dysfunction by evaluating diffusivity in the three axes. The diffusivity in the x-axis is perpendicular to the ventricles and major tracts and contains the glymphatic flow. Conversely, the y- and z-axes for the projection and association areas, respectively, are perpendicular to both the major tracts and glymphatic flow. By comparing the diffusivity in these areas using the ALPS-index equation (below), we can estimate glymphatic flow in the x-axis, excluding random diffusion values.\u003c/p\u003e \u003cp\u003eALPS-index\u0026thinsp;=\u0026thinsp;mean (D\u003csub\u003exx projection\u003c/sub\u003e, D\u003csub\u003exx association\u003c/sub\u003e) / mean (D\u003csub\u003eyy projection\u003c/sub\u003e, D\u003csub\u003ezz association\u003c/sub\u003e)\u003c/p\u003e \u003cp\u003eIn most studies that utilized the DTI-ALPS method, the ROI selection was performed manually. This introduces a possibility of human error and rater bias \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Several papers used an atlas-based automatic ROI selection method \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, which does not share this limitation. To exclude undesirable voxels, some studies even utilized subsequent fractional anisotropy-based thresholding \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study aimed to use the DTI-ALPS method to evaluate glymphatic system function in patients with de novo PD to investigate its relation to OSA severity and to parameters of sleep disruption, and to compare these associations with healthy controls. We hypothesize that 1) glymphatic dysfunction is correlated with OSA intensity and parameters of sleep fragmentation, and 2) these relations will be more pronounced in PD compared to the control group.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eFifty-four treatment-naive PD patients newly diagnosed at our Movement Disorders Center at the Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University, and General University Hospital in Prague were consecutively included in the study. The diagnosis was confirmed by a movement disorders specialist (PD) according to the Movement Disorders Society (MDS) clinical diagnostic criteria \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The exclusion criteria were treatment with antiparkinsonian medication before baseline examination, clinical, imaging, or laboratory signs of atypical parkinsonism, and normal findings on dopamine transporter single-photon emission computed tomography (DAT-SPECT) examination \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Thirty-two control subjects were recruited from the general community through advertisements. To be eligible for the study, controls had to be free of major neurologic disorders, active oncologic illness, and abuse of psychoactive substances.\u003c/p\u003e \u003cp\u003eAll study participants underwent a protocol consisting of a comprehensive medical history, a neurological examination including the Unified Parkinson's Disease Rating Scale (MDS-UPDRS), polysomnography, and brain MRI.\u003c/p\u003e \u003cp\u003e The study was approved by the Ethics Committee of the General University Hospital in Prague, and participants signed written informed consent before entering the study in accordance with the Helsinki Declaration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePolysomnography\u003c/h2\u003e \u003cp\u003eNocturnal polysomnography was performed using a digital polysomnography system (RemLogic, version 3.4.1, Embla Systems). It consisted of electrooculography, electroencephalography (F3-M2, C3-M2, O1-M2, F4-M1, C4-M1, O2-M1), surface electromyography (EMG) of the bilateral mentalis and tibialis anterior muscles, electrocardiography, nasal pressure, nasal and oral airflow, thoracic and abdominal respiratory effort, oxygen saturation, microphone, and digitally synchronized video monitoring, measured during the period from 10 p.m. to 6 a.m. according to the American Academy of Sleep Medicine (AASM) recommendation \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. All features on PSG were analyzed visually. The sleep stages, respiratory events, limb movements, and arousals were scored according to the AASM Manual for the Scoring of Sleep and Associated Events version 2.2 2015 \u003csup\u003e33\u003c/sup\u003e. Thus, we obtained the following sleep characteristics: total sleep time (TST), sleep latency (SL), sleep efficiency (SE), the ratio of wakefulness, sleep stage N1, N2, N3, and REM sleep (R) during sleep, arousal index, apnea-hypopnea index (AHI), oxygen desaturation index (ODI) and periodic limb movements index (PLMI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eImaging acquisition protocol\u003c/h2\u003e \u003cp\u003eMRI examination was carried out on a 3T scanner (Siemens Skyra, Siemens Healthcare, Erlangen, Germany) with a 32-channel head coil. The protocol included 1) a diffusion tensor MRI with repetition time (TR)\u0026thinsp;=\u0026thinsp;10.5 s; echo time (TE)\u0026thinsp;=\u0026thinsp;93 ms; total 72 slices with voxel resolution of 2 mm isotropic; 30 noncolinear directions with b-value of 1000 s/m2, one b\u0026thinsp;=\u0026thinsp;0 s/m2 image in antero-posterior and one b\u0026thinsp;=\u0026thinsp;0 s/m2 image in postero-anterior phase encoding direction, 2) an axial 3D T1-weighted Magnetization Prepared Rapid Gradient Echo (MPRAGE, repetition time (TR) 2,200 ms; echo time (TE) 2.4 ms; inversion time (TI) 900 ms; flip angle (FA) 8\u0026deg;; field of view (FOV) 230\u0026times;197\u0026times;176 mm; voxel resolution 1\u0026times;1\u0026times;1 mm3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDTI-ALPS calculation\u003c/h2\u003e \u003cp\u003eWe measured the mean ALPS index of both hemispheres using an automatic atlas-based approach with subsequent color-coded fractional anisotropy thresholding. We chose this approach to avoid ROI selection bias and developed a color-coded fractional anisotropy thresholding to exclude voxels with a low inclination towards the desired axes. The analysis was performed in each subject\u0026rsquo;s native diffusion space and the DWI data were corrected for distortion, movement artifacts and eddy currents using FSL\u0026rsquo;s \u003cem\u003etopup\u003c/em\u003e and \u003cem\u003eeddy\u003c/em\u003e tools.\u003c/p\u003e \u003cp\u003eWe used the \u0026ldquo;JHU ICBM tracts maxprob thr25 1mm\u0026rdquo; atlas\u0026rsquo;s labels of superior longitudinal fasciculus (SLF) and corticospinal tract (CST) for the association and projection areas, respectively \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). We restricted these labels to areas at the top of the lateral ventricles and areas with a low probability of intruding into the cortex during transformations to each subject\u0026rsquo;s diffusion space with the resulting masks (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). These restricted labels were then used as precursors to the final ROIs and were transformed from the MNI152 space to each subject\u0026rsquo;s native diffusion space. To achieve these transformations, each T1 image was first transformed into MNI152 space using FSL\u0026rsquo;s \u003cem\u003eflirt\u003c/em\u003e and \u003cem\u003efnirt\u003c/em\u003e tools \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Subsequently, the original T1 image was coregistered to its corresponding diffusion image via \u003cem\u003eflirt\u003c/em\u003e. The \u003cem\u003econvertwarp\u003c/em\u003e function was then employed to generate a single transformation from MNI152 space to the subject\u0026rsquo;s native diffusion space. Each transformation was manually inspected for errors.\u003c/p\u003e \u003cp\u003eUsing FSL\u0026rsquo;s \u003cem\u003edtifit\u003c/em\u003e, we acquired each voxel\u0026rsquo;s principal eigenvector, defined by three volumes representing the x/y/z axes, and transformed these into absolute values (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). As a result, we obtained three volumes representing the principal eigenvector\u0026rsquo;s inclination to each of the x/y/z/ axes, respectively, but we lost the precise direction of the principal eigenvector.\u003c/p\u003e \u003cp\u003eWe analyzed the ROIs for each hemisphere separately. For the projection ROIs, we subtracted the x- and y-volumes from the z-volume. The resulting image favored the \u0026ldquo;purest\u0026rdquo; diffusion in the z-axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). An analogous approach was used to obtain the association area ROIs. The 87% highest-value voxels restricted to the CST and SLF masks were used as the projection and association area ROIs, respectively.\u003c/p\u003e \u003cp\u003eSubsequently, we acquired the diffusivity images in the x/y/z axes using FSL\u0026rsquo;s \u003cem\u003edtifit\u003c/em\u003e. The diffusivity values used in the ALPS-index Eq.\u0026nbsp;(1) were then extracted from these images using the projection and association area ROIs acquired in the previous steps. The average ALPS-index of both hemispheres was calculated for each subject. All resource-heavy computation steps were performed on the MetaCentrum distributed computing infrastructure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing atlas regions of interest (a), restricting to the desired areas (b). Then, using color-coded fractional anisotropy thresholding by acquiring absolute values of the principal eigenvector (c) and subtracting the nuisance vectors from the desired one (d) and multiplying the result by fractional anisotropy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe Shapiro-Wilk test was used to test the normality of the data distribution. Comparisons were conducted using the Mann\u0026ndash;Whitney U test. Spearman's rank correlation coefficient (rho) was used for the correlation analysis. Statistical significance was defined as a two-tailed \u003cem\u003ep\u003c/em\u003e-value less than 0.05. The Bonferroni method was applied to correct familywise error. All statistical analyses were performed using SPSS Software (IBM SPSS Statistics Version 26).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of study participants\u003c/h2\u003e \u003cp\u003eCompared to controls, PD patients had higher MDS UPDRS scores, lower stage R ratios, and lower arousal indexes. Other observed parameters, including age, ALPS index, AHI, and ODI, did not show significant differences in case-control analysis. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents an overview of demographic and polysomnographic parameters and the ALPS-index.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of demographic, clinical and polysomnographic parameters and DTI-ALPS index.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePD (N\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eControls (N\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (F/M)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e17/37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e10/22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody Mass Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMDS UPDRS III\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALPS-Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Sleep Time (min.)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e255.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e332.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep Latency (min.)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep Efficiency (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWake (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage N1 (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage N2 (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage N3 (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage R (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eApnea-Hypopnea Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxygen Desaturation Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeriodic Limb Movements Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArousal Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eLegend: F \u0026ndash; females, M \u0026ndash; males, MDS-UPDRS \u0026ndash; Unified Parkinson's Disease Rating Scale, ALPS \u0026ndash; analysis along the perivascular space, N1, 2, 3 \u0026ndash; non-rapid eye movement sleep stage 1, 2, 3, R \u0026ndash; rapid eye movement sleep, PD \u0026ndash; Parkinson\u0026rsquo;s disease, N \u0026ndash; number, SD \u0026ndash; standard deviation. Results are presented as mean and standard deviation. Mann-Whitney U test was applied. *: Difference is significant at the 0.05 level. \u003cb\u003eBold\u003c/b\u003e: Significant after Bonferroni correction.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of DTI-ALPS with observed parameters\u003c/h2\u003e \u003cp\u003eThe ALPS index was negatively correlated with age in both groups. In the PD group ALPS index was significantly negatively correlated with AHI, ODI, sleep stage N1 ratio, and arousal index, and significantly positively correlated with sleep stage R ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This contrasted with the control group, where the ALPS-index was only correlated with age (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between ALPS-index and observed parameters.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrelations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALPS-index vs.:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003erho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003erho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.36**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.56**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody Mass Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMDS UPDRS III\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Sleep Time\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep Latency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep Efficiency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWake ratio (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage N1 ratio (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.42**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage N2 ratio (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage N3 ratio (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage R ratio (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eApnea-Hypopnea Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.41**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxygen Desaturation Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.38**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeriodic Limb Movements Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArousal Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.42*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eLegend: ALPS \u0026ndash; analysis along the perivascular space, MDS-UPDRS \u0026ndash; Unified Parkinson's Disease Rating Scale, N1, 2, 3 \u0026ndash; non-rapid eye movement sleep stage 1, 2, 3, R \u0026ndash; rapid eye movement sleep, PD \u0026ndash; Parkinson\u0026rsquo;s disease. ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). \u003cb\u003eBold\u003c/b\u003e: Significant after Bonferroni correction.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe relationship of glymphatic function to gender was investigated using a comparison of ALPS-indices between females and males. In the PD group, males showed lower ALPS-indices compared to females (1.254\u0026thinsp;\u0026plusmn;\u0026thinsp;0.163 vs.1.355\u0026thinsp;\u0026plusmn;\u0026thinsp;0.148; p\u0026thinsp;=\u0026thinsp;0.040), while in the control group, no differences in ALPS-indices were found when comparing gender.\u003c/p\u003e \u003cp\u003eAmong other relevant parameters in PD, AHI and ODI correlated with sleep efficiency (AHI: rho = -0.34; p\u0026thinsp;=\u0026thinsp;0.012, ODI: rho = -0.34; p\u0026thinsp;=\u0026thinsp;0.015), with wakefulness ratio (AHI: rho\u0026thinsp;=\u0026thinsp;0.37; p\u0026thinsp;=\u0026thinsp;0.007; ODI: rho\u0026thinsp;=\u0026thinsp;0.37; p\u0026thinsp;=\u0026thinsp;0.008), with sleep stage N1 ratio (AHI: rho\u0026thinsp;=\u0026thinsp;0.31; p\u0026thinsp;=\u0026thinsp;0.025, ODI: rho = -0.41; p\u0026thinsp;=\u0026thinsp;0.003, with sleep stage N3 ratio (AHI: rho = -0.36; p\u0026thinsp;=\u0026thinsp;0.008; ODI: rho = -0.34; p\u0026thinsp;=\u0026thinsp;0.017), with sleep stage R (AHI: rho = -0.53; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, ODI: rho = -0.49; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). While the arousal index was correlated with ODI (rho\u0026thinsp;=\u0026thinsp;0.40; p\u0026thinsp;=\u0026thinsp;0.036), we could not find a correlation with AHI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCorrelation analysis between ALPS-index and age (A), arousal index (B), sleep stage N1 ratio (C), sleep stage R ratio (D), apnea-hypopnea index (E), and oxygen desaturation index (F) in Parkinson\u0026rsquo;s disease. Legend: ALPS \u0026ndash; analysis along the perivascular space, N1 \u0026ndash; non-rapid eye movement sleep stage 1, R \u0026ndash; rapid eye movement sleep.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to elucidate previously unexplored associations of glymphatic dysfunction in de novo PD with OSA parameters and other polysomnographic features. The major finding of this study was that in de novo PD patients, glymphatic system function was related to several parameters, particularly polysomnographic, which were absent in controls. The glymphatic function was negatively correlated with OSA severity, with sleep stage N1 ratio and arousal index, and positively correlated with sleep stage R ratio. Glymphatic system function was negatively correlated with age in both PD and controls.\u003c/p\u003e \u003cp\u003eThe ALPS-index, suggesting dysfunction of the glymphatic system, is presumably associated with the process of neurodegeneration and with neurodegenerative diseases, including PD \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In our study, glymphatic system dysfunction was related to OSA severity in PD patients. This finding is consistent with a recent report performed on a cohort of individuals with severe OSA \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. OSA is frequent in PD, and it exacerbates both motor and non-motor symptoms of PD \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The influence of comorbid OSA on PD through dysfunction of the glymphatic system has only been hypothesized without direct evidence. This study documents the missing link between OSA and glymphatic system dysfunction in PD. PD patients in our study showed no difference in OSA intensity compared to controls of equal age. This suggests that OSA affects patients with PD more severely, already at the time of the diagnosis. It can be speculated that the neurodegenerative process in PD makes a substrate for the brain to become more vulnerable to OSA and its consequences.\u003c/p\u003e \u003cp\u003eConcidering that glymphatic system is most active in sleep, particularly dependent on TST and deep sleep stages (N3) amounts, fragmented sleep is considered as a pathogenetic mechanism that leads to glymphatic dysfunction \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In this study, in PD, we found a negative association of glymphatic function with frequency of arousals, which is a direct parameter of sleep fragmentation, and further with the sleep stage N1 ratio, the shallowest sleep stage. We also consider this to be a manifestation of sleep disruption, as N1 is the initial sleep stage following a sleep interruption manifested as arousal, and its ratio increases proportionally when the ratio of deeper sleep stages decreases. In accordance with this, we found a positive association of glymphatic function with the proportion of sleep stage R, the normal proportion of which is conditioned by the complete uninterrupted course of the entire sleep cycle, so it can be considered a manifestation of maintained sleep continuity. Therefore, our results are in line with the assumption that glymphatic dysfunction has a relationship with sleep fragmentation in PD. It has been implied that the relationship between sleep fragmentation, glymphatic dysfunction, and the process of neurodegeneration is linked in potential bidirectional relationships that could accelerate pathophysiological processes over time \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Our results show that this \u0026ldquo;vicious triangle\u0026rdquo; starts already at the time of PD diagnosis; it can indeed be expected that the condition will progress during the PD progression and that the relationships between the mentioned parameters will be stronger in more advanced stages of the disease.\u003c/p\u003e \u003cp\u003eSleep fragmentation is a known consequence of OSA. In our study, we showed that most of the parameters that correlated with glymphatic dysfunction were the result of OSA since they correlated with AHI and ODI as well, in particular, decline in sleep efficiency, increase in sleep stage N1 ratio, and decrease of sleep stage R ratio. The finding that the arousal index was not related to AHI, although it correlated with DTI-ALPS, suggests that other mechanisms could mediate this association. Fragmentation of sleep is well documented in PD \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e with multiple causes besides OSA, for example, muscle cramps, periodic limb movements, increased muscle tension, and pathophysiology of the disease itself \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. However, the arousal index was related to ODI, which points to the hypoxic events and therefore, still supports OSA as the probable cause.\u003c/p\u003e \u003cp\u003eBesides sleep fragmentation, OSA leads to recurrent hypoxia. Hypoxia was suggested as a likely mechanism through which OSA increases the risk for cognitive impairment \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. It was demonstrated that OSA is associated with declines in memory, attention, and executive functions in adults \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Further it was shown that glymphatic system dysfunction is correlated with ODI \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, which is consistent with our findings demonstrating the same relationship. We conclude that OSA enters the \u0026ldquo;vicious triangle\u0026rdquo; pathogenesis with worsening sleep disruption and hypoxic burden. Therefore, we stress that OSA should be actively screened for at the moment of PD diagnosis.\u003c/p\u003e \u003cp\u003eSurprisingly, the relationship of glymphatic dysfunction to OSA and other sleep parameters observed in PD was not expressed in the control group. The mere presence of early PD unmasked these relationships, which did not exceed the statistical significance threshold in the control group. It can be concluded that the neurodegenerative process creates a pathogenetic substrate of vulnerable brain tissue for the pathophysiological relationships and events described above. At this point, it is appropriate to emphasize that control subjects did not differ in the intensity of OSA or the proportions of NREM sleep stages, and the frequency of arousals was even higher in the control group. It seems that in control subjects, there could be a compensation mechanism that prevents the development of these relationships observed in de novo PD.\u003c/p\u003e \u003cp\u003eOur results on the relation of OSA to glymphatic impairment in PD are consistent with a very recent study reporting the relation of glymphatic dysfunction to body mass index (BMI) in PD \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. OSA is known to be tightly associated with obesity \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Therefore, it is reasonable to consider that OSA may have contributed to the mediation of the main finding of the mentioned study. Surprisingly, in our study, the ALPS-index did not correlate with BMI, and BMI was not significantly different between PD patients and controls.\u003c/p\u003e \u003cp\u003eIn our study, we did not find a significant change in glymphatic dysfunction in PD patients compared to controls; however, the apparent trend to lower ALPS-index values is in agreement with a recent study \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Unlike previous studies investigating glymphatic function in PD, in our study, the patients with PD were at the very threshold of diagnosis. That could be why their glymphatic function impairment may not have been so pronounced.\u003c/p\u003e \u003cp\u003eThe negative correlation of glymphatic function with age in both groups is consistent with the available literature \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The gradual decrease of glymphatic function during the aging of the brain is apparently a parallel process, most likely a result of changes in cerebrovascular function with age that lie in the background of the events discussed above. Our control group's results show that it does not avoid even healthy individuals.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe demonstrated that glymphatic system dysfunction is related to the severity of OSA and parameters of sleep fragmentation in newly diagnosed PD but not in controls. Thus, we documented for the first time the link between OSA and glymphatic system dysfunction in PD. It can be implied that OSA contributes to the neurodegenerative process via glymphatic impairment in PD. Therefore, we suggest that OSA should be actively screened for at the moment of PD diagnosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eAll authors declare no financial or non-financial competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eJN designed the study concept, analyzed the polysomnography, performed the statistical analysis, interpreted the data and wrote the draft of the manuscript. SM analyzed the MRI and contributed to the critical edition of the manuscript. VR analyzed the MRI. SD analyzed the polysomnography and contributed to the critical edition of the manuscript. TK performed MRI preprocessing analysis. JK performed and evaluated the MRI. KS analyzed the polysomnography and contributed to the critical edition of the manuscript. PD performed clinical examinations, contributed to the data acquisition and to the critical edition of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe study was funded by National Institute for Neurological Research (Programme EXCELES, ID Project No. LX22NPO5107) - Funded by the European Union \u0026ndash; Next Generation EU; Charles University: Cooperatio Program in Neuroscience; General University Hospital in Prague and Ministry of Health of the Czech Republic project MH CZ-DRO-VFN64165; Na Homolce Hospital project CZ-DRO-NHH00023884 and Czech Health Research Council grant NU21\u0026ndash;04\u0026ndash;00535.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIliff, J. 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Front Aging Neurosci 15, 1173221 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnagi.2023.1173221\u003c/span\u003e\u003cspan address=\"10.3389/fnagi.2023.1173221\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-parkinsons-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjparkd","sideBox":"Learn more about [npj Parkinson's Disease](http://www.nature.com/npjparkd/)","snPcode":"41531","submissionUrl":"https://submission.springernature.com/new-submission/41531/3","title":"npj Parkinson's Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4673004/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4673004/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlymphatic dysfunction can contribute to Parkinson\u0026rsquo;s disease (PD). Obstructive sleep apnea (OSA) disturbs sleep, which is necessary for its proper function, and is frequent in PD. We investigated the glymphatic function in de novo PD and its relation to OSA. Fifty-four PD patients (mean age 58.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2 years) and 32 controls (mean age 59.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3 years) underwent polysomnography and 3T magnetic resonance imaging of the brain. Diffusion tensor imaging along the perivascular space (DTI-ALPS) was calculated using atlas-based automatic regions of interest selection. In PD ALPS-index negatively correlated with apnea-hypopnea index (rho=-0.41; p\u0026thinsp;=\u0026thinsp;0.002), oxygen desaturation index (rho=-0.38; p\u0026thinsp;=\u0026thinsp;0.006), sleep stage N1 (rho=-0.42; p\u0026thinsp;=\u0026thinsp;0.002), and arousal index (rho=-0.24; p\u0026thinsp;=\u0026thinsp;0.018), and positively correlated with sleep stage R (rho\u0026thinsp;=\u0026thinsp;0.32; p\u0026thinsp;=\u0026thinsp;0.023), while in controls no such correlations were observed. Glymphatic dysfunction is related to OSA severity in de novo PD but not in controls. We imply that OSA contributes to neurodegeneration via glymphatic impairment in PD.\u003c/p\u003e","manuscriptTitle":"Glymphatic dysfunction evidenced by DTI-ALPS is related to obstructive sleep apnea intensity in newly diagnosed Parkinson’s disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 00:46:26","doi":"10.21203/rs.3.rs-4673004/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-03-04T12:06:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-01-21T13:42:50+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-01-21T12:43:51+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-09-07T14:52:17+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-08-20T15:39:31+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-08-12T10:06:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-04T13:23:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-04T04:45:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Parkinson's Disease","date":"2024-07-02T22:36:54+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2024-07-02T16:48:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-parkinsons-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjparkd","sideBox":"Learn more about [npj Parkinson's Disease](http://www.nature.com/npjparkd/)","snPcode":"41531","submissionUrl":"https://submission.springernature.com/new-submission/41531/3","title":"npj Parkinson's Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c779a20b-936b-4a90-969a-232468a050d8","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34131839,"name":"Health sciences/Neurology/Neurological disorders/Parkinson's disease"},{"id":34131840,"name":"Health sciences/Neurology/Neurological disorders/Neurodegenerative diseases/Parkinson's disease"},{"id":34131841,"name":"Health sciences/Neurology/Neurological disorders/Movement disorders/Parkinson's disease"},{"id":34131842,"name":"Health sciences/Neurology/Neurological disorders/Neurodegeneration"},{"id":34131843,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-06-12T07:09:27+00:00","versionOfRecord":{"articleIdentity":"rs-4673004","link":"https://doi.org/10.1038/s41531-025-01018-8","journal":{"identity":"npj-parkinsons-disease","isVorOnly":false,"title":"npj Parkinson's Disease"},"publishedOn":"2025-06-11 04:00:00","publishedOnDateReadable":"June 11th, 2025"},"versionCreatedAt":"2024-10-08 00:46:26","video":"","vorDoi":"10.1038/s41531-025-01018-8","vorDoiUrl":"https://doi.org/10.1038/s41531-025-01018-8","workflowStages":[]},"version":"v1","identity":"rs-4673004","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4673004","identity":"rs-4673004","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}