Regional Glymphatic Dysfunction in Patients with Spinocerebellar Ataxia Type 3

Regional Glymphatic Dysfunction in Patients with Spinocerebellar Ataxia Type 3 | 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 Regional Glymphatic Dysfunction in Patients with Spinocerebellar Ataxia Type 3 Zhen Yuan, Lin Hua, Manxi Xu, Linwei Zhang, Fei Gao, Xinglin Zeng, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6891437/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Abnormal accumulation of polyglutamine proteins is considered a core pathogenic factor in spinocerebellar ataxia type 3 (SCA3). The glymphatic system, a lymphatic-like fluid transport system, plays a crucial role in maintaining the balance between protein production and clearance in the brain. However, it remains unclear whether SCA3 is associated with impairments in glymphatic function. Using multimodal imaging data, 34 SCA3 patients and 36 age-, sex- and educational matched healthy controls (HCs) were compared using multiple glymphatic measurements, including choroid plexus (CP) volume, cerebrospinal fluid (CSF) volume, diffusion tensor imaging along the perivascular (DTI-ALPS) index, and coupling relationship between blood-oxygen-level-dependent signals and CSF flow (BOLD-CSF coupling). Then, we evaluated regional glymphatic function by dividing DTI-ALPS and BOLD-CSF coupling into anterior, middle, posterior, and cerebellum regions, thereby identifying the spatial variation of glymphatic function in the two groups. We demonstrated that compared with HCs, larger CP and CSF volumes were found in SCA3 patients. More importantly, for DTI-ALPS index and BOLD-CSF coupling, these surrogate markers for glymphatic clearance were weaker in SCA3 patients. Furthermore, altered regional glymphatic functions were most prominent in midbrain, cerebellum and middle cortical regions. Crucially, the altered midbrain, cerebellum, middle and global glymphatic functions were accompanied by the severity of ataxia and other SCA3 symptoms. Similar to other neurodegenerative disorders, the associations between multiple glymphatic markers and SCA3 symptoms suggest that waste clearance is disrupted in SCA3 patients, shedding light on the pathogenesis of the disease from a glymphatic perspective. Our findings highlighted the dysregulated glymphatic function as a novel biomarker for SCA3 and provide insights for exploring effective treatment strategies. Health sciences/Biomarkers Biological sciences/Neuroscience cerebrospinal fluid flow resting-state fMRI signal choroid plexus DTI-ALPS glymphatic system spinocerebellar ataxia type 3 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Spinocerebellar ataxia type 3 (SCA3), also known as Machado-Joseph disease (MJD), is the most prevalent subtype of spinocerebellar ataxia and an autosomal dominant neurodegenerative disorder. Patients with SCA3 typically present a spectrum of motor and non-motor symptoms, including pyramidal signs, extrapyramidal features, cognitive decline, and REM sleep behavior disorder (RBD) 1 – 4 . The disease's pathogenesis is primarily driven by the expansion of CAG repeat sequences within the ATXN3 gene, which causes protein misfolding and the accumulation of abnormal polyglutamine proteins 5 , 6 . This accumulation disrupts cellular function and can ultimately lead to neuronal death 7 . An imbalance between the production and clearance of these abnormal proteins is crucial to SCA3 pathogenesis; however, the mechanisms regulating pathogenic protein clearance remain poorly understood. The glymphatic system is a critical “waste clearance” mechanism in the central nervous system 8 , 9 , facilitating the exchange of cerebrospinal fluid (CSF) and interstitial fluid (ISF) to efficiently remove metabolic waste and abnormal proteins 10 – 12 . This system is vital in the pathology of various neurodegenerative diseases, including Alzheimer’s disease (AD) 13 , 14 and Parkinson’s disease (PD) 15 , 16 . However, its functionality in SCA3 remains insufficiently studied. Importantly, glymphatic activity significantly increases during sleep, a crucial period for effective waste clearance 17 . SCA3 patients frequently experience sleep disturbances, which can impair glymphatic function and diminish clearance efficiency 3 , 4 . This impairment may exacerbate the accumulation of pathological proteins and accelerate disease progression. Consequently, developing a neuroimaging biomarker to detect changes in the glymphatic system of SCA3 patients is essential for monitoring disease advancement. Glymphatic magnetic resonance imaging (MRI) typically requires intrathecal injection of gadolinium-based contrast agents to visualize the glymphatic system 18 . However, the widespread use of gadolinium poses a risk of severe neurotoxic complications, highlighting the need for non-invasive assessment methods. Recently, a technique known as perivascular space diffusion tensor imaging analysis (DTI-ALPS) has been introduced to evaluate glymphatic function without the need for contrast agents 19 , and it has been successfully applied in several neurodegenerative diseases 20 , 21 . Additionally, the choroid plexus (CP), an integral part of the lymphatic system, serves as a surrogate marker for CSF production 22 , 23 . The coupling of low-frequency (< 0.1 Hz) resting-state fMRI (rs-fMRI) BOLD signals with CSF dynamics in the ventricles further facilitates metabolite clearance 24 , 25 . By integrating various non-invasive neuroimaging techniques, we can comprehensively assess the clearance function of the brain's glymphatic system 26 – 29 . This study aims to utilize multimodal imaging data—including structural MRI, resting-state fMRI, and diffusion tensor imaging—to evaluate glymphatic system dysfunction in patients with SCA3. Specific analyses will include changes in the morphology of the choroid plexus (CP) and CSF, the coupling between BOLD signals and CSF flow in the ventricles, and the DTI-ALPS index. These analyses will reveal functional impairments in the glymphatic system of SCA3 patients and explore their relationship with neuropsychological assessments. This research will provide new insights into the complex pathological mechanisms of SCA3 and establish a theoretical foundation for future treatment strategies. Methods Subjects From June 2021 to April 2024, a prospective cohort of 120 subjects were categorized into HCs ( N = 50) and SCA patients ( N = 70). The SCA patients were recruited from the outpatient clinic of the China-Japan Friendship Hospital while the HCs from the local communities (Fig. 1 ). The inclusion criteria of SCA3 patients were as follow: (1) diagnosed as SCA3 by expert neurologists according to the family history, neurological manifestations, genetic and molecular tests, and routine brain MRI findings; (2) at least complete T1-weighted structural MRI scanning and one ataxia assessment; (3) age ≥ 18 years; (4) right-handed. The exclusion criteria were: (1) challenging to cooperate during MRI examination, and the image quality is too poor for image analysis; (2) a history of other brains organic and metabolic diseases; (3) pregnant and lactating women; and (4) other MRI contraindications. 14 HCs and 17 SCA patients were excluded for further analysis due to poor image quality (see Supporting Information for Image Quality Control). Genetic testing was then conducted to exclude those diagnosed with Multiple System Atrophy (MSA), Autosomal Recessive Cerebellar Ataxia (ARCA), and other types of SCA (SCA1, SCA2, SCA6, and SCA7). 34 symptomatic patients with genetically confirmed SCA3 were included in the analysis. These SCA3 patients were further screened by ataxia assessments including the Scale for the Assessment and Rating of Ataxia (SARA) and the International Cooperative Ataxia Rating Scale (ICARS) 30 . In addition to ataxia assessments, SCA3 patients were performed extraordinary psychiatric assessments using Self-Rating Anxiety Scale (SAS) 31 and Self-Rating Depression Scale (SDS) 32 . Additionally, to evaluate the SCA3 patients’ sleep quality, Self-Rating Scale of Sleep (SRSS) 33 was also involved. The HCs were matched for age, sex, and educational level with SCA3 patients, free of any neuropsychiatric or neurodegenerative diseases. Finally, after controlling clinical assessments, genetic and molecular tests, and the MRI imaging quality, 36 HCs and 34 SCA3 patients with structural MRI, 35 HCs and 27 SCA3 patients with rs-fMRI data and 35 HCs and 28 SCA3 patients with DTI data were selected for further CP and CSF volumes, BOLD-CSF coupling and DTI-ALPS analysis, respectively. Image acquisition and preprocessing All MRI data (structural MRI, rs-fMRI, and DTI data) was collected in the 3.0 Tesla MR scanner (General Electric, Discovery MR750, Milwaukee, WI, United States). Each imaging session included a three-dimensional fast spoiled gradient-echo sequence (3D FSPGR) with the following parameters: repetition time (TR) = 6.7 ms, echo time (TE) = 2.9 ms, flip angle = 12°, slice thickness = 1 mm, field of view (FOV) = 256 × 256 mm 2 , matrix = 256 × 256, voxel size = 1 × 1 × 1 mm 3 . For rs-fMRI acquisition, 240 fMRI volumes were captured with an echo-planar image (EPI) sequence (TR = 2000 ms, TE = 30 ms, filp angle = 90°, slice thickness = 3.5 mm, FOV = 224 × 224 mm 2 , matrix = 64 × 64, voxel size = 3.5 × 3.5 × 4.2 mm 3 ). The DTI images with 8 b-value of 0 s/mm 2 and 64 b-value of 1000 s/mm 2 were acquired using a diffusion weighted spin-echo EPI (TR = 8028 ms, TE = 81.8 ms, flip angle = 90°, slice thickness = 2 mm, FOV = 240 × 240 mm 2 , matrix = 120 ×120, voxel size = 2 × 2 × 2 mm 3 ). During the MRI scanning session, foam paddings were given inside the head coil to restrict potential head motions. Additional routine MR sequences, including axial T2-weighted imaging (T2WI), T2-FLAIR, and diffusion-weighted imaging (DWI), were performed to identify brain abnormalities in SCA3 patients. The rs-fMRI data was preprocessed using the 1000 Functional Connectomes Project script (version 1.1-beta; https://www.nitrc.org/frs/?group_id=296 ), which underwent a pipeline similar to a previous study examining neurofluid coupling in neurodegeneration diseases 26 – 29 , 34 . Raw rs-fMRI images for each subject underwent preprocessing steps including slice timing, motion correction, skull stripping, spatial smoothing with a 4mm full-width half-maximum (FWHM) kernel, 0.01–0.1 Hz bandpass filtering, as well as linear and quadratic temporal trend removal. Subsequently, rs-fMRI images of the subjects were co-registered to their high-resolution T1-weighted structural MRI images and then to the 152-brain Montreal Neurological Institute (MNI-152) space. As our analysis focused on the coupling between CSF signal and global BOLD (gBOLD) signal, regression of global signal and CSF signal was omitted by our preprocessing pipeline. Additionally, under the same rationale regression of motion parameters was also skipped as it may associate with gBOLD signal 35 . The DTI data was preprocessed using the FSL package (version 6.0.5; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki ). The general procedures for DTI data preprocessing included skull stripping, the correction for head motion, eddy current-induced distortions, EPI-induced susceptibility distortions, and bias field. The fractional anisotropy (FA) map of each subject was then registered to the FA map of the Johns Hopkins University atlas (JHU atlas) template 36 , and the transformation matrix obtained from the previous step of co-registration was applied to other diffusion metric maps to obtain all DTI-based maps in the space of the JHU atlas (spatial resolution, 1 × 1 × 1 mm 3 ; Fig. 2 D). CP and CSF segmentation The CP has been reported as the principal locus of CSF secretion, thus being considered as a potential imaging marker for indirectly evaluating CSF production and toxic clearance. Using the FreeSurfer cortical reconstruction pipeline (version 6.0.0; https://surfer.nmr.mgh.harvard.edu/fswiki ), segmentation of the CP within the lateral ventricles was automatically accomplished from the T1-weighted structural MRI image (Fig. 2 A). Manual scrutiny and correction of CP segmentation was conducted by two neuroimaging researchers, ensuring its accuracy and reliability. These revised CP segmentations underwent further independent quality assessment and finalization by one neuroradiologist. Total intracranial volume (ICV), cortical gray matter (GM) volume, CSF volume, and CP volume were extracted from the whole brain segmentation. To reduce inter-subject variability, CP and CSF volumes were separately normalized as the ratio of CP and CSF volumes to ICV 22 . Quantification of the coupling between BOLD signal and CSF inflow The gBOLD signals were obtained from the cortical GM regions of cerebrum and cerebellum, delineated by the automated anatomical labeling 2 (AAL2) atlas 37 from preprocessed functional images in individual spaces (Fig. 2 B). To assess the regional distribution of glymphatic function, the AAL2 atlas was divided into the anterior, middle, and posterior parts based on the atlas labels of the frontal, temporoparietal, occipital, and cerebellum regions (Fig. 2 B). To ensure precise alignment between the cortical GM masks and the functional space, the cortical GM masks were transformed from the MNI-152 space to individual functional space for each subject using the inversed concatenated transformation matrix from the previous co-registration process (functional-structural-standard). Transformed cortical masks were visually inspected to assure accurate spatial registration with the preprocessed functional images. The CSF mask was placed in the bottom slices of the rs-fMRI image 26 – 29 , 34 (Fig. 2 B). These bottom slices consistently encompassed the cerebellum of all subjects as confirmed by visual inspection, capturing the CSF through-slice inflow effect as previously described. To quantify gBOLD-CSF coupling, the cross-correlation function was computed between the gBOLD signals and the CSF signals across different time lags ranging from − 9s to 9s for each subject (Fig. 2 C). The negative correlation coefficients peaked at the lag of + 3s. This negative peak signified the point at which the gBOLD and CSF signals exhibited their strongest inverse relationship, capturing the interplay between hemodynamic signal and CSF dynamics. It was therefore used to quantify the strength of the gBOLD-CSF coupling for each subject 17 . Moreover, we also calculated the cross-correlation function between the negative derivative of the BOLD signal and the CSF signal to ensure that the CSF signal matched the negative derivative of the BOLD oscillation when setting the negative value to zero 17 . Finally, the cross-correlation function between the CSF signal and the anterior, middle, posterior, cerebellum BOLD signals was computed to obtain the regional BOLD-CSF coupling (aBOLD-CSF, mBOLD-CSF, pBOLD-CSF, and cbBOLD-CSF couplings). Calculation of the DTI-ALPS index The DTI-ALPS index, evaluating the water diffusivity along the PVS at the level of the lateral ventricle, is used to reflect the activity of the glymphatic system 19 , 38 – 40 . The direction of PVS at the lateral ventricle level is primarily along the x-axis, perpendicular to the ventricle wall. Furthermore, the projection fibers that pass along the lateral ventricular wall in the z-axis are adjacent to the association fibers that go through at the more lateral area in the y-axis. Therefore, the major distinction in water molecule behavior between diffusivity along the x-axis in both fibers (D xx,proj and D xx,assoc ) and diffusivity perpendicular to them (D yy,proj and D zz,assoc ) relates to the diffusivity of the PVS. The regions of interests (ROIs) in the current study were defined as 3-mm-diameter spherical areas within the projection (including the anterior, superior, and posterior corona radiata) and the association fiber (superior longitudinal fasciculus), localized at the level of the lateral ventricle (MNI coordinates z = 26) based on JHU atlas labels (Fig. 2 D). Mean diffusion values of these ROIs were extracted to compute the global DTI-ALPS. Specifically, the DTI-ALPS index was calculated as follows 19 : $$\:DTI-ALPS\:index\:=\frac{mean({D}_{xx,proj},{D}_{xx,assoc})}{mean({D}_{yy,proj},{D}_{zz,assoc})}$$ Subsequently, these ROIs were then subdivided into the anterior, middle, and posterior regions to obtain the anterior DTI-ALPS (aDTI-ALPS), middle DTI-ALPS (mDTI-ALPS), and posterior DTI-ALPS (pDTI-ALPS), respectively (Fig. 2 D). Finally, DTI-ALPS gradient was calculated from the ROIs with 3 × 3 × 3 mm 3 at 1 mm intervals to comprehensively understanding the glymphatic activity from anterior to posterior (Fig. 2 D). A total of 46 pairs of ROIs were generated, and each pair of DTI-ALPS was calculated. Relationship between regional glymphatic measurements and neuropsychological assessments The strength of gBOLD-CSF coupling was identified at the lag of + 3s (the negative peak of the cross-correlation function). Confounding effects of age and sex were estimated by the linear mixed effect model and regressed out from further relationship analyses. Next, partial correlation analyses were conducted to evaluate the association within multiple regional glymphatic measurements in the whole cohort and in the two groups separately. Finally, the relationship between neuropsychological assessments, performed in SCA3 patients to assess ataxia, psychiatry as well as sleep symptoms, and CP ratio, CSF ratio, a/m/p/cb/gBOLD-CSF coupling strength and a/m/p/gDTI-ALPS strength were assessed by regression analyses after z-score normalization and adjusting for age and sex. Statistical analysis To ascertain the statistical significance of the a/m/p/cb/gBOLD-CSF correlations, permutation method was employed to create a null distribution for the a/m/p/cb/gBOLD-CSF correlations, respectively. Specifically, a/m/p/cb/gBOLD signals and CSF signals from different subject were randomly paired, and cross-correlation coefficients were recalculated. This process was repeated for 1000 times, generating a null distribution for the mean a/m/p/cb/gBOLD-CSF cross-correlation function. The p value was estimated by calculating the percentage of the correlation value of permutation data higher than the correlation value of real data at different lags. Furthermore, between-group comparisons were performed by two-sample t-test for continuous variables and χ 2 test for categorical variables. Multiple comparisons were corrected by the false discovery rate (FDR) approach. Statistical significance was defined as p < 0.05. Results Demographic and clinical characteristics A total of 70 subjects (44.21 ± 12.64 years; 28 females) including 34 SCA3 patients (42.62 ± 12.01 years; 10 females) was entered in the formal analyses (Table 1 ). 36 HCs (45.72 ± 13.2 years; 18 females) were age- ( t (68) = 1.03, p = 0.308), sex- ( χ 2 (1) = 3.09, p = 0.079) and education- ( t (64) = 1.38, p = 0.171) matched with SCA3 patients. No significant differences were found in ICV ( t (68) = -0.57, p = 0.572) and GMV ( t (68) = 1.35, p = 0.183) between HCs and SCA3 patients. The mean distributions of disease durations and CAG repeat numbers in SCA3 patients were 9.04 ± 8.5 and 70.39 ± 3.67, respectively. Moreover, SCA3 patients underwent multiple neuropsychological assessments, including ataxia assessments of SARA (17.12 ± 9.2) and ICARS (44.74 ± 21.45), psychiatric assessments of SAS standard score (44.87 ± 12.45) and SAS standard score (47.1 ± 13.96), and sleep assessment of SRSS (26.32 ± 8.6). Table 1 Demographic and clinical characteristics of HCs and SCA3 patients. Characteristics HCs SCA3 T/χ² values p values Age (years) 45.72 ± 13.2 ( N = 36) 42.62 ± 12.01 ( N = 34) 1.03 a 0.308 Sex (M/F) 18/18 ( N = 36) 24/10 ( N = 34) 3.09 b 0.079 Education (years) 14 ± 4.11 ( N = 36) 12.67 ± 3.61 ( N = 30) 1.38 a 0.171 ICV (ml) 1490.14 ± 180.86 ( N = 36) 1512.83 ± 150.66 ( N = 34) -0.57 a 0.572 GMV (ml) 493.3 ± 61.91 ( N = 36) 475.91 ± 44.24 ( N = 34) 1.35 a 0.183 Duration (years) / 9.04 ± 8.5 ( N = 23) / / CAG / 70.39 ± 3.67 ( N = 31) / / SARA / 17.12 ± 9.2 ( N = 34) / / ICARS / 44.74 ± 21.45 ( N = 34) / / SAS standard / 44.87 ± 12.45 ( N = 31) / / SDS standard / 47.1 ± 13.96 ( N = 31) / / SRSS / 26.32 ± 8.6 ( N = 31) / / Note: Data is presented as mean ± standard deviations (SD). a: Independent-samples t test, b: Chi-square test. HCs = Healthy controls, SCA3 = Spinocerebellar ataxia type 3, M = Male, F = Female, ICV = Intracranial volume, GMV = Cortical gray matter volume. CAG = Cytosine-adenine‐guanine repeat number, SARA = Scale for the Assessment and Rating of Ataxia, ICARS = International Cooperative Ataxia Rating Scale, SAS standard = Standard score of Self-Rating Anxiety Scale, SDS standard = Standard score of Self-Rating Depression Scale, SRSS = Self-Rating Scale of Sleep. The gBOLD-CSF coupling, CP volume, and CSF volume alteration in SCA3 The gBOLD-CSF coupling was initially quantified by cross-correlation coefficient between gBOLD and CSF signals and averaged within HCs and SCA3 patients. Then, a/m/p/cbBOLD-CSF was obtained from the anterior, middle, posterior, and cerebellum regions of the BOLD signal coupled with the CSF signal, respectively. Consistent with previous report 17 , 26 – 29 , 34 , the strength of gBOLD-CSF coupling for both groups ( r = -0.29, p < 0.001 for HCs; r = -0.22, p < 0.001 for SCA3 patients; 1000 times permutation test) and a/m/p/cbBOLD-CSF coupling for the whole samples ( r = -0.25, p < 0.001 for aBOLD-CSF coupling; r = -0.28, p < 0.001 for mBOLD-CSF coupling; r = -0.26, p < 0.001 for pBOLD-CSF coupling; r = -0.21, p < 0.001 for cbBOLD-CSF coupling; 1000 times permutation test) was identified with the negative peak of cross-correlation function at the lag of + 3 (Fig. 3 A and 3 B). At the individual level, the coupling strength did not correlate with head motion as quantified by mean frame-wise displacement ( r = 0.12, p = 0.356; Figure S1 ). The peak of coupling strength and time lag was further confirmed by calculating the cross-correlation function between the negative derivative of the gBOLD signal and the CSF signal within HCs (Fig. 3 C) and SCA3 patients or between the negative derivative of the a/m/p/cbBOLD signal and the CSF signal across the entire sample (Fig. 3 D). Therefore, for further comparisons between two groups, the cross-correlation strength at + 3s for each subject was used as a surrogate marker for glymphatic function. The gBOLD-CSF coupling strength of HCs was significantly stronger than the coupling of SCA3 patients ( t (60) = -2.08, p = 0.041; Fig. 3 A and Table 2 ), suggesting that glymphatic function deficiency may be involved in SCA3 patients. Furthermore, Between-group comparisons of CP (Fig. 4 B) and CSF (Fig. 4 C) volumes showed that SCA3 patients had higher CP ( t (68) = 2.64, p = 0.01) and CSF ( t (68) = 3.75, p < 0.001) volumes than HCs, especially after controlling the inter-subject variability ( t (68) = 2.87, p = 0.006 for CP ratio and t (68) = 4.44, p < 0.001 for CSF ratio; Table 2 ). Table 2 Comparisons of multiple glymphatic measurements between HCs and SCA3 patients. Glymphatic measurements SCA3 HCs T values p values CSF volume N = 34 N = 36 CSF (ml) 1.25 ± 0.26 1.04 ± 0.23 3.75 < 0.001 CSF (ratio of ICV x 10 3 ) 0.83 ± 0.14 0.69 ± 0.12 4.44 < 0.001 CP volume N = 34 N = 36 CP (ml) 1.19 ± 0.4 0.91 ± 0.47 2.64 0.01 CP (ratio of ICV x 10 3 ) 0.78 ± 0.23 0.6 ± 0.28 2.87 0.006 BOLD-CSF coupling N = 27 N = 35 aBOLD-CSF -0.22 ± 0.11 -0.27 ± 0.13 1.54 0.128 mBOLD-CSF -0.23 ± 0.11 -0.34 ± 0.18 3.06 0.003 pBOLD-CSF -0.23 ± 0.12 -0.28 ± 0.16 1.41 0.165 cbBOLD-CSF -0.15 ± 0.12 -0.25 ± 0.18 2.34 0.023 gBOLD-CSF -0.22 ± 0.12 -0.29 ± 0.15 2.08 0.041 DTI-ALPS N = 28 N = 35 aDTI-ALPS 1.18 ± 0.13 1.26 ± 0.18 -2 0.05 mDTI-ALPS 1.24 ± 0.2 1.39 ± 0.18 -3.1 0.003 pDTI-ALPS 1.13 ± 0.16 1.19 ± 0.18 -1.41 0.165 gDTI-ALPS 1.16 ± 0.12 1.27 ± 0.15 -3.02 0.004 Note: Data is presented as mean ± SD. HCs = Healthy controls, SCA3 = Spinocerebellar ataxia type 3, ICV = Intracranial volume, CSF = Cerebrospinal fluid, CP = Choroid plexus, a/m/p/cb/gBOLD-CSF coupling = The coupling between anterior/middle/posterior/cerebellum/global cortical gray matter of blood-oxygen-level-dependent signals and cerebrospinal fluid signals; a/m/p/gDTI-ALPS = Anterior/middle/posterior/global diffusion tensor image analysis along the perivascular space. Group comparisons of regional glymphatic measurements between HCs and SCA3 To further confirm the regional glymphatic impairment in SCA3, multiple regional glymphatic differences were calculated between HCs and SCA3 patients. When examining the regional DTI-ALPS between the two groups (Table 2 ), mDTI-ALPS ( t (61) = -3.1, p = 0.003) and gDTI-ALPS ( t (61) = -3.02, p = 0.004) were statistically weaker in SCA3 patients than that in HCs. No significant difference was found in aDTI-ALPS ( t (61) = -2, p = 0.05) and pDTI-ALPS ( t (61) = -1.41, p = 0.165) between the two groups. This regional DTI-ALPS pattern was consistent with that of the regional BOLD-CSF coupling, which was also detected with significant differences in mBOLD-CSF coupling ( t (60) = 3.06, p = 0.003), cbBOLD-CSF coupling ( t (60) = 2.34, p = 0.023) and gBOLD-CSF coupling ( t (60) = 2.08, p = 0.041), but not in aBOLD-CSF ( t (60) = 1.54, p = 0.128) coupling and pBOLD-CSF coupling ( t (60) = 1.41, p = 0.165; Table 2 ). These findings demonstrated that the glymphatic function in the midbrain, cerebellum, and middle regions was severely impaired in SCA3 patients. The spatial variation and correlation of regional glymphatic measurements We then investigated how DTI-ALPS and BOLD-CSF coupling as indexes for glymphatic function varied spatially across cortical regions and fibers in HCs and SCA3 patients. In line with a prior report, DTI-ALPS gradients in both groups demonstrated an ascending trend from anterior to middle regions, followed by a decline from middle to posterior regions 34 (Fig. 4 A). Significant differences were mainly found in the anterior ( t (61) range: 2.12 to 2.27, p range: 0.027 to 0.038) and middle regions ( t (61) range: 2.16 to 3.04, p range: 0.004 to 0.035) between HCs and SCA3 patients. In both groups, the mDTI-ALPS was stronger than the pDTI-ALPS ( t (68) = 4.65, p < 0.001 for HCs; t (54) = 2.38, p = 0.021 for SCA3 patients; FDR correction), while the mDTI-ALPS surpassed the aDTI-ALPS only in HCs ( t (68) = 3.05, p = 0.003; FDR correction; Fig. 4 D). Moreover, the mBOLD-CSF coupling was stronger than the cbBOLD-CSF coupling in the two groups ( t (68) = -2.12, p = 0.038 for HCs; t (52) = -2.25, p = 0.029 for SCA3 patients; FDR correction), with the mBOLD-CSF coupling being stronger than the aBOLD-CSF coupling exclusively in HCs ( t (68) = -2.1, p = 0.039; FDR correction), and the pBOLD-CSF coupling being stronger than cbBOLD-CSF coupling was observed solely in SCA3 patients ( t (52) = -2.24, p = 0.029 for SCA3 patients; FDR correction; Fig. 4 E). Furthermore, statistically significant positive correlations were primarily showed between mBOLD-CSF coupling, cbBOLD-CSF coupling, and gBOLD-CSF coupling with CP volume ( r range: 0.33 to 0.39, p range: 0.013 to 0.042), along with negative correlations with mDTI-ALPS ( r range: -0.53 to -0.48, all p < = 0.001) and gDTI-ALPS ( r range: -0.49 to -0.43, p range: 0.001 to 0.009) across the entire subjects (Fig. 5 A and 5 B). These results highlighted the different spatial pattern of glymphatic function between HCs and SCA3 patients. Relationship between aberrant glymphatic function and SCA3 symptoms Our final analysis was to understand the association between altered glymphatic function and SCA3 symptoms. After adjusting for age and sex, and controlling for FDR, significant linear relationship was found between regional glymphatic measurements and SCA3 disease duration ( β = 0.48, p = 0.001 for CSF ratio; β = 0.15 and 0.13, p = 0.044 and 0.047 for m/cbBOLD-CSF coupling; β = -0.18 and − 0.15, p = 0.038 and 0.044 for m/gDTI-ALPS; Fig. 6 ). Specifically, the CP ratio and CSF ratio were positively associated with ataxia and sleep assessments ( β range: 0.35 to 0.53, p range: 0.001 to 0.013; Fig. 6 ). Neuropsychological assessments for ataxia and sleep symptoms were mainly significantly correlated with mBOLD-CSF ( β range: 0.13 to 0.25, p range: 0.007 to 0.047) coupling strength, and mDTI-ALPS strength ( β range: -0.15 to -0.19, p range: 0.009 to 0.041) with the exception of psychiatric assessments (Fig. 6 C and 6 D), implying that the SCA3 disease severity increased with weakened glymphatic function. Further, these relationships indicated that impaired glymphatic function, as depicted by regional glymphatic measurements, could serve as predictive indicators for the clinical symptoms of SCA3. Discussion Our study comprehensively assessed glymphatic system function and its spatial distribution, finding regional glymphatic dysfunction in SCA3 patients and correlations between glymphatic function metrics and SCA3-related symptoms. We estimated CSF morphology, CSF production, subcortical and cortical glymphatic movements using CSF volume, CP volume, DTI-ALPS, and BOLD-CSF coupling, respectively. Regional glymphatic function was then delineated to ascertain spatial patterns and correlated with clinical assessments in SCA3 patients. Significant alterations in glymphatic function were observed, particularly within the midbrain, cerebellum, and middle regions, among SCA3 patients. These discernible deviations in regional glymphatic functions exhibited spatial variation, which were further associated with SCA3-related symptoms. Together, the current results suggested that the altered glymphatic functions affect pathophysiology of SCA3 in a spatially differentiated way, presumably through its effect on glymphatic clearance. We hypothesize that impaired glymphatic clearance is a critical pathological factor in the development of SCA3 symptoms. The present study showed abnormally elevated CSF and CP volumes in SCA3 patients compared to HCs. This enlargement in CSF and CP volume has been reported to be potentially linked to neuroinflammation and glymphatic impairment in neurodegenerative diseases 22 , 23 , 34 and psychosis spectrum 41 , 42 . Therefore, the impaired glymphatic clearance mechanisms in SCA3 may mitigate glymphatic dysfunction by enhancing waste clearance through increased production and transmission of functional units in the CSF and CP 43 . Furthermore, the between-group comparison results demonstrated that in contrast to HCs, SCA3 patients manifested weaker m/cb/gBOLD-CSF coupling and lower m/gDTI-ALPS. In line with the previous studies, SCA3 patients in the current study exhibiting predominant atrophic changes in the precentral gyrus, paracentral lobes, and cerebellum also witnessed the deposition of toxic proteins (e.g., tau and polyglutamine protein) 44 – 47 and neuro-damage markers 48 , 49 . The bidirectional flow within the glymphatic system may facilitate the clearance of aberrant proteins from the ISF into the ventricular or subarachnoid compartments 50 , 51 . Consequently, slowed glymphatic movement can result in waste accumulation across brain cortex and subcortex in SCA3 patients. Additionally, these findings also revealed impairment of glymphatic function within midbrain, cerebellum, and middle regions in patients with SCA3. The pathological underpinnings of SCA3 were primarily localized to the cerebellum-neostriatum-motor and association cortical circuits 52 , consistent with the distributional pattern of glymphatic disfunction. Another noteworthy finding from the current research was that, in regional glymphatic distributions we tested, the cortical and subcortical clearance showed an ascending trend from anterior to middle regions, followed by a descending trend from middle to posterior and cerebellum regions. The spatial dynamics in subcortical and cortical clearance were consistent with the anterior-posterior gradient observed in white matter microstructural diffusivity 53 and the motor-sensory dominant pattern of regional BOLD 27 , 54 , respectively. Given the consistent utilization of the same CSF signal for each subject, any spatial variation in the regional BOLD-CSF coupling can be exclusively ascribed to alterations in regional BOLD signals. This pattern of brain co-activation was concomitant with specific deactivation in subcortical regions associated with arousal regulation, notably the basal ganglia and brainstem 27 , 54 , and was strongly related to SCA3 etiology since the accumulation and aggregation of expanded polyglutamine stretches within susceptible brain nuclei may precipitate direct or indirect neurotoxic effects, ultimately culminating in neuronal loss and cerebral atrophy 45 – 47 . Furthermore, in mice, neural response in the sensory motor cortex induced by whisker stimulation was recently found to be accompanied by accelerated CSF flow in PVS 55 . This suggested that neurovascular coupling was involved in both supplying metabolites and removal of their waste products. Therefore, inadequate regional glymphatic clearance may lead to the accumulation and dissemination of neurotoxic proteins within the he midbrain, cerebellum, and middle regions, thereby exacerbating the pathogenesis of SCA3. With regard to the relationship between multiple glymphatic approaches and SCA3 symptoms, our results demonstrated significant correlations between these glymphatic indexes and SCA3-related symptoms as well as the disease severity. These findings highlighted the potential of glymphatic dysfunction as a biomarker for disease severity and progression of SCA3. Similar associations have been reported in other neurodegenerative diseases, emphasizing the pervasive impact of glymphatic dysfunction on neurological function 19 , 22 , 23 , 26 – 29 , 34 , 38 – 40 . Furthermore, the resting-state BOLD signal performed sleep dependence similar to glymphatic function, which was notably strong during drowsiness and sleep states 56 , 57 . Thus, the sleep disturbances in SCA3 patients may cause impaired glymphatic clearance, resulting in the weaker glymphatic indexes. Further investigations could extend this line of research to explore whether enhanced glymphatic clearance could improve SCA3 symptoms. Finally, some caveats need to be noted regarding the present study. Our study did not directly evaluate the accumulation of neurotoxic wastes (proteins or small molecules). Further studies adopting PET data of the relevant proteins (e.g., tau-PET images) could provide empirical evidence with the relationship between regional glymphatic function and proteins deposition, especially in the midbrain, cerebellum, and middle regions. Moreover, as subjects in the present study consisted of a cohort, we were unable to offer insights into the longitudinal changes of spatial distribution of the glymphatic function in SCA3. As such, our data warrants future follow-up studies, with larger sample sizes and in independent cohorts, to validate and extend these findings. In summary, drawing on multiple glymphatic measurements, the current study provided comprehensive and compelling evidence for the involvement of glymphatic dysfunction in SCA3 patients. Notably, SCA3 patients exhibited spatial specificity of glymphatic dysfunction in midbrain, cerebellum, and middle regions, which revealed the pathological patterns of SCA3. Furthermore, the regional glymphatic alterations were closely associated with the SCA3 symptoms. Our findings provided valuable insights into the interplay between glymphatic dysfunction, brain structural alterations, and clinical symptoms, contributing to a deeper understanding of the pathophysiology of SCA3. Declarations Ethics statement The study was conducted in accordance with the declaration of Helsinki and was approved by the ethics committee of China-Japan Friendship Hospital (Approval No. 2023-KY-299). All the participants and/or their relatives were informed about this study and provided their written informed consent. The information of all participants had been fully anonymized. Competing interests The authors declare no competing interests Supplementary material Supplementary material is available in supplementary files. Author contribution L.H.: conceptualization, methodology, validation, investigation, formal analysis, writing—original draft, writing—review and editing. MX.X: conceptualization, methodology, validation, investigation, formal analysis, writing—original draft, writing—review and editing. LW.Z.: methodology, resources, data curation, validation. F.G.: methodology, validation, writing—review and editing. LX.Z.: methodology, validation, formal analysis. AC.Y.: methodology, data curation. JX.L.: methodology, data curation. C.L.: investigation, formal analysis. F.H.: formal analysis. GL.M.: conceptualization resources, funding acquisition, project administration, writing—review and editing. Z.Y.: conceptualization resources, funding acquisition, project administration, writing—review and editing. Acknowledgements This work was supported by Macao Science and Technology Development Fund (No. 0020/2019/AMJ and 0011/2018/A1), the University of Macau (No. MYRG2020-00067-FHS, MYRG2019-00082-FHS, and MYRG2018-00081-FHS), Higher Education Fund of Macao SAR Government (No. CP-UMAC-2020-01), National Natural Science Foundation of China (No. 82271953), Guangzhou Science and Technology Planning Project (No. 202103010001), STI2030-Major Projects (No. 2022ZD0213300), Capital’s Funds for Health Improvement and Research (No. 2022-1-2031) and Beijing Municipal Science and Technology Project (No. Z211100003521009) and Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning (No. CNLZD2303). Data availability The data that support the findings of this study are available from the corresponding authors upon reasonable request. Code availability All analyses used open-source software with URL links already included in Methods. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6891437","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":475848858,"identity":"435c63c7-5701-4fbd-a2f4-d9d087e8b865","order_by":0,"name":"Zhen Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYDACCSjNDybZSNEi2UCyFoMDxGqRn9187DFvm12e8Y3sBIYPZYcZdNsb8GthnHMs3Zi3LbnY7EbuBsYZ5w4zmJ05gF8Ls0SOmTRvG3PiNqAWZt42oJYbCfi1sEnkfwNqqU/cPAOo5S8xWngkctiAWg4nbpAAamEkRouERJqZ5JxzxxNnnHm74WDPuXQegn6Rn5H8TOJNWXVif3vuxgc/yqzlzI434NcCAkw8UAbIeB48ChGA8QdRykbBKBgFo2DEAgC3VEOKf1tpcgAAAABJRU5ErkJggg==","orcid":"","institution":"Faculty of Health Sciences, University of Macau","correspondingAuthor":true,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Yuan","suffix":""},{"id":475848859,"identity":"739b6f9a-d7bc-4fcc-9b86-2f0334b4730d","order_by":1,"name":"Lin Hua","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Hua","suffix":""},{"id":475848860,"identity":"ccf5a30d-d429-4cc2-b889-45ca33e5511e","order_by":2,"name":"Manxi Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Manxi","middleName":"","lastName":"Xu","suffix":""},{"id":475848861,"identity":"780c62e1-bbe7-40a0-8c5a-556dffd95c70","order_by":3,"name":"Linwei Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Linwei","middleName":"","lastName":"Zhang","suffix":""},{"id":475848862,"identity":"c0267d85-78f8-4529-a669-fee24e412110","order_by":4,"name":"Fei Gao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Gao","suffix":""},{"id":475848863,"identity":"da64f4dc-646b-417a-8fbc-f3c25554858f","order_by":5,"name":"Xinglin Zeng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xinglin","middleName":"","lastName":"Zeng","suffix":""},{"id":475848864,"identity":"1c9beb6b-b439-497b-abe3-7aec74ef8f9e","order_by":6,"name":"Aocai Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Aocai","middleName":"","lastName":"Yang","suffix":""},{"id":475848865,"identity":"18e6cd07-9de9-4d70-90be-9409bcd92db9","order_by":7,"name":"Jixin Luan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jixin","middleName":"","lastName":"Luan","suffix":""},{"id":475848866,"identity":"e34bb5f9-21b9-4f66-92c1-081dd20780a4","order_by":8,"name":"Chen Liu","email":"","orcid":"https://orcid.org/0000-0001-5149-2496","institution":"Southwest Hospital, Army Medical University (Third Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Liu","suffix":""},{"id":475848867,"identity":"e42d2a9b-baf0-47bd-85a3-e386eb5ef1a6","order_by":9,"name":"Fang Han","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Han","suffix":""},{"id":475848868,"identity":"35fcdc60-a35a-4121-a11d-e964805cd40d","order_by":10,"name":"Guolin Ma","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Guolin","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-06-14 03:05:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6891437/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6891437/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85649023,"identity":"c143956d-c639-4230-b11e-2a9f74958c78","added_by":"auto","created_at":"2025-06-30 08:54:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":112552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of participant recruitment.\u003c/strong\u003e SARA = Scale for the Assessment and Rating of Ataxia; ICARS = the International Cooperative Ataxia Rating Scale; SAS = Self-Rating Anxiety Scale; SDS = Self-Rating Depression Scale; SRSS = Self-Rating Scale of Sleep; MSA = multiple system atrophy; ARCA = autosomal recessive cerebellar ataxia; SCA1 = spinocerebellar ataxia type 1; SCA2 = spinocerebellar ataxia type 2; SCA3 = spinocerebellar ataxia type 3; SCA7 = spinocerebellar ataxia type 7.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891437/v1/7c737c2444ce72ec22b31177.jpg"},{"id":85649037,"identity":"88598e3d-0c9d-47da-bb77-b3e927855ac8","added_by":"auto","created_at":"2025-06-30 08:54:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103261,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWhole procedures of characterizing multiple glymphatic measurements.\u003c/strong\u003e (A) Choroid plexus within the lateral ventricles and cerebrospinal fluid were automatically segmented and manually corrected in each subject’s structural MRI. (B) The global BOLD signal was extracted from gray matter regions (including the cerebellum), and then further separated into anterior BOLD, middle BOLD, posterior BOLD, and cerebellum BOLD regions. The CSF signal was extracted from CSF regions at the bottom slice of the rs-fMRI image and referenced to the structural MRI image. CSF inflow effect was detected to identify the boundaries of CSF regions at the bright areas of the rs-fMRI image. (C) Representative time series of normalized cortical gray-matter BOLD signal and CSF signal changes were showed by corresponding amplitude fluctuations. (D) DTI images were preprocessed and calculated to generate the FA map. Then, the FA map was registered to the JHU atlas and segmented into anterior DTI-ALPS, middle DTI-ALPS, and posterior DTI-ALPS regions according to the atlas labels (anterior, superior, posterior corona radiata, and superior longitudinal fasciculus). The glymphatic gradient was calculated by the DTI-ALPS from 46 pairs of 3 × 3 × 3 mm\u003csup\u003e3\u003c/sup\u003e ROIs with 1 mm intervals from anterior to posterior in the projection and association fibers.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891437/v1/63ddaa71fb7113207d75f8ec.jpg"},{"id":85649061,"identity":"887dd7e3-34e2-49fb-9093-b683d642464e","added_by":"auto","created_at":"2025-06-30 08:54:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47657,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe BOLD-CSF coupling strength is differed between HC and SCA3 groups, as well as across brain regions.\u003c/strong\u003e (A) The mean gBLOD-CSF cross-correlation function averaged within HCs (\u003cem\u003eN\u003c/em\u003e = 35) and SCA3 patients (\u003cem\u003eN\u003c/em\u003e = 27). The vertical dashed line marks the +3s time lag (\u003cem\u003er\u003c/em\u003e = -0.29, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 for HCs’ gBOLD-CSF coupling; \u003cem\u003er\u003c/em\u003e = -0.22, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 for SCA3 patients’ gBOLD-CSF coupling; 1000 times permutation test; the negative peak of the mean cross-correlation). (B) The mean a/m/p/cbBLOD-CSF cross-correlation function averaged across all subjects (\u003cem\u003eN\u003c/em\u003e = 62). The vertical dashed line marks the same +3s time lag as gBOLD-CSF coupling (\u003cem\u003er\u003c/em\u003e = -0.25, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 for aBOLD-CSF coupling; \u003cem\u003er\u003c/em\u003e = -0.28, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 for mBOLD-CSF coupling; \u003cem\u003er\u003c/em\u003e= -0.26, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 for pBOLD-CSF coupling; \u003cem\u003er\u003c/em\u003e = -0.21, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001 for cbBOLD-CSF coupling; 1000 times permutation test). (C) and (D) Mean cross-correlation between the zero-threshold negative derivative of a/m/p/cb/gBOLD and CSF signals showed the strongest correlation at 3s (vertical dashed line). Shaded areas are 95% interval of the mean correlation coefficient across subjects.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891437/v1/0daa2c82138da32f0dec9602.jpg"},{"id":85648999,"identity":"24ba043f-94f9-40e9-8c0a-71ddfa17f9bb","added_by":"auto","created_at":"2025-06-30 08:54:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55418,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlymphatic gradient and the relationship between different regional glymphatic function.\u003c/strong\u003e (A) The dissociating gradient of DTI-ALPS from anterior to posterior regions between HCs (\u003cem\u003eN\u003c/em\u003e = 35) and SCA3 patients (\u003cem\u003eN\u003c/em\u003e = 28). Shaded areas are 95% interval of the glymphatic gradient within HCs and SCA3 patients. Blue and magenta lines denote HCs and SCA3 patients. (B) and (C) The different CP and CSF ratio of ICV between HCs (\u003cem\u003eN\u003c/em\u003e = 36) and SCA3 patients (\u003cem\u003eN\u003c/em\u003e = 34). (D) The distribution of BOLD-CSF coupling from anterior to cerebellum regions within HCs (\u003cem\u003eN\u003c/em\u003e = 35) and SCA3 patients (\u003cem\u003eN\u003c/em\u003e = 27). Blue and magenta bars denoteHCs and SCA3 patients. Error bars represent the SD. (E) The distribution of DTI-ALPS from anterior to posterior regions within HCs (\u003cem\u003eN\u003c/em\u003e = 35) and SCA3 patients (\u003cem\u003eN\u003c/em\u003e = 28). Error bars represent the 1.5*SD. \u003cem\u003eP\u003c/em\u003e values in heatmaps were adjusted for age and sex. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891437/v1/4cd4a4cf2c7b8e3cc5e1be00.jpg"},{"id":85649003,"identity":"558ae7cf-44f4-474d-a9e3-b2b775332309","added_by":"auto","created_at":"2025-06-30 08:54:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":39904,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship among multiple glymphatic measurements\u003c/strong\u003e. (A) All subjects. (B) Within HCs and SCA3 patients, separately. \u003cem\u003eP\u003c/em\u003e values in heatmaps were adjusted for age and sex. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891437/v1/3ac6513e87475ffba7e21a22.jpg"},{"id":85649032,"identity":"be1983a2-acbf-42f5-a738-b7dc51608442","added_by":"auto","created_at":"2025-06-30 08:54:46","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":37543,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegression results between different regional glymphatic indexes and SCA3 symptoms.\u003c/strong\u003e (A-D) The CP ratio, CSF ratio, a/m/p/cb/gBOLD-CSF coupling, and a/m/p/gDTI-ALPS normalized by z score and adjusted for age and sex were correlated with different clinical assessments. The X axis denotes the \u003cem\u003eβ\u003c/em\u003e value of regression analysis. \u003cem\u003eP\u003c/em\u003e values in forest plots were adjusted for age and sex. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891437/v1/ce5a276afd8ea7b7ed5e8745.jpg"},{"id":91453513,"identity":"a99092f0-5e76-4f17-b0ac-c94d49040bce","added_by":"auto","created_at":"2025-09-16 15:50:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1680238,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6891437/v1/1741d21b-b413-4886-a681-0eb39adeb637.pdf"},{"id":85649044,"identity":"dcd8f864-f72b-485c-a730-39a24e99ddc9","added_by":"auto","created_at":"2025-06-30 08:54:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":169295,"visible":true,"origin":"","legend":"Supporting Information","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6891437/v1/d19eeb45589746533476fec8.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Regional Glymphatic Dysfunction in Patients with Spinocerebellar Ataxia Type 3","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSpinocerebellar ataxia type 3 (SCA3), also known as Machado-Joseph disease (MJD), is the most prevalent subtype of spinocerebellar ataxia and an autosomal dominant neurodegenerative disorder. Patients with SCA3 typically present a spectrum of motor and non-motor symptoms, including pyramidal signs, extrapyramidal features, cognitive decline, and REM sleep behavior disorder (RBD)\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The disease's pathogenesis is primarily driven by the expansion of CAG repeat sequences within the ATXN3 gene, which causes protein misfolding and the accumulation of abnormal polyglutamine proteins\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This accumulation disrupts cellular function and can ultimately lead to neuronal death\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. An imbalance between the production and clearance of these abnormal proteins is crucial to SCA3 pathogenesis; however, the mechanisms regulating pathogenic protein clearance remain poorly understood.\u003c/p\u003e \u003cp\u003eThe glymphatic system is a critical \u0026ldquo;waste clearance\u0026rdquo; mechanism in the central nervous system\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, facilitating the exchange of cerebrospinal fluid (CSF) and interstitial fluid (ISF) to efficiently remove metabolic waste and abnormal proteins\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. This system is vital in the pathology of various neurodegenerative diseases, including Alzheimer\u0026rsquo;s disease (AD)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and Parkinson\u0026rsquo;s disease (PD)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, its functionality in SCA3 remains insufficiently studied. Importantly, glymphatic activity significantly increases during sleep, a crucial period for effective waste clearance\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. SCA3 patients frequently experience sleep disturbances, which can impair glymphatic function and diminish clearance efficiency\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This impairment may exacerbate the accumulation of pathological proteins and accelerate disease progression. Consequently, developing a neuroimaging biomarker to detect changes in the glymphatic system of SCA3 patients is essential for monitoring disease advancement.\u003c/p\u003e \u003cp\u003eGlymphatic magnetic resonance imaging (MRI) typically requires intrathecal injection of gadolinium-based contrast agents to visualize the glymphatic system\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, the widespread use of gadolinium poses a risk of severe neurotoxic complications, highlighting the need for non-invasive assessment methods. Recently, a technique known as perivascular space diffusion tensor imaging analysis (DTI-ALPS) has been introduced to evaluate glymphatic function without the need for contrast agents\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, and it has been successfully applied in several neurodegenerative diseases\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Additionally, the choroid plexus (CP), an integral part of the lymphatic system, serves as a surrogate marker for CSF production \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The coupling of low-frequency (\u0026lt;\u0026thinsp;0.1 Hz) resting-state fMRI (rs-fMRI) BOLD signals with CSF dynamics in the ventricles further facilitates metabolite clearance\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. By integrating various non-invasive neuroimaging techniques, we can comprehensively assess the clearance function of the brain's glymphatic system\u003csup\u003e\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study aims to utilize multimodal imaging data\u0026mdash;including structural MRI, resting-state fMRI, and diffusion tensor imaging\u0026mdash;to evaluate glymphatic system dysfunction in patients with SCA3. Specific analyses will include changes in the morphology of the choroid plexus (CP) and CSF, the coupling between BOLD signals and CSF flow in the ventricles, and the DTI-ALPS index. These analyses will reveal functional impairments in the glymphatic system of SCA3 patients and explore their relationship with neuropsychological assessments. This research will provide new insights into the complex pathological mechanisms of SCA3 and establish a theoretical foundation for future treatment strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eFrom June 2021 to April 2024, a prospective cohort of 120 subjects were categorized into HCs (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50) and SCA patients (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;70). The SCA patients were recruited from the outpatient clinic of the China-Japan Friendship Hospital while the HCs from the local communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The inclusion criteria of SCA3 patients were as follow: (1) diagnosed as SCA3 by expert neurologists according to the family history, neurological manifestations, genetic and molecular tests, and routine brain MRI findings; (2) at least complete T1-weighted structural MRI scanning and one ataxia assessment; (3) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (4) right-handed. The exclusion criteria were: (1) challenging to cooperate during MRI examination, and the image quality is too poor for image analysis; (2) a history of other brains organic and metabolic diseases; (3) pregnant and lactating women; and (4) other MRI contraindications. 14 HCs and 17 SCA patients were excluded for further analysis due to poor image quality (see Supporting Information for Image Quality Control). Genetic testing was then conducted to exclude those diagnosed with Multiple System Atrophy (MSA), Autosomal Recessive Cerebellar Ataxia (ARCA), and other types of SCA (SCA1, SCA2, SCA6, and SCA7). 34 symptomatic patients with genetically confirmed SCA3 were included in the analysis. These SCA3 patients were further screened by ataxia assessments including the Scale for the Assessment and Rating of Ataxia (SARA) and the International Cooperative Ataxia Rating Scale (ICARS)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In addition to ataxia assessments, SCA3 patients were performed extraordinary psychiatric assessments using Self-Rating Anxiety Scale (SAS)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and Self-Rating Depression Scale (SDS)\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Additionally, to evaluate the SCA3 patients\u0026rsquo; sleep quality, Self-Rating Scale of Sleep (SRSS)\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e was also involved. The HCs were matched for age, sex, and educational level with SCA3 patients, free of any neuropsychiatric or neurodegenerative diseases. Finally, after controlling clinical assessments, genetic and molecular tests, and the MRI imaging quality, 36 HCs and 34 SCA3 patients with structural MRI, 35 HCs and 27 SCA3 patients with rs-fMRI data and 35 HCs and 28 SCA3 patients with DTI data were selected for further CP and CSF volumes, BOLD-CSF coupling and DTI-ALPS analysis, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImage acquisition and preprocessing\u003c/h3\u003e\n\u003cp\u003eAll MRI data (structural MRI, rs-fMRI, and DTI data) was collected in the 3.0 Tesla MR scanner (General Electric, Discovery MR750, Milwaukee, WI, United States). Each imaging session included a three-dimensional fast spoiled gradient-echo sequence (3D FSPGR) with the following parameters: repetition time (TR)\u0026thinsp;=\u0026thinsp;6.7 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;2.9 ms, flip angle\u0026thinsp;=\u0026thinsp;12\u0026deg;, slice thickness\u0026thinsp;=\u0026thinsp;1 mm, field of view (FOV)\u0026thinsp;=\u0026thinsp;256 \u0026times; 256 mm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, matrix\u0026thinsp;=\u0026thinsp;256 \u0026times; 256, voxel size\u0026thinsp;=\u0026thinsp;1 \u0026times; 1 \u0026times; 1 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. For rs-fMRI acquisition, 240 fMRI volumes were captured with an echo-planar image (EPI) sequence (TR\u0026thinsp;=\u0026thinsp;2000 ms, TE\u0026thinsp;=\u0026thinsp;30 ms, filp angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, slice thickness\u0026thinsp;=\u0026thinsp;3.5 mm, FOV\u0026thinsp;=\u0026thinsp;224 \u0026times; 224 mm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, matrix\u0026thinsp;=\u0026thinsp;64 \u0026times; 64, voxel size\u0026thinsp;=\u0026thinsp;3.5 \u0026times; 3.5 \u0026times; 4.2 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e). The DTI images with 8 b-value of 0 s/mm\u003csup\u003e2\u003c/sup\u003e and 64 b-value of 1000 s/mm\u003csup\u003e2\u003c/sup\u003e were acquired using a diffusion weighted spin-echo EPI (TR\u0026thinsp;=\u0026thinsp;8028 ms, TE\u0026thinsp;=\u0026thinsp;81.8 ms, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, slice thickness\u0026thinsp;=\u0026thinsp;2 mm, FOV\u0026thinsp;=\u0026thinsp;240 \u0026times; 240 mm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, matrix\u0026thinsp;=\u0026thinsp;120 \u0026times;120, voxel size\u0026thinsp;=\u0026thinsp;2 \u0026times; 2 \u0026times; 2 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e). During the MRI scanning session, foam paddings were given inside the head coil to restrict potential head motions. Additional routine MR sequences, including axial T2-weighted imaging (T2WI), T2-FLAIR, and diffusion-weighted imaging (DWI), were performed to identify brain abnormalities in SCA3 patients.\u003c/p\u003e \u003cp\u003eThe rs-fMRI data was preprocessed using the 1000 Functional Connectomes Project script (version 1.1-beta; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nitrc.org/frs/?group_id=296\u003c/span\u003e\u003cspan address=\"https://www.nitrc.org/frs/?group_id=296\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which underwent a pipeline similar to a previous study examining neurofluid coupling in neurodegeneration diseases\u003csup\u003e\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Raw rs-fMRI images for each subject underwent preprocessing steps including slice timing, motion correction, skull stripping, spatial smoothing with a 4mm full-width half-maximum (FWHM) kernel, 0.01\u0026ndash;0.1 Hz bandpass filtering, as well as linear and quadratic temporal trend removal. Subsequently, rs-fMRI images of the subjects were co-registered to their high-resolution T1-weighted structural MRI images and then to the 152-brain Montreal Neurological Institute (MNI-152) space. As our analysis focused on the coupling between CSF signal and global BOLD (gBOLD) signal, regression of global signal and CSF signal was omitted by our preprocessing pipeline. Additionally, under the same rationale regression of motion parameters was also skipped as it may associate with gBOLD signal\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe DTI data was preprocessed using the FSL package (version 6.0.5; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki\u003c/span\u003e\u003cspan address=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The general procedures for DTI data preprocessing included skull stripping, the correction for head motion, eddy current-induced distortions, EPI-induced susceptibility distortions, and bias field. The fractional anisotropy (FA) map of each subject was then registered to the FA map of the Johns Hopkins University atlas (JHU atlas) template\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, and the transformation matrix obtained from the previous step of co-registration was applied to other diffusion metric maps to obtain all DTI-based maps in the space of the JHU atlas (spatial resolution, 1 \u0026times; 1 \u0026times; 1 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCP and CSF segmentation\u003c/h3\u003e\n\u003cp\u003eThe CP has been reported as the principal locus of CSF secretion, thus being considered as a potential imaging marker for indirectly evaluating CSF production and toxic clearance. Using the FreeSurfer cortical reconstruction pipeline (version 6.0.0; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://surfer.nmr.mgh.harvard.edu/fswiki\u003c/span\u003e\u003cspan address=\"https://surfer.nmr.mgh.harvard.edu/fswiki\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), segmentation of the CP within the lateral ventricles was automatically accomplished from the T1-weighted structural MRI image (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Manual scrutiny and correction of CP segmentation was conducted by two neuroimaging researchers, ensuring its accuracy and reliability. These revised CP segmentations underwent further independent quality assessment and finalization by one neuroradiologist. Total intracranial volume (ICV), cortical gray matter (GM) volume, CSF volume, and CP volume were extracted from the whole brain segmentation. To reduce inter-subject variability, CP and CSF volumes were separately normalized as the ratio of CP and CSF volumes to ICV\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eQuantification of the coupling between BOLD signal and CSF inflow\u003c/h3\u003e\n\u003cp\u003eThe gBOLD signals were obtained from the cortical GM regions of cerebrum and cerebellum, delineated by the automated anatomical labeling 2 (AAL2) atlas\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e from preprocessed functional images in individual spaces (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). To assess the regional distribution of glymphatic function, the AAL2 atlas was divided into the anterior, middle, and posterior parts based on the atlas labels of the frontal, temporoparietal, occipital, and cerebellum regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). To ensure precise alignment between the cortical GM masks and the functional space, the cortical GM masks were transformed from the MNI-152 space to individual functional space for each subject using the inversed concatenated transformation matrix from the previous co-registration process (functional-structural-standard). Transformed cortical masks were visually inspected to assure accurate spatial registration with the preprocessed functional images. The CSF mask was placed in the bottom slices of the rs-fMRI image\u003csup\u003e\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These bottom slices consistently encompassed the cerebellum of all subjects as confirmed by visual inspection, capturing the CSF through-slice inflow effect as previously described.\u003c/p\u003e \u003cp\u003eTo quantify gBOLD-CSF coupling, the cross-correlation function was computed between the gBOLD signals and the CSF signals across different time lags ranging from \u0026minus;\u0026thinsp;9s to 9s for each subject (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The negative correlation coefficients peaked at the lag of +\u0026thinsp;3s. This negative peak signified the point at which the gBOLD and CSF signals exhibited their strongest inverse relationship, capturing the interplay between hemodynamic signal and CSF dynamics. It was therefore used to quantify the strength of the gBOLD-CSF coupling for each subject\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Moreover, we also calculated the cross-correlation function between the negative derivative of the BOLD signal and the CSF signal to ensure that the CSF signal matched the negative derivative of the BOLD oscillation when setting the negative value to zero\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Finally, the cross-correlation function between the CSF signal and the anterior, middle, posterior, cerebellum BOLD signals was computed to obtain the regional BOLD-CSF coupling (aBOLD-CSF, mBOLD-CSF, pBOLD-CSF, and cbBOLD-CSF couplings).\u003c/p\u003e\n\u003ch3\u003eCalculation of the DTI-ALPS index\u003c/h3\u003e\n\u003cp\u003eThe DTI-ALPS index, evaluating the water diffusivity along the PVS at the level of the lateral ventricle, is used to reflect the activity of the glymphatic system\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The direction of PVS at the lateral ventricle level is primarily along the x-axis, perpendicular to the ventricle wall. Furthermore, the projection fibers that pass along the lateral ventricular wall in the z-axis are adjacent to the association fibers that go through at the more lateral area in the y-axis. Therefore, the major distinction in water molecule behavior between diffusivity along the x-axis in both fibers (D\u003csub\u003exx,proj\u003c/sub\u003e and D\u003csub\u003exx,assoc\u003c/sub\u003e) and diffusivity perpendicular to them (D\u003csub\u003eyy,proj\u003c/sub\u003e and D\u003csub\u003ezz,assoc\u003c/sub\u003e) relates to the diffusivity of the PVS. The regions of interests (ROIs) in the current study were defined as 3-mm-diameter spherical areas within the projection (including the anterior, superior, and posterior corona radiata) and the association fiber (superior longitudinal fasciculus), localized at the level of the lateral ventricle (MNI coordinates z\u0026thinsp;=\u0026thinsp;26) based on JHU atlas labels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Mean diffusion values of these ROIs were extracted to compute the global DTI-ALPS. Specifically, the DTI-ALPS index was calculated as follows\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:DTI-ALPS\\:index\\:=\\frac{mean({D}_{xx,proj},{D}_{xx,assoc})}{mean({D}_{yy,proj},{D}_{zz,assoc})}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSubsequently, these ROIs were then subdivided into the anterior, middle, and posterior regions to obtain the anterior DTI-ALPS (aDTI-ALPS), middle DTI-ALPS (mDTI-ALPS), and posterior DTI-ALPS (pDTI-ALPS), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Finally, DTI-ALPS gradient was calculated from the ROIs with 3 \u0026times; 3 \u0026times; 3 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e at 1 mm intervals to comprehensively understanding the glymphatic activity from anterior to posterior (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). A total of 46 pairs of ROIs were generated, and each pair of DTI-ALPS was calculated.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between regional glymphatic measurements and neuropsychological assessments\u003c/h2\u003e \u003cp\u003eThe strength of gBOLD-CSF coupling was identified at the lag of +\u0026thinsp;3s (the negative peak of the cross-correlation function). Confounding effects of age and sex were estimated by the linear mixed effect model and regressed out from further relationship analyses. Next, partial correlation analyses were conducted to evaluate the association within multiple regional glymphatic measurements in the whole cohort and in the two groups separately. Finally, the relationship between neuropsychological assessments, performed in SCA3 patients to assess ataxia, psychiatry as well as sleep symptoms, and CP ratio, CSF ratio, a/m/p/cb/gBOLD-CSF coupling strength and a/m/p/gDTI-ALPS strength were assessed by regression analyses after z-score normalization and adjusting for age and sex.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo ascertain the statistical significance of the a/m/p/cb/gBOLD-CSF correlations, permutation method was employed to create a null distribution for the a/m/p/cb/gBOLD-CSF correlations, respectively. Specifically, a/m/p/cb/gBOLD signals and CSF signals from different subject were randomly paired, and cross-correlation coefficients were recalculated. This process was repeated for 1000 times, generating a null distribution for the mean a/m/p/cb/gBOLD-CSF cross-correlation function. The \u003cem\u003ep\u003c/em\u003e value was estimated by calculating the percentage of the correlation value of permutation data higher than the correlation value of real data at different lags. Furthermore, between-group comparisons were performed by two-sample t-test for continuous variables and χ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e test for categorical variables. Multiple comparisons were corrected by the false discovery rate (FDR) approach. Statistical significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and clinical characteristics\u003c/h2\u003e \u003cp\u003eA total of 70 subjects (44.21\u0026thinsp;\u0026plusmn;\u0026thinsp;12.64 years; 28 females) including 34 SCA3 patients (42.62\u0026thinsp;\u0026plusmn;\u0026thinsp;12.01 years; 10 females) was entered in the formal analyses (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). 36 HCs (45.72\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2 years; 18 females) were age- (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(68)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.308), sex- (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e(1)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.079) and education- (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(64)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.171) matched with SCA3 patients. No significant differences were found in ICV (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(68)\u003c/sub\u003e = -0.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.572) and GMV (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(68)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.35, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.183) between HCs and SCA3 patients. The mean distributions of disease durations and CAG repeat numbers in SCA3 patients were 9.04\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5 and 70.39\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67, respectively. Moreover, SCA3 patients underwent multiple neuropsychological assessments, including ataxia assessments of SARA (17.12\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2) and ICARS (44.74\u0026thinsp;\u0026plusmn;\u0026thinsp;21.45), psychiatric assessments of SAS standard score (44.87\u0026thinsp;\u0026plusmn;\u0026thinsp;12.45) and SAS standard score (47.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.96), and sleep assessment of SRSS (26.32\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6).\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\u003eDemographic and clinical characteristics of HCs and SCA3 patients.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCA3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT/χ\u0026sup2; values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.72\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.62\u0026thinsp;\u0026plusmn;\u0026thinsp;12.01\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (M/F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18/18\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24/10\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.09\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.11\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.61\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICV (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1490.14\u0026thinsp;\u0026plusmn;\u0026thinsp;180.86\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1512.83\u0026thinsp;\u0026plusmn;\u0026thinsp;150.66\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.57\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGMV (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e493.3\u0026thinsp;\u0026plusmn;\u0026thinsp;61.91\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e475.91\u0026thinsp;\u0026plusmn;\u0026thinsp;44.24\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.04\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.39\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSARA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.12\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICARS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.74\u0026thinsp;\u0026plusmn;\u0026thinsp;21.45\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAS standard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.87\u0026thinsp;\u0026plusmn;\u0026thinsp;12.45\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDS standard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.96\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.32\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Data is presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD). a: Independent-samples t test, b: Chi-square test. HCs\u0026thinsp;=\u0026thinsp;Healthy controls, SCA3\u0026thinsp;=\u0026thinsp;Spinocerebellar ataxia type 3, M\u0026thinsp;=\u0026thinsp;Male, F\u0026thinsp;=\u0026thinsp;Female, ICV\u0026thinsp;=\u0026thinsp;Intracranial volume, GMV\u0026thinsp;=\u0026thinsp;Cortical gray matter volume. CAG\u0026thinsp;=\u0026thinsp;Cytosine-adenine‐guanine repeat number, SARA\u0026thinsp;=\u0026thinsp;Scale for the Assessment and Rating of Ataxia, ICARS\u0026thinsp;=\u0026thinsp;International Cooperative Ataxia Rating Scale, SAS standard\u0026thinsp;=\u0026thinsp;Standard score of Self-Rating Anxiety Scale, SDS standard\u0026thinsp;=\u0026thinsp;Standard score of Self-Rating Depression Scale, SRSS\u0026thinsp;=\u0026thinsp;Self-Rating Scale of Sleep.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe gBOLD-CSF coupling, CP volume, and CSF volume alteration in SCA3\u003c/h2\u003e \u003cp\u003eThe gBOLD-CSF coupling was initially quantified by cross-correlation coefficient between gBOLD and CSF signals and averaged within HCs and SCA3 patients. Then, a/m/p/cbBOLD-CSF was obtained from the anterior, middle, posterior, and cerebellum regions of the BOLD signal coupled with the CSF signal, respectively. Consistent with previous report\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, the strength of gBOLD-CSF coupling for both groups (\u003cem\u003er\u003c/em\u003e = -0.29, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for HCs; \u003cem\u003er\u003c/em\u003e = -0.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for SCA3 patients; 1000 times permutation test) and a/m/p/cbBOLD-CSF coupling for the whole samples (\u003cem\u003er\u003c/em\u003e = -0.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for aBOLD-CSF coupling; \u003cem\u003er\u003c/em\u003e = -0.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for mBOLD-CSF coupling; \u003cem\u003er\u003c/em\u003e = -0.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for pBOLD-CSF coupling; \u003cem\u003er\u003c/em\u003e = -0.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for cbBOLD-CSF coupling; 1000 times permutation test) was identified with the negative peak of cross-correlation function at the lag of +\u0026thinsp;3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). At the individual level, the coupling strength did not correlate with head motion as quantified by mean frame-wise displacement (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.356; Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The peak of coupling strength and time lag was further confirmed by calculating the cross-correlation function between the negative derivative of the gBOLD signal and the CSF signal within HCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) and SCA3 patients or between the negative derivative of the a/m/p/cbBOLD signal and the CSF signal across the entire sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Therefore, for further comparisons between two groups, the cross-correlation strength at +\u0026thinsp;3s for each subject was used as a surrogate marker for glymphatic function.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe gBOLD-CSF coupling strength of HCs was significantly stronger than the coupling of SCA3 patients (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(60)\u003c/sub\u003e = -2.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting that glymphatic function deficiency may be involved in SCA3 patients. Furthermore, Between-group comparisons of CP (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) and CSF (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) volumes showed that SCA3 patients had higher CP (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(68)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) and CSF (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(68)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) volumes than HCs, especially after controlling the inter-subject variability (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(68)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006 for CP ratio and \u003cem\u003et\u003c/em\u003e\u003csub\u003e(68)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for CSF ratio; 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\u003eComparisons of multiple glymphatic measurements between HCs and SCA3 patients.\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlymphatic measurements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCA3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHCs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e values\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\u003eCSF volume\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF (ratio of ICV x 10\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCP volume\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCP (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCP (ratio of ICV x 10\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBOLD-CSF coupling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaBOLD-CSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emBOLD-CSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epBOLD-CSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecbBOLD-CSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egBOLD-CSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDTI-ALPS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaDTI-ALPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emDTI-ALPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epDTI-ALPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egDTI-ALPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Data is presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. HCs\u0026thinsp;=\u0026thinsp;Healthy controls, SCA3\u0026thinsp;=\u0026thinsp;Spinocerebellar ataxia type 3, ICV\u0026thinsp;=\u0026thinsp;Intracranial volume, CSF\u0026thinsp;=\u0026thinsp;Cerebrospinal fluid, CP\u0026thinsp;=\u0026thinsp;Choroid plexus, a/m/p/cb/gBOLD-CSF coupling\u0026thinsp;=\u0026thinsp;The coupling between anterior/middle/posterior/cerebellum/global cortical gray matter of blood-oxygen-level-dependent signals and cerebrospinal fluid signals; a/m/p/gDTI-ALPS\u0026thinsp;=\u0026thinsp;Anterior/middle/posterior/global diffusion tensor image analysis along the perivascular space.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGroup comparisons of regional glymphatic measurements between HCs and SCA3\u003c/h2\u003e \u003cp\u003eTo further confirm the regional glymphatic impairment in SCA3, multiple regional glymphatic differences were calculated between HCs and SCA3 patients. When examining the regional DTI-ALPS between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), mDTI-ALPS (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(61)\u003c/sub\u003e = -3.1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and gDTI-ALPS (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(61)\u003c/sub\u003e = -3.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) were statistically weaker in SCA3 patients than that in HCs. No significant difference was found in aDTI-ALPS (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(61)\u003c/sub\u003e = -2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05) and pDTI-ALPS (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(61)\u003c/sub\u003e = -1.41, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.165) between the two groups. This regional DTI-ALPS pattern was consistent with that of the regional BOLD-CSF coupling, which was also detected with significant differences in mBOLD-CSF coupling (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(60)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), cbBOLD-CSF coupling (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(60)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) and gBOLD-CSF coupling (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(60)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041), but not in aBOLD-CSF (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(60)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.128) coupling and pBOLD-CSF coupling (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(60)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.41, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.165; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings demonstrated that the glymphatic function in the midbrain, cerebellum, and middle regions was severely impaired in SCA3 patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThe spatial variation and correlation of regional glymphatic measurements\u003c/h2\u003e \u003cp\u003eWe then investigated how DTI-ALPS and BOLD-CSF coupling as indexes for glymphatic function varied spatially across cortical regions and fibers in HCs and SCA3 patients. In line with a prior report, DTI-ALPS gradients in both groups demonstrated an ascending trend from anterior to middle regions, followed by a decline from middle to posterior regions\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Significant differences were mainly found in the anterior (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(61)\u003c/sub\u003e range: 2.12 to 2.27, \u003cem\u003ep\u003c/em\u003e range: 0.027 to 0.038) and middle regions (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(61)\u003c/sub\u003e range: 2.16 to 3.04, \u003cem\u003ep\u003c/em\u003e range: 0.004 to 0.035) between HCs and SCA3 patients.\u003c/p\u003e \u003cp\u003eIn both groups, the mDTI-ALPS was stronger than the pDTI-ALPS (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(68)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for HCs; \u003cem\u003et\u003c/em\u003e\u003csub\u003e(54)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021 for SCA3 patients; FDR correction), while the mDTI-ALPS surpassed the aDTI-ALPS only in HCs (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(68)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; FDR correction; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Moreover, the mBOLD-CSF coupling was stronger than the cbBOLD-CSF coupling in the two groups (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(68)\u003c/sub\u003e = -2.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038 for HCs; \u003cem\u003et\u003c/em\u003e\u003csub\u003e(52)\u003c/sub\u003e = -2.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029 for SCA3 patients; FDR correction), with the mBOLD-CSF coupling being stronger than the aBOLD-CSF coupling exclusively in HCs (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(68)\u003c/sub\u003e = -2.1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039; FDR correction), and the pBOLD-CSF coupling being stronger than cbBOLD-CSF coupling was observed solely in SCA3 patients (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(52)\u003c/sub\u003e = -2.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029 for SCA3 patients; FDR correction; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Furthermore, statistically significant positive correlations were primarily showed between mBOLD-CSF coupling, cbBOLD-CSF coupling, and gBOLD-CSF coupling with CP volume (\u003cem\u003er\u003c/em\u003e range: 0.33 to 0.39, \u003cem\u003ep\u003c/em\u003e range: 0.013 to 0.042), along with negative correlations with mDTI-ALPS (\u003cem\u003er\u003c/em\u003e range: -0.53 to -0.48, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.001) and gDTI-ALPS (\u003cem\u003er\u003c/em\u003e range: -0.49 to -0.43, \u003cem\u003ep\u003c/em\u003e range: 0.001 to 0.009) across the entire subjects (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). These results highlighted the different spatial pattern of glymphatic function between HCs and SCA3 patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between aberrant glymphatic function and SCA3 symptoms\u003c/h2\u003e \u003cp\u003eOur final analysis was to understand the association between altered glymphatic function and SCA3 symptoms. After adjusting for age and sex, and controlling for FDR, significant linear relationship was found between regional glymphatic measurements and SCA3 disease duration (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 for CSF ratio; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15 and 0.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044 and 0.047 for m/cbBOLD-CSF coupling; \u003cem\u003eβ\u003c/em\u003e = -0.18 and \u0026minus;\u0026thinsp;0.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038 and 0.044 for m/gDTI-ALPS; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Specifically, the CP ratio and CSF ratio were positively associated with ataxia and sleep assessments (\u003cem\u003eβ\u003c/em\u003e range: 0.35 to 0.53, \u003cem\u003ep\u003c/em\u003e range: 0.001 to 0.013; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Neuropsychological assessments for ataxia and sleep symptoms were mainly significantly correlated with mBOLD-CSF (\u003cem\u003eβ\u003c/em\u003e range: 0.13 to 0.25, \u003cem\u003ep\u003c/em\u003e range: 0.007 to 0.047) coupling strength, and mDTI-ALPS strength (\u003cem\u003eβ\u003c/em\u003e range: -0.15 to -0.19, \u003cem\u003ep\u003c/em\u003e range: 0.009 to 0.041) with the exception of psychiatric assessments (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), implying that the SCA3 disease severity increased with weakened glymphatic function. Further, these relationships indicated that impaired glymphatic function, as depicted by regional glymphatic measurements, could serve as predictive indicators for the clinical symptoms of SCA3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study comprehensively assessed glymphatic system function and its spatial distribution, finding regional glymphatic dysfunction in SCA3 patients and correlations between glymphatic function metrics and SCA3-related symptoms. We estimated CSF morphology, CSF production, subcortical and cortical glymphatic movements using CSF volume, CP volume, DTI-ALPS, and BOLD-CSF coupling, respectively. Regional glymphatic function was then delineated to ascertain spatial patterns and correlated with clinical assessments in SCA3 patients. Significant alterations in glymphatic function were observed, particularly within the midbrain, cerebellum, and middle regions, among SCA3 patients. These discernible deviations in regional glymphatic functions exhibited spatial variation, which were further associated with SCA3-related symptoms. Together, the current results suggested that the altered glymphatic functions affect pathophysiology of SCA3 in a spatially differentiated way, presumably through its effect on glymphatic clearance. We hypothesize that impaired glymphatic clearance is a critical pathological factor in the development of SCA3 symptoms.\u003c/p\u003e \u003cp\u003eThe present study showed abnormally elevated CSF and CP volumes in SCA3 patients compared to HCs. This enlargement in CSF and CP volume has been reported to be potentially linked to neuroinflammation and glymphatic impairment in neurodegenerative diseases\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and psychosis spectrum\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Therefore, the impaired glymphatic clearance mechanisms in SCA3 may mitigate glymphatic dysfunction by enhancing waste clearance through increased production and transmission of functional units in the CSF and CP\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Furthermore, the between-group comparison results demonstrated that in contrast to HCs, SCA3 patients manifested weaker m/cb/gBOLD-CSF coupling and lower m/gDTI-ALPS. In line with the previous studies, SCA3 patients in the current study exhibiting predominant atrophic changes in the precentral gyrus, paracentral lobes, and cerebellum also witnessed the deposition of toxic proteins (e.g., tau and polyglutamine protein)\u003csup\u003e\u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e and neuro-damage markers\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. The bidirectional flow within the glymphatic system may facilitate the clearance of aberrant proteins from the ISF into the ventricular or subarachnoid compartments\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Consequently, slowed glymphatic movement can result in waste accumulation across brain cortex and subcortex in SCA3 patients. Additionally, these findings also revealed impairment of glymphatic function within midbrain, cerebellum, and middle regions in patients with SCA3. The pathological underpinnings of SCA3 were primarily localized to the cerebellum-neostriatum-motor and association cortical circuits\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, consistent with the distributional pattern of glymphatic disfunction.\u003c/p\u003e \u003cp\u003eAnother noteworthy finding from the current research was that, in regional glymphatic distributions we tested, the cortical and subcortical clearance showed an ascending trend from anterior to middle regions, followed by a descending trend from middle to posterior and cerebellum regions. The spatial dynamics in subcortical and cortical clearance were consistent with the anterior-posterior gradient observed in white matter microstructural diffusivity\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e and the motor-sensory dominant pattern of regional BOLD\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, respectively. Given the consistent utilization of the same CSF signal for each subject, any spatial variation in the regional BOLD-CSF coupling can be exclusively ascribed to alterations in regional BOLD signals. This pattern of brain co-activation was concomitant with specific deactivation in subcortical regions associated with arousal regulation, notably the basal ganglia and brainstem\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, and was strongly related to SCA3 etiology since the accumulation and aggregation of expanded polyglutamine stretches within susceptible brain nuclei may precipitate direct or indirect neurotoxic effects, ultimately culminating in neuronal loss and cerebral atrophy\u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Furthermore, in mice, neural response in the sensory motor cortex induced by whisker stimulation was recently found to be accompanied by accelerated CSF flow in PVS\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. This suggested that neurovascular coupling was involved in both supplying metabolites and removal of their waste products. Therefore, inadequate regional glymphatic clearance may lead to the accumulation and dissemination of neurotoxic proteins within the he midbrain, cerebellum, and middle regions, thereby exacerbating the pathogenesis of SCA3.\u003c/p\u003e \u003cp\u003eWith regard to the relationship between multiple glymphatic approaches and SCA3 symptoms, our results demonstrated significant correlations between these glymphatic indexes and SCA3-related symptoms as well as the disease severity. These findings highlighted the potential of glymphatic dysfunction as a biomarker for disease severity and progression of SCA3. Similar associations have been reported in other neurodegenerative diseases, emphasizing the pervasive impact of glymphatic dysfunction on neurological function\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Furthermore, the resting-state BOLD signal performed sleep dependence similar to glymphatic function, which was notably strong during drowsiness and sleep states\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Thus, the sleep disturbances in SCA3 patients may cause impaired glymphatic clearance, resulting in the weaker glymphatic indexes. Further investigations could extend this line of research to explore whether enhanced glymphatic clearance could improve SCA3 symptoms.\u003c/p\u003e \u003cp\u003eFinally, some caveats need to be noted regarding the present study. Our study did not directly evaluate the accumulation of neurotoxic wastes (proteins or small molecules). Further studies adopting PET data of the relevant proteins (e.g., tau-PET images) could provide empirical evidence with the relationship between regional glymphatic function and proteins deposition, especially in the midbrain, cerebellum, and middle regions. Moreover, as subjects in the present study consisted of a cohort, we were unable to offer insights into the longitudinal changes of spatial distribution of the glymphatic function in SCA3. As such, our data warrants future follow-up studies, with larger sample sizes and in independent cohorts, to validate and extend these findings.\u003c/p\u003e \u003cp\u003eIn summary, drawing on multiple glymphatic measurements, the current study provided comprehensive and compelling evidence for the involvement of glymphatic dysfunction in SCA3 patients. Notably, SCA3 patients exhibited spatial specificity of glymphatic dysfunction in midbrain, cerebellum, and middle regions, which revealed the pathological patterns of SCA3. Furthermore, the regional glymphatic alterations were closely associated with the SCA3 symptoms. Our findings provided valuable insights into the interplay between glymphatic dysfunction, brain structural alterations, and clinical symptoms, contributing to a deeper understanding of the pathophysiology of SCA3.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e The study was conducted in accordance with the declaration of Helsinki and was approved by the ethics committee of China-Japan Friendship Hospital (Approval No. 2023-KY-299). All the participants and/or their relatives were informed about this study and provided their written informed consent. The information of all participants had been fully anonymized.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eSupplementary material\u003c/h2\u003e \u003cp\u003eSupplementary material is available in supplementary files.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contribution\u003c/h2\u003e \u003cp\u003eL.H.: conceptualization, methodology, validation, investigation, formal analysis, writing\u0026mdash;original draft, writing\u0026mdash;review and editing. MX.X: conceptualization, methodology, validation, investigation, formal analysis, writing\u0026mdash;original draft, writing\u0026mdash;review and editing. LW.Z.: methodology, resources, data curation, validation. F.G.: methodology, validation, writing\u0026mdash;review and editing. LX.Z.: methodology, validation, formal analysis. AC.Y.: methodology, data curation. JX.L.: methodology, data curation. C.L.: investigation, formal analysis. F.H.: formal analysis. GL.M.: conceptualization resources, funding acquisition, project administration, writing\u0026mdash;review and editing. Z.Y.: conceptualization resources, funding acquisition, project administration, writing\u0026mdash;review and editing.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported by Macao Science and Technology Development Fund (No. 0020/2019/AMJ and 0011/2018/A1), the University of Macau (No. MYRG2020-00067-FHS, MYRG2019-00082-FHS, and MYRG2018-00081-FHS), Higher Education Fund of Macao SAR Government (No. CP-UMAC-2020-01), National Natural Science Foundation of China (No. 82271953), Guangzhou Science and Technology Planning Project (No. 202103010001), STI2030-Major Projects (No. 2022ZD0213300), Capital\u0026rsquo;s Funds for Health Improvement and Research (No. 2022-1-2031) and Beijing Municipal Science and Technology Project (No. Z211100003521009) and Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning (No. CNLZD2303).\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\u003ch2\u003eCode availability\u003c/h2\u003e \u003cp\u003eAll analyses used open-source software with URL links already included in Methods. Code used in the analyses described in this paper will be made available upon acceptance of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYap, K.H., Kessels, R.P., Azmin, S., van de Warrenburg, B., and Mohamed Ibrahim, N. (2022). Neurocognitive changes in spinocerebellar ataxia type 3: a systematic review with a narrative design. The Cerebellum, 1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePilotto, F., and Saxena, S. (2018). Epidemiology of inherited cerebellar ataxias and challenges in clinical research. Clinical and translational neuroscience \u003cem\u003e2\u003c/em\u003e, 2514183X18785258.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSch\u0026ouml;ls, L., Haan, J., Riess, O., Amoiridis, G., and Przuntek, H. (1998). Sleep disturbance in spinocerebellar ataxias: is the SCA3 mutation a cause of restless legs syndrome? 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Human brain mapping \u003cem\u003e29\u003c/em\u003e, 671\u0026ndash;682.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cerebrospinal fluid flow, resting-state fMRI signal, choroid plexus, DTI-ALPS, glymphatic system, spinocerebellar ataxia type 3","lastPublishedDoi":"10.21203/rs.3.rs-6891437/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6891437/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAbnormal accumulation of polyglutamine proteins is considered a core pathogenic factor in spinocerebellar ataxia type 3 (SCA3). The glymphatic system, a lymphatic-like fluid transport system, plays a crucial role in maintaining the balance between protein production and clearance in the brain. However, it remains unclear whether SCA3 is associated with impairments in glymphatic function. Using multimodal imaging data, 34 SCA3 patients and 36 age-, sex- and educational matched healthy controls (HCs) were compared using multiple glymphatic measurements, including choroid plexus (CP) volume, cerebrospinal fluid (CSF) volume, diffusion tensor imaging along the perivascular (DTI-ALPS) index, and coupling relationship between blood-oxygen-level-dependent signals and CSF flow (BOLD-CSF coupling). Then, we evaluated regional glymphatic function by dividing DTI-ALPS and BOLD-CSF coupling into anterior, middle, posterior, and cerebellum regions, thereby identifying the spatial variation of glymphatic function in the two groups. We demonstrated that compared with HCs, larger CP and CSF volumes were found in SCA3 patients. More importantly, for DTI-ALPS index and BOLD-CSF coupling, these surrogate markers for glymphatic clearance were weaker in SCA3 patients. Furthermore, altered regional glymphatic functions were most prominent in midbrain, cerebellum and middle cortical regions. Crucially, the altered midbrain, cerebellum, middle and global glymphatic functions were accompanied by the severity of ataxia and other SCA3 symptoms. Similar to other neurodegenerative disorders, the associations between multiple glymphatic markers and SCA3 symptoms suggest that waste clearance is disrupted in SCA3 patients, shedding light on the pathogenesis of the disease from a glymphatic perspective. Our findings highlighted the dysregulated glymphatic function as a novel biomarker for SCA3 and provide insights for exploring effective treatment strategies.\u003c/p\u003e","manuscriptTitle":"Regional Glymphatic Dysfunction in Patients with Spinocerebellar Ataxia Type 3","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-30 08:53:54","doi":"10.21203/rs.3.rs-6891437/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d799fc0b-e84c-4a15-a1d8-c5ab75f033e8","owner":[],"postedDate":"June 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50528812,"name":"Health sciences/Biomarkers"},{"id":50528813,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2025-09-16T15:42:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-30 08:53:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6891437","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6891437","identity":"rs-6891437","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}