Choroid Plexus Free-Water Correlates with Glymphatic function and Neurodegeneration in Alzheimer’s Disease

Choroid Plexus Free-Water Correlates with Glymphatic function and Neurodegeneration in Alzheimer’s Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Choroid Plexus Free-Water Correlates with Glymphatic function and Neurodegeneration in Alzheimer’s Disease Binyin Li, Xiaomeng Xu, Xinyuan Yang, Junfang Zhang, Yan Wang, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5322986/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 Free-water imaging of the choroid plexus (CP) is an index revealing components of the CP, which may improve the evaluation of Alzheimer's disease (AD). Our study evaluated free water fraction (FWf) of CP in 216 participants (133 Aβ+ participants and 83 Aβ- controls) continuously enrolled in the Ruijin NeuroBank of Alzheimer's Disease and Dementia (RJNB-D) cohort. The ADNI dataset was used for external validation. Assessments of AD neurodegeneration included Aβ-PET, Tau-PET, synaptic vesicle glycoprotein 2A-PET scans, and blood biomarkers included glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL), neurogranin (NRGN), and Tumor Necrosis Factor-α (TNF-α). The CP FWf and diffusion tensor image analysis along the perivascular space (DTI-ALPS) index were independently associated with Aβ positivity in both RJNB-D and ADNI datasets. Within the Aβ+ group, the negative correlation between CP FWf and DTI-ALPS was validated by two datasets. Furthermore, we observed a partial mediation effect of DTI-ALPS between CP FWf and periventricular white matter hyperintensity (pWMH). Elevated CP FWf was linked to worse Mini-Mental State Examination, increased Tau accumulation, reduced synaptic density, and elevated levels of NFL, GFAP, NRGN, and TNF-α. Longitudinally, CP FWf increased faster in Aβ+ participants than Aβ- controls (time × group interaction effect p = 0.046). The growth of CP FWf was associated with a reduction in DTI-ALPS (ρ = -0.42, p = 0.006), and the growth rate of CP FWf surpassed that of pWMH, Tau, and GFAP. Overall, our findings suggest that elevated CP FWf indicates impaired glymphatic function and AD neurodegeneration. Trial registration The study is registered on ClinicalTrials.gov (NCT05623124). Health sciences/Biomarkers/Diagnostic markers Health sciences/Biomarkers/Predictive markers Biological sciences/Neuroscience Health sciences/Biomarkers/Prognostic markers Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Alzheimer's disease (AD) is a neurodegenerative disorder marked by cognitive decline, β-amyloid plaques, hyperphosphorylated Tau, and cortical atrophy[ 1 ]. Moreover, dysfunction in the cerebrospinal fluid (CSF) glymphatic clearance and white matter hyperintensity (WMH) has also been identified as contributing factors [ 2 ]. The choroid plexus (CP) produces CSF and regulates its dynamics[ 3 ]. Comprising a single-layer epithelium, fenestrated capillaries, connective tissue, and immune cells, the CP forms the blood-CSF barrier and aids immune surveillance[ 3 ]. However, the mechanisms behind the CP in neurodegeneration and AD are unclear. Despite this, there is a scarcity of research focusing on components of the CP. Given its role in producing CSF and its location within the ventricles, fluid is one of the predominant components within the CP structure. Free water refers to unconstrained water molecules in fluid. Diffusion MRI techniques can measure free water in brain tissue[ 4 ]. Previous study showed increased free-water fraction (FWf) in cortical and subcortical regions as neurodegeneration progresses[ 5 ]. Given the choroid plexus's role in CSF regulation, CP FWf may be crucial for brain fluid circulation. Furthermore, in the brain glymphatic system, CSF enters the brain parenchyma through the perivascular spaces surrounding arteries to clear interstitial fluid waste products[ 6 ]. Prior studies using diffusion tensor image analysis along the perivascular space (DTI-ALPS) index have shown an association between glymphatic dysfunction and the severity of WMH[ 7 ]. Meanwhile, previous study revealed an association between WMH and WMH-linked cortical AD biomarkers[ 8 ]. However, the relationship between CP FWf and glymphatic clearance or WMH remains unclear. We hypothesized that CP FWf in AD correlates with glymphatic function and AD severity across the disease continuum. We undertook this study to: 1) investigate CP FWf changes in AD; 2) examine the relationship between CP FWf and glymphatic function, WMH, as well as AD pathological biomarkers; 3) explore potential mediation factors between CP FWf and cognition; and 4) validate the impact of CP FWf in AD using longitudinal data over a 12-month follow-up period. Subjects and Methods Participants and study design This retrospective study utilized consecutive collected data from the Ruijin NeuroBank of Alzheimer's Disease and Dementia (RJNB-D) cohort between November 2021 and March 2024. The cohort is a prospective cohort for all-cause dementia from memory clinics and cognitively normal volunteers from community, with detailed information in our previous study [ 9 ]. The cross-sectional analysis included 216 participants: 133 Aβ + individuals and 83 Aβ- controls. The Aβ + group was further diagnosed as AD or mild cognitive impairment (MCI) according to 2016 NIA-AA criteria.[ 10 ]. For longitudinal analysis, 76 of 216 baseline participants completed 12-month follow-up assessments, including repeated neuropsychological tests, multimodal MRI, and PET scans. All follow-up assessments were conducted within two weeks of the 12-month interview. For external validation, Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset was utilized. Participants continuously enrolled in ADNI 3 between June 2017 and June 2022 were included in the study. Apart from the ADNI enrollment criteria, the following additional exclusion criteria were applied: 1. without multi-shell diffusion MRI scans necessary for FWf calculation; 2. lack of 18F-Florbetapir (FBP)-PET scans required to determine Aβ status. Data collection In RJNB-D, the participants underwent multimodal MRI, PET scans, neuropsychological tests, and blood sample collection (Supplementary Fig. 1A). Multimodal MRI included 3D high-resolution T1, fluid-attenuated inversion recovery (FLAIR), multi-shell diffusion MRI (dMRI) and pseudo-continuous arterial spin labelled (pCASL) perfusion MRI (Supplementary Fig. 1B, Supplementary Method 1). PET scans included FBP for Aβ, 18 F-MK-6240 for Tau, and 18 F-SynVesT-1 for synaptic density (Supplementary Method 2). Neuropsychological assessments included Mini-Mental State Examination (MMSE, Chinese Version)[ 11 ], 20-minute auditory verbal learning test (AVLT)[ 12 ], Clock-drawing test (CDT)[ 13 ], Boston naming test (BNT), shape-trail test (STT-A and STT-B)[ 14 ], and animal fluency test (AFT) [ 14 ]. Venous blood samples were used for ApoE genotyping and glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL), neurogranin (NRGN), and Tumor Necrosis Factor-α (TNF-α) (Supplementary Method 3). MRI acquisition and analysis MRI data were acquired using a 3.0-T scanner with a 64-channel head coil (uMR 890, United Imaging Healthcare, Shanghai, China). Detailed parameters of T1-weighted, 3D FLAIR, diffusion and pCASL sequences are presented in Supplementary Method 1. Volumetric analysis was performed using FreeSurfer 7.1.1 ( https://surfer.nmr.mgh.harvard.edu ). WMH was defined and segmented using Brain Intensity AbNormality Classification Algorithm (BIANCA, version 6.0, FMRIB Software Library, Oxford, UK), and was further categorized into pWMH and deep white matter hyperintensity (dWMH) by 10-mm distance threshold from the ventricles[ 8 ]. Diffusion MRI data underwent pre-processing and tensor fitting to generate ractional anisotropy (FA) and mean diffusivity (MD), and to calculate DTI-ALPS index using FSL (version 6.0.3, http://www.fmrib.ox.ac.uk/fsl ). Free-water imaging of CP was assessed using the neurite orientation dispersion and density imaging (NODDI) model. The CP FWf was calculated as the average of the left and right CP ROI registered with T1-weighted image. cerebral blood flow (CBF) in the CP was estimated using the FSL BASIL toolbox. Detailed information of MRI analysis is presented in Supplementary Method 1. PET-MRI acquisition and analysis The PET scans were performed using a 3T whole-body PET/MR scanner (uPMR 790, United Imaging, China). Participants received intravenous FBP for Aβ, 18 F-MK-6240 for Tau, and 18 F-SynVesT-1 for synaptic vesicle glycoprotein 2A (SV2A) to assess synaptic loss as previously described[ 15 ]. An automated pipeline derived cortical standardized uptake value ratios (SUVR) using the PETSurfer toolbox in FreeSurfer 7.1.1 ( https://surfer.nmr.mgh.harvard.edu ), with the cerebellum as a reference. High-resolution segmentation from T1 images facilitated partial volume correction, followed by registration and validation of PET and anatomical images. Detailed information of PET-MRI acquisition and analysis is presented in Supplementary Method 2. Statistical analyses Group comparisons used t-tests for continuous variables and Chi-square tests for categorical variables. Non-normal distributions were log-transformed. Logistic regression was used to adjust for age, sex, and ApoE genotype. Within each group, correlations were assessed using Pearson's or Spearman's methods based on variable distribution. Multivariable linear regression analyzed CP FWf and other imaging measures, adjusted for age, sex, and ApoE genotype. Bonferroni correction addressed multiple comparisons. Vertex-wise correlation analysis using generalized linear model, controlled for age and sex, demonstrated spatial correlations between CP FWf and AD markers. Permutation tests reduced false positivity rates. Mediation analyses were conducted using PROCESS v3.2 for SPSS (version 25.0, IBM), adjusted for age. Longitudinal analysis was performed using repeated measures ANOVA. Alteration rates between Aβ + participants and Aβ- controls were compared using linear mixed model. The relationship between CP FWf and DTI-ALPS changes was explored in the Aβ + group. Rates of longitudinal changes in CP FWf and related metrics were compared after standardizing baseline measures. Analyses were conducted using R (version 4.3.3, The R Foundation) or SPSS (version 25.0, IBM), with significance at P < 0.05 (two-tailed). All analyses were independently performed by two authors (XX and XY). Study approval This study was approved by the ethics committee at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China. All participants or caregivers provided written informed consent. The study adhered to the 1964 Declaration of Helsinki and its amendments. The study was registered on ClinicalTrials.gov (NCT05623124). Respectively, numbers of 94, 107, 120 participants of the same cohort overlapped previous publications[ 8 , 9 , 15 ], regarding to WMH-related neurodegeneration[ 8 , 15 ] and synaptic loss in AD[ 9 ]. This study furthered the previous findings by evaluating the influence of free-water in the CP on glymphatic dysfunction and WMH in AD. Results Characteristics and group comparison Table 1 summarizes the baseline characteristics of all participants from our RJNB-D cohort (n = 216, mean age, 69.20 years ± 8.00 [SD]; 128 females). Table 2 lists the characteristics of the 76 participants completed 12-month follow-up (mean age, 70.09 years ± 7.29 [SD]; 49 females). Table 1 Baseline Demographic and MRI Characteristics in RJNB cohort(n = 216) Aβ+ (n = 133) Aβ- (n = 83) p-value Age 70.59 ± 7.62 66.98 ± 8.13 0.001 Female 77 (58%) 51 (61%) 0.605 Education 11.56 ± 4.08 12.16 ± 3.43 0.266 ApoE 4 carrier $ 67 (62%) 15 (23%) < 0.001 MMSE 21.07 ± 6.36 27.18 ± 4.02 < 0.001 CP FWf 0.78 ± 0.04 0.76 ± 0.06 0.002 CPV 0.98 ± 0.20 0.94 ± 0.23 0.143 CP FA # -1.65 ± 0.25 -1.64 ± 0.23 0.841 CP MD # -7.64 ± 0.16 -7.67 ± 0.10 0.091 CP CBF # 3.80 ± 0.53 3.77 ± 0.85 0.746 DTI-ALPS 1.15 ± 0.17 1.23 ± 0.16 0.001 pWMH # -3.83 ± 1.52 -4.78 ± 1.73 < 0.001 dWMH # -5.74 ± 1.70 -6.44 ± 1.73 0.005 #, The log-transformed values were used $, Different sample size for ApoE genotype: Aβ+, n = 108; Aβ-, n = 66. Table 2 Month 12 Demographic and MRI Characteristics in RJNB cohort (n = 76) Aβ+ (n = 46) Aβ- (n = 30) p-value Age 72.33 ± 6.22 66.67 ± 7.59 0.001 Female 28 (61%) 21 (70%) 0.416 ApoE 4 carrier $ 27(63%) 5 (19%) < 0.001 Baseline CP FWf 0.77 ± 0.04 0.75 ± 0.05 0.020 Month 12 CP FWf 0.80 ± 0.04 0.76 ± 0.06 0.001 ΔCP FWf 0.03 ± 0.04 0.01 ± 0.04 0.046 Baseline DTI-ALPS 1.20 ± 0.14 1.27 ± 0.15 0.081 Month 12 DTI-ALPS 1.13 ± 0.18 1.23 ± 0.20 0.052 ΔDTI-ALPS -0.08 ± 0.11 -0.05 ± 0.10 0.226 Baseline pWMH # -4.01 ± 1.52 -4.83 ± 1.49 0.022 Month 12 pWMH # -4.07 ± 2.20 -4.82 ± 1.96 0.126 ΔpWMH 0.01 ± 0.03 0.00 ± 0.01 0.078 Baseline Tau SUVR # 0.64 ± 0.46 -0.03 ± 0.14 < 0.001 Month 12 Tau SUVR # 0.67 ± 0.51 -0.10 ± 0.16 < 0.001 Baseline GFAP 153.05 ± 93.36 76.94 ± 64.01 < 0.001 Month 12 GFAP 167.52 ± 84.90 128.61 ± 71.91 0.146 #, Nature logarithm was taken for normalization. $, Different sample size for ApoE genotype: Aβ+, n = 43; Aβ-, n = 26. Compared to Aβ- group, increased CP FWf (0.78 ± 0.04 vs 0.76 ± 0.06, p = 0.002), pWMH volume ( -3.83 ± 1.52 vs -4.78 ± 1.73, p < 0.001), dWMH volume ( -5.74 ± 1.70 vs -5.74 ± 1.70, p = 0.005, both log-transformed), and decreased DTI-ALPS (1.15 ± 0.17 vs 1.23 ± 0.16, p = 0.001) were observed in Aβ + participants (Table 1 , Fig. 1 A). The FA, MD, CBF and volumes of CP (CPV) were similar between groups (Table 1 , all p > 0.050). After adjustment for age, sex and ApoE genotype, CP FWf (β = 10.02, p = 0.015), DTI-ALPS (β = -3.19, p = 0.007), pWMH (β = 0.33, p = 0.007) and dWMH (β = 0.28, p = 0.013) independently indicated Aβ positivity (Supplementary Table 1–4), respectively. The CP FWf associated with DTI-ALPS and WMH Among all participants, CP FWf correlated with DTI-ALPS (r = -0.47, p < 0.001), pWMH (r = 0.46, p < 0.001), and dWMH (r = 0.21, p = 0.003). With adjustment for age, mediation analysis showed DTI-ALPS partially mediated the association between CP FWf and pWMH (indirect effect standardized-β = 0.26, p < 0.001, mediated effect = 32.59%; Fig. 1 B), but not dWMH (indirect effect standardized-β = 0.09, p = 0.236). Within the Aβ + group, CP FWf correlated with DTI-ALPS (r = -0.45, p < 0.001) and pWMH (r = 0.40, p < 0.001), but not dWMH (r = 0.11, p = 0.213). Partial mediation effect of DTI-ALPS between CP FWf and pWMH was also observed (indirect effect standardized-β = 0.25, p = 0.003, mediated effect = 35.76%; Fig. 1 C), adjusted for age. In Aβ- controls, CP FWf was also related to DTI-ALPS (r = -0.44, p < 0.001) and pWMH (r = 0.45, p < 0.001), yet the mediation effect of DTI-ALPS between CP FWf and pWMH was insignificant (Fig. 1 D), adjusted for age. Validation of CP FWf measurements The measurements of CP FWf were repeated with an eroded CP mask (by zeroing non-zero voxels when zero voxels found in kernel of 2 mm 3 box in the original CP mask) to minimize the contamination of CSF. The FWf in the eroded CP FWf was highly correlated with the original CP FWf (ρ = 0.76, p < 0.001), as well as DTI-ALPS (ρ = -0.35, p < 0.001) and pWMH (ρ = 0.18, p = 0.044, Supplementary Fig. 2). We also test the FWf measurement in another non-neuronal tissue: CSF. The distribution of FWf values in the central part of lateral ventricles was skewed, with the majority of values equaling to 1 (Supplementary Fig. 3). It validated our application of FWf measurement since the main component of CSF in ventricles were free water. External validation was further performed using the public ADNI dataset. Supplementary Table 5 summarizes the baseline characteristics of all participants (n = 168, mean age, 72.52 years ± 7.77 [SD]; 93 females). In consistency with our cohort, increased CP FWf (0.81 ± 0.07 vs 0.78 ± 0.07, p = 0.006) and decreased DTI-ALPS (1.08 ± 0.15 vs 1.14 ± 0.17, p = 0.018) were observed in Aβ + participants from the ADNI dataset (Fig. 1 C). In the ADNI dataset, CP FWf was also highly correlated with DTI-ALPS among all the participants (r = -0.53, p < 0.001), in the Aβ + subgroup (r = -0.57, p < 0.001), and in the Aβ- subgroup (r = -0.48, p < 0.001). In addition, predictive values of CP FWf calculated by ROC was similar between ADNI (AUC = 0.625) and our cohort (AUC = 0.623), and the predictive values were moderately improved when adjusted for age and ApoE genotype (ADNI: AUC = 0.815; RJNB: AUC = 0.770, Fig. 1 D). CP FWf across the AD continuum CP FWf correlated with AD imaging and blood biomarkers In the Aβ + group of RJNB-D cohort, CP FWf showed significant relationship with AD imaging markers, including cortical Tau SUVR, cortical SV2A SUVR, hippocampus volume, cortex volume, as well as MMSE score (Fig. 2 A, Supplementary Table 6). Adjusted for age, sex, and ApoE genotype, elevated CP FWf linked to increased cortical Tau accumulation (Tau SUVR, β = 9.27, p = 0.002, Fig. 2 C, Supplementary Table 7) and decreased cortical synaptic density (SV2A SUVR, β = -3.43, p = 0.017, Fig. 2 C, Supplementary Table 8). However, CP FWf did not influence FBP SUVR (β = 0.98, p = 0.103). Vertex-wise analysis illustrated that CP FWf negatively correlated with cortical thickness in bilateral post cingulate gyrus, precuneus, temporal, and insular lobes, but positively correlated with Tau SUVR in bilateral insular, temporal regions, and precuneus (Fig. 2 B). Moreover, CP FWf negatively correlated with SV2A SUVR in the right inferior parietal gyrus (Supplementary Fig. 4) Within the Aβ + group, CP FWf correlated with NFL (β = 3.12, p = 0.026, n = 92), GFAP (β = 5.28, p < 0.001, n = 92), NRGN (β = 5.70, p = 0.028, n = 82), and TNF-α (β = 10.83, p = 0.009, n = 81) (Supplementary Table 9–12), adjusted for age, sex and ApoE genotype. Due to the different availability of adequate blood samples, the sample sizes were marked respectively. AD biomarkers mediated CP FWf effect on cognition Increased CP FWf linked to worse cognitive performance, including overall cognition (MMSE, β = -53.36, p < 0.001), verbal function (AFT, β = -29.83, p = 0.014), and executive function (CDT, β = -35.29, p = 0.025) within the Aβ + group (Fig. 2 C, Supplementary Table 13–15), adjusted for age, sex, and ApoE genotype. Partial mediation effects of Tau SUVR (indirect effect standardized-β = -0.18, p = 0.043, mediated effect = 40.88%, n = 110, Fig. 3 A) and SV2A SUVR (indirect effect standardized-β = -0.21, p = 0.089, mediated effect = 21.73%, n = 64, Fig. 3 B) were observed between CP FWf and MMSE, adjusted for age. Although NFL, NRGN, and TNF-α failed to mediate the association between CP FWf and MMSE (Fig. 3 D), GFAP exhibited a full mediation effect (mediated effect = 38.69%, n = 111, Fig. 3 C), adjusted for age. These findings suggested that the impact of CP FWf on cognition is mediated via Tau- and GFAP-related pathways. The effect of longitudinal CP FWf changes During the 12-month follow-up, CP FWf increased drastically in Aβ + participants (F-statistic = 16.673, corrected p < 0.001, Supplementary Table 16). Moreover, Aβ + participants demonstrated faster growth rate than Aβ- controls (time × group interaction effect: F-statistic = 4.118, corrected p = 0.046, Fig. 4 A, Supplementary Table 16). The growth rates of CP FWf did not differ between ApoE 4 carriers (n = 27) and non-carriers (n = 16) in the Aβ + group (F-statistic = 0.004, p = 0.949, Fig. 4 B). In Aβ + participants, annual changes in CP FWf, DTI-ALPS, and pWMH volume were calculated as ΔCP FWf, ΔDTI-ALPS, and ΔpWMH. Spearman’s correlation analysis suggested ΔCP FWf paralleled ΔDTI-ALPS (ρ = -0.42, p = 0.006, Fig. 4 C), but not ΔpWMH (ρ = 0.21, p = 0.173). And the association between ΔCP FWf and ΔDTI-ALPS remained significant upon adjustment for age and sex (β = -1.02, p = 0.016). The growth rate of CP FWf exceeded that of pWMH (interaction effect, F-statistic = 11.201, corrected p = 0.001), Tau SUVR (interaction effect, F-statistic = 6.804, corrected p = 0.011) and GFAP (interaction effect, F-statistic = 4.430, corrected p = 0.039, Fig. 4 D). Discussion Our study observed increased CP FWf and decreased DTI-ALPS index at baseline in Aβ + participants. And we noted elevated CP FWf exacerbates pWMH, partially mediated by the glymphatic system, revealing a potential mechanism linking CSF dynamics to white matter lesions. We also observed significant associations between CP FWf and pivotal AD imaging markers (cortical Tau SUVR, cortical SV2A SUVR, hippocampus volume, cortex volume) and cognitive performance. In longitudinal analyses, we observed a significant and faster increase in CP FWf during a 12-month follow-up period in Aβ + participants compared to Aβ- controls, and the rate of progression of CP FWf exceeded that of WMH and Tau accumulation. The CP is a highly vascularized structure that regulates CSF, clears neurotoxins, and plays a crucial role in the glymphatic system[ 16 , 17 ]. Studies in AD demonstrated that CPV was linked to cognitive impairment[ 3 , 18 – 20 ], and levels of Aβ and Tau in the CSF, indicating that CP may participate the clearance of Aβ and Tau[ 18 ]. Prior results regarding the relationship between CPV and Aβ positivity were inconsistent[ 3 , 19 , 20 ]. These disagreements may be attributed to the contamination of partial volume effect of CSF in calculation CPV by traditional methods[ 21 ], and also the morphological nature of CPV, which may display a relatively delayed response to Aβ deposition. In the current study, we also failed to verify the association between CPV and Aβ positivity. However, after evaluating various novel imaging biomarkers that reflects CP microstructure, including CP FWf, CP FA, CP MD and CP CBF, we found that CP FWf, derived from free-water mapping, was the only index that significant differed between Aβ + and Aβ- groups. Free-water mapping uses a bi-tensor model to differentiate water diffusion in brain tissue and extracellular space, providing better microstructural insights than traditional single-tensor model[ 22 , 23 ]. The DTI-ALPS index is a reliable parameter of glymphatic system[ 24 ]. Previous studies showed that enlarged CP and reduced DTI-ALPS were both associated with WMH growth[ 7 , 19 ]. Moreover, DTI-ALPS mediated the association between CP and WMH burden, suggesting that WMH growth can result from glymphatic disorder[ 7 ]. In our study, CP FWf showed solid correlation with DTI-ALPS and WMH, suggesting that CP FWf reliably indicated glymphatic function. We noticed that in Aβ + participants CP FWf was associated with pWMH that anatomically located nearer to the CP, but not with dWMH, and there is a partial mediating effect of DTI-ALPS between CP FWf and pWMH. A recent study by Jeong et al demonstrated a positive correlation between CP enlargement and Aβ burden, but this correlation was only significant in the AD non-dementia group[ 20 ], likely due to the “ceiling effect”, as Aβ is saturated in dementia state[ 25 ]. Similarly, our study showed that CP FWf was significantly associated with Aβ positivity, but did not correlate with Aβ burden measured by FBP SUVR. A recent report indicated a positive correlation between global Tau burden and CP enlargement in AD[ 26 ]. We further these findings by vertex-wise analysis, and showed that CP FWf changed in parallel with Tau accumulation primarily in bilateral insular, temporal lobes, and precuneus. Furthermore, we found that increased Tau burden mediated the detrimental effects of CP FWf on cognition. This finding adds clinical evidence to a recent study indicating that glymphatic dysfunction impairs cognition through poor Tau clearance in AD mouse models[ 27 ]. In addition to the clearance of neurotoxins, glymphatic system can also modulate neuroinflammation[ 28 ]. We explored the relationship between peripheral neuroinflammatory markers and CP FWf. GFAP correlated with CP FWf, and substantially mediated the negative impact of CP FWf on cognitive function. GFAP is a structural protein in astrocytes and is a marker of reactive gliosis related to aging[ 29 ]. Astrocytes are critical components of glymphatic system by ensheathing the brain vasculature with their endfeet[ 6 , 28 ]. Previous study showed that blood GFAP correlated to impaired glymphatic function and global cognition in community-dwelling older adults[ 30 ]. Our findings, in consistent with the previous publications, provided new evidence for involvement of GFAP-related pathway in glymphatic dysfunction in AD pathology. The current study is the first study to calculate the FWf of CP. In order to further validate the solidity of our measurements, the FWf values of non-neuronal free water in lateral ventricles and eroded part of CP were extracted. In consistency with our expectancy, the FWf of CSF in the lateral ventricles was approximate to 1, and the FWf of eroded CP was highly correlated to CP. Finally, the diagnostic power (AUC) of CP FWf were highly consistent in ADNI and our cohort. Although as a single parameter, the CP FWf failed to present optimal AUCs, the values were moderately improved with adjustment of age and ApoE genotype, indicating its potential value as one of the contributors in a composite predictive frame. Our study has limitations. First, the small sample size of longitudinal study, as the cohort is newly established with about half of the participants not reaching the 12-month follow-up. Also, this duration may be too short to observe significant changes in imaging markers like pWMH volume, suggesting a longer follow-up is warranted. However, ΔCP FWf already showed remarkable changes, indicating its sensitivity in monitoring disease progression. Additionally, the limited number of blood samples and analyses may influence the results, so findings related to blood markers should be viewed as exploratory and hypothesis-generating. In conclusion, our results indicate that CP FWF serves as a novel imaging marker that reflecting various AD pathological features, complementing structural MRI and PET imaging. By capturing the multifaceted aspects and longitudinal changes in AD, CP FWF holds potential for early diagnosis and precise monitoring of disease progression, offering the possibility of personalized and timely treatment interventions. Declarations Competing Interest The authors have declared that no conflict of interest exist. Author contributions XX and BL designed the study; XX, XY, MS, and BL wrote the initial draft of the manuscript; XX, XY, JZ, RS, FX and BL performed data analysis; YW, YG, JL and YD consulted on statistical methods; XX, XY and WX generated tables and figures; YZ, QH, LS, WW performed neurological function evaluation and collected blood samples and imaging data; all authors read the manuscript and provided input. XX, XY, MS and BL finalized the manuscript. FX and BL oversaw the study and provided direction, funding, and resources. Acknowledgements See Supplemental Acknowledgments for the complete list of ADNI investigators and their affiliations. We obtained part of the data from the ADNI database in preparation for this article. The investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this research. We would like to thank all the researchers and participants in the ADNI initiative. The authors thank the participants and their families for their participation in this study. This study was supported by National Natural Science Foundation of China (82271441, 82171473, 81901180), Shanghai Rising-Star Program (21QA1405800), National Key Research and Development Program of China, Scientific and technological innovation 2030 - Major Projects (2022ZD0213800). Data availability For all main figures with RJNB-D datasets, the extracted traces from the raw image files are available on Figshare ( https://figshare.com/articles/dataset/Choroid_Plexus_Free-Water_Correlates_with_Glymphatic_Dysfunction_and_Tracks_Neurodegeneration_in_Alzheimer_s_Disease_The_RJNB-D_Study/26043871 ). Values for all data points in graphs are reported in the Supporting Data Values file. References Jack CR, Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB, et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016;87:539–547. Garnier-Crussard A, Cotton F, Krolak-Salmon P, Chételat G. White matter hyperintensities in Alzheimer’s disease: Beyond vascular contribution. Alzheimers Dement. 2023;19:3738–3748. Choi JD, Moon Y, Kim H-J, Yim Y, Lee S, Moon W-J. Choroid Plexus Volume and Permeability at Brain MRI within the Alzheimer Disease Clinical Spectrum. Radiology. 2022;304:635–645. Wang Y, Wang Q, Haldar JP, Yeh F-C, Xie M, Sun P, et al. Quantification of increased cellularity during inflammatory demyelination. Brain. 2011;134:3590–3601. Chiu SY, Chen R, Wang W-E, Armstrong MJ, Boeve BF, Savica R, et al. Longitudinal Free-Water Changes in Dementia with Lewy Bodies. Mov Disord. 2024;39:836–846. Hablitz LM, Nedergaard M. The glymphatic system. Curr Biol. 2021;31:R1371–R1375. Li Y, Zhou Y, Zhong W, Zhu X, Chen Y, Zhang K, et al. Choroid Plexus Enlargement Exacerbates White Matter Hyperintensity Growth through Glymphatic Impairment. Ann Neurol. 2023;94:182–195. Zhang J, Chen H, Wang J, Huang Q, Xu X, Wang W, et al. Linking white matter hyperintensities to regional cortical thinning, amyloid deposition, and synaptic density loss in Alzheimer’s disease. Alzheimers Dement. 2024. 22 April 2024. https://doi.org/10.1002/alz.13845 . Li B, Chen H, Zheng Y, Xu X, You Z, Huang Q, et al. Loss of synaptic density in nucleus basalis of meynert indicates distinct neurodegeneration in Alzheimer’s disease: the RJNB-D study. Eur J Nucl Med Mol Imaging. 2024. 8 August 2024. https://doi.org/10.1007/s00259-024-06862-z . Cummings J. The National Institute on Aging-Alzheimer’s Association Framework on Alzheimer’s disease: Application to clinical trials. Alzheimers Dement. 2019;15:172–178. Katzman R, Zhang MY, Ouang-Ya-Qu null, Wang ZY, Liu WT, Yu E, et al. A Chinese version of the Mini-Mental State Examination; impact of illiteracy in a Shanghai dementia survey. J Clin Epidemiol. 1988;41:971–978. Zhao Q, Lv Y, Zhou Y, Hong Z, Guo Q. Short-term delayed recall of auditory verbal learning test is equivalent to long-term delayed recall for identifying amnestic mild cognitive impairment. PLoS One. 2012;7:e51157. Mendez MF, Ala T, Underwood KL. Development of scoring criteria for the clock drawing task in Alzheimer’s disease. J Am Geriatr Soc. 1992;40:1095–1099. Zhao Q, Guo Q, Hong Z. Clustering and switching during a semantic verbal fluency test contribute to differential diagnosis of cognitive impairment. Neurosci Bull. 2013;29:75–82. Zhang J, Wang J, Xu X, You Z, Huang Q, Huang Y, et al. In vivo synaptic density loss correlates with impaired functional and related structural connectivity in Alzheimer’s disease. J Cereb Blood Flow Metab. 2023;43:977–988. Dai Z, Yang Z, Chen X, Zheng W, Zhuang Z, Liao Y, et al. The aging of glymphatic system in human brain and its correlation with brain charts and neuropsychological functioning. Cereb Cortex. 2023;33:7896–7903. Čarna M, Onyango IG, Katina S, Holub D, Novotny JS, Nezvedova M, et al. Pathogenesis of Alzheimer’s disease: Involvement of the choroid plexus. Alzheimer’s & Dementia. 2023;19:3537–3554. Tadayon E, Pascual-Leone A, Press D, Santarnecchi E, Alzheimer’s Disease Neuroimaging Initiative. Choroid plexus volume is associated with levels of CSF proteins: relevance for Alzheimer’s and Parkinson’s disease. Neurobiol Aging. 2020;89:108–117. Hong H, Hong L, Luo X, Zeng Q, Li K, Wang S, et al. The relationship between amyloid pathology, cerebral small vessel disease, glymphatic dysfunction, and cognition: a study based on Alzheimer’s disease continuum participants. Alzheimers Res Ther. 2024;16:43. Jeong SH, Park CJ, Cha J, Kim S-Y, Lee S-K, Kim YJ, et al. Choroid Plexus Volume, Amyloid Burden, and Cognition in the Alzheimer’s Disease Continuum. Aging Dis. 2024. 23 January 2024. https://doi.org/10.14336/AD.2024.0118 . Saito Y, Kamagata K, Uchida W, Takabayashi K, Aoki S. The partial volume effect of choroid plexus in pathogenesis of Alzheimer’s disease. Alzheimer’s & Dementia. 2023;19:4756–4757. Pasternak O, Sochen N, Gur Y, Intrator N, Assaf Y. Free water elimination and mapping from diffusion MRI. Magn Reson Med. 2009;62:717–730. Zhou L, Li G, Zhang Y, Zhang M, Chen Z, Zhang L, et al. Increased free water in the substantia nigra in idiopathic REM sleep behaviour disorder. Brain. 2021;144:1488–1497. Butler T, Zhou L, Ozsahin I, Wang XH, Garetti J, Zetterberg H, et al. Glymphatic clearance estimated using diffusion tensor imaging along perivascular spaces is reduced after traumatic brain injury and correlates with plasma neurofilament light, a biomarker of injury severity. Brain Commun. 2023;5:fcad134. Bjorkli C, Sandvig A, Sandvig I. Bridging the Gap Between Fluid Biomarkers for Alzheimer’s Disease, Model Systems, and Patients. Front Aging Neurosci. 2020;12:272. Ota M, Sato N, Nakaya M, Shigemoto Y, Kimura Y, Chiba E, et al. Relationship between the tau protein and choroid plexus volume in Alzheimer’s disease. Neuroreport. 2023;34:546–550. Lopes DM, Wells JA, Ma D, Wallis L, Park D, Llewellyn SK, et al. Glymphatic inhibition exacerbates tau propagation in an Alzheimer’s disease model. Alzheimer’s Research & Therapy. 2024;16:71. Mogensen FL-H, Delle C, Nedergaard M. The Glymphatic System (En)during Inflammation. Int J Mol Sci. 2021;22:7491. Sabbatini M, Barili P, Bronzetti E, Zaccheo D, Amenta F. Age-related changes of glial fibrillary acidic protein immunoreactive astrocytes in the rat cerebellar cortex. Mech Ageing Dev. 1999;108:165–172. Wei Y-C, Hsu C-CH, Huang W-Y, Lin C, Chen C-K, Chen Y-L, et al. Vascular risk factors and astrocytic marker for the glymphatic system activity. Radiol Med. 2023;128:1148–1161. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files FigureS1.tif FigureS2.tif FigureS3.tif FigureS4.tif MPSupplementADNIAcknowledgementList.pdf MPSupplementaryTables.docx MPSupplementaryfigurelegend.docx MPSupplementarymethods.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5322986","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":370347720,"identity":"a27ac1fb-3c58-4451-a46f-1df7d214532e","order_by":0,"name":"Binyin Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACPmYehgMMDBIM/MzMBx8QpYUNpkWynS3ZgDgtDDwQhsF5HjMB4rSw8x48XNhmkWd8mMGMgaHGJpoIh/ElHJ7ZJlFsdpgh7QHDsbTcBsJaeAwO87ZJJG47zHDcgLHhMAlaNjcztkmQpmUDMzMbsVqAfuE5J5E44zAbs0ECMX7h5z97+DNPWV1if//5jw8+1NgQ1gIGjGxQRgJRysHgD/FKR8EoGAWjYAQCAOMSNyXCAWwmAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1953-382X","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Binyin","middleName":"","lastName":"Li","suffix":""},{"id":370347721,"identity":"b6b7f2f5-bf03-4f27-80ae-f096add84811","order_by":1,"name":"Xiaomeng Xu","email":"","orcid":"https://orcid.org/0000-0002-1717-538X","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaomeng","middleName":"","lastName":"Xu","suffix":""},{"id":370347722,"identity":"681dd1ba-4290-4b57-b2fe-9ef50fa495e0","order_by":2,"name":"Xinyuan Yang","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xinyuan","middleName":"","lastName":"Yang","suffix":""},{"id":370347723,"identity":"97e78b95-ce49-403c-bf31-090d5f9e118b","order_by":3,"name":"Junfang Zhang","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Junfang","middleName":"","lastName":"Zhang","suffix":""},{"id":370347724,"identity":"0e47c801-e757-4d94-b433-afad7e608bdc","order_by":4,"name":"Yan Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Wang","suffix":""},{"id":370347725,"identity":"727a746d-cee1-46d3-9b03-f7983a77a3ba","order_by":5,"name":"Magdy Selim","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Magdy","middleName":"","lastName":"Selim","suffix":""},{"id":370347726,"identity":"a2e4a96a-ce08-4abc-b208-5d7c77330fa1","order_by":6,"name":"Yingting Zheng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yingting","middleName":"","lastName":"Zheng","suffix":""},{"id":370347727,"identity":"d7def924-8e5c-4a25-9228-892c12d2cc7f","order_by":7,"name":"Ruinan Shen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ruinan","middleName":"","lastName":"Shen","suffix":""},{"id":370347728,"identity":"3a25d594-353f-4ccd-96a5-4c22db5ab2a4","order_by":8,"name":"Lipeng Sun","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lipeng","middleName":"","lastName":"Sun","suffix":""},{"id":370347729,"identity":"91ca8009-815d-424b-a007-80a644590096","order_by":9,"name":"Qi Huang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Huang","suffix":""},{"id":370347730,"identity":"ce4ff03e-9f28-4601-a906-49544cfbfde4","order_by":10,"name":"Wenjing Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wenjing","middleName":"","lastName":"Wang","suffix":""},{"id":370347731,"identity":"b5a0b88a-0863-49b4-a35c-1e753d22a37b","order_by":11,"name":"Wei Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Xu","suffix":""},{"id":370347732,"identity":"7102f133-1f77-485b-ae4c-d4adb9988bf9","order_by":12,"name":"Yihui Guan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yihui","middleName":"","lastName":"Guan","suffix":""},{"id":370347733,"identity":"e395f114-19e7-4fbf-9a54-7b3ea0cee4fb","order_by":13,"name":"Jun Liu","email":"","orcid":"https://orcid.org/0000-0001-8300-8646","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Liu","suffix":""},{"id":370347734,"identity":"3b69a237-ec93-4dcd-b1a1-b0c0aebb14a7","order_by":14,"name":"Yulei Deng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yulei","middleName":"","lastName":"Deng","suffix":""},{"id":370347735,"identity":"ba1082e2-fbb1-4558-9340-cf61a148b931","order_by":15,"name":"Fang Xie","email":"","orcid":"https://orcid.org/0000-0003-2667-281X","institution":"Department of Nuclear Medicine \u0026 PET Center, Huashan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2024-10-24 06:00:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5322986/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5322986/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71556963,"identity":"b72cb68c-2b15-4e2c-8aca-d96c7eba9cfa","added_by":"auto","created_at":"2024-12-16 16:29:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":506355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGroup difference and correlation between CP FWf and glymphatic markers. (A)\u003c/strong\u003e Increased CP FWf (p = 0.002) and decreased DTI-ALPS (p = 0.001) were observed in Aβ+ participants in RJNB cohort. \u003cstrong\u003e(B)\u003c/strong\u003e Across all participants, mediation analysis revealed a partial mediation effect of DTI-ALPS between CP FWf and pWMH. Within the Aβ+ group, a significant partial mediation effect of DTI-ALPS between CP FWf and pWMH was observed.\u003cstrong\u003e \u003c/strong\u003eThe Aβ- controls displayed the trend of mediation effect of DTI-ALPS between CP FWf and pWMH.\u003c/p\u003e\n\u003cp\u003eThe log-transformed pWMH volume was used.\u003cstrong\u003e \u003c/strong\u003eAll the mediation analysis was adjusted for age.\u003cstrong\u003e (C)\u003c/strong\u003e Increased CP FWf (p = 0.006) and decreased DTI-ALPS (p = 0.018) were observed in Aβ+ participants in ADNI dataset. \u003cstrong\u003e(D) \u003c/strong\u003ePredictive values of CP FWf alone (blue) and with age and ApoE genotype (red) for Aβ positivity calculated by ROC. The AUCs were similar for CP FWf alone (RJNB: AUC = 0.623; ADNI: AUC = 0.625), and with age and ApoE genotype (RJNB: AUC = 0.770; ADNI: AUC = 0.815). DTI-ALPS = diffusion tensor image analysis along the perivascular space; CP = choroid plexus; FWf = free-water fraction; pWMH = periventricular white matter hyperintensity.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5322986/v1/0d3f4231f360aff3134a01fc.png"},{"id":71556964,"identity":"e906a708-907d-46e4-9cb5-bdb0bd75c980","added_by":"auto","created_at":"2024-12-16 16:29:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":534477,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCP FWf correlated with imaging metrics and blood biomarkers in AD. (A) \u003c/strong\u003eSpearman’s correlation analysis between the potential imaging biomarkers of CP, glymphatic function markers, AD imaging markers and global cognitive performance within the Aβ+ group. The CP FWf showed significant association with CP FA, CPV, DTI-ALPS, pWMH, cortical Tau SUVR, cortical SV2A SUVR, hippocampus volume, cortex volume and MMSE. The log-transformed pWMH volume was used. \u003cstrong\u003e(B) \u003c/strong\u003eThe vertex-wise GLM analysis revealed a negative correlation map between CP FWf and cortical thickness, and Tau SUVR exhibited a positive correlation map with CP FWf. Age and sex were included as covariates in the GLM. Only the significant clusters with corrected p \u0026lt; 0.05 after permutations are colored. The color bar represents uncorrected vertex-wise p values. \u003cstrong\u003e(C) \u003c/strong\u003eCP FWf correlated with peripheral blood AD biomarkers and cognition. Within the Aβ+ group, there was a positive correlation between CP FWf and NFL (β = 3.117, p = 0.026), GFAP (β = 5.28, p \u0026lt; 0.001), NRGN (β = 5.70, p = 0.028), TNF-α (β = 10.83, p = 0.009) and global Tau (β = 9.27, p = 0.002).\u003cstrong\u003e \u003c/strong\u003eThe log-transformed levels of blood AD biomarkers were used. Moreover, CP FWf was negatively associated with cognitive performance, including MMSE, AFT and CDT and SV2A.\u003cstrong\u003e \u003c/strong\u003eAll the regressions were adjusted for age, sex, and ApoE genotype. CP = choroid plexus; FWf = free-water fraction; FA = fractional anisotropy; MD = mean diffusivity; CBF = cerebral blood flow; CPV = volume of CP; DTI-ALPS = diffusion tensor image analysis along the perivascular space; pWMH = periventricular white matter hyperintensity; SUVR = standardized uptake value ratios; NFL = neurofilament light chain; GFAP = glial fibrillary acidic protein; NRGN = neurogranin; TNF-α = Tumor Necrosis Factor-α; MMSE = Mini-Mental State Examination; AFT = animal fluency test; CDT = clock drawing test.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5322986/v1/e2efda5113255096f8297a97.png"},{"id":71558166,"identity":"673ca331-0b1f-4499-8630-59c990dd98dc","added_by":"auto","created_at":"2024-12-16 16:37:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":270399,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediation effects of AD biomarkers on the relationship between CP FWf and MMSE. \u003c/strong\u003eWe observed a partial mediation effect of Tau \u003cstrong\u003e(A)\u003c/strong\u003e, and marginal effect of SV2A \u003cstrong\u003e(B)\u003c/strong\u003e. Notably, GFAP exhibited a full mediation effect \u003cstrong\u003e(C)\u003c/strong\u003e. On the other hand, NFL, NRGN, and TNF-α did not significantly mediate the association between CP FWf and MMSE \u003cstrong\u003e(D)\u003c/strong\u003e. All the mediation analysis was adjusted for age. CP = choroid plexus; FWf = free-water fraction; SV2A = synaptic vesicle glycoprotein 2A; GFAP = glial fibrillary acidic protein; NRGN = neurogranin; TNF-α = Tumor Necrosis Factor-α.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5322986/v1/74300c4fb24d2bdfa180ffe7.png"},{"id":71556966,"identity":"326e83bb-b765-46ea-831b-28fe85b6f9b4","added_by":"auto","created_at":"2024-12-16 16:29:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":530494,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLongitudinal changes in CP FWf indicated neurodegeneration. \u003c/strong\u003eThe linear mixed model analysis indicated that Aβ+ participants exhibited a more rapid increase in CP FWf compared to Aβ- controls. Individual changes are represented by dashed lines, while the average changes in the group are depicted by solid lines \u003cstrong\u003e(A)\u003c/strong\u003e. In the Aβ+ group, both ApoE 4 carriers and non-carriers showed similar rates of CP FWf increase \u003cstrong\u003e(B)\u003c/strong\u003e. The annual changes in CP FWf (ΔCP FWf) were significantly associated with alterations in DTI-ALPS (ΔDTI-ALPS) \u003cstrong\u003e(C)\u003c/strong\u003e. Furthermore, linear mixed models with standardized preprocessing revealed that the growth rate of CP FWf surpassed that of pWMH, Tau SUVR, and GFAP \u003cstrong\u003e(D)\u003c/strong\u003e. CP = choroid plexus; FWf = free-water fraction; DTI-ALPS = diffusion tensor image analysis along the perivascular space; pWMH = periventricular white matter hyperintensity; SUVR = standardized uptake value ratios; GFAP = glial fibrillary acidic protein.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5322986/v1/cfc7ef689841d59f678f6946.png"},{"id":73267315,"identity":"75a445fe-0e42-4d62-b587-b13286068969","added_by":"auto","created_at":"2025-01-08 10:29:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2123036,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5322986/v1/6d7cd1a7-b11f-4851-8353-2a3bb57b4e16.pdf"},{"id":71556973,"identity":"f8474970-5cf6-470e-9d65-df536c06e363","added_by":"auto","created_at":"2024-12-16 16:29:29","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11248908,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-5322986/v1/ea0df594139ba4cc6918eabd.tif"},{"id":71558168,"identity":"d00a557f-63e4-4a51-9217-b85a9f2591d4","added_by":"auto","created_at":"2024-12-16 16:37:29","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":533880,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-5322986/v1/ede164d91b474abc0243fae2.tif"},{"id":71556974,"identity":"6dfe0b6e-72ec-4368-bce0-775770fe5442","added_by":"auto","created_at":"2024-12-16 16:29:29","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":657752,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-5322986/v1/f3d0c0233ba130f54cce21ba.tif"},{"id":71559415,"identity":"e8e33dbb-3b91-4538-9d88-8423c831c944","added_by":"auto","created_at":"2024-12-16 16:45:29","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":732584,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-5322986/v1/bb7a8034f2748ec8211e2a72.tif"},{"id":71560555,"identity":"5fe8b93f-31c1-4a9e-8873-cba571e59f5f","added_by":"auto","created_at":"2024-12-16 16:53:32","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":284541,"visible":true,"origin":"","legend":"","description":"","filename":"MPSupplementADNIAcknowledgementList.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5322986/v1/ff6c8ea74dc8cafa1204bc1e.pdf"},{"id":71556968,"identity":"cb9e0381-3efd-4c72-a437-aa31f90ba4f5","added_by":"auto","created_at":"2024-12-16 16:29:29","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":30966,"visible":true,"origin":"","legend":"","description":"","filename":"MPSupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5322986/v1/c0ec84306604a9fc2e902112.docx"},{"id":71558165,"identity":"957bfc6c-6388-47fb-89ec-5fdb2e75ffae","added_by":"auto","created_at":"2024-12-16 16:37:29","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":19589,"visible":true,"origin":"","legend":"","description":"","filename":"MPSupplementaryfigurelegend.docx","url":"https://assets-eu.researchsquare.com/files/rs-5322986/v1/7338ceda4dbabb7cec3bbe6a.docx"},{"id":71556970,"identity":"827b02b7-4cf9-4949-a25b-5cb8dc655e6f","added_by":"auto","created_at":"2024-12-16 16:29:29","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":40716,"visible":true,"origin":"","legend":"","description":"","filename":"MPSupplementarymethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-5322986/v1/4b7b4b6e35274667eb7eb740.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Choroid Plexus Free-Water Correlates with Glymphatic function and Neurodegeneration in Alzheimer’s Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer's disease (AD) is a neurodegenerative disorder marked by cognitive decline, β-amyloid plaques, hyperphosphorylated Tau, and cortical atrophy[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Moreover, dysfunction in the cerebrospinal fluid (CSF) glymphatic clearance and white matter hyperintensity (WMH) has also been identified as contributing factors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe choroid plexus (CP) produces CSF and regulates its dynamics[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Comprising a single-layer epithelium, fenestrated capillaries, connective tissue, and immune cells, the CP forms the blood-CSF barrier and aids immune surveillance[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the mechanisms behind the CP in neurodegeneration and AD are unclear. Despite this, there is a scarcity of research focusing on components of the CP. Given its role in producing CSF and its location within the ventricles, fluid is one of the predominant components within the CP structure.\u003c/p\u003e \u003cp\u003eFree water refers to unconstrained water molecules in fluid. Diffusion MRI techniques can measure free water in brain tissue[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Previous study showed increased free-water fraction (FWf) in cortical and subcortical regions as neurodegeneration progresses[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Given the choroid plexus's role in CSF regulation, CP FWf may be crucial for brain fluid circulation.\u003c/p\u003e \u003cp\u003eFurthermore, in the brain glymphatic system, CSF enters the brain parenchyma through the perivascular spaces surrounding arteries to clear interstitial fluid waste products[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Prior studies using diffusion tensor image analysis along the perivascular space (DTI-ALPS) index have shown an association between glymphatic dysfunction and the severity of WMH[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Meanwhile, previous study revealed an association between WMH and WMH-linked cortical AD biomarkers[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the relationship between CP FWf and glymphatic clearance or WMH remains unclear.\u003c/p\u003e \u003cp\u003eWe hypothesized that CP FWf in AD correlates with glymphatic function and AD severity across the disease continuum. We undertook this study to: 1) investigate CP FWf changes in AD; 2) examine the relationship between CP FWf and glymphatic function, WMH, as well as AD pathological biomarkers; 3) explore potential mediation factors between CP FWf and cognition; and 4) validate the impact of CP FWf in AD using longitudinal data over a 12-month follow-up period.\u003c/p\u003e"},{"header":"Subjects and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and study design\u003c/h2\u003e \u003cp\u003eThis retrospective study utilized consecutive collected data from the Ruijin NeuroBank of Alzheimer's Disease and Dementia (RJNB-D) cohort between November 2021 and March 2024. The cohort is a prospective cohort for all-cause dementia from memory clinics and cognitively normal volunteers from community, with detailed information in our previous study [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe cross-sectional analysis included 216 participants: 133 Aβ\u0026thinsp;+\u0026thinsp;individuals and 83 Aβ- controls. The Aβ\u0026thinsp;+\u0026thinsp;group was further diagnosed as AD or mild cognitive impairment (MCI) according to 2016 NIA-AA criteria.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For longitudinal analysis, 76 of 216 baseline participants completed 12-month follow-up assessments, including repeated neuropsychological tests, multimodal MRI, and PET scans. All follow-up assessments were conducted within two weeks of the 12-month interview.\u003c/p\u003e \u003cp\u003eFor external validation, Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI) dataset was utilized. Participants continuously enrolled in ADNI 3 between June 2017 and June 2022 were included in the study. Apart from the ADNI enrollment criteria, the following additional exclusion criteria were applied: 1. without multi-shell diffusion MRI scans necessary for FWf calculation; 2. lack of 18F-Florbetapir (FBP)-PET scans required to determine Aβ status.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eIn RJNB-D, the participants underwent multimodal MRI, PET scans, neuropsychological tests, and blood sample collection (Supplementary Fig.\u0026nbsp;1A). Multimodal MRI included 3D high-resolution T1, fluid-attenuated inversion recovery (FLAIR), multi-shell diffusion MRI (dMRI) and pseudo-continuous arterial spin labelled (pCASL) perfusion MRI (Supplementary Fig.\u0026nbsp;1B, Supplementary Method 1). PET scans included FBP for Aβ, \u003csup\u003e18\u003c/sup\u003eF-MK-6240 for Tau, and \u003csup\u003e18\u003c/sup\u003eF-SynVesT-1 for synaptic density (Supplementary Method 2). Neuropsychological assessments included Mini-Mental State Examination (MMSE, Chinese Version)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], 20-minute auditory verbal learning test (AVLT)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], Clock-drawing test (CDT)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], Boston naming test (BNT), shape-trail test (STT-A and STT-B)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and animal fluency test (AFT) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Venous blood samples were used for ApoE genotyping and glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL), neurogranin (NRGN), and Tumor Necrosis Factor-α (TNF-α) (Supplementary Method 3).\u003c/p\u003e\n\u003ch3\u003eMRI acquisition and analysis\u003c/h3\u003e\n\u003cp\u003eMRI data were acquired using a 3.0-T scanner with a 64-channel head coil (uMR 890, United Imaging Healthcare, Shanghai, China). Detailed parameters of T1-weighted, 3D FLAIR, diffusion and pCASL sequences are presented in Supplementary Method 1.\u003c/p\u003e \u003cp\u003eVolumetric analysis was performed using FreeSurfer 7.1.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://surfer.nmr.mgh.harvard.edu\u003c/span\u003e\u003cspan address=\"https://surfer.nmr.mgh.harvard.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). WMH was defined and segmented using Brain Intensity AbNormality Classification Algorithm (BIANCA, version 6.0, FMRIB Software Library, Oxford, UK), and was further categorized into pWMH and deep white matter hyperintensity (dWMH) by 10-mm distance threshold from the ventricles[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Diffusion MRI data underwent pre-processing and tensor fitting to generate ractional anisotropy (FA) and mean diffusivity (MD), and to calculate DTI-ALPS index using FSL (version 6.0.3, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fmrib.ox.ac.uk/fsl\u003c/span\u003e\u003cspan address=\"http://www.fmrib.ox.ac.uk/fsl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Free-water imaging of CP was assessed using the neurite orientation dispersion and density imaging (NODDI) model. The CP FWf was calculated as the average of the left and right CP ROI registered with T1-weighted image. cerebral blood flow (CBF) in the CP was estimated using the FSL BASIL toolbox. Detailed information of MRI analysis is presented in Supplementary Method 1.\u003c/p\u003e\n\u003ch3\u003ePET-MRI acquisition and analysis\u003c/h3\u003e\n\u003cp\u003eThe PET scans were performed using a 3T whole-body PET/MR scanner (uPMR 790, United Imaging, China). Participants received intravenous FBP for Aβ, \u003csup\u003e18\u003c/sup\u003eF-MK-6240 for Tau, and \u003csup\u003e18\u003c/sup\u003eF-SynVesT-1 for synaptic vesicle glycoprotein 2A (SV2A) to assess synaptic loss as previously described[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn automated pipeline derived cortical standardized uptake value ratios (SUVR) using the PETSurfer toolbox in FreeSurfer 7.1.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://surfer.nmr.mgh.harvard.edu\u003c/span\u003e\u003cspan address=\"https://surfer.nmr.mgh.harvard.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with the cerebellum as a reference. High-resolution segmentation from T1 images facilitated partial volume correction, followed by registration and validation of PET and anatomical images. Detailed information of PET-MRI acquisition and analysis is presented in Supplementary Method 2.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eGroup comparisons used t-tests for continuous variables and Chi-square tests for categorical variables. Non-normal distributions were log-transformed. Logistic regression was used to adjust for age, sex, and ApoE genotype.\u003c/p\u003e \u003cp\u003eWithin each group, correlations were assessed using Pearson's or Spearman's methods based on variable distribution. Multivariable linear regression analyzed CP FWf and other imaging measures, adjusted for age, sex, and ApoE genotype. Bonferroni correction addressed multiple comparisons. Vertex-wise correlation analysis using generalized linear model, controlled for age and sex, demonstrated spatial correlations between CP FWf and AD markers. Permutation tests reduced false positivity rates. Mediation analyses were conducted using PROCESS v3.2 for SPSS (version 25.0, IBM), adjusted for age.\u003c/p\u003e \u003cp\u003eLongitudinal analysis was performed using repeated measures ANOVA. Alteration rates between Aβ\u0026thinsp;+\u0026thinsp;participants and Aβ- controls were compared using linear mixed model. The relationship between CP FWf and DTI-ALPS changes was explored in the Aβ\u0026thinsp;+\u0026thinsp;group. Rates of longitudinal changes in CP FWf and related metrics were compared after standardizing baseline measures. Analyses were conducted using R (version 4.3.3, The R Foundation) or SPSS (version 25.0, IBM), with significance at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed). All analyses were independently performed by two authors (XX and XY).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy approval\u003c/h2\u003e \u003cp\u003e This study was approved by the ethics committee at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China. All participants or caregivers provided written informed consent. The study adhered to the 1964 Declaration of Helsinki and its amendments. The study was registered on ClinicalTrials.gov (NCT05623124). Respectively, numbers of 94, 107, 120 participants of the same cohort overlapped previous publications[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], regarding to WMH-related neurodegeneration[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and synaptic loss in AD[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This study furthered the previous findings by evaluating the influence of free-water in the CP on glymphatic dysfunction and WMH in AD.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics and group comparison\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline characteristics of all participants from our RJNB-D cohort (n\u0026thinsp;=\u0026thinsp;216, mean age, 69.20 years\u0026thinsp;\u0026plusmn;\u0026thinsp;8.00 [SD]; 128 females). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists the characteristics of the 76 participants completed 12-month follow-up (mean age, 70.09 years\u0026thinsp;\u0026plusmn;\u0026thinsp;7.29 [SD]; 49 females).\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\u003eBaseline Demographic and MRI Characteristics in RJNB cohort(n\u0026thinsp;=\u0026thinsp;216)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAβ+ (n\u0026thinsp;=\u0026thinsp;133)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAβ- (n\u0026thinsp;=\u0026thinsp;83)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.59\u0026thinsp;\u0026plusmn;\u0026thinsp;7.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.98\u0026thinsp;\u0026plusmn;\u0026thinsp;8.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.56\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.16\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoE 4 carrier\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.07\u0026thinsp;\u0026plusmn;\u0026thinsp;6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.18\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eCP FWf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCP FA\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCP MD\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCP CBF\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTI-ALPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epWMH\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003edWMH\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e#, The log-transformed values were used\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e$, Different sample size for ApoE genotype: Aβ+, n\u0026thinsp;=\u0026thinsp;108; Aβ-, n\u0026thinsp;=\u0026thinsp;66.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\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\u003eMonth 12 Demographic and MRI Characteristics in RJNB cohort (n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAβ+ (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAβ- (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.67\u0026thinsp;\u0026plusmn;\u0026thinsp;7.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoE 4 carrier\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eBaseline CP FWf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth 12 CP FWf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔCP FWf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline DTI-ALPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth 12 DTI-ALPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔDTI-ALPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline pWMH\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth 12 pWMH\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.07\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔpWMH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Tau SUVR\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eMonth 12 Tau SUVR\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eBaseline GFAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153.05\u0026thinsp;\u0026plusmn;\u0026thinsp;93.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.94\u0026thinsp;\u0026plusmn;\u0026thinsp;64.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eMonth 12 GFAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167.52\u0026thinsp;\u0026plusmn;\u0026thinsp;84.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.61\u0026thinsp;\u0026plusmn;\u0026thinsp;71.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e#, Nature logarithm was taken for normalization.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e$, Different sample size for ApoE genotype: Aβ+, n\u0026thinsp;=\u0026thinsp;43; Aβ-, n\u0026thinsp;=\u0026thinsp;26.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCompared to Aβ- group, increased CP FWf (0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 vs 0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06, p\u0026thinsp;=\u0026thinsp;0.002), pWMH volume ( -3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52 vs -4.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), dWMH volume ( -5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70 vs -5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70, p\u0026thinsp;=\u0026thinsp;0.005, both log-transformed), and decreased DTI-ALPS (1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17 vs 1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16, p\u0026thinsp;=\u0026thinsp;0.001) were observed in Aβ\u0026thinsp;+\u0026thinsp;participants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The FA, MD, CBF and volumes of CP (CPV) were similar between groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, all p\u0026thinsp;\u0026gt;\u0026thinsp;0.050).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter adjustment for age, sex and ApoE genotype, CP FWf (β\u0026thinsp;=\u0026thinsp;10.02, p\u0026thinsp;=\u0026thinsp;0.015), DTI-ALPS (β = -3.19, p\u0026thinsp;=\u0026thinsp;0.007), pWMH (β\u0026thinsp;=\u0026thinsp;0.33, p\u0026thinsp;=\u0026thinsp;0.007) and dWMH (β\u0026thinsp;=\u0026thinsp;0.28, p\u0026thinsp;=\u0026thinsp;0.013) independently indicated Aβ positivity (Supplementary Table\u0026nbsp;1\u0026ndash;4), respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe CP FWf associated with DTI-ALPS and WMH\u003c/h2\u003e \u003cp\u003eAmong all participants, CP FWf correlated with DTI-ALPS (r = -0.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), pWMH (r\u0026thinsp;=\u0026thinsp;0.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and dWMH (r\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;=\u0026thinsp;0.003). With adjustment for age, mediation analysis showed DTI-ALPS partially mediated the association between CP FWf and pWMH (indirect effect standardized-β\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, mediated effect\u0026thinsp;=\u0026thinsp;32.59%; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), but not dWMH (indirect effect standardized-β\u0026thinsp;=\u0026thinsp;0.09, p\u0026thinsp;=\u0026thinsp;0.236).\u003c/p\u003e \u003cp\u003eWithin the Aβ\u0026thinsp;+\u0026thinsp;group, CP FWf correlated with DTI-ALPS (r = -0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and pWMH (r\u0026thinsp;=\u0026thinsp;0.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not dWMH (r\u0026thinsp;=\u0026thinsp;0.11, p\u0026thinsp;=\u0026thinsp;0.213). Partial mediation effect of DTI-ALPS between CP FWf and pWMH was also observed (indirect effect standardized-β\u0026thinsp;=\u0026thinsp;0.25, p\u0026thinsp;=\u0026thinsp;0.003, mediated effect\u0026thinsp;=\u0026thinsp;35.76%; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), adjusted for age.\u003c/p\u003e \u003cp\u003eIn Aβ- controls, CP FWf was also related to DTI-ALPS (r = -0.44, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and pWMH (r\u0026thinsp;=\u0026thinsp;0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), yet the mediation effect of DTI-ALPS between CP FWf and pWMH was insignificant (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), adjusted for age.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eValidation of CP FWf measurements\u003c/h2\u003e \u003cp\u003eThe measurements of CP FWf were repeated with an eroded CP mask (by zeroing non-zero voxels when zero voxels found in kernel of 2 mm\u003csup\u003e3\u003c/sup\u003e box in the original CP mask) to minimize the contamination of CSF. The FWf in the eroded CP FWf was highly correlated with the original CP FWf (ρ\u0026thinsp;=\u0026thinsp;0.76, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as DTI-ALPS (ρ = -0.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and pWMH (ρ\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;=\u0026thinsp;0.044, Supplementary Fig.\u0026nbsp;2). We also test the FWf measurement in another non-neuronal tissue: CSF. The distribution of FWf values in the central part of lateral ventricles was skewed, with the majority of values equaling to 1 (Supplementary Fig.\u0026nbsp;3). It validated our application of FWf measurement since the main component of CSF in ventricles were free water.\u003c/p\u003e \u003cp\u003eExternal validation was further performed using the public ADNI dataset. \u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e summarizes the baseline characteristics of all participants (n\u0026thinsp;=\u0026thinsp;168, mean age, 72.52 years\u0026thinsp;\u0026plusmn;\u0026thinsp;7.77 [SD]; 93 females). In consistency with our cohort, increased CP FWf (0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 vs 0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07, p\u0026thinsp;=\u0026thinsp;0.006) and decreased DTI-ALPS (1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 vs 1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17, p\u0026thinsp;=\u0026thinsp;0.018) were observed in Aβ\u0026thinsp;+\u0026thinsp;participants from the ADNI dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). In the ADNI dataset, CP FWf was also highly correlated with DTI-ALPS among all the participants (r = -0.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), in the Aβ\u0026thinsp;+\u0026thinsp;subgroup (r = -0.57, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and in the Aβ- subgroup (r = -0.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn addition, predictive values of CP FWf calculated by ROC was similar between ADNI (AUC\u0026thinsp;=\u0026thinsp;0.625) and our cohort (AUC\u0026thinsp;=\u0026thinsp;0.623), and the predictive values were moderately improved when adjusted for age and ApoE genotype (ADNI: AUC\u0026thinsp;=\u0026thinsp;0.815; RJNB: AUC\u0026thinsp;=\u0026thinsp;0.770, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCP FWf across the AD continuum\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eCP FWf correlated with AD imaging and blood biomarkers\u003c/h2\u003e \u003cp\u003eIn the Aβ\u0026thinsp;+\u0026thinsp;group of RJNB-D cohort, CP FWf showed significant relationship with AD imaging markers, including cortical Tau SUVR, cortical SV2A SUVR, hippocampus volume, cortex volume, as well as MMSE score (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Supplementary Table\u0026nbsp;6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdjusted for age, sex, and ApoE genotype, elevated CP FWf linked to increased cortical Tau accumulation (Tau SUVR, β\u0026thinsp;=\u0026thinsp;9.27, p\u0026thinsp;=\u0026thinsp;0.002, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, Supplementary Table\u0026nbsp;7) and decreased cortical synaptic density (SV2A SUVR, β = -3.43, p\u0026thinsp;=\u0026thinsp;0.017, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, Supplementary Table\u0026nbsp;8). However, CP FWf did not influence FBP SUVR (β\u0026thinsp;=\u0026thinsp;0.98, p\u0026thinsp;=\u0026thinsp;0.103). Vertex-wise analysis illustrated that CP FWf negatively correlated with cortical thickness in bilateral post cingulate gyrus, precuneus, temporal, and insular lobes, but positively correlated with Tau SUVR in bilateral insular, temporal regions, and precuneus (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Moreover, CP FWf negatively correlated with SV2A SUVR in the right inferior parietal gyrus (Supplementary Fig.\u0026nbsp;4)\u003c/p\u003e \u003cp\u003eWithin the Aβ\u0026thinsp;+\u0026thinsp;group, CP FWf correlated with NFL (β\u0026thinsp;=\u0026thinsp;3.12, p\u0026thinsp;=\u0026thinsp;0.026, n\u0026thinsp;=\u0026thinsp;92), GFAP (β\u0026thinsp;=\u0026thinsp;5.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, n\u0026thinsp;=\u0026thinsp;92), NRGN (β\u0026thinsp;=\u0026thinsp;5.70, p\u0026thinsp;=\u0026thinsp;0.028, n\u0026thinsp;=\u0026thinsp;82), and TNF-α (β\u0026thinsp;=\u0026thinsp;10.83, p\u0026thinsp;=\u0026thinsp;0.009, n\u0026thinsp;=\u0026thinsp;81) (Supplementary Table\u0026nbsp;9\u0026ndash;12), adjusted for age, sex and ApoE genotype. Due to the different availability of adequate blood samples, the sample sizes were marked respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAD biomarkers mediated CP FWf effect on cognition\u003c/h2\u003e \u003cp\u003eIncreased CP FWf linked to worse cognitive performance, including overall cognition (MMSE, β = -53.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), verbal function (AFT, β = -29.83, p\u0026thinsp;=\u0026thinsp;0.014), and executive function (CDT, β = -35.29, p\u0026thinsp;=\u0026thinsp;0.025) within the Aβ\u0026thinsp;+\u0026thinsp;group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, Supplementary Table\u0026nbsp;13\u0026ndash;15), adjusted for age, sex, and ApoE genotype.\u003c/p\u003e \u003cp\u003ePartial mediation effects of Tau SUVR (indirect effect standardized-β = -0.18, p\u0026thinsp;=\u0026thinsp;0.043, mediated effect\u0026thinsp;=\u0026thinsp;40.88%, n\u0026thinsp;=\u0026thinsp;110, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and SV2A SUVR (indirect effect standardized-β = -0.21, p\u0026thinsp;=\u0026thinsp;0.089, mediated effect\u0026thinsp;=\u0026thinsp;21.73%, n\u0026thinsp;=\u0026thinsp;64, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) were observed between CP FWf and MMSE, adjusted for age. Although NFL, NRGN, and TNF-α failed to mediate the association between CP FWf and MMSE (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), GFAP exhibited a full mediation effect (mediated effect\u0026thinsp;=\u0026thinsp;38.69%, n\u0026thinsp;=\u0026thinsp;111, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), adjusted for age. These findings suggested that the impact of CP FWf on cognition is mediated via Tau- and GFAP-related pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eThe effect of longitudinal CP FWf changes\u003c/h2\u003e \u003cp\u003eDuring the 12-month follow-up, CP FWf increased drastically in Aβ\u0026thinsp;+\u0026thinsp;participants (F-statistic\u0026thinsp;=\u0026thinsp;16.673, corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Supplementary Table\u0026nbsp;16). Moreover, Aβ\u0026thinsp;+\u0026thinsp;participants demonstrated faster growth rate than Aβ- controls (time \u0026times; group interaction effect: F-statistic\u0026thinsp;=\u0026thinsp;4.118, corrected p\u0026thinsp;=\u0026thinsp;0.046, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Supplementary Table\u0026nbsp;16). The growth rates of CP FWf did not differ between ApoE 4 carriers (n\u0026thinsp;=\u0026thinsp;27) and non-carriers (n\u0026thinsp;=\u0026thinsp;16) in the Aβ\u0026thinsp;+\u0026thinsp;group (F-statistic\u0026thinsp;=\u0026thinsp;0.004, p\u0026thinsp;=\u0026thinsp;0.949, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Aβ\u0026thinsp;+\u0026thinsp;participants, annual changes in CP FWf, DTI-ALPS, and pWMH volume were calculated as ΔCP FWf, ΔDTI-ALPS, and ΔpWMH. Spearman\u0026rsquo;s correlation analysis suggested ΔCP FWf paralleled ΔDTI-ALPS (ρ = -0.42, p\u0026thinsp;=\u0026thinsp;0.006, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), but not ΔpWMH (ρ\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;=\u0026thinsp;0.173). And the association between ΔCP FWf and ΔDTI-ALPS remained significant upon adjustment for age and sex (β = -1.02, p\u0026thinsp;=\u0026thinsp;0.016). The growth rate of CP FWf exceeded that of pWMH (interaction effect, F-statistic\u0026thinsp;=\u0026thinsp;11.201, corrected p\u0026thinsp;=\u0026thinsp;0.001), Tau SUVR (interaction effect, F-statistic\u0026thinsp;=\u0026thinsp;6.804, corrected p\u0026thinsp;=\u0026thinsp;0.011) and GFAP (interaction effect, F-statistic\u0026thinsp;=\u0026thinsp;4.430, corrected p\u0026thinsp;=\u0026thinsp;0.039, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study observed increased CP FWf and decreased DTI-ALPS index at baseline in Aβ\u0026thinsp;+\u0026thinsp;participants. And we noted elevated CP FWf exacerbates pWMH, partially mediated by the glymphatic system, revealing a potential mechanism linking CSF dynamics to white matter lesions. We also observed significant associations between CP FWf and pivotal AD imaging markers (cortical Tau SUVR, cortical SV2A SUVR, hippocampus volume, cortex volume) and cognitive performance. In longitudinal analyses, we observed a significant and faster increase in CP FWf during a 12-month follow-up period in Aβ\u0026thinsp;+\u0026thinsp;participants compared to Aβ- controls, and the rate of progression of CP FWf exceeded that of WMH and Tau accumulation.\u003c/p\u003e \u003cp\u003eThe CP is a highly vascularized structure that regulates CSF, clears neurotoxins, and plays a crucial role in the glymphatic system[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Studies in AD demonstrated that CPV was linked to cognitive impairment[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and levels of Aβ and Tau in the CSF, indicating that CP may participate the clearance of Aβ and Tau[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Prior results regarding the relationship between CPV and Aβ positivity were inconsistent[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These disagreements may be attributed to the contamination of partial volume effect of CSF in calculation CPV by traditional methods[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and also the morphological nature of CPV, which may display a relatively delayed response to Aβ deposition. In the current study, we also failed to verify the association between CPV and Aβ positivity. However, after evaluating various novel imaging biomarkers that reflects CP microstructure, including CP FWf, CP FA, CP MD and CP CBF, we found that CP FWf, derived from free-water mapping, was the only index that significant differed between Aβ\u0026thinsp;+\u0026thinsp;and Aβ- groups. Free-water mapping uses a bi-tensor model to differentiate water diffusion in brain tissue and extracellular space, providing better microstructural insights than traditional single-tensor model[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe DTI-ALPS index is a reliable parameter of glymphatic system[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Previous studies showed that enlarged CP and reduced DTI-ALPS were both associated with WMH growth[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, DTI-ALPS mediated the association between CP and WMH burden, suggesting that WMH growth can result from glymphatic disorder[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In our study, CP FWf showed solid correlation with DTI-ALPS and WMH, suggesting that CP FWf reliably indicated glymphatic function. We noticed that in Aβ\u0026thinsp;+\u0026thinsp;participants CP FWf was associated with pWMH that anatomically located nearer to the CP, but not with dWMH, and there is a partial mediating effect of DTI-ALPS between CP FWf and pWMH.\u003c/p\u003e \u003cp\u003eA recent study by Jeong \u003cem\u003eet al\u003c/em\u003e demonstrated a positive correlation between CP enlargement and Aβ burden, but this correlation was only significant in the AD non-dementia group[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], likely due to the \u0026ldquo;ceiling effect\u0026rdquo;, as Aβ is saturated in dementia state[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Similarly, our study showed that CP FWf was significantly associated with Aβ positivity, but did not correlate with Aβ burden measured by FBP SUVR. A recent report indicated a positive correlation between global Tau burden and CP enlargement in AD[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We further these findings by vertex-wise analysis, and showed that CP FWf changed in parallel with Tau accumulation primarily in bilateral insular, temporal lobes, and precuneus. Furthermore, we found that increased Tau burden mediated the detrimental effects of CP FWf on cognition. This finding adds clinical evidence to a recent study indicating that glymphatic dysfunction impairs cognition through poor Tau clearance in AD mouse models[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to the clearance of neurotoxins, glymphatic system can also modulate neuroinflammation[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. We explored the relationship between peripheral neuroinflammatory markers and CP FWf. GFAP correlated with CP FWf, and substantially mediated the negative impact of CP FWf on cognitive function. GFAP is a structural protein in astrocytes and is a marker of reactive gliosis related to aging[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Astrocytes are critical components of glymphatic system by ensheathing the brain vasculature with their endfeet[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Previous study showed that blood GFAP correlated to impaired glymphatic function and global cognition in community-dwelling older adults[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Our findings, in consistent with the previous publications, provided new evidence for involvement of GFAP-related pathway in glymphatic dysfunction in AD pathology.\u003c/p\u003e \u003cp\u003eThe current study is the first study to calculate the FWf of CP. In order to further validate the solidity of our measurements, the FWf values of non-neuronal free water in lateral ventricles and eroded part of CP were extracted. In consistency with our expectancy, the FWf of CSF in the lateral ventricles was approximate to 1, and the FWf of eroded CP was highly correlated to CP. Finally, the diagnostic power (AUC) of CP FWf were highly consistent in ADNI and our cohort. Although as a single parameter, the CP FWf failed to present optimal AUCs, the values were moderately improved with adjustment of age and ApoE genotype, indicating its potential value as one of the contributors in a composite predictive frame.\u003c/p\u003e \u003cp\u003eOur study has limitations. First, the small sample size of longitudinal study, as the cohort is newly established with about half of the participants not reaching the 12-month follow-up. Also, this duration may be too short to observe significant changes in imaging markers like pWMH volume, suggesting a longer follow-up is warranted. However, ΔCP FWf already showed remarkable changes, indicating its sensitivity in monitoring disease progression. Additionally, the limited number of blood samples and analyses may influence the results, so findings related to blood markers should be viewed as exploratory and hypothesis-generating.\u003c/p\u003e \u003cp\u003eIn conclusion, our results indicate that CP FWF serves as a novel imaging marker that reflecting various AD pathological features, complementing structural MRI and PET imaging. By capturing the multifaceted aspects and longitudinal changes in AD, CP FWF holds potential for early diagnosis and precise monitoring of disease progression, offering the possibility of personalized and timely treatment interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interest\u003c/h2\u003e \u003cp\u003eThe authors have declared that no conflict of interest exist.\u003c/p\u003e \u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eXX and BL designed the study; XX, XY, MS, and BL wrote the initial draft of the manuscript; XX, XY, JZ, RS, FX and BL performed data analysis; YW, YG, JL and YD consulted on statistical methods; XX, XY and WX generated tables and figures; YZ, QH, LS, WW performed neurological function evaluation and collected blood samples and imaging data; all authors read the manuscript and provided input. XX, XY, MS and BL finalized the manuscript. FX and BL oversaw the study and provided direction, funding, and resources.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eSee Supplemental Acknowledgments for the complete list of ADNI investigators and their affiliations. We obtained part of the data from the ADNI database in preparation for this article. The investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this research. We would like to thank all the researchers and participants in the ADNI initiative. The authors thank the participants and their families for their participation in this study. This study was supported by National Natural Science Foundation of China (82271441, 82171473, 81901180), Shanghai Rising-Star Program (21QA1405800), National Key Research and Development Program of China, Scientific and technological innovation 2030 - Major Projects (2022ZD0213800).\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eFor all main figures with RJNB-D datasets, the extracted traces from the raw image files are available on Figshare (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/articles/dataset/Choroid_Plexus_Free-Water_Correlates_with_Glymphatic_Dysfunction_and_Tracks_Neurodegeneration_in_Alzheimer_s_Disease_The_RJNB-D_Study/26043871\u003c/span\u003e\u003cspan address=\"https://figshare.com/articles/dataset/Choroid_Plexus_Free-Water_Correlates_with_Glymphatic_Dysfunction_and_Tracks_Neurodegeneration_in_Alzheimer_s_Disease_The_RJNB-D_Study/26043871\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Values for all data points in graphs are reported in the Supporting Data Values file.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJack CR, Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB, et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016;87:539\u0026ndash;547.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarnier-Crussard A, Cotton F, Krolak-Salmon P, Ch\u0026eacute;telat G. White matter hyperintensities in Alzheimer\u0026rsquo;s disease: Beyond vascular contribution. Alzheimers Dement. 2023;19:3738\u0026ndash;3748.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi JD, Moon Y, Kim H-J, Yim Y, Lee S, Moon W-J. Choroid Plexus Volume and Permeability at Brain MRI within the Alzheimer Disease Clinical Spectrum. Radiology. 2022;304:635\u0026ndash;645.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Wang Q, Haldar JP, Yeh F-C, Xie M, Sun P, et al. Quantification of increased cellularity during inflammatory demyelination. Brain. 2011;134:3590\u0026ndash;3601.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiu SY, Chen R, Wang W-E, Armstrong MJ, Boeve BF, Savica R, et al. Longitudinal Free-Water Changes in Dementia with Lewy Bodies. Mov Disord. 2024;39:836\u0026ndash;846.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHablitz LM, Nedergaard M. The glymphatic system. Curr Biol. 2021;31:R1371\u0026ndash;R1375.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Zhou Y, Zhong W, Zhu X, Chen Y, Zhang K, et al. Choroid Plexus Enlargement Exacerbates White Matter Hyperintensity Growth through Glymphatic Impairment. Ann Neurol. 2023;94:182\u0026ndash;195.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Chen H, Wang J, Huang Q, Xu X, Wang W, et al. Linking white matter hyperintensities to regional cortical thinning, amyloid deposition, and synaptic density loss in Alzheimer\u0026rsquo;s disease. Alzheimers Dement. 2024. 22 April 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/alz.13845\u003c/span\u003e\u003cspan address=\"10.1002/alz.13845\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi B, Chen H, Zheng Y, Xu X, You Z, Huang Q, et al. Loss of synaptic density in nucleus basalis of meynert indicates distinct neurodegeneration in Alzheimer\u0026rsquo;s disease: the RJNB-D study. Eur J Nucl Med Mol Imaging. 2024. 8 August 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00259-024-06862-z\u003c/span\u003e\u003cspan address=\"10.1007/s00259-024-06862-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCummings J. The National Institute on Aging-Alzheimer\u0026rsquo;s Association Framework on Alzheimer\u0026rsquo;s disease: Application to clinical trials. Alzheimers Dement. 2019;15:172\u0026ndash;178.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatzman R, Zhang MY, Ouang-Ya-Qu null, Wang ZY, Liu WT, Yu E, et al. A Chinese version of the Mini-Mental State Examination; impact of illiteracy in a Shanghai dementia survey. J Clin Epidemiol. 1988;41:971\u0026ndash;978.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Q, Lv Y, Zhou Y, Hong Z, Guo Q. Short-term delayed recall of auditory verbal learning test is equivalent to long-term delayed recall for identifying amnestic mild cognitive impairment. PLoS One. 2012;7:e51157.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMendez MF, Ala T, Underwood KL. Development of scoring criteria for the clock drawing task in Alzheimer\u0026rsquo;s disease. J Am Geriatr Soc. 1992;40:1095\u0026ndash;1099.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Q, Guo Q, Hong Z. Clustering and switching during a semantic verbal fluency test contribute to differential diagnosis of cognitive impairment. Neurosci Bull. 2013;29:75\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Wang J, Xu X, You Z, Huang Q, Huang Y, et al. In vivo synaptic density loss correlates with impaired functional and related structural connectivity in Alzheimer\u0026rsquo;s disease. J Cereb Blood Flow Metab. 2023;43:977\u0026ndash;988.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai Z, Yang Z, Chen X, Zheng W, Zhuang Z, Liao Y, et al. The aging of glymphatic system in human brain and its correlation with brain charts and neuropsychological functioning. Cereb Cortex. 2023;33:7896\u0026ndash;7903.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eČarna M, Onyango IG, Katina S, Holub D, Novotny JS, Nezvedova M, et al. Pathogenesis of Alzheimer\u0026rsquo;s disease: Involvement of the choroid plexus. Alzheimer\u0026rsquo;s \u0026amp; Dementia. 2023;19:3537\u0026ndash;3554.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTadayon E, Pascual-Leone A, Press D, Santarnecchi E, Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative. Choroid plexus volume is associated with levels of CSF proteins: relevance for Alzheimer\u0026rsquo;s and Parkinson\u0026rsquo;s disease. Neurobiol Aging. 2020;89:108\u0026ndash;117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong H, Hong L, Luo X, Zeng Q, Li K, Wang S, et al. The relationship between amyloid pathology, cerebral small vessel disease, glymphatic dysfunction, and cognition: a study based on Alzheimer\u0026rsquo;s disease continuum participants. Alzheimers Res Ther. 2024;16:43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeong SH, Park CJ, Cha J, Kim S-Y, Lee S-K, Kim YJ, et al. Choroid Plexus Volume, Amyloid Burden, and Cognition in the Alzheimer\u0026rsquo;s Disease Continuum. Aging Dis. 2024. 23 January 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14336/AD.2024.0118\u003c/span\u003e\u003cspan address=\"10.14336/AD.2024.0118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaito Y, Kamagata K, Uchida W, Takabayashi K, Aoki S. The partial volume effect of choroid plexus in pathogenesis of Alzheimer\u0026rsquo;s disease. Alzheimer\u0026rsquo;s \u0026amp; Dementia. 2023;19:4756\u0026ndash;4757.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePasternak O, Sochen N, Gur Y, Intrator N, Assaf Y. Free water elimination and mapping from diffusion MRI. Magn Reson Med. 2009;62:717\u0026ndash;730.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou L, Li G, Zhang Y, Zhang M, Chen Z, Zhang L, et al. Increased free water in the substantia nigra in idiopathic REM sleep behaviour disorder. Brain. 2021;144:1488\u0026ndash;1497.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eButler T, Zhou L, Ozsahin I, Wang XH, Garetti J, Zetterberg H, et al. Glymphatic clearance estimated using diffusion tensor imaging along perivascular spaces is reduced after traumatic brain injury and correlates with plasma neurofilament light, a biomarker of injury severity. Brain Commun. 2023;5:fcad134.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBjorkli C, Sandvig A, Sandvig I. Bridging the Gap Between Fluid Biomarkers for Alzheimer\u0026rsquo;s Disease, Model Systems, and Patients. Front Aging Neurosci. 2020;12:272.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOta M, Sato N, Nakaya M, Shigemoto Y, Kimura Y, Chiba E, et al. Relationship between the tau protein and choroid plexus volume in Alzheimer\u0026rsquo;s disease. Neuroreport. 2023;34:546\u0026ndash;550.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopes DM, Wells JA, Ma D, Wallis L, Park D, Llewellyn SK, et al. Glymphatic inhibition exacerbates tau propagation in an Alzheimer\u0026rsquo;s disease model. Alzheimer\u0026rsquo;s Research \u0026amp; Therapy. 2024;16:71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMogensen FL-H, Delle C, Nedergaard M. The Glymphatic System (En)during Inflammation. Int J Mol Sci. 2021;22:7491.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabbatini M, Barili P, Bronzetti E, Zaccheo D, Amenta F. Age-related changes of glial fibrillary acidic protein immunoreactive astrocytes in the rat cerebellar cortex. Mech Ageing Dev. 1999;108:165\u0026ndash;172.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei Y-C, Hsu C-CH, Huang W-Y, Lin C, Chen C-K, Chen Y-L, et al. Vascular risk factors and astrocytic marker for the glymphatic system activity. Radiol Med. 2023;128:1148\u0026ndash;1161.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-5322986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5322986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFree-water imaging of the choroid plexus (CP) is an index revealing components of the CP, which may improve the evaluation of Alzheimer's disease (AD).\u003cstrong\u003e \u003c/strong\u003eOur study evaluated free water fraction (FWf) of CP in 216 participants (133 Aβ+ participants and 83 Aβ- controls) continuously enrolled in the Ruijin NeuroBank of Alzheimer's Disease and Dementia (RJNB-D) cohort. The ADNI dataset was used for external validation. \u0026nbsp;Assessments of AD neurodegeneration included Aβ-PET, Tau-PET, synaptic vesicle glycoprotein 2A-PET scans, and blood biomarkers included glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL), neurogranin (NRGN), and Tumor Necrosis Factor-α (TNF-α). The CP FWf and diffusion tensor image analysis along the perivascular space (DTI-ALPS) index were independently associated with Aβ positivity in both RJNB-D and ADNI datasets. Within the Aβ+ group, the negative correlation between CP FWf and DTI-ALPS was validated by two datasets. Furthermore, we observed a partial mediation effect of DTI-ALPS between CP FWf and periventricular white matter hyperintensity (pWMH). Elevated CP FWf was linked to worse Mini-Mental State Examination, increased Tau accumulation, reduced synaptic density, and elevated levels of NFL, GFAP, NRGN, and TNF-α. Longitudinally, CP FWf increased faster in Aβ+ participants than Aβ- controls (time × group interaction effect p = 0.046). The growth of CP FWf was associated with a reduction in DTI-ALPS (ρ = -0.42, p = 0.006), and the growth rate of CP FWf surpassed that of pWMH, Tau, and GFAP. Overall, our findings suggest that\u003cstrong\u003e \u003c/strong\u003eelevated CP FWf indicates impaired glymphatic function and AD neurodegeneration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is registered on ClinicalTrials.gov (NCT05623124).\u003c/p\u003e","manuscriptTitle":"Choroid Plexus Free-Water Correlates with Glymphatic function and Neurodegeneration in Alzheimer’s Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 16:29:24","doi":"10.21203/rs.3.rs-5322986/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":"0e6fbf96-2a7d-4e35-946f-c2e767c097a1","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39417604,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":39417605,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":39417606,"name":"Biological sciences/Neuroscience"},{"id":39417607,"name":"Health sciences/Biomarkers/Prognostic markers"}],"tags":[],"updatedAt":"2025-01-08T10:21:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 16:29:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5322986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5322986","identity":"rs-5322986","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}