Choroid Plexus Free-Water Correlates with Glymphatic function in Alzheimer Disease: The RJNB-D Study

Choroid Plexus Free-Water Correlates with Glymphatic function in Alzheimer Disease: The RJNB-D Study | 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 in Alzheimer Disease: The RJNB-D Study Binyin Li, Xiaomeng Xu, Xinyuan Yang, Junfang Zhang, Yan Wang, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4680360/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 The free water imaging of choroid plexus (CP) may improve the evaluation of Alzheimer's disease (AD). Our study investigated the role of free water fraction (FWf) of CP in AD by including 216 participants (133 Aβ + participants and 83 Aβ- controls) continuously enrolled in the NeuroBank-Dementia cohort at Ruijin Hospital (RJNB-D). At baseline, Aβ + participants showed higher CP free water fraction (FWf), increased white matter hyperintensity (WMH) volume, and decreased diffusion tensor image analysis of the perivascular space (DTI-ALPS). In Aβ + participants, DTI-ALPS mediated the association between CP FWf and periventricular WMH. CP FWf was associated with cortical Tau accumulation, synaptic loss, hippocampal and cortical atrophy, and cognitive performance. During follow-up, CP FWf increased faster in Aβ + participants than in controls. The findings suggest that elevated CP FWf may indicate impaired glymphatic function and AD neurodegeneration, potentially serving as a valuable biomarker for AD evaluation and progression. Health sciences/Neurology/Neurological disorders/Dementia/Alzheimer's disease Biological sciences/Computational biology and bioinformatics/Image processing Alzheimer Disease Free-water mapping choroid plexus diffusion tensor image analysis of the perivascular space white matter hyperintensity 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) have also been identified as contributing factors 2,3 . The choroid plexus (CP) produces 80% of CSF and regulates its dynamics 4 . Comprising a single-layer epithelium, fenestrated capillaries, connective tissue, and immune cells, CP forms the blood-CSF barrier 5 and aids immune surveillance 6,7 . CP enlargement occurs in neurodegenerative 8 , neuroinflammatory 5 , and cerebral small vessel diseases 9 . While previously linked to aging and reduced function 10,11 , the mechanisms behind CP in neurodegeneration 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 12,13 . Studies show increased free water fraction (FWf) in white matter tracts 14 , cortical and subcortical regions as AD progresses 15,16 . 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 17 . Prior studies using diffusion tensor image analysis along the perivascular space (DTI-ALPS) have shown an association between glymphatic dysfunction and the severity of WMH 9,18 . Meanwhile, our previous study revealed an association between WMH and remote cortical AD biomarkers 19 . However, the relationship between choroid plexus free water fraction (CP FWf) and glymphatic clearance or WMH remains unclear. We hypothesized that elevated CP FWf in AD correlates with glymphatic function and AD biomarkers 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 clearance function, WMH, as well as positron emission tomography (Aβ-PET, Tau-PET, synaptic vesicle glycoprotein 2A density-PET) and blood markers other than Aβ and Tau (glial fibrillary acidic protein, GFAP; neurofilament light chain, NFL; Neurogranin, NRGN; and Tumor Necrosis Factor-α, TNFα); 3) explore potential mediation factors (such as Tau accumulation, synaptic density) between CP FWF and cognitive tests; and 4) validate the impact of CP FWf in AD using longitudinal data over a 12-month follow-up period. Results Demographics and group comparisons Table 1 summarizes the demographic and imaging characteristics stratified by Aβ status for all patients (n = 216, Fig. 1 A) included in the cross-sectional analyses. The average age was 69.20 ± 8.00 years; 128 were females. Table 2 lists the characteristics of the 76 participants included in the longitudinal analyses. Their average age was 70.09 ± 7.29 years; 49 were females. Table 1 Baseline Demographic and MRI Characteristics (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 (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. At baseline, age (70.59 ± 7.62 vs 66.98 ± 8.13, p = 0.001) and ApoE 4 carrier status (62% vs 23%, p < 0.001) differed between Aβ + group and Aβ- controls. Increased FWf of CP (0.78 ± 0.04 vs 0.76 ± 0.06, p = 0.002), increased volume of WMH (pWMH, -3.83 ± 1.52 vs -4.78 ± 1.73, p < 0.001; dWMH, -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. 2 A-C). Conversely, the Aβ + group and Aβ- controls exhibited similar volumes of CP, as well as similar values for FA, MD, and CBF of CP (Table 1 , all p > 0.050). After adjusting for age, sex and ApoE genotype, FWf of CP (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) respectively demonstrated an independent association with Aβ positivity (Supplementary Table 1–4). The relationship between CP FWf, glymphatic clearance and WMH Across all participants, the 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). Mediation analysis revealed a partial mediation effect of DTI-ALPS for the association between CP FWf and pWMH (indirect effect standardized-β = 0.26, p < 0.001, mediated effect = 32.59%; Fig. 2 D). However, no mediation effect of DTI-ALPS was observed for the association between CP FWf and 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), as well as between CP FWf and pWMH (r = 0.40, p < 0.001) but not dWMH (r = 0.11, p = 0.213). A partial mediation effect of DTI-ALPS for the association between CP FWf and pWMH was observed (indirect effect standardized-β = 0.25, p = 0.003, mediated effect = 35.76%; Fig. 2 E). The Aβ- controls displayed similar associations between CP FWf and DTI-ALPS (r = -0.44, p < 0.001), and pWMH (r = 0.45, p < 0.001). A marginal mediation effect of DTI-ALPS for the association between CP FWf and pWMH was observed (indirect effect standardized-β = 0.25, p = 0.034, mediated effect = 24.67%; Fig. 2 F). The impact of CP FWf across the AD continuum The relationship between CP FWf, glymphatics and AD imaging markers We observed significant associations between CP FWf and AD imaging markers (cortical Tau SUVR, cortical SV2A SUVR, hippocampus volume, cortex volume), as well as MMSE score (Fig. 2 G, Supplementary Table 5). After adjusting for age, sex, and ApoE genotype in multivariable linear regression models, an increase in CP FWf was correlated with increased Tau-PET accumulation (Tau SUVR, β = 9.27, p = 0.002, Fig. 3 A, Supplementary Table 6) and decreased synaptic density (SV2A SUVR, β = -3.43, p = 0.017, Fig. 3 A, Supplementary Table 7). However, CP FWf did not corelated with FBP SUVR (β = 0.98, p = 0.103). The vertex-wise GLM analysis revealed a negative correlation between CP FWf and cortical thickness primarily in the bilateral post cingulate gyrus, precuneus, temporal, and insular lobes. Conversely, CP FWf positively correlated with Tau SUVR in the bilateral insular, temporal regions, and precuneus (Fig. 2 H). CP FWf negatively correlated with SV2A SUVR in the right inferior parietal gyrus (Extended Data Fig. 1 ) The relationship between CP FWf and blood-based neural biomarkers Due to restrictions imposed by variability in the availability of adequate blood samples in some patients of the Aβ + cohorts, we specifically marked the sample size in these analyses. Within the Aβ + group, CP FWf was associated 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 8–11). The relationship between CP FWf, glymphatic clearance, WMH and cognition Increased CP FWf was associated with 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 (CDT20, β = -35.29, p = 0.025) within the Aβ + group (Fig. 3 A, Supplementary Table 12–14). Similarly, DTI-ALPS and pWMH were also correlated with MMSE (DTI-ALPS, β = 13.17, p < 0.001; pWMH, β = -1.57, p < 0.001), AFT (DTI-ALPS, β = 8.19, p = 0.006; pWMH, β = -0.92, p = 0.003), and CDT20 (DTI-ALPS, β = 13.02, p < 0.001; pWMH, β = -1.60, p < 0.001) We observed a partial mediation effect of Tau SUVR (indirect effect standardized-β = -0.18, p = 0.043, mediated effect = 40.88%, n = 110, Fig. 3 B) and SV2A SUVR (indirect effect standardized-β = -0.21, p = 0.089, mediated effect = 21.73%, n = 64, Fig. 3 C) on the association between CP FWf and MMSE. While GFAP exhibited a full mediation effect (mediated effect = 38.69%, n = 111, Fig. 3 D), NFL, NRGN, and TNF-α did not significantly mediate the association between CP FWf and global cognitive performance assessed by MMSE (Fig. 3 E-G). These findings suggest that the impact of CP FWf on cognitive performance is mediated via Tau- and GFAP-related pathways. Longitudinal study CP FWf increased faster in Aβ + than Aβ- participants. We observed a significant increase in CP FWf during 12-month follow-up period than baseline (F-statistic = 16.673, corrected p < 0.001, Supplementary Table 15) in Aβ + patients. More importantly, Aβ + patients demonstrated a faster increment in CP FWf compared to Aβ- controls (time × group interaction effect: F-statistic = 4.118, corrected p = 0.046, Fig. 4 A, Supplementary Table 15). The rates of increase in 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). Δ CP FWf paralleled Δ DTI-ALPS, and was faster than Δ pWMH , Δ Tau-PET and Δ GFAP In Aβ + participants, annual changes in CP FWf, DTI-ALPS, and pWMH volume from baseline to the 12-month follow-up were calculated as ΔCP FWf, ΔDTI-ALPS, and ΔpWMH, respectively. Through Spearman correlation analysis, it was found the ΔCP FWf during the 12-month follow-up period paralleled ΔDTI-ALPS (ρ = -0.42, p = 0.006, Fig. 4 C), but not ΔpWMH (ρ = 0.21, p = 0.173). After adjusting for age and sex, the association between ΔCP FWf and ΔDTI-ALPS remained significant (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-F). Discussion In this study, we found increased FWf of CP, increased volume of WMH, and decreased DTI-ALPS at baseline in Aβ + participants compared to Aβ- controls. And we noted elevated CP FWf exacerbates pWMH, partially mediated by the glymphatic system (DTI-ALPS), revealing a potential mechanism linking CSF dynamics to white matter lesions. We also observed significant associations between CP FWf and 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 + patients compared to Aβ- controls, and the rate of progression of CP FWf exceeded that of WMH. The CP is a highly vascularized structure that regulates CSF production and clears neurotoxic proteins, playing a crucial role in the glymphatic system. Recent studies show the glymphatic system's pivotal role in aging and neurodegeneration 20 , 21 . Post-mortem and in vivo studies have revealed the CP plays a role in the pathogenesis of AD 22 . Other studies have found an association between increased CP volume and greater cognitive impairment in AD. 8 , 23 – 25 Additionally, CP volume has been linked to levels of pathological proteins like Aβ and Tau in the CSF, indicating that the CP may participate the clearance of Aβ and Tau 23 . Prior results regarding the relationship between CP volume and Aβ positivity were inconsistent 8 , 24 , 25 . In our study, CP volume did not relate to Aβ positivity. These disagreements may be partially attributed to the contamination of partial volume effect of CSF in calculation CP volume by traditional methods 26 , and also the morphological nature of CP volume, which may display a relatively delayed response to Aβ deposition. In the current study, we evaluated various novel imaging biomarkers which reflects CP content and microstructure, including CP FWf, CP FA, CP MD, and CP CBF. CP FWf was the only marker that displayed a significant association with Aβ positivity. Free-water mapping is derived from a bi-tensor model that enables the distinction of diffusion properties between water in brain tissue and water in extracellular space (free water), thus offering more comprehensive microstructural insights compared to the traditional single-tensor model 13 , 27 . The DTI-ALPS index is a reliable parameter for evaluating glymphatic system, reflecting its function in maintaining CSF homeostasis and clearance of toxic proteins 28 , 29 . Previous studies showed that in subjects with WMH or CSVD, CP enlargement and reduced DTI-ALPS were both associated with WMH growth 9 , 24 . Moreover, DTI-ALPS mediated the association between CP and WMH burden, suggesting that the pathogenesis of WMH may result from impaired glymphatic clearance 9 . We demonstrated that CP FWf is a reliable indicator of glymphatic function by examining its relationship with ALPS and WMH. We noticed that in Aβ + population CP FWf was associated with pWMH, 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 25 . However, subgroup analysis of this study suggested that the correlation was only significant in the AD non-dementia group, but not in the AD dementia group 25 , which is possibly due to the ceiling effect of Aβ burden, as Aβ burden is saturated in dementia state 30 . Likewise, although we also found that CP FWf was significantly related to Aβ positivity, we did not detect a correlation with Aβ burden quantified by FBP SUVR. A recent report demonstrated a positive correlation between global Tau burden and CP enlargement in AD 31 , We expanded on these findings by using vertex-wise analysis to provide topographical information and showed that CP FWf changed in parallel with Tau accumulation mainly in bilateral insular, temporal lobes, and precuneus. Furthermore, we found that increased Tau burden mediated the detrimental effects of CP FWf on cognitive performance. This finding adds clinical evidence and support to a recent study indicating that glymphatic dysfunction impairs cognition through poor Tau clearance in AD mouse models 32 . In addition to the clearance of neurotoxic proteins, glymphatic system can also modulate neuroinflammation 33 , 34 . We explored the relationship between peripheral neuroinflammatory factors in the blood and CP FWf. GFAP correlated with CP FWf, and substantially mediated the negative impact of CP FWf on cognitive function. Astrocytes are serving as critical components of glymphatic system by ensheathing the brain vasculature with their specialized endfeet. 17 , 34 GFAP is a structural protein in astrocytes and is a marker of reactive gliosis related to aging 35 . Our findings, albeit preliminary, are in agreement with the results of another study, showing that blood GFAP is related to impaired glymphatic function and global cognition in community-dwelling older adults 36 and provide new evidence for involvement of GFAP-related pathway in glymphatic dysfunction in AD pathology. Our study has limitations. The sample size of the longitudinal cohort is relatively small, partly due to the cohort being relatively new, resulting in approximately half of the enrolled subjects not reaching the 12-month follow-up time point. Furthermore, the 12-month follow-up duration is relatively short, and certain imaging markers such as the volume of pWMH may not exhibit significant changes within this timeframe. A longer follow-up period may be necessary to capture more pronounced ΔpWMH. However, ΔCP FWf was marked within the short time interval, indicating the sensitivity of CP FWf in monitoring disease progression. Lastly, the number of blood samples and analyses of various blood markers were limited and may have influenced the related results. Therefore, the results of our blood markers analyses should be considered exploratory and hypothesis generating. In conclusion, our findings suggest that CP FWf emerges as a novel imaging biomarker complementing structural MRI and molecular PET imaging for assessing glymphatic function and CP involvement in AD. By reflecting the multifactorial nature of AD, CP FWf shows promise for early diagnosis and accurate monitoring of AD progression, potentially enabling tailored and timely treatment strategies. Methods 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). Participants and study design The data for this study were obtained from the Ruijin NeuroBank of Alzheimer's Disease and Dementia (RJNB-D) cohort study. The participants underwent comprehensive assessments including multimodal MRI, Aβ PET, Tau PET, 18 F-SynVesT-1 PET measuring synaptic density, neuropsychological tests, ApoE genotyping and blood tests for GFAP, NFL, NRGN and TNFα (Fig. 1 A). 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. Neuropsychological assessments included Mini-Mental State Examination (MMSE, Chinese Version) 37 , 20-minute auditory verbal learning test (AVLT) 38 , Clock-drawing test (CDT) 39 , Boston naming test (BNT), shape-trail test (STT-A and STT-B) 40 , and animal fluency test (AFT) 40 . We collected a 20mL venous blood sample from participants at the visits. Samples were centrifuged to isolate serum and plasma before being stored at -80℃. For blood biomarkers, protein concentrations in ethylenediaminetetraacetic acid (EDTA) plasma were assessed using the ultra-sensitive single-molecule array (Simoa) platform. Specifically, GFAP and NFL levels were quantified utilizing the Neurology 4-Plex E Advantage kit (QTX-103670, Quanterix, Billerica, Massachusetts, United States). Serum concentrations of NRGN and TNFα were determined using the Luminex 200 system (Luminex, Austin, TX, USA) with the Human ProcartaPlex Mix & Match 3-plex kit (PPX-03-MXMFYR9, Thermo Fisher Scientific). In the cross-sectional analysis, we studied a total of 216 participants. Among them, the 133 participants with cognitive complaints and Aβ positivity were defined as Aβ + group. As across the AD continuum, participants in the Aβ + group were further diagnosed as AD or mild cognitive impairment (MCI). The criteria for diagnosing AD and MCI adhered to the research criteria established by the National Institute on Aging-Alzheimer's Association (NIA-AA) workgroups in 2016 41 . Meanwhile, another 83 cognitively normal participants with negative Aβ were included as controls. For the longitudinal analysis, 76 out of the 216 participants at baseline underwent complete 12-month follow-up assessments, during which they were repeatedly administered neuropsychological tests, multimodal MRI and PET scans. The follow-up assessments were performed 12 months after baseline visit and the assessments (including neuropsychological tests, MRI, PET, etc.) were completed within two weeks. Acquisition of MRI and PET images MRI data were acquired on a 3T scanner (uMR 890, United Imaging Healthcare, Shanghai, China) with a dedicated 64-channel head coil. As our previous protocol of RJNB-Dementia dataset 19 , 42 , the T1-weighted structural images were acquired with a 3D fast spoiled gradient-echo sequence, with 0.75 mm isotropic voxels (field of view read: 240 mm, output images of 360 × 439× 480 slices, repetition time (TR) / inversion time (TI) = 7.5 ms /1100 ms, echo time (TE) = 3.4 ms, flip angle = 7 degree). The 3D FLAIR acquisition parameters were: TR = 6000 ms, TE = 408 ms, and TI = 1841ms, with 0.75 mm isotropic voxels. Diffusion images were acquired with an echo-planar imaging (EPI) sequence, and the parameters were: TR/TE = 3500/77.3 ms, voxel size = 1.5 × 1.5 × 1.5 mm, 100 slices, multiple band = 10, diffusion direction = 32/64, b = 1500/3000, phase encoding direction = PA. A pair of four b0 images with reversed phase-encode blips (AP and PA) was acquired to correct susceptibility-induced distortion. The ASL scans were performed using a 5-delay pCASL protocol with background suppressed 3D GRASE (gradient and spin echo) readout (labeling pulse duration = 1.8 s, post label delay = 0.5-1.0-1.5-2.0-2.5 s, matrix = 64 × 64, voxel size = 3.5 × 3.5 × 3.5 mm, 6 pairs of tag and control for each delay). We utilized PET scans to evaluate the spatial accumulation of Aβ and Tau, as well as synaptic density in the study. The PET scans were conducted using a 3T whole-body PET/MR scanner (uPMR 790, United Imaging, China). Participants were administered an intravenous injection of 18 F Florbetapir (FBP) at an average dose of 3.7 MBq/kg body weight to visualize Aβ accumulation. Static FBP-PET data were acquired in sinogram mode 50 minutes after the injection. Additionally, all participants underwent a static 18 F-MK-6240 PET scan to image Tau. Furthermore, a 18 F-SynVesT-1 PET scan was performed to visualize synaptic vesicle glycoprotein 2A (SV2A) as previously described 42 . Participants refrained from taking drugs targeting SV2A for at least 24 hours before the 18 F-SynVesT-1 PET scan. A 30-minute static PET scan was initiated at 60 minutes post-injection of 18F-SynVesT-1 (~ 3.7 MBq/kg body weight). Each participant had the three PET scans on different days within two weeks; the scans were scheduled at least three days apart from each other. CP and WMH segmentation The region of interest (ROI) of CP was segmented from 3D-T1 weighted images. Both baseline and follow-up 3D-T1 images were segmented automatically based on FreeSurfer 7.1.1 platform as previously described 43 , 44 . The segmentation of brain volumes is based on an atlas containing probabilistic information regarding the location of structures. This pre-processing step involves motion correction of 3D T1 images, brain extraction, segmentation of subcortical white matter and deep gray matter volumetric structures, intensity normalization, white matter surface smoothing, topology correction, and optimization of pial and white matter surfaces. The total intracranial volume (TIV) and the CP of the lateral ventricle were determined through this process. In addition, the cortical thickness was determined by measuring the distance between the white matter and pial surfaces. All final automatic segmentations underwent quality checks by a senior neurologist (X.X.) who was blinded to clinical and other image data. The CP ROI were used as mask for analysis after registration with the following MRI modalities. The CP volumes (CPV) were expressed as ratios to TIV. The WMH was defined as subcortical hyperintensities without cavitation on T2 FLAIR based on the recommendations of Standards for Reporting Vascular Changes on Neuroimaging (STRIVE) 45 . We utilized FSL's Brain Intensity AbNormality Classification Algorithm (BIANCA, FMRIB Software Library, Oxford, UK), version 6.0, a fully automated, supervised k-nearest neighbor (k-NN) algorithm for WMH segmentation. The binary masks of total hyperintensities were generated using BIANCA and then carefully inspected and manually corrected to produce the final WMH binary masks. These derived WMH masks were further categorized into periventricular WMH (pWMH) and deep WMH (dWMH) by applying a 10 mm distance threshold from the ventricles 19 . Subsequently, all maps were visually inspected and manually corrected as needed. The volumes of pWMH and dWMH were expressed as ratios to the total volume of white matter. Diffusion MRI analysis Pre-processing: The pre-processing pipelines for diffusion MRI generally followed the protocols of an image-processing pipeline developed by UK Biobank 46 . The original diffusion MRI data were corrected for susceptibility-induced distortion by top-up method 47 . Then the data were further corrected for head motion and eddy currents using the Eddy tool in FSL. The output data were computed into diffusion tensor maps using the “DTIFIT” function of FSL. Fitting diffusion tensors: The DTIFIT estimated a diffusion tensor at each voxel of the brain, and outputs a set of images including fractional anisotropy (FA) and mean diffusivity (MD), as well as voxel-wise diffusivity in the directions of the x-axis (Dx), y-axis (Dy), and z-axis (Dz) images. We linearly aligned individual FA/MD maps with the T1-weighted image. The registration process is a combination of affine transformation 48 . Then, the CP mask in T1-weighted image could be used to extract mean FA and MD value in the CP. Visual examinations were performed to identify any co-registration bias. DTI-ALPS: The FA map of each subject was registered to the FA map template in the standard MNI space, and the accuracy of the co-registration was visually confirmed. The Dx, Dy, Dz maps of the subject were then warped based on the registration matrix obtained from the FA map. The ROIs corresponding to the left projection fibers (superior and posterior corona radiata) and association fibers (superior longitudinal fasciculus) were extracted using atlas labels. The range of ROIs in the craniocaudal direction was limited to where the x-axis lines were perpendicular to the lateral ventricle bodies. A mask with FA values greater than 0.2 was applied to exclude CSF voxels. Finally, the Dx, Dy, and Dz values in these ROIs on projection fibers and association fibers were determined as Dxproj, Dyproj, Dzproj, Dxassoc, Dyassoc, Dzassoc, respectively. The DTI-ALPS index was automatically computed using the following Eq. 4 9 : DTI-ALPS index= [(Dxproj + Dxassoc) / (Dyproj + Dzassoc)] Free-Water image of CP: The FWf can be measured quantitatively by neurite orientation dispersion and density imaging (NODDI) model derived from multi-shell diffusion MRI. NODDI models the voxel contents as neurites (thin pipes, nominally axons), the spaces between them, and freely diffusing water 50 , 51 . NODDI estimation for FWf was done using Accelerated Microstructure Imaging via Convex Optimization 46 . After registered with T1-weighted image, the averaged Fwf of CP was calculated from the left and right CP ROI (Fig. 1 B). Cerebral blood flow in CP We used FSL BASIL toolbox to estimate perfusion in the CP. The key steps in BASIL pipeline includes motion correction, registration, calibration using M0 image, and Bayesian kinetic modelling to estimate perfusion maps, with spatial regularization and partial volume correction (PVC). Following these steps, the whole brain perfusion map was registered to the T1-weighted image, and the accuracy of the co-registration was visually confirmed. Subsequently, the cerebral blood flow (CBF) of the choroid plexus was extracted from the CP region of interest segmented from the T1-weighted image. Surface-based PET images for Aβ, Tau and SV2A An automated pipeline was utilized to derive cortical standardized uptake value ratios (SUVR) using the PETSurfer toolbox in Freesurfer, with the cerebellum cortex serving as the reference region 52 . Initially, high-resolution segmentation was generated from structural T1 images to facilitate PVC techniques. Subsequently, registration of PET and anatomical images was conducted and visually validated. To mitigate partial volume effects resulting from cortical atrophy in AD, the extended Muller–Gartner method was employed as a PVC approach 53 . Surface-based maps of Aβ, Tau, and SV2A were smoothed on a two-dimensional surface using a Gaussian kernel with a full width at half maximum (FWHM) of 5mm. These biomarker maps were then utilized for vertex-wise analysis and mean cortical SUVRs were extracted for spatial correlation analyses. 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, adjusting for age, sex, and ApoE genotype. Bonferroni correction addressed multiple comparisons. Vertex-wise correlation analysis using generalized linear model (GLM), 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, adjusted for age. Models tested glymphatic function's mediation between CP FWf and WMH, and blood markers' mediation between CP FWf and cognition. Longitudinal CP FWf changes were assessed using repeated measures ANOVA. Alteration rates between Aβ + patients and Aβ- controls were compared using a 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 (4.3.3) or SPSS, with significance at P < 0.05 (two-tailed). Declarations Data availability For all main figures with 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). Code availability The preprocessing codes for multimodal MR and PET are available as a Github repository (https://github.com/colorfulbrain/CP_FWf). The R/SPSS code used for the main analysis are available from the corresponding author upon request. Acknowledgements 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). Author contributions X. X. and B. L. designed the study; X. X., X. Y. M. S. and B. 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NeuroImage 92:225–236. 10.1016/j.neuroimage.2013.12.021 Greve DN, Salat DH, Bowen SL et al (2016) Different partial volume correction methods lead to different conclusions: An (18)F-FDG-PET study of aging. NeuroImage 132:334–343. 10.1016/j.neuroimage.2016.02.042 Additional Declarations There is NO Competing Interest. Supplementary Files SIfigure2.pdf Supplement1for0624.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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4680360","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":331854937,"identity":"8ff19661-c456-4edf-bcf0-704d5acec4c5","order_by":0,"name":"Binyin 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1","display":"","copyAsset":false,"role":"figure","size":2119298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of the present study.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The participants inclusion pipeline of the study. (B) The multimodal MRI scans and processing steps includingT1, T2 FLAIR, multi-shell diffusion MRI and arterial spin labelled (ASL) perfusion MRI sequences.\u003c/p\u003e","description":"","filename":"SIfigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4680360/v1/2eb4478bd0f27b892b4ac4a6.png"},{"id":61808960,"identity":"3241ed6c-f54c-4c82-95cc-fe69479ae339","added_by":"auto","created_at":"2024-08-05 20:12:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":535700,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGroup difference and correlation between CP FWf and glymphatic markers. (A-C)\u003c/strong\u003e Increased FWf of CP (p = 0.002), increased volume of WMH (pWMH, p \u0026lt; 0.001), and decreased DTI-ALPS (p = 0.001) were observed in Aβ+ participants. The log-transformed pWMH volume was used.\u003cstrong\u003e (D)\u003c/strong\u003e Across all participants, mediation analysis revealed a partial mediation effect of DTI-ALPS between CP FWf and pWMH. \u003cstrong\u003e(E)\u003c/strong\u003eWithin the Aβ+ group, a significant partial mediation effect of DTI-ALPS between CP FWf and pWMH was observed. \u003cstrong\u003e(F) \u003c/strong\u003eThe Aβ- controls displayed the trend of mediation effect of DTI-ALPS between CP FWf and pWMH. \u003cstrong\u003e(G) \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. The color bar represents uncorrected vertex-wise p values. \u003cstrong\u003e(H) \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. 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; MMSE = Mini-Mental State Examination; GLM = generalized linear model.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4680360/v1/fb02160f7c841efbc1e27603.png"},{"id":61808958,"identity":"d8b53fd4-5db4-4675-85d6-b1ff38e0b01a","added_by":"auto","created_at":"2024-08-05 20:12:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":463062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCP FWf correlated with AD biomarkers and cognition. (A)\u003c/strong\u003eWithin the Aβ+ group, there was a positive correlation between CP FWf and NFL (β = 3.12, 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\u003eWe observed a partial mediation effect of Tau \u003cstrong\u003e(B)\u003c/strong\u003e, and marginal effect of SV2A \u003cstrong\u003e(C)\u003c/strong\u003e. Notably, GFAP exhibited a full mediation effect \u003cstrong\u003e(D)\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(E-G)\u003c/strong\u003e. CP = choroid plexus; FWf = free-water fraction; NFL = neurofilament light chain; GFAP = glial fibrillary acidic protein; NRGN = neurogranin; TNFα = Tumor Necrosis Factor-α; MMSE = Mini-Mental State Examination; AFT = animal fluency test; CDT = clock drawing test; SV2A = synaptic vesicle glycoprotein 2A; GFAP = glial fibrillary acidic protein; NRGN = neurogranin; TNFα = Tumor Necrosis Factor-α.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4680360/v1/2c4f5e43f44810031b2032cd.png"},{"id":61811533,"identity":"b78e3a34-3912-4e68-a298-7189060863a3","added_by":"auto","created_at":"2024-08-05 20:28:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":486293,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLongitudinal changes in CP FWf indicated neurodegeneration. \u003c/strong\u003eThe linear mixed model analysis indicated that Aβ+ patients 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-F)\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":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4680360/v1/af3145ed9fe8c2d7811064a1.png"},{"id":61812104,"identity":"6ab87dac-eacd-4d7e-b789-4257f70694bf","added_by":"auto","created_at":"2024-08-05 20:44:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5455558,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4680360/v1/63d6baff-e10e-43c5-bbdf-c1643991b59b.pdf"},{"id":61810309,"identity":"96442de9-3bda-4da1-a19c-ee531f31b9c6","added_by":"auto","created_at":"2024-08-05 20:20:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":199038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SIfigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4680360/v1/1bf21164c0bff3361f0ac03c.pdf"},{"id":61808954,"identity":"c5ab5709-ce63-4ecf-92f1-7974c6e8dcfd","added_by":"auto","created_at":"2024-08-05 20:12:07","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":29214,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement1for0624.docx","url":"https://assets-eu.researchsquare.com/files/rs-4680360/v1/173c4c1a0814c2187d1b52fb.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Choroid Plexus Free-Water Correlates with Glymphatic function in Alzheimer Disease: The RJNB-D Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer\u0026apos;s disease (AD) is a neurodegenerative disorder marked by cognitive decline, \u0026beta;-amyloid plaques, hyperphosphorylated Tau, and cortical atrophy\u003csup\u003e1\u003c/sup\u003e.\u0026nbsp;Moreover,\u0026nbsp;dysfunction in the cerebrospinal fluid (CSF) glymphatic clearance and white matter hyperintensity (WMH) have also been identified as contributing factors\u0026nbsp;\u003csup\u003e2,3\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe choroid plexus (CP) produces 80% of CSF and regulates its dynamics\u003csup\u003e4\u003c/sup\u003e.\u0026nbsp;Comprising a single-layer epithelium, fenestrated capillaries, connective tissue, and immune cells, CP forms the blood-CSF barrier\u003csup\u003e5\u003c/sup\u003e and aids immune surveillance\u003csup\u003e6,7\u003c/sup\u003e. CP enlargement occurs in neurodegenerative\u003csup\u003e8\u003c/sup\u003e, neuroinflammatory\u003csup\u003e5\u003c/sup\u003e, and cerebral small vessel diseases\u003csup\u003e9\u003c/sup\u003e. While previously linked to aging and reduced function\u003csup\u003e10,11\u003c/sup\u003e, the mechanisms behind CP in neurodegeneration are unclear.\u0026nbsp;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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFree water refers to unconstrained water molecules in fluid. Diffusion MRI techniques can measure free water in brain tissue\u003csup\u003e12,13\u003c/sup\u003e. Studies show increased free water fraction (FWf) in white matter tracts\u003csup\u003e14\u003c/sup\u003e, cortical and subcortical regions as AD progresses\u003csup\u003e15,16\u003c/sup\u003e. Given the choroid plexus\u0026apos;s role in CSF regulation, CP FWf may be crucial for brain fluid circulation.\u003c/p\u003e\n\u003cp\u003eFurthermore, in the brain glymphatic system, CSF enters the brain parenchyma through the perivascular spaces surrounding arteries to clear interstitial fluid waste products\u003csup\u003e17\u003c/sup\u003e. Prior studies using diffusion tensor image analysis along the perivascular space (DTI-ALPS) have shown an association between glymphatic dysfunction and the severity of WMH\u003csup\u003e9,18\u003c/sup\u003e. Meanwhile, our previous study revealed an association between WMH and remote cortical AD biomarkers\u003csup\u003e19\u003c/sup\u003e. However, the relationship between choroid plexus free water fraction (CP FWf) and glymphatic clearance or WMH remains unclear.\u003c/p\u003e\n\u003cp\u003eWe hypothesized that elevated CP FWf in AD correlates with glymphatic function and AD biomarkers 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 clearance function, WMH, as well as positron emission tomography (A\u0026beta;-PET, Tau-PET, synaptic vesicle glycoprotein 2A density-PET) and blood markers other than A\u0026beta; and Tau (glial fibrillary acidic protein, GFAP; neurofilament light chain, NFL; Neurogranin, NRGN; and Tumor Necrosis Factor-\u0026alpha;, TNF\u0026alpha;); 3) explore potential mediation factors (such as Tau accumulation, synaptic density) between CP FWF and cognitive tests; and 4) validate the impact of CP FWf in AD using longitudinal data over a 12-month follow-up period.\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003eDemographics and group comparisons\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the demographic and imaging characteristics stratified by Aβ status for all patients (n\u0026thinsp;=\u0026thinsp;216, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) included in the cross-sectional analyses. The average age was 69.20\u0026thinsp;\u0026plusmn;\u0026thinsp;8.00 years; 128 were females. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists the characteristics of the 76 participants included in the longitudinal analyses. Their average age was 70.09\u0026thinsp;\u0026plusmn;\u0026thinsp;7.29 years; 49 were 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 (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 \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 (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\u003eAt baseline, age (70.59\u0026thinsp;\u0026plusmn;\u0026thinsp;7.62 vs 66.98\u0026thinsp;\u0026plusmn;\u0026thinsp;8.13, p\u0026thinsp;=\u0026thinsp;0.001) and ApoE 4 carrier status (62% vs 23%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) differed between Aβ\u0026thinsp;+\u0026thinsp;group and Aβ- controls. Increased FWf of CP (0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 vs 0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06, p\u0026thinsp;=\u0026thinsp;0.002), increased volume of WMH (pWMH, -3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52 vs -4.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; dWMH, -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=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). Conversely, the Aβ\u0026thinsp;+\u0026thinsp;group and Aβ- controls exhibited similar volumes of CP, as well as similar values for FA, MD, and CBF of CP (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 adjusting for age, sex and ApoE genotype, FWf of CP (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) respectively demonstrated an independent association with Aβ positivity (Supplementary Table\u0026nbsp;1\u0026ndash;4).\u003c/p\u003e\n\u003ch3\u003eThe relationship between CP FWf, glymphatic clearance and WMH\u003c/h3\u003e\n\u003cp\u003eAcross all participants, the 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). Mediation analysis revealed a partial mediation effect of DTI-ALPS for 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=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). However, no mediation effect of DTI-ALPS was observed for the association between CP FWf and 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), as well as between CP FWf 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). A partial mediation effect of DTI-ALPS for the association between CP FWf and pWMH was observed (indirect effect standardized-β\u0026thinsp;=\u0026thinsp;0.25, p\u0026thinsp;=\u0026thinsp;0.003, mediated effect\u0026thinsp;=\u0026thinsp;35.76%; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eThe Aβ- controls displayed similar associations between CP FWf and DTI-ALPS (r = -0.44, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and pWMH (r\u0026thinsp;=\u0026thinsp;0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A marginal mediation effect of DTI-ALPS for the association between CP FWf and pWMH was observed (indirect effect standardized-β\u0026thinsp;=\u0026thinsp;0.25, p\u0026thinsp;=\u0026thinsp;0.034, mediated effect\u0026thinsp;=\u0026thinsp;24.67%; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e\n\u003ch3\u003eThe impact of CP FWf across the AD continuum\u003c/h3\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eThe relationship between CP FWf, glymphatics and AD imaging markers\u003c/h2\u003e \u003cp\u003eWe observed significant associations between CP FWf and AD imaging markers (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\u003eG, Supplementary Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eAfter adjusting for age, sex, and ApoE genotype in multivariable linear regression models, an increase in CP FWf was correlated with increased Tau-PET accumulation (Tau SUVR, β\u0026thinsp;=\u0026thinsp;9.27, p\u0026thinsp;=\u0026thinsp;0.002, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Supplementary Table\u0026nbsp;6) and decreased synaptic density (SV2A SUVR, β = -3.43, p\u0026thinsp;=\u0026thinsp;0.017, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Supplementary Table\u0026nbsp;7). However, CP FWf did not corelated with FBP SUVR (β\u0026thinsp;=\u0026thinsp;0.98, p\u0026thinsp;=\u0026thinsp;0.103). The vertex-wise GLM analysis revealed a negative correlation between CP FWf and cortical thickness primarily in the bilateral post cingulate gyrus, precuneus, temporal, and insular lobes. Conversely, CP FWf positively correlated with Tau SUVR in the bilateral insular, temporal regions, and precuneus (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). CP FWf negatively correlated with SV2A SUVR in the right inferior parietal gyrus (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe relationship between CP FWf and blood-based neural biomarkers\u003c/h3\u003e\n\u003cp\u003eDue to restrictions imposed by variability in the availability of adequate blood samples in some patients of the Aβ\u0026thinsp;+\u0026thinsp;cohorts, we specifically marked the sample size in these analyses. Within the Aβ\u0026thinsp;+\u0026thinsp;group, CP FWf was associated 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;8\u0026ndash;11).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eThe relationship between CP FWf, glymphatic clearance, WMH and cognition\u003c/h2\u003e \u003cp\u003eIncreased CP FWf was associated with 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 (CDT20, β = -35.29, p\u0026thinsp;=\u0026thinsp;0.025) within the Aβ\u0026thinsp;+\u0026thinsp;group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Supplementary Table\u0026nbsp;12\u0026ndash;14). Similarly, DTI-ALPS and pWMH were also correlated with MMSE (DTI-ALPS, β\u0026thinsp;=\u0026thinsp;13.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; pWMH, β = -1.57, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), AFT (DTI-ALPS, β\u0026thinsp;=\u0026thinsp;8.19, p\u0026thinsp;=\u0026thinsp;0.006; pWMH, β = -0.92, p\u0026thinsp;=\u0026thinsp;0.003), and CDT20 (DTI-ALPS, β\u0026thinsp;=\u0026thinsp;13.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; pWMH, β = -1.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003cp\u003eWe observed a partial mediation effect 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\u003eB) 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\u003eC) on the association between CP FWf and MMSE. While 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\u003eD), NFL, NRGN, and TNF-α did not significantly mediate the association between CP FWf and global cognitive performance assessed by MMSE (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-G). These findings suggest that the impact of CP FWf on cognitive performance is mediated via Tau- and GFAP-related pathways.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLongitudinal study\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eCP FWf increased faster in Aβ\u0026thinsp;+\u0026thinsp;than Aβ- participants.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe observed a significant increase in CP FWf during 12-month follow-up period than baseline (F-statistic\u0026thinsp;=\u0026thinsp;16.673, corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Supplementary Table\u0026nbsp;15) in Aβ\u0026thinsp;+\u0026thinsp;patients. More importantly, Aβ\u0026thinsp;+\u0026thinsp;patients demonstrated a faster increment in CP FWf compared to 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;15).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe rates of increase in 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\u003eΔ\u003cb\u003eCP FWf paralleled\u003c/b\u003e Δ\u003cb\u003eDTI-ALPS, and was faster than\u003c/b\u003e Δ\u003cb\u003epWMH\u003c/b\u003e, Δ\u003cb\u003eTau-PET and\u003c/b\u003e Δ\u003cb\u003eGFAP\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn Aβ\u0026thinsp;+\u0026thinsp;participants, annual changes in CP FWf, DTI-ALPS, and pWMH volume from baseline to the 12-month follow-up were calculated as ΔCP FWf, ΔDTI-ALPS, and ΔpWMH, respectively. Through Spearman correlation analysis, it was found the ΔCP FWf during the 12-month follow-up period 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). After adjusting for age and sex, the association between ΔCP FWf and ΔDTI-ALPS remained significant (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-F).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we found increased FWf of CP, increased volume of WMH, and decreased DTI-ALPS at baseline in Aβ\u0026thinsp;+\u0026thinsp;participants compared to Aβ- controls. And we noted elevated CP FWf exacerbates pWMH, partially mediated by the glymphatic system (DTI-ALPS), revealing a potential mechanism linking CSF dynamics to white matter lesions. We also observed significant associations between CP FWf and 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;patients compared to Aβ- controls, and the rate of progression of CP FWf exceeded that of WMH.\u003c/p\u003e \u003cp\u003eThe CP is a highly vascularized structure that regulates CSF production and clears neurotoxic proteins, playing a crucial role in the glymphatic system. Recent studies show the glymphatic system's pivotal role in aging and neurodegeneration\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Post-mortem and in vivo studies have revealed the CP plays a role in the pathogenesis of AD\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Other studies have found an association between increased CP volume and greater cognitive impairment in AD.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Additionally, CP volume has been linked to levels of pathological proteins like Aβ and Tau in the CSF, indicating that the CP may participate the clearance of Aβ and Tau\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Prior results regarding the relationship between CP volume and Aβ positivity were inconsistent\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In our study, CP volume did not relate to Aβ positivity. These disagreements may be partially attributed to the contamination of partial volume effect of CSF in calculation CP volume by traditional methods\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and also the morphological nature of CP volume, which may display a relatively delayed response to Aβ deposition. In the current study, we evaluated various novel imaging biomarkers which reflects CP content and microstructure, including CP FWf, CP FA, CP MD, and CP CBF. CP FWf was the only marker that displayed a significant association with Aβ positivity. Free-water mapping is derived from a bi-tensor model that enables the distinction of diffusion properties between water in brain tissue and water in extracellular space (free water), thus offering more comprehensive microstructural insights compared to the traditional single-tensor model\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe DTI-ALPS index is a reliable parameter for evaluating glymphatic system, reflecting its function in maintaining CSF homeostasis and clearance of toxic proteins\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Previous studies showed that in subjects with WMH or CSVD, CP enlargement and reduced DTI-ALPS were both associated with WMH growth\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Moreover, DTI-ALPS mediated the association between CP and WMH burden, suggesting that the pathogenesis of WMH may result from impaired glymphatic clearance\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. We demonstrated that CP FWf is a reliable indicator of glymphatic function by examining its relationship with ALPS and WMH. We noticed that in Aβ\u0026thinsp;+\u0026thinsp;population CP FWf was associated with pWMH, 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\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. However, subgroup analysis of this study suggested that the correlation was only significant in the AD non-dementia group, but not in the AD dementia group\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, which is possibly due to the ceiling effect of Aβ burden, as Aβ burden is saturated in dementia state\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Likewise, although we also found that CP FWf was significantly related to Aβ positivity, we did not detect a correlation with Aβ burden quantified by FBP SUVR. A recent report demonstrated a positive correlation between global Tau burden and CP enlargement in AD\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, We expanded on these findings by using vertex-wise analysis to provide topographical information and showed that CP FWf changed in parallel with Tau accumulation mainly in bilateral insular, temporal lobes, and precuneus. Furthermore, we found that increased Tau burden mediated the detrimental effects of CP FWf on cognitive performance. This finding adds clinical evidence and support to a recent study indicating that glymphatic dysfunction impairs cognition through poor Tau clearance in AD mouse models\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition to the clearance of neurotoxic proteins, glymphatic system can also modulate neuroinflammation\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. We explored the relationship between peripheral neuroinflammatory factors in the blood and CP FWf. GFAP correlated with CP FWf, and substantially mediated the negative impact of CP FWf on cognitive function. Astrocytes are serving as critical components of glymphatic system by ensheathing the brain vasculature with their specialized endfeet.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e GFAP is a structural protein in astrocytes and is a marker of reactive gliosis related to aging\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Our findings, albeit preliminary, are in agreement with the results of another study, showing that blood GFAP is related to impaired glymphatic function and global cognition in community-dwelling older adults\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and provide new evidence for involvement of GFAP-related pathway in glymphatic dysfunction in AD pathology.\u003c/p\u003e \u003cp\u003eOur study has limitations. The sample size of the longitudinal cohort is relatively small, partly due to the cohort being relatively new, resulting in approximately half of the enrolled subjects not reaching the 12-month follow-up time point. Furthermore, the 12-month follow-up duration is relatively short, and certain imaging markers such as the volume of pWMH may not exhibit significant changes within this timeframe. A longer follow-up period may be necessary to capture more pronounced ΔpWMH. However, ΔCP FWf was marked within the short time interval, indicating the sensitivity of CP FWf in monitoring disease progression. Lastly, the number of blood samples and analyses of various blood markers were limited and may have influenced the related results. Therefore, the results of our blood markers analyses should be considered exploratory and hypothesis generating.\u003c/p\u003e \u003cp\u003eIn conclusion, our findings suggest that CP FWf emerges as a novel imaging biomarker complementing structural MRI and molecular PET imaging for assessing glymphatic function and CP involvement in AD. By reflecting the multifactorial nature of AD, CP FWf shows promise for early diagnosis and accurate monitoring of AD progression, potentially enabling tailored and timely treatment strategies.\u003c/p\u003e"},{"header":"Methods","content":"\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).\u003c/p\u003e\n\u003ch3\u003eParticipants and study design\u003c/h3\u003e\n\u003cp\u003eThe data for this study were obtained from the Ruijin NeuroBank of Alzheimer's Disease and Dementia (RJNB-D) cohort study. The participants underwent comprehensive assessments including multimodal MRI, Aβ PET, Tau PET, \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF-SynVesT-1 PET measuring synaptic density, neuropsychological tests, ApoE genotyping and blood tests for GFAP, NFL, NRGN and TNFα (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). 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. Neuropsychological assessments included Mini-Mental State Examination (MMSE, Chinese Version)\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, 20-minute auditory verbal learning test (AVLT)\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, Clock-drawing test (CDT)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, Boston naming test (BNT), shape-trail test (STT-A and STT-B)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, and animal fluency test (AFT) \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe collected a 20mL venous blood sample from participants at the visits. Samples were centrifuged to isolate serum and plasma before being stored at -80℃. For blood biomarkers, protein concentrations in ethylenediaminetetraacetic acid (EDTA) plasma were assessed using the ultra-sensitive single-molecule array (Simoa) platform. Specifically, GFAP and NFL levels were quantified utilizing the Neurology 4-Plex E Advantage kit (QTX-103670, Quanterix, Billerica, Massachusetts, United States). Serum concentrations of NRGN and TNFα were determined using the Luminex 200 system (Luminex, Austin, TX, USA) with the Human ProcartaPlex Mix \u0026amp; Match 3-plex kit (PPX-03-MXMFYR9, Thermo Fisher Scientific).\u003c/p\u003e \u003cp\u003eIn the cross-sectional analysis, we studied a total of 216 participants. Among them, the 133 participants with cognitive complaints and Aβ positivity were defined as Aβ\u0026thinsp;+\u0026thinsp;group. As across the AD continuum, participants in the Aβ\u0026thinsp;+\u0026thinsp;group were further diagnosed as AD or mild cognitive impairment (MCI). The criteria for diagnosing AD and MCI adhered to the research criteria established by the National Institute on Aging-Alzheimer's Association (NIA-AA) workgroups in 2016\u003csup\u003e41\u003c/sup\u003e. Meanwhile, another 83 cognitively normal participants with negative Aβ were included as controls.\u003c/p\u003e \u003cp\u003eFor the longitudinal analysis, 76 out of the 216 participants at baseline underwent complete 12-month follow-up assessments, during which they were repeatedly administered neuropsychological tests, multimodal MRI and PET scans. The follow-up assessments were performed 12 months after baseline visit and the assessments (including neuropsychological tests, MRI, PET, etc.) were completed within two weeks.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition of MRI and PET images\u003c/h2\u003e \u003cp\u003eMRI data were acquired on a 3T scanner (uMR 890, United Imaging Healthcare, Shanghai, China) with a dedicated 64-channel head coil. As our previous protocol of RJNB-Dementia dataset\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, the T1-weighted structural images were acquired with a 3D fast spoiled gradient-echo sequence, with 0.75 mm isotropic voxels (field of view read: 240 mm, output images of 360 \u0026times; 439\u0026times; 480 slices, repetition time (TR) / inversion time (TI)\u0026thinsp;=\u0026thinsp;7.5 ms /1100 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;3.4 ms, flip angle\u0026thinsp;=\u0026thinsp;7 degree). The 3D FLAIR acquisition parameters were: TR\u0026thinsp;=\u0026thinsp;6000 ms, TE\u0026thinsp;=\u0026thinsp;408 ms, and TI\u0026thinsp;=\u0026thinsp;1841ms, with 0.75 mm isotropic voxels. Diffusion images were acquired with an echo-planar imaging (EPI) sequence, and the parameters were: TR/TE\u0026thinsp;=\u0026thinsp;3500/77.3 ms, voxel size\u0026thinsp;=\u0026thinsp;1.5 \u0026times; 1.5 \u0026times; 1.5 mm, 100 slices, multiple band\u0026thinsp;=\u0026thinsp;10, diffusion direction\u0026thinsp;=\u0026thinsp;32/64, b\u0026thinsp;=\u0026thinsp;1500/3000, phase encoding direction\u0026thinsp;=\u0026thinsp;PA. A pair of four b0 images with reversed phase-encode blips (AP and PA) was acquired to correct susceptibility-induced distortion. The ASL scans were performed using a 5-delay pCASL protocol with background suppressed 3D GRASE (gradient and spin echo) readout (labeling pulse duration\u0026thinsp;=\u0026thinsp;1.8 s, post label delay\u0026thinsp;=\u0026thinsp;0.5-1.0-1.5-2.0-2.5 s, matrix\u0026thinsp;=\u0026thinsp;64 \u0026times; 64, voxel size\u0026thinsp;=\u0026thinsp;3.5 \u0026times; 3.5 \u0026times; 3.5 mm, 6 pairs of tag and control for each delay).\u003c/p\u003e \u003cp\u003eWe utilized PET scans to evaluate the spatial accumulation of Aβ and Tau, as well as synaptic density in the study. The PET scans were conducted using a 3T whole-body PET/MR scanner (uPMR 790, United Imaging, China). Participants were administered an intravenous injection of \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF Florbetapir (FBP) at an average dose of 3.7 MBq/kg body weight to visualize Aβ accumulation. Static FBP-PET data were acquired in sinogram mode 50 minutes after the injection. Additionally, all participants underwent a static \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF-MK-6240 PET scan to image Tau. Furthermore, a \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF-SynVesT-1 PET scan was performed to visualize synaptic vesicle glycoprotein 2A (SV2A) as previously described\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Participants refrained from taking drugs targeting SV2A for at least 24 hours before the \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF-SynVesT-1 PET scan. A 30-minute static PET scan was initiated at 60 minutes post-injection of 18F-SynVesT-1 (~\u0026thinsp;3.7 MBq/kg body weight). Each participant had the three PET scans on different days within two weeks; the scans were scheduled at least three days apart from each other.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCP and WMH segmentation\u003c/h2\u003e \u003cp\u003eThe region of interest (ROI) of CP was segmented from 3D-T1 weighted images. Both baseline and follow-up 3D-T1 images were segmented automatically based on FreeSurfer 7.1.1 platform as previously described\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The segmentation of brain volumes is based on an atlas containing probabilistic information regarding the location of structures. This pre-processing step involves motion correction of 3D T1 images, brain extraction, segmentation of subcortical white matter and deep gray matter volumetric structures, intensity normalization, white matter surface smoothing, topology correction, and optimization of pial and white matter surfaces. The total intracranial volume (TIV) and the CP of the lateral ventricle were determined through this process. In addition, the cortical thickness was determined by measuring the distance between the white matter and pial surfaces. All final automatic segmentations underwent quality checks by a senior neurologist (X.X.) who was blinded to clinical and other image data. The CP ROI were used as mask for analysis after registration with the following MRI modalities. The CP volumes (CPV) were expressed as ratios to TIV.\u003c/p\u003e \u003cp\u003eThe WMH was defined as subcortical hyperintensities without cavitation on T2 FLAIR based on the recommendations of Standards for Reporting Vascular Changes on Neuroimaging (STRIVE)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. We utilized FSL's Brain Intensity AbNormality Classification Algorithm (BIANCA, FMRIB Software Library, Oxford, UK), version 6.0, a fully automated, supervised k-nearest neighbor (k-NN) algorithm for WMH segmentation. The binary masks of total hyperintensities were generated using BIANCA and then carefully inspected and manually corrected to produce the final WMH binary masks. These derived WMH masks were further categorized into periventricular WMH (pWMH) and deep WMH (dWMH) by applying a 10 mm distance threshold from the ventricles\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Subsequently, all maps were visually inspected and manually corrected as needed. The volumes of pWMH and dWMH were expressed as ratios to the total volume of white matter.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDiffusion MRI analysis\u003c/h2\u003e \u003cp\u003ePre-processing: The pre-processing pipelines for diffusion MRI generally followed the protocols of an image-processing pipeline developed by UK Biobank\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The original diffusion MRI data were corrected for susceptibility-induced distortion by top-up method\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Then the data were further corrected for head motion and eddy currents using the Eddy tool in FSL. The output data were computed into diffusion tensor maps using the \u0026ldquo;DTIFIT\u0026rdquo; function of FSL.\u003c/p\u003e \u003cp\u003eFitting diffusion tensors: The DTIFIT estimated a diffusion tensor at each voxel of the brain, and outputs a set of images including fractional anisotropy (FA) and mean diffusivity (MD), as well as voxel-wise diffusivity in the directions of the x-axis (Dx), y-axis (Dy), and z-axis (Dz) images. We linearly aligned individual FA/MD maps with the T1-weighted image. The registration process is a combination of affine transformation\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Then, the CP mask in T1-weighted image could be used to extract mean FA and MD value in the CP. Visual examinations were performed to identify any co-registration bias.\u003c/p\u003e \u003cp\u003eDTI-ALPS: The FA map of each subject was registered to the FA map template in the standard MNI space, and the accuracy of the co-registration was visually confirmed. The Dx, Dy, Dz maps of the subject were then warped based on the registration matrix obtained from the FA map. The ROIs corresponding to the left projection fibers (superior and posterior corona radiata) and association fibers (superior longitudinal fasciculus) were extracted using atlas labels. The range of ROIs in the craniocaudal direction was limited to where the x-axis lines were perpendicular to the lateral ventricle bodies. A mask with FA values greater than 0.2 was applied to exclude CSF voxels. Finally, the Dx, Dy, and Dz values in these ROIs on projection fibers and association fibers were determined as Dxproj, Dyproj, Dzproj, Dxassoc, Dyassoc, Dzassoc, respectively. The DTI-ALPS index was automatically computed using the following Eq.\u0026nbsp;4\u003csup\u003e9\u003c/sup\u003e:\u003c/p\u003e \u003cp\u003eDTI-ALPS index= [(Dxproj\u0026thinsp;+\u0026thinsp;Dxassoc) / (Dyproj\u0026thinsp;+\u0026thinsp;Dzassoc)]\u003c/p\u003e \u003cp\u003eFree-Water image of CP: The FWf can be measured quantitatively by neurite orientation dispersion and density imaging (NODDI) model derived from multi-shell diffusion MRI. NODDI models the voxel contents as neurites (thin pipes, nominally axons), the spaces between them, and freely diffusing water\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. NODDI estimation for FWf was done using Accelerated Microstructure Imaging via Convex Optimization\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. After registered with T1-weighted image, the averaged Fwf of CP was calculated from the left and right CP ROI (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCerebral blood flow in CP\u003c/h2\u003e \u003cp\u003eWe used FSL BASIL toolbox to estimate perfusion in the CP. The key steps in BASIL pipeline includes motion correction, registration, calibration using M0 image, and Bayesian kinetic modelling to estimate perfusion maps, with spatial regularization and partial volume correction (PVC). Following these steps, the whole brain perfusion map was registered to the T1-weighted image, and the accuracy of the co-registration was visually confirmed. Subsequently, the cerebral blood flow (CBF) of the choroid plexus was extracted from the CP region of interest segmented from the T1-weighted image.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSurface-based PET images for Aβ, Tau and SV2A\u003c/h2\u003e \u003cp\u003eAn automated pipeline was utilized to derive cortical standardized uptake value ratios (SUVR) using the PETSurfer toolbox in Freesurfer, with the cerebellum cortex serving as the reference region\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Initially, high-resolution segmentation was generated from structural T1 images to facilitate PVC techniques. Subsequently, registration of PET and anatomical images was conducted and visually validated. To mitigate partial volume effects resulting from cortical atrophy in AD, the extended Muller\u0026ndash;Gartner method was employed as a PVC approach\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Surface-based maps of Aβ, Tau, and SV2A were smoothed on a two-dimensional surface using a Gaussian kernel with a full width at half maximum (FWHM) of 5mm. These biomarker maps were then utilized for vertex-wise analysis and mean cortical SUVRs were extracted for spatial correlation analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \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, adjusting for age, sex, and ApoE genotype. Bonferroni correction addressed multiple comparisons. Vertex-wise correlation analysis using generalized linear model (GLM), 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, adjusted for age. Models tested glymphatic function's mediation between CP FWf and WMH, and blood markers' mediation between CP FWf and cognition.\u003c/p\u003e \u003cp\u003eLongitudinal CP FWf changes were assessed using repeated measures ANOVA. Alteration rates between Aβ\u0026thinsp;+\u0026thinsp;patients and Aβ- controls were compared using a 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 (4.3.3) or SPSS, with significance at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor all main figures with 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).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe preprocessing codes for multimodal MR and PET are available as a Github repository (https://github.com/colorfulbrain/CP_FWf). The R/SPSS code used for the main analysis are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis 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\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX. X. and B. L. designed the study; X. X., X. Y. M. S. and B. L. wrote the initial draft of the manuscript; X. X., X. Y., J. Z., R. S., F. X. and B. L. performed data analysis; Y. W., Y. G., J. L., and Y. D. consulted on statistical methods; X. X., X. Y. and W. X. generated tables and figures; Y. Z.,Q. H., W., W. performed neurological function evaluation and collected blood samples and imaging data; all authors read the manuscript and provided input and consultation. X. X., X. Y., M. S. and B. L. finalized the manuscript. F. X. and B. L. oversaw the study and provided direction, funding, and resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll human subjects provided informed consent.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJack CR, Bennett DA, Blennow K et al (2016) A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. 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NeuroImage 132:334\u0026ndash;343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2016.02.042\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2016.02.042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":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":"Alzheimer Disease, Free-water mapping, choroid plexus, diffusion tensor image analysis of the perivascular space, white matter hyperintensity","lastPublishedDoi":"10.21203/rs.3.rs-4680360/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4680360/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe free water imaging of choroid plexus (CP) may improve the evaluation of Alzheimer's disease (AD). Our study investigated the role of free water fraction (FWf) of CP in AD by including 216 participants (133 Aβ\u0026thinsp;+\u0026thinsp;participants and 83 Aβ- controls) continuously enrolled in the NeuroBank-Dementia cohort at Ruijin Hospital (RJNB-D). At baseline, Aβ\u0026thinsp;+\u0026thinsp;participants showed higher CP free water fraction (FWf), increased white matter hyperintensity (WMH) volume, and decreased diffusion tensor image analysis of the perivascular space (DTI-ALPS). In Aβ\u0026thinsp;+\u0026thinsp;participants, DTI-ALPS mediated the association between CP FWf and periventricular WMH. CP FWf was associated with cortical Tau accumulation, synaptic loss, hippocampal and cortical atrophy, and cognitive performance. During follow-up, CP FWf increased faster in Aβ\u0026thinsp;+\u0026thinsp;participants than in controls. The findings suggest that elevated CP FWf may indicate impaired glymphatic function and AD neurodegeneration, potentially serving as a valuable biomarker for AD evaluation and progression.\u003c/p\u003e","manuscriptTitle":"Choroid Plexus Free-Water Correlates with Glymphatic function in Alzheimer Disease: The RJNB-D Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-05 20:12:02","doi":"10.21203/rs.3.rs-4680360/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":"August 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35139077,"name":"Health sciences/Neurology/Neurological disorders/Dementia/Alzheimer's disease"},{"id":35139078,"name":"Biological sciences/Computational biology and bioinformatics/Image processing"}],"tags":[],"updatedAt":"2024-08-05T20:12:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-05 20:12:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4680360","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4680360","identity":"rs-4680360","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}