Quantifying Ventricular CSF Clearance in the Human Brain Using Dynamic 18F-FDG PET: Insights into Age-Related Glymphatic Impairment

Quantifying Ventricular CSF Clearance in the Human Brain Using Dynamic 18F-FDG PET: Insights into Age-Related Glymphatic Impairment | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Quantifying Ventricular CSF Clearance in the Human Brain Using Dynamic 18F-FDG PET: Insights into Age-Related Glymphatic Impairment Zeyu Zhou , Tianyun Zhao , John Gardus III , Qiuting Wen , Yang Feng , Christine DeLorenzo , Ramin Parsey , View ORCID Profile Chuan Huang doi: https://doi.org/10.1101/2025.04.07.25325241 Zeyu Zhou 1 Department of Radiology and Imaging Sciences, Emory University School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tianyun Zhao 1 Department of Radiology and Imaging Sciences, Emory University School of Medicine 2 Department of Biomedical Engineering, Stony Brook University Find this author on Google Scholar Find this author on PubMed Search for this author on this site John Gardus III 3 Department of Psychiatry and Behavior Sciences, Stony Brook University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Qiuting Wen 4 Department of Radiology and Imaging Sciences, Indiana University School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yang Feng 5 Department of Biostatistics, New York University School of Global Public Health Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christine DeLorenzo 2 Department of Biomedical Engineering, Stony Brook University 3 Department of Psychiatry and Behavior Sciences, Stony Brook University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ramin Parsey 3 Department of Psychiatry and Behavior Sciences, Stony Brook University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chuan Huang 1 Department of Radiology and Imaging Sciences, Emory University School of Medicine 6 Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chuan Huang For correspondence: chuan.huang{at}emory.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Purpose The glymphatic system facilitates brain waste clearance via cerebrospinal fluid (CSF) flow, and its dysfunction has been linked to aging and neurodegeneration. However, clinically accessible methods to quantify glymphatic function in humans remain limited. This study aimed to examine the potential of dynamic 18F-FDG PET for measuring ventricular CSF clearance - as a surrogate marker of glymphatic function. Specifically, we evaluated its association with age, its test–retest reliability, and the feasibility of reduced scan durations for clinical applicability. Methods We analyzed 72 baseline 18F-FDG PET scans from participants enrolled in a prior depression trial. Time–activity curves (TACs) were extracted from the lateral ventricles and fitted with a γ-variate model to estimate influx ( μ in ) and clearance ( μ out ) parameters. Associations with age and clinical factors were examined using correlation and multiple linear regression. Test–retest reliability was assessed in 11 placebo-treated participants who underwent repeat scans eight weeks apart. A feasibility analysis tested whether shorter scan windows could yield comparable clearance estimates. Results μ out showed a strong negative correlation with age (r = –0.680, p < 0.001), while μ in was not significantly age-related. Age remained a significant predictor of μ out after adjusting for sex, ventricle size, and depression severity. A positive association between μ out and depression severity was observed after covariate adjustment. Test–retest analysis yielded an intraclass correlation coefficient of 0.702 for μ out , indicating moderate-to-good reproducibility. A shortened 30-minute scan window (starting 30 minutes post injection) preserved strong correlations with both μ out and age, supporting the potential for abbreviated imaging protocols. Conclusion Dynamic 18F-FDG PET provides a reliable and noninvasive method to quantify ventricular CSF clearance, revealing age-related decline indicative of glymphatic impairment. The method demonstrates reproducibility over time and retains key clearance metrics even with shortened scan durations. These findings establish a clinically feasible 18F-FDG PET-based approach for studying brain clearance and glymphatic function in aging and disease. Introduction The glymphatic system is a waste clearance pathway in the brain that plays a crucial role in maintaining neural homeostasis by facilitating the exchange of cerebrospinal fluid (CSF) and interstitial fluid (ISF) through perivascular spaces. Dysfunction of this system has been implicated in a range of neurodegenerative disorders, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and vascular dementia, where impaired clearance of metabolic waste may contribute to disease progression 1 , 2 . In addition, glymphatic dysfunction has been linked to cerebrovascular and traumatic brain disorders, such as small vessel disease 3 , traumatic brain injury 4 , stroke-related impairment 5 , and idiopathic normal pressure hydrocephalus 6 . CSF flow is a fundamental component of glymphatic function, serving as the driving force behind solute transport and waste removal from the brain 7 , 8 . In parallel with pharmacological and lifestyle interventions, novel surgical procedures such as lymphovenous anastomosis (LVA) have been proposed to create a direct channel from venous to lymphatic pathways – often considered the outflow rout of the glymphatic system – to enhance metabolic waste clearance. 9 The glymphatic system operates through a three-phase process: (1) CSF influx into the periarterial space, (2) CSF-ISF convection driven by arterial pulsations and aquaporin-4 (AQP4) water channels, and (3) ISF efflux through perivenous spaces toward lymphatic drainage 1 , 2 . Within this framework, the role of ventricular CSF is critical yet often underemphasized. Ventricular CSF, produced predominantly by the choroid plexus, provides the initial fluid volume and driving pressure necessary for subsequent glymphatic circulation. Its flow dynamics— regulated by anatomical structures and physiological states such as sleep and wakefulness— directly influence the distribution of CSF into periarterial spaces, thus affecting the efficiency of the overall glymphatic clearance pathway. Despite its significance, many aspects of glymphatic physiology remain poorly understood, with ongoing debates regarding its driving forces, efficiency in humans, and its alterations in aging and disease 10 , 11 . Age-related changes, including vascular stiffening, AQP4 mislocalization, and alterations in CSF production, have been linked to glymphatic dysfunction and may underlie the increased risk of neurodegenerative diseases in older individuals 2 , 12 , 13 . Accurate and clinically feasible measures of glymphatic function, therefore, are essential for assessing metabolic waste removal efficiency and monitoring therapeutic interventions designed to enhance brain fluid clearance, such as LVA. This further underscores the need for a clinically feasible technique to quantify glymphatic function and assess the efficacy of interventions designed to restore or enhance fluid clearance. Current human imaging approaches primarily rely on magnetic resonance imaging (MRI) techniques, including dynamic contrast-enhanced MRI (DCE-MRI) with intrathecal tracers, phase-contrast MRI (PC-MRI), and diffusion-weighted imaging (DWI), each providing valuable insights into glymphatic flow 8 . However, these MRI methods have notable limitations: DCE-MRI, while valuable for visualizing solute transport, often requires invasive intrathecal administration of gadolinium-based agents, posing risks of discomfort, increased intracranial pressure, and gadolinium deposition in brain tissues 5 . PC-MRI, capable of assessing bulk CSF flow, struggles to detect slower glymphatic dynamics due to limited sensitivity at lower flow velocities 6 . DWI, on the other hand, enables the estimation of molecular diffusion and fluid dynamics 14 , yet its ability to directly assess glymphatic transport remains limited by low spatial resolution and susceptibility to motion artifacts 15 . These technical and practical challenges underscore the critical need for alternative, noninvasive imaging biomarkers. Positron emission tomography (PET), particularly with small-molecule tracers such as 18F-THK and 11C-PiB, has recently been explored for quantifying ventricular CSF clearance as a surrogate of glymphatic function 13 , 16 , 17 . Among PET tracers, 18F-fluorodeoxyglucose (18F-FDG) - an analog of glucose - presents a unique opportunity for glymphatic imaging, as preclinical MRI studies using glucose-based tracers have highlighted the relationship between CSF clearance and AD pathology of neurodegenerative diseases such as AD and Huntington’s Disease 18 , 19 . Unlike receptor-binding PET tracers, 18F-FDG uptake is not confounded by ligand-receptor interactions, allowing for a more direct assessment of CSF dynamics. Furthermore, 18F-FDG is widely available and routinely used in clinical imaging, making it a promising candidate for translational research. In this study, we propose the use of dynamic 18F-FDG PET to quantify ventricular CSF clearance as a surrogate marker of glymphatic function, leveraging insights from preclinical MRI studies. Our objectives are threefold: (1) to characterize the relationship between ventricular CSF clearance and aging to demonstrate the surrogate markers potential in assessing glymphatic function, (2) to evaluate the reliability of dynamic PET measures using test-retest assessments, and (3) to assess the feasibility of shorter acquisition windows, enhancing clinical applicability. This work aims to establish 18F-FDG PET as a clinically viable tool for indirectly assessing glymphatic function, providing a foundation for future studies on neurodegenerative disease biomarkers and therapeutic interventions. Materials and Methods Study Population This study is a secondary analysis of data originally acquired in a randomized clinical trial investigating cerebral glucose metabolism in major depressive disorder 20 . A total of 85 participants (56 females, 29 males; age range 18.1-64.5 years; mean ± SD: 30.3 ± 13.9) underwent baseline 18F-FDG PET imaging. All participants provided written informed consent in accordance with institutional review board guidelines and were compensated for their participation. Of the 85 baseline scans, five were excluded due to irrecoverable reconstruction issues (e.g. missing attenuation maps or corrupted listmode/normalization files), and additional five were removed due to severe image corruption from excessive motion or instrument error. Three scans were also excluded due to abnormal blood glucose levels (outside 70-150 mg/dL or with fluctuations > 50% between pre- and post-scan). This yielded 72 participants for baseline analysis ( Figure 1 ). Download figure Open in new tab Figure 1. CONSORT-style flowchart detailing participant inclusion and exclusion criteria. A total of 85 individuals completed baseline FDG PET imaging; after quality control and glucose-level screening, 72 were included in baseline analyses. Of these, 49 completed post-treatment scans following an 8-week intervention. For test–retest analysis, only participants from the placebo arm with HDRS-17 change ≤5 were retained (N=11). Of these, 49 participants completed an 8-week treatment protocol followed by a second/post-treatment PET scan. As part of the trial, participants were randomized to receive escitalopram (N=24) or placebo (N=25). Since there is evidence that escitalopram affects glymphatic function 21 , only participants from the placebo group were considered for test-retest evaluation. Among them, 14 were excluded due to Hamilton Depression Rating Scale (HDRS-17) changes larger than 5 from pre- to post-treatment, resulting in 11 participants for the test-retest analysis ( Figure 1 ). PET/MR Data Acquisition Each participant received an intravenous injection of 148–185 MBq (4–5 mCi) of 18F-FDG and was placed in a Siemens Biograph mMR PET/MR scanner for a continuous 60-minute scan. PET data were acquired in list-mode format throughout the 60-minute session, then binned into thirty 2-minute frames to capture tracer uptake kinetics. Reconstructions were performed using Siemens E7 Tools with a maximum-likelihood expectation maximization (MLEM) algorithm for 75 iterations. We used 75 iterations of the MLEM algorithm based on the contrast-to-noise ratio (CNR) between gray and white matter. CNR plateaued after around 75 iterations, indicating sufficient image quality without further improvement 22 . Concurrently, high-resolution anatomical MR images were acquired using a T1-weighted MPRAGE (Magnetization Prepared Rapid Gradient Echo) sequence with typical parameters: repetition time = 2300 ms, echo time = 2.98 ms, inversion time = 900 ms, flip angle = 9°, 1 mm isotropic resolution. MR-based attenuation correction was applied using the “Boston method,” 23 which uses tissue segmentation and atlas registration to model soft tissue and bone structures. Participants were instructed to remain still and awake throughout the scan, and no sedation was used. The resulting co-registered PET and T1-weighted MR data provided the basis for subsequent analyses of glymphatic clearance dynamics. Images processing The 30 dynamic PET frames were first averaged to create a composite mean PET image, which served as a reference for motion correction. Each original frame was rigidly realigned (six degrees of freedom) to this composite mean. Then the same averaging and realignment steps were repeated once more to minimize residual head motion. After motion correction, the aligned PET frames were co-registered to the corresponding T1-weighted MR image using Freesurfer 7.4.1. Subsequently, for each subject, the radioactivity concentration of all 30 frames was normalized by body weight and injected dose to generate standardized uptake value (SUV) images. A subject-specific region of interest (ROI) encompassing the lateral ventricles was extracted using FreeSurfer, which was then eroded using a single-voxel morphological erosion to reduce partial-volume effects from surrounding tissues. Time-activity curves (TACs) were finally extracted from the eroded ventricular ROI across all dynamic frames and used for subsequent kinetic modeling and analyses. Modeling of the Time-Activity Curves To model the dynamic behavior of 18F-FDG in the lateral ventricles, we employed the γ-variate model 24 , which provides a physiologically plausible representation of bolus kinetics. As a glucose analog, FDG exhibits distribution and clearance characteristics similar to native glucose following intravenous injection. Prior work using dynamic glucose-enhanced MRI has demonstrated that the γ-variate model accurately captures the temporal profile of glucose concentrations in both brain parenchyma and CSF, including the initial rise and subsequent clearance phase. Using this model, the TAC of the lateral ventricle can be modeled as: where C(t) is the standardized uptake value (SUV) at time t, A represents the overall amplitude, μ in represents the 18F-FDG influx rate into the ventricles, and μ in indicates the CSF clearance (efflux) rate, respectively. In general, μ in quantifies how fast the TAC curve rises and μ out quantifies how fast the TAC curve drops. Note that the two variables do not act independently but jointly determine the shape of the curve. Test-retest analysis To assess the stability of ventricular CSF clearance measures, we performed a test-retest analysis in N=11 participants ( Figure 1 ) who received placebo and completed both baseline and 8-week follow-up PET scans. The 8-week interval was deemed insufficient for aging-related effects to impact glymphatic dynamics. Only participants with no larger than 5-point change on the HDRS-17 from pre- to post-treatment were included, consistent with the minimal clinically important difference 25 , to minimize confounding effects from clinically meaningful mood changes. The exclusion of large HDRS-17 change is due to the association between depression level and CSF clearance, which will be justified in the Results section. The same image preprocessing and modeling pipeline was applied at both time points. Fitted μ out values were compared to evaluate test-retest reliability and repeatability. Feasibility Analysis of Reduced Scan Duration To evaluate the clinical feasibility of shorter imaging protocols, we investigated whether scanning durations of less than one hour could still capture reliable information about CSF clearance. Beginning at 10 minutes post-injection, we varied the analysis window by adjusting the end time, with each interval spanning a minimum of 10 minutes. For each start–end time window [ t start , t end ], a TAC was extracted from the eroded lateral ventricle ROI. A linear model was fitted to the SUV values within the interval to estimate a clearance slope. This slope was then normalized by the mean SUV of the same interval to enable inter-subject comparison. Thus, the normalized clearance slope between start time t start and end time t end is: Correlation analysis was performed between the normalized slopes and both the efflux rate ( μ out ) and participants’ age across all 72 baseline scans. By tracking how these correlations varied across different start-end time windows, we identified shortened scan durations that preserved the sensitivity to individual differences in CSF clearance. Statistical Analysis The primary outcome measures, μ in (influx rate) and μ out (efflux rate) , were first examined for normality using visual inspection and the Shapiro-Wilk test. As both variables conformed to normality assumptions, their associations with age were assessed using Pearson correlation. To account for potential confounders, separate multiple linear regression models were constructed for μ in and μ out , incorporating age, lateral ventricle size, baseline HDRS-17 score, and sex, as predictors. Test–retest reliability was assessed in a subset of N=11 participants. The intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of μ out across the two scan sessions. For the feasibility analysis of reduced scan duration, Pearson correlation test was used to assess the relationship between normalized clearance slopes (derived from truncated TAC intervals) and both μ out and age. Correlation strength was tracked across varying start–end time windows to evaluate the reliability of shortened imaging protocols. All statistical tests were two-tailed, with significance defined as p < 0.05, unless otherwise specified. Analyses were performed using SPSS 29.0.2, and figures were generated with Python 3.11 using matplotlib 3.7.2. Results Baseline Analysis: Association Between Ventricular CSF Clearance and Age To investigate age-related differences in ventricular CSF clearance, we analyzed 72 baseline (pre-treatment) FDG PET scans, minimizing potential confounds from treatment or placebo effects. Demographic characteristics by sex are summarized in Table 1 . Although females were slightly older on average than males, no significant sex differences were observed in baseline depression severity (HDRS-17) or lateral ventricle size. View this table: View inline View popup Download powerpoint Table 1. Baseline Participant Demographics. Figure 2 displays representative dynamic PET images and fitted time-activity curves (TACs) with the γ-variate model for a young participant in early adulthood and an older participant in their 60s. In both cases, 18F-FDG progressively accumulates in the lateral ventricles, but notable differences in curve shape are evident. The younger participant’s TAC plateaus and declines after ∼30 minutes, yielding a positive μ out value consistent with intact CSF clearance. In contrast, the older participant’s TAC continues to rise throughout the scan, producing a negative μ out value indicative of impaired clearance. Download figure Open in new tab Figure 2. Representative dynamic PET/MR images (a, c) and corresponding lateral ventricle time-activity curves (TACs; b, d) (b, d) for one younger participant and one older participant. The influx ( μ in ) and clearance ( μ out ) rates were estimated using γ-variate model. The younger participant’s curve plateaus and declines after ∼30 minutes, indicating effective CSF clearance (positive μ out ), whereas the older participant’s curve continues to rise throughout the scan, yielding a negative μ out consistent with impaired clearance. To evaluate these patterns across the cohort, lateral ventricle TACs were normalized by their 2-norm ( Figure 3a ) and fitted by the γ-variate model ( Figure 3b ). A clear age-related gradient was observed: older participants (depicted in red/yellow) exhibited flatter, continuously rising curves, while younger individuals (blue/green) showed earlier plateaus suggestive of more efficient CSF clearance. Download figure Open in new tab Figure 3. Normalized lateral ventricle TACs (a) and corresponding γ-variate model fits (b) for all 72 participants. Curves are color-coded by age. We then quantified the relationship between fitted parameters and age. As shown in Figure 4 , μ in was not significantly associated with age (r = 0.203, p = 0.087; Fig. 3a ), whereas μ out exhibited a strong negative correlation (r = –0.680, p < 0.001; Fig. 3b ), indicating that CSF clearance declines with age while influx remains stable. Download figure Open in new tab Figure 4. Scatter plot of fitted parameters (a) μ in and (b) μ out of the γ-variate model to the subjects’ TACs against age, along with Pearson correlation test results. To further assess whether lateral ventricle size, baseline HDRS-17 score or sex influenced CSF clearance, we performed a multiple linear regression analysis. Table 2 shows the analysis results. For μ out , age emerged as the strongest predictor (standardized beta = -0.584, p < 0.001), and baseline HDRS-17 was also significant (standardized beta = 0.200, p = 0.021), whereas ventricle size approached borderline significance (standardized beta = -0.191, p = 0.052) and sex showed no association (p = 0.956). In contrast, analysis of μ in revealed significant effects for both age (standardized beta = 0.361, p = 0.006) and lateral ventricle size (standardized beta = -0.362, p = 0.007), but no significant associations with baseline HDRS-17 (p = 0.289) or sex (p = 0.160). These findings confirmed age as a robust predictor of both influx ( μ in ) and efflux ( μ out ) parameters. Depression severity (as captured by HDRS-17) is selectively linked to efflux that higher μ out was associated with more severe depression. Lateral ventricle size may also modulate these CSF dynamics. View this table: View inline View popup Download powerpoint Table 2. Multiple linear regression results for influx rate μ in and clearance rate μ out . Covariates included age, lateral ventricle size, HDRS-17 score, and sex. To further explore the influence of depression severity, we regressed out age, lateral ventricle size, and sex from μ out and correlated with the residuals with HDRS-17 ( Figure 5 ). A significant positive association remains (r = 0.276, p = 0.019), indicating the independent influence of depression severity on CSF clearance. Download figure Open in new tab Figure 5: Relationship between HDRS-17 scores and μ out residuals after adjusting for age, lateral ventricle size, and sex through multiple linear regression ( Table 2 ). Pearson correlation test result is displayed. Test-retest analysis We next assessed the reliability of μ out in a subset of 11 participants who received placebo and exhibited minimal change in HDRS-17 scores (<5 points) between baseline and post-treatment scans. Figure 6 shows the comparison of μ out values across timepoints, with an intraclass correlation coefficient (ICC) of 0.702. This indicates moderate to good test–retest reliability of ventricular CSF clearance measured by μ out . Download figure Open in new tab Figure 6. Test–retest reliability of μ out across two scans spaced 8 weeks apart in N=11 placebo-treated participants. Dashed line represents identity. ICC (intraclass correlation coefficient) = 0.702. Feasibility Analysis of Reduced Scan Duration To evaluate the potential for shorter PET imaging protocols, we examined whether clearance metrics could be preserved using truncated scan windows. For each pair of start and end times (minimum 10 minutes apart) beginning at 10 minutes post-injection, we fit a linear model to the ventricular TAC and normalized the slope by the mean SUV over the interval. Figures 7a and 7b display the Pearson correlation between these normalized slopes and both μ out and age across varying scan windows. Correlation strength increased with longer scan durations, peaking when the end time approached 60 minutes. Download figure Open in new tab Figure 7. Evaluation of reduced scan duration. Correlation map of normalized slope of a shortened TAC versus (a) μ out and (b) age, across various scan (y-axia) start time and (x-axis) end time. (c) Correlation curves between the normalized slope and μ out (and age), with fixed end time at 60 minutes. Fixing the end time at 60 minutes and varying the start time ( Figure 7c ) showed that correlations with μ out increased up to ∼30 minutes and then declined, while correlations with age remained stable until ∼30 minutes and then dropped sharply. This suggests that a 30–60 minute scan window preserves maximal association with both μ out and age. These results demonstrate that a reduced protocol—from 30 to 60 minutes post-injection— could capture essential CSF clearance dynamics with minimal loss of sensitivity, offering a feasible path toward abbreviated clinical imaging strategies. Discussion There findings provide direct in vivo evidence that ventricular CSF clearance decreases with advancing age, consistent with earlier preclinical MRI studies demonstrating that aging slows CSF turnover and impairs metabolic waste elimination 26 . As illustrated in Figures 2 and 3 , older participants exhibited reduced 18F-FDG clearance in the lateral ventricles, suggesting that the glymphatic function—of which CSF flow is a major driving component—may be compromised in later life. This observation aligns with previous reports of reduced ISF flow after age 50 27 , potentially driven by factors such as reduced arterial pulsatility and AQP4 mislocalization 28 . Multiple linear regression analysis ( Table 2 ) indicated that, although older individuals tend to have structurally enlarged ventricles 29 , age-related clearance decline could not be explained by this morphological feature. Nor does sex appear to play a significant role. These results support the notion that age-related deficits in glymphatic clearance reflect a physiological process beyond simple structural change. Compared to existing MRI-based glymphatic imaging techniques, our PET-derived measure of ventricular CSF clearance offers several notable advantages. As discussed in the Introduction, PC-MRI is well-suited for quantifying high-velocity bulk flow but struggles to reliably detect slow-moving CSF within small anatomical regions like the ventricles. Extended MRI acquisition times required to enhance signal-to-noise ratio also pose logistical difficulties, especially for older adults and cognitively impaired individuals. DCE-MRI methods, which utilize intrathecal administration of gadolinium-based contrast agents, are invasive, time-consuming, and may alter physiological conditions due to higher molecular-weight tracers. By contrast, PET imaging with the widely available 18F-FDG tracer circumvents these limitations. It requires only standard intravenous injection and accommodates a practical scanning duration suitable for clinical populations. Crucially, the relatively long half-life and minimal binding complexity of 18F-FDG allow for accurate detection of subtle, prolonged clearance patterns characteristic of impaired glymphatic function in aging populations. Together, these attributes position PET imaging as a valuable alternative or complementary approach to existing MRI techniques, effectively addressing critical gaps related to invasiveness, patient comfort, logistical feasibility, and sensitivity to subtle glymphatic alterations. Test-retest analysis ( Figure 6 ) showed that μ out exhibited moderate-to-good reliability (ICC = 0.702) over an 8-week interval in placebo-treated participants with stable clinical status. Although the sample size was limited (N=11), this degree of reproducibility supports μ out as a reliable CSF clearance marker for longitudinal monitoring, potentially enabling detection of changes over time in disease progression or response to therapeutic intervention. One observation from our study is that many participants, particularly older ones, displayed a continuously rising TAC at the 60-minute mark, suggesting that a one-hour scan captures only the early phase of CSF clearance. This observation implies that clearance dynamics may be prolonged in older adults, and longer scans could be necessary to fully characterize tracer outflow. While the 60-minute window provides useful information about CSF clearance kinetics, extended observation may yield more robust estimates of μ out —particularly in detecting subtle or protracted changes among older adults. If future studies employ longer imaging sessions, it may be possible to differentiate more definitively between normal and compromised ventricular clearance rates in humans, thereby enhancing the clinical utility of 18F-FDG PET for glymphatic assessment. Interestingly, μ out remained positively associated with HDRS-17 scores after controlling for age, ventricle size, and sex ( Figure 5 ), indicating that individuals with more severe depressive symptoms may exhibit higher CSF clearance rates. While the underlying mechanisms remain speculative, one possibility is that elevated stress-related hormones or altered neurovascular regulation in depression could transiently augment CSF outflow. Alternatively, this correlation may be caused by a compensatory mechanism in which the brain attempts to clear potentially harmful metabolic byproducts more rapidly under heightened psychiatric distress. However, the direction and causality of this relationship remain unclear, warranting future studies that incorporate additional physiological and neuroendocrine measures to clarify underlying mechanisms. Our feasibility analysis demonstrated that a truncated 30-minute window starting at 30-minutes post injection captured much of the information contained in the full 60-minute scan. Normalized slope estimates from this window remained strongly correlated with both μ out and age ( Figure 7 ). These findings support the potential use of abbreviated scanning protocols, which holds promise for clinical applications and may be particularly beneficial in older or clinically populations who may struggle with long acquisition times. Future studies that validate the reproducibility and diagnostic value of such abbreviated protocols in larger, more diverse populations and longer acquisition time will help determine whether a reduced scan duration could become a standard practice in assessing CSF clearance. Specifically, the strength of 18F-FDG PET in assessing CSF clearance lies in its relatively low binding complexity compared to other PET tracers that have been employed for a similar purpose. Tau radioligands (e.g., 18F-THK5117 16 , 18F-THK5351 13 ), amyloid-beta agents (e.g., 11C-PiB 17 ), and inflammation-targeting tracers (e.g., 18F-FEPPA 30 ) all interact with specific binding sites, introducing complexities due to variations in receptor expression, affinity, and tissue uptake kinetics. As a result, these tracers often exhibit a rapid drop in blood activity during the early phase, and their late-phase interpretation is confounded by multipass blood supply, binding competition, and transporter limitations. Furthermore, for extended studies (∼120 minutes), the residual tracer signal in the ventricles may become insufficient to clearly distinguish inter-individual differences in clearance rates. By contrast, 18F-FDG combines the advantages of an 18F label with a 110-minute half-life, a widely available clinical protocol, and the absence of a receptor-binding step, which simplifies kinetic modeling of ventricle outflow over an extended timeframe. Moreover, 18F-FDG is a glucose analog, making it directly comparable to preclinical studies that use glucose-based tracers to evaluate CSF and glymphatic dynamics. While non-binding, water-like tracers such as 11C-butanol 31 or 15O-H2O 32 circumvent receptor interactions, their very short half-lives (20 and 2 minutes, respectively) make prolonged imaging impractical. Taken together, these attributes make 18F-FDG a practical and potentially more robust tracer for quantifying CSF dynamics and evaluating glymphatic function in both research and clinical settings. This work’s generalizability is limited by the sample, drawn exclusively from individuals with major depressive disorder. Although convenient for secondary analysis of age-related changes, depression itself — with its associated physiological and metabolic alterations — may influence CSF clearance and glymphatic function. In fact, we observed a positive correlation between depression severity and μ out in this study ( Figure 5 ). Prospective studies including healthy controls and individuals with diverse neuropsychiatric profiles are needed. In addition, the γ-variate model—while physiologically grounded—assumes a single bolus-shaped clearance trajectory, which may oversimplify the multi-compartmental nature of CSF movement and the multiphasic behavior of glymphatic dynamics, especially over extended time periods or in pathological conditions. It may also fail to account for partial reabsorption over longer intervals, as well as the kinetic nuances introduced when FDG is phosphorylated and becomes trapped intracellularly. Future modeling efforts may benefit from compartmental or multiphasic frameworks to better capture these complexities. Conclusion This study used dynamic 18F-FDG PET imaging to quantify ventricular CSF clearance, which is shown to decline with age, implying impaired glymphatic function in later life. Using a γ-variate model, we demonstrated that the CSF clearance quantification is reliable and reproducible. Our results suggest that even abbreviated imaging protocols – focusing on the 30-60 minute post-injection window – can potentially preserve key clearance metrics, facilitating broader clinical application. Compared to existing MRI-based approaches, 18F-FDG PET offers practical, noninvasive advantages for assessing ventricular CSF circulation. Future studies incorporating longer scan sessions and healthy control populations are needed to further validate this approach. Additionally, exploring more advanced models may help better capture the complex kinetics of ventricular tracer dynamics. Together, these findings support the use of dynamic 18F-FDG PET as a promising tool for investigating glymphatic function in aging and neuropsychiatric conditions, with potential implications for early detection, monitoring, and therapeutic targeting of brain clearance deficits. Disclosure Data used in this study were originally acquired with the support of NIH/NIMH grant R01MH104512 (CD). The authors declare no competing interests. No financial relationships, activities, or affiliations exist that could be perceived as having influenced the submitted work beyond the stated funding support. Data Availability Statement All data produced in the present study are available upon reasonable request to the authors References 1. ↵ Jessen , N. A. , Munk , A. S. F. , Lundgaard , I. & Nedergaard , M. The Glymphatic System: A Beginner’s Guide . Neurochem Res 40 , 2583 – 2599 ( 2015 ). OpenUrl CrossRef PubMed 2. ↵ Benveniste , H. et al. Glymphatic Cerebrospinal Fluid and Solute Transport Quantified by MRI and PET Imaging . Neuroscience 474 , 63 – 79 ( 2021 ). OpenUrl CrossRef PubMed 3. ↵ Mestre , H. , Kostrikov , S. , Mehta , R. I. & Nedergaard , M. Perivascular spaces, glymphatic dysfunction, and small vessel disease . Clinical Science 131 , 2257 – 2274 ( 2017 ). OpenUrl Abstract / FREE Full Text 4. ↵ Venkat , P. et al. White matter damage and glymphatic dysfunction in a model of vascular dementia in rats with no prior vascular pathologies . Neurobiology of Aging 50 , 96 – 106 ( 2017 ). OpenUrl CrossRef PubMed 5. ↵ Eide , P. K. & Ringstad , G. MRI with intrathecal MRI gadolinium contrast medium administration: a possible method to assess glymphatic function in human brain . Acta Radiologica Open 4 , 2058460115609635 ( 2015 ). OpenUrl PubMed 6. ↵ Ringstad , G. , Vatnehol , S. A. S. & Eide , P. K. Glymphatic MRI in idiopathic normal pressure hydrocephalus . Brain 140 , 2691 – 2705 ( 2017 ). OpenUrl CrossRef PubMed 7. ↵ Ringstad , G. et al. Brain-wide glymphatic enhancement and clearance in humans assessed with MRI . JCI Insight 3 , e121537 ( 2018 ). OpenUrl 8. ↵ Taoka , T. & Naganawa , S. Glymphatic imaging using MRI . Journal of Magnetic Resonance Imaging 51 , 11 – 24 ( 2020 ). OpenUrl CrossRef PubMed 9. ↵ Li , X. et al. Promising outcomes 5 weeks after a surgical cervical shunting procedure to unclog cerebral lymphatic systems in a patient with Alzheimer’s disease . Gen Psychiatr 37 , e101641 ( 2024 ). OpenUrl 10. ↵ Bohr , T. et al. The glymphatic system: Current understanding and modeling . iScience 25 , ( 2022 ). 11. ↵ Ding , Z. et al. The glymphatic system: a new perspective on brain diseases . Front. Aging Neurosci . 15 , ( 2023 ). 12. ↵ Reddy , O. C. & van der Werf , Y. D. The Sleeping Brain: Harnessing the Power of the Glymphatic System through Lifestyle Choices . Brain Sciences 10 , 868 ( 2020 ). OpenUrl PubMed 13. ↵ Li , Y. et al. Decreased CSF clearance and increased brain amyloid in Alzheimer’s disease . Fluids Barriers CNS 19 , 21 ( 2022 ). OpenUrl CrossRef PubMed 14. ↵ Wright , A. M. , Wu , Y.-C. , Feng , L. & Wen , Q. Diffusion magnetic resonance imaging of cerebrospinal fluid dynamics: Current techniques and future advancements . NMR Biomed 37 , e5162 ( 2024 ). OpenUrl CrossRef PubMed 15. ↵ Prasuhn , J. , Xu , J. , Hua , J. , van Zijl , P. & Knutsson , L. Exploring neurodegenerative disorders using advanced magnetic resonance imaging of the glymphatic system . Front. Psychiatry 15 , ( 2024 ). 16. ↵ Leon , M. J. de et al . Cerebrospinal Fluid Clearance in Alzheimer Disease Measured with Dynamic PET . Journal of Nuclear Medicine 58 , 1471 – 1476 ( 2017 ). OpenUrl Abstract / FREE Full Text 17. ↵ Schubert , J. J. et al. Dynamic 11C-PiB PET Shows Cerebrospinal Fluid Flow Alterations in Alzheimer Disease and Multiple Sclerosis . Journal of Nuclear Medicine 60 , 1452 – 1460 ( 2019 ). OpenUrl PubMed 18. ↵ Huang , J. et al. Altered d-glucose in brain parenchyma and cerebrospinal fluid of early Alzheimer’s disease detected by dynamic glucose-enhanced MRI . Science Advances 6 , eaba3884 ( 2020 ). OpenUrl FREE Full Text 19. ↵ Liu , H. et al. Interrogation of dynamic glucose-enhanced MRI and fluorescence-based imaging reveals a perturbed glymphatic network in Huntington’s disease . bioRxiv 2023.04.03.535397 ( 2023 ) doi: 10.1101/2023.04.03.535397 . OpenUrl Abstract / FREE Full Text 20. ↵ Hill , K. R. et al. Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial . NeuroImage: Clinical 32 , 102858 ( 2021 ). OpenUrl PubMed 21. ↵ Sheline , Y. I. et al. Effect of escitalopram dose and treatment duration on CSF Aβ Levels in healthy older adults . Neurology 95 , e2658 – e2665 ( 2020 ). OpenUrl PubMed 22. ↵ Mehranian , A. et al. Multi-modal synergistic PET and MR reconstruction using mutually weighted quadratic priors . Magn Reson Med 81 , 2120 – 2134 ( 2019 ). OpenUrl PubMed 23. ↵ Poynton , C. B. et al. Probabilistic atlas-based segmentation of combined T1-weighted and DUTE MRI for calculation of head attenuation maps in integrated PET/MRI scanners . Am J Nucl Med Mol Imaging 4 , 160 – 171 ( 2014 ). OpenUrl PubMed 24. ↵ Huang , J. et al. Altered d-glucose in brain parenchyma and cerebrospinal fluid of early Alzheimer’s disease detected by dynamic glucose-enhanced MRI . Science Advances 6 , eaba3884 ( 2020 ). OpenUrl FREE Full Text 25. ↵ Hengartner , M. P. & Plöderl , M. Estimates of the minimal important difference to evaluate the clinical significance of antidepressants in the acute treatment of moderate-to-severe depression . BMJ Evidence-Based Medicine 27 , 69 – 73 ( 2022 ). OpenUrl 26. ↵ Kress , B. T. et al. Impairment of paravascular clearance pathways in the aging brain . Ann Neurol 76 , 845 – 861 ( 2014 ). OpenUrl CrossRef PubMed 27. ↵ Suzuki , Y. , Nakamura , Y. & Igarashi , H. Interstitial fluid flow decreases with age, especially after 50 years . Neurobiol Aging 141 , 14 – 20 ( 2024 ). OpenUrl PubMed 28. ↵ Ding , Z. et al. The glymphatic system: a new perspective on brain diseases . Front. Aging Neurosci . 15 , ( 2023 ). 29. ↵ Carmichael , O. T. et al. Cerebral ventricular changes associated with transitions between normal cognitive function, mild cognitive impairment, and dementia . Alzheimer Dis Assoc Disord 21 , 14 – 24 ( 2007 ). OpenUrl CrossRef PubMed 30. ↵ Zhou , Z. et al. Impaired Cerebrospinal Fluid 18F-FEPPA Clearance in Long COVID Suggests Altered Glymphatic System Function . Journal of Nuclear Medicine 65 , 242407 – 242407 ( 2024 ). OpenUrl 31. ↵ Mehta , N. H. et al. [1-11C]-Butanol Positron Emission Tomography reveals an impaired brain to nasal turbinates pathway in aging amyloid positive subjects . Fluids and Barriers of the CNS 21 , 30 ( 2024 ). OpenUrl 32. ↵ Suzuki , Y. , Nakamura , Y. & Igarashi , H. Interstitial fluid flow decreases with age, especially after 50 years . Neurobiology of Aging 141 , 14 – 20 ( 2024 ). OpenUrl PubMed View the discussion thread. Back to top Previous Next Posted April 09, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Quantifying Ventricular CSF Clearance in the Human Brain Using Dynamic 18F-FDG PET: Insights into Age-Related Glymphatic Impairment Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. 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