Coexisting Alzheimer’s disease (AD) pathology is common in Parkinson’s disease (PD). However, the implications of genetic risk scores (GRS) for AD have not been elucidated in PD. In 413 de novo PD and 195 healthy controls from the Parkinson’s Progression Marker Initiative database, the effects of GRS for AD (GRS-AD) and PD (GRS-PD) on the risk of PD and longitudinal CSF biomarkers and clinical outcomes were explored. Higher GRS-PD and lower baseline CSF α-synuclein were associated with an increased risk of PD. In the PD group, GRS-AD was correlated positively with CSF p-tau/Aβ and negatively with CSF α-synuclein. Higher GRS-PD was associated with faster CSF p-tau/Aβ increase, and GRS-AD and GRS-PD were interactively associated with CSF α-synuclein. In the PD group, higher GRS-AD was associated with poor visuospatial function, and baseline CSF p-tau/Aβ was associated with faster cognitive decline. Higher GRS-PD was associated with better semantic fluency and frontal-related cognition and motor function given the same levels of CSF biomarkers and dopamine transporter uptake. Taken together, our results suggest that higher GRS-AD and CSF p-tau/Aβ, reflecting AD-related pathophysiology, may be associated with cognitive decline in PD patients. Accordingly, multiple studies have focused on predicting AD pathology using cerebrospinal fluid (CSF) biomarkers, such as phosphorylated-tau, total-tau, and β-amyloid (Aβ), or apolipoprotein E ε4 allele (APOE4) in PD7. However, the contribution of CSF biomarkers on clinical progression in PD patients is conflicting. Discrepancies could originate from concomitant AD pathology3, which leads to neuronal damage and release of both neuronal tau and αSyn into CSF9 given the same level of αSyn pathology2. Furthermore, the status of CSF biomarkers is evolving over time10. Research has shown that de novo PD patients have lower CSF αSyn levels7 and that CSF αSyn decreases longitudinally in PD patients12. However, there are controversies regarding the implication of CSF αSyn in PD patients, particularly whether lower CSF αSyn is associated with motor severity6 and cognition7, or not12. Therefore, simultaneous evaluation of CSF AD biomarkers and CSF αSyn is needed to determine their exact relationships with neurodegeneration in PD patients.Coexisting Alzheimer’s disease (AD) pathology is observed in approximately 30% of autopsy-confirmed Parkinson’s disease (PD) patients13. Higher GRS for PD (GRS-PD) is associated with earlier symptom onset15 and faster progression17 in PD patients. However, other studies have reported protective or no association between higher GRS-PD and the progression of PD18. As the clinical symptoms and prognosis of PD are affected by concomitant AD pathology3 and since there is genetic pleiotropy between AD and synucleinopathy19, a model that consider both GRS and CSF biomarkers of AD and PD is required. Our previous study on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset showed that one such approach successfully revealed correlations between GRS and CSF biomarkers for AD and PD20.Genetic risk score (GRS), based on disease-associated risk loci, captures the polygenicity of diseases and identifies an individual’s disease riskIn this study, we computed GRS for AD (GRS-AD) and GRS-PD from the Parkinson’s Progression Marker Initiative (PPMI) database and evaluated the effects of GRS-AD and GRS-PD on longitudinal CSF biomarkers and clinical outcomes in de novo PD patients. We hypothesized that GRS-AD would predict CSF biomarkers of AD and αSyn pathologies, and that GRS-AD and CSF biomarker of AD would be related to clinical outcomes in PD patients.APOE4 and GRS-AD were comparable between the two groups. Compared to the HC group, the PD group had lower mean baseline CSF Aβ, total tau (t-tau), phosphorylated tau (p-tau), and αSyn levels. Baseline CSF p-tau/Aβ and amyloid positivity did not differ between the two groups. The PD group had worse motor severity scores, lower dopamine transporter (DAT) availability, and worse cognitive scores in all tests.There were no significant differences in baseline age, sex, and follow-up duration between the PD and healthy control (HC) groups. The HC group had higher education than the PD group Table . GRS-PD Logistic regression analyses performed in all participants showed that higher GRS-PD model 1) and lower baseline CSF αSyn model 2) were associated with an increased risk of PD  for time-varying CSF p-tau/Aβ showed that GRS-AD, time, and GRS-PD*time were associated with time-varying CSF p-tau/Aβ, while GRS-PD, GRS-AD*time, and time-varying CSF αSyn were not Table . These rIn HC subjects, LMM for time-varying CSF p-tau/Aβ showed that GRS-AD and time-varying αSyn were significantly associated with time-varying CSF p-tau/Aβ, suggesting that higher GRS-AD was associated with higher CSF p-tau/Aβ and time-varying CSF αSyn positively correlates with time-varying CSF p-tau/Aβ. LMM for time-varying CSF αSyn showed that time-varying CSF p-tau/Aβ was significantly associated with time-varying CSF αSyn, but the effects of other predictors were not significant.In PD patients, model 1 LMM for longitudinal cognitive scores showed that GRS-AD and GRS-AD*GRS-PD were significantly associated with visuospatial score; no predictors were associated with memory score; GRS-PD was associated with semantic fluency score; and time and GRS-PD*time were associated with frontal/executive score Table . Model 2In PD patients, model 1 LMM for longitudinal motor severity scores showed that the effects of time and GRS-PD*time on motor severity scores were significant, suggesting that motor severity scores increased over time and that higher GRS-PD was associated with slower aggravation in motor severity scores Table . Model 2GBA) mutation. When the effects of GBA mutation on longitudinal cognitive scores were investigated, model 1 LMM showed that the presence of GBA mutation was associated with a faster decline in semantic fluency score  correction. When the effects of GBA mutation on longitudinal motor severity scores were investigated, the presence of GBA mutation was associated with faster aggravation in motor severity scores given the same levels of time-varying CSF biomarkers (Supplementary Table Fourteen of 413 PD patients (3.4%) had glucocerebrosidase  was reversed, such that higher GRS-AD was associated with slower decrease in CSF αSyn. A possible explanation for this complex relationship between GRS-AD and CSF αSyn could be that higher GRS-AD may not only aggravate αSyn pathology but also induce faster neuronal damage leading to relative increases in CSF αSyn level.Higher GRS-AD was associated with higher CSF p-tau/Aβ and lower CSF αSyn levels. The direction of β coefficients for time in Table 10, the effects of CSF αSyn were not simultaneously considered in the study due to high correlation between CSF biomarkers24. In our study, we used CSF p-tau/Aβ to avoid issues with multi-collinearity. Considering that concomitant AD co-pathology in LBD patients is reflected in CSF biomarkers, including tau and Aβ2, our results are consistent with previous studies showing a strong influence of AD pathology on cognitive dysfunction in PD patients26. Thus, our results suggest that CSF p-tau/Aβ could be a biomarker for AD-related pathophysiology in PD patients when CSF αSyn is simultaneously considered. It is noteworthy that higher GRS-AD was associated with visuospatial dysfunction, but CSF p-tau/Aβ was not. Considering that visuospatial dysfunction is a cognitive characteristic of DLB24 and a mixed disease of AD with LBD28, unknown LB-related mechanisms, such as the interaction of Aβ and LB-related brain changes converging in the occipital cortex28, could explain visuospatial dysfunction related to GRS-AD.Higher baseline CSF p-tau/Aβ was associated with a faster decline in memory, semantic fluency, and frontal/executive function in PD patients. Although a previous study in the PPMI cohort found an association between CSF Aβ and working memory, rather than episodic memory17. The discrepancy could be attributable to the different set of SNPs used for GRS, demographic differences of the cohorts, and further consideration of CSF biomarkers and AD-GRS in our study. To evaluate the validity of the model used in our study, we conducted sensitivity analyses using the presence of GBA mutation as a predictor instead of PD-GRS , those with GBA-associated PD showed faster cognitive declines in semantic fluency scores and faster motor progression, consistent with previous studies30. There is a PPMI-based study that showed slower striatal dopaminergic degeneration and motor outcomes in those with higher GRS-PD, possibly due to the heterogenetic effects of alleles used for GRS on the progression of PD18. Considering that higher GRS-PD was associated with the risk of PD, PD patients with higher GRS-PD may be more susceptible to the clinical symptoms of PD with the same burden of αSyn pathology rather than more rapid spreading of αSyn pathology. As higher GRS-PD was associated with slower cognitive and motor progression given the same CSF biomarker burden after disease onset, the effect of higher GRS-PD in PD patients resembled the effect of lower education in AD patients31. As a proxy marker for cognitive reserve, lower education is associated with a higher risk of developing AD dementia given the same amount of AD pathology, but it is related to slower symptom progression after disease onset. Relative genetic composition of GRS-PD, either related to the burden of αSyn pathology or functional reserve against αSyn pathology, could explain the discrepancy in the previously reported effect of GRS-PD on clinical progression, either protective18 or harmful17. Inconsistency on the effects of GRS-PD requires further application of GRS-PD on multiple cohorts and large-sized meta-analyses.Higher GRS-PD was associated with better semantic fluency domain, slower decline in frontal/executive function, and better motor symptoms, given the same level of CSF biomarkers. These findings are not consistent with previous studies showing faster cognitive decline and motor progression in PD patients with higher GRS-PDAPOE region was included in the computation of GRS-AD and limits the interpretation of GRS-AD other than APOE4, which are known to increase the risk of cognitive decline in PD33. In the sensitivity analyses using GRS-AD excluding a SNP in APOE region , which is an observational multicenter study with clinical, imaging, and biological data to identify biomarkers of PD progression. The analyses of this study are based on the initial phase of the PPMI, which enrolled 423 de novo PD participants without rare genetic mutations. We further selected 413 PD patients and 195 HCs with available whole-genome sequencing data. All procedures in the study involving human participants were performed in accordance with the ethical standards committee at each participating institution, and written informed consent was obtained from all participants. The study was registered at clinicaltrials.gov (NCT01141023).The data used in this study were obtained from the PPMI database , deviation from Hardy–Weinberg equilibrium (P < 1E-6), and high rates of missing genotypes (>0.05). GRS-AD was computed using genotypes of 24 risk loci for AD , we conducted the analyses with GRS-PD based on the 87 bi-allelic risk loci. Each GRS was calculated by summing the number of risk alleles weighted by log-transformed odds ratios (OR). To account for genomic population structures, we conducted a principal component (PC) analysis using each participant’s SNP data using PLINK36. The first four PCs were included as covariates in all statistical analyses. To identify the effects of SNPs other than APOE on cognitive scores, we computed GRS-AD without the SNP rs429358 on the APOE region. To evaluate the effects of mutations in GBA, which is known to affect cognition in PD patients, the presence of GBA mutation was investigated including 84GG, N370S, L444P, A456P, and IVS2 + 1 G > A.DNA samples were extracted from whole blood and sequenced using an Illumina HiSeq X Ten Sequencer. Paired-end reads were aligned to the reference genome, and single nucleotide polymorphisms (SNPs) were called using the Genome Analysis Tool Kitm P < 1E-, and higBaseline and longitudinal scores for cognitive domains were measured as follows: (i) memory, delayed recall score of Hopkins Verbal Learning Test Revised; (ii) semantic fluency domain; (iii) frontal/executive, Symbol Digit Modality score, and (iv) visuospatial function, Benton Judgment of Line Orientation score. For motor severity score, we used the Movement Disorders Society modified Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part III total score.Roche Diagnostics). The range of quantitation for CSF Aβ is from 0.2 ng/mL to 1.7 ng/mL37, and the extrapolated value for CSF Aβ over 1.7 ng/mL was used. The cutoff for CSF amyloid-positivity was set to 1.1 ng/mL38. CSF αSyn was measured using ELISA (BioLegend).Baseline and longitudinal (up to 3 years from baseline) CSF biomarkers, including Aβ, t-tau, and p-tau, were measured using Elecsys immunoassays on the Cobas e 601 platform -1, for the bilateral putamen was selected for analyses using the DAT imaging biomarker.All participants underwent DAT scans using Statistical analyses of the demographic and clinical data were performed using IBM SPSS version 26.0  and R Statistical Software . Analysis of variance and chi-square test were used to compare demographic variables between PD patients and HCs. Due to strong correlations between CSF tau, p-tau, and αSyn, CSF p-tau/Aβ and CSF αSyn were selected as predictors to avoid multicollinearity issues in the following analyses. All analyses included age, sex, education, and the first four PCs as covariates. A logistic regression model was used to evaluate ORs for the diagnosis of PD using either GRS-AD, GRS-PD (model 1), CSF biomarkers (model 2), or both (model 3) as predictors. To evaluate the performance of GRS and CSF biomarkers in the classification of LBD patients from HCs, ROC curve analyses were performed in all participants using GRS-AD and GRS-PD (Model 1), CSF biomarkers (Model 2), and GRS-AD, GRS-PD, and CSF biomarkers (Model 3). The optimal cutoff points on the ROC curves were determined using the Youden index, and the area under the curve of each model was compared to that of Model 2 using DeLong’s test.lme4 package of R. Follow-up years from baseline were used for time predictors in the model. Interaction terms between GRSs and time (year) were used as predictors to measure the effect of GRS on the rate of changes in outcome variables. To evaluate the association between longitudinal CSF biomarkers and GRS, LMMs for time-varying CSF biomarkers were performed using GRS-AD, GRS-PD, time (in years), GRS-AD*time, and GRS-PD*time as predictors in PD patients and HCs separately. Significant interactions between the predictors and the remaining CSF biomarkers were further included as predictors. The effects of GRS-AD and GRS-PD on longitudinal cognitive scores were evaluated using LMMs in PD patients. Predictors included GRS, time, GRS*time, and significant interactions between the predictors (model 1). To evaluate the independent effects of GRS and baseline CSF biomarkers on longitudinal cognitive scores, baseline CSF p-tau/Aβ, baseline CSF αSyn, and their interactions with time were added to the predictors from model 1 (model 2). As we performed LMMs for four cognitive scores, the FDR method was used to correct for multiple comparisons.To evaluate the association between longitudinal effects of GRSs on clinical outcomes and CSF and imaging biomarkers, we conducted LMMs with a random slope and a random intercept using the To evaluate the longitudinal effects of GRS on motor severity scores in PD patients, LMMs for time-varying motor severity scores were performed using GRS, time, and GRS*time as predictors (model 1). To evaluate the effects of GRS given the same level of either CSF biomarkers or DAT-putamen, time-varying CSF biomarkers (model 2), time-varying DAT-putamen (model 3), or both (model 4) were included as predictors.GBA mutation on longitudinal cognitive scores and motor severity scores, sensitivity analyses were performed using the presence of GBA mutation as a predictor instead of GRS-PD. Also, to evaluate the effects of GRS-AD other than APOE, additional sensitivity analyses using the recalculated GRS-AD excluding the rs429358 on APOE region were performed.To determine the effects of Further information on research design is available in the Supplementary_materialsReporting Summary Checklist


Cerebrospinal fluid (CSF) hypovolemia is the core of spontaneous intracranial hypotension (SIH). More than 1000 magnetic resonance myelography (MRM) images are required to evaluate each subject. An effective spinal CSF quantification method is needed. In this study, we proposed a cascade artificial intelligence (AI) model to automatically segment spinal CSF. From January 2014 to December 2019, patients with SIH and 12 healthy volunteers (HVs) were recruited. We evaluated the performance of AI models which combined object detection (YOLO v3) and semantic segmentation (U-net or U-net++). The network of performance was evaluated using intersection over union (IoU). The best AI model was used to quantify spinal CSF in patients. We obtained 25,603 slices of MRM images from 13 patients and 12 HVs. We divided the images into training, validation, and test datasets with a ratio of 4:1:5. The IoU of Cascade YOLO v3 plus U-net++ (0.9374) was the highest. Applying YOLO v3 plus U-net++ to another 13 SIH patients showed a significant decrease in the volume of spinal CSF measured (59.32 ± 10.94 mL) at disease onset compared to during their recovery stage (70.61 ± 15.31 mL). The cascade AI model provided a satisfactory performance with regard to the fully automatic segmentation of spinal CSF from MRM images. The spinal CSF volume obtained through its measurements could reflect a patient’s clinical status. Spontaneous intracranial hypotension (SIH) is caused by cerebrospinal fluid (CSF) leakage through a spinal dura defect, leading to CSF hypovolemia. The incidence of SIH is 5 per 100,000/year, with a female-to-male ratio of 2:1. Diagnosis of SIH depends on typical clinical presentation, such as orthostatic headache and typical radiological findings ,4 or comEpidural blood patch (EBP) is the treatment of choice for SIH patients who have been nonresponsive to conservative hydration treatment. Evaluation of disease severity and treatment response usually relies on the grading of headache scores, the visual analog scale (VAS), and MRI findings . ResearcKrizhevsky et al. developed deep convolutional neural network architectures and formed a team (SuperVision) that won the ImageNet competition in 2012 by a large margin . Since tIn addition to semantic segmentation, border detection is the crucial function for fully automated computer-aided diagnosis. Redmon et al. created You Only Look Once (YOLO) in 2016, and it has been widely used for medical image studies [In this study, we proposed a cascade architecture that could individually perform the object detection task and the semantic segmentation task. Then, two types of aforementioned output were fused by a union operation. We expected the proposed cascade AI to provide both object detection and semantic segmentation functions. Meanwhile, it may provide satisfactory segmentation performance to measure spinal CSF volume and evaluate CSF hypovolemia in SIH patients. Furthermore, we investigated whether the segmented spinal CSF volume would be consistent with other MR findings and correlate with disease status.From January 2014 to December 2019, patients who had been diagnosed with spinal CSF leakage and received treatment to complete recovery at our hospital were retrospectively reviewed. They were assessed using MRI and MRM at disease onset and at their complete recovery stage, which was defined as there being neither clinical symptoms nor abnormal radiological findings. Patients with poor-quality MR images were excluded. The definition of poor-quality images was that the scan area did not include the whole spinal canal, uneven signal intensity through the spinal canal, or presence of large imaging artifacts. Healthy volunteers (HVs) with no known spinal disease were recruited and received MRM examination.Twenty-five subjects , and informed consent was obtained.We divided the image data of 25,603 slices of MRM images acquired from 25 subjects (13 patients and 12 HVs) into training and validation datasets and a test dataset with a ratio of 4:1:5. The training dataset contained 7192 slices and 6201 slices of annotated MR images from 7 patients and 6 HVs, respectively. Overall, 13,393 slices of annotated MR images were used to train and validate the proposed model. The test dataset contained 6332 slices and 5878 slices of annotated MR images from 6 patients and 6 HVs, respectively. Overall, 12,210 slices of annotated MR images were used to test the model performance. The ratio of images used in the training phase and test phase was 52%:48% .3, matrix size = 320 × 320 pixels, and field of view (FOV) = 200 mm. Fat suppression and generalized autocalibrating partially parallel acquisition (GRAPPA) imaging reconstruction with an acceleration factor of two were used. Images were obtained volumetrically in the coronal plane parallel to the cervical-to-thoracic and thoracic-to-lumbar regions of the spine, and then axial multi-planar reformation (MPR) images were reconstructed with a slice thickness of 5 mm for spinal CSF leakage number assessment.A 1.5 T MRI scanner  was used. Whole-spine MRM was performed in all patients at disease onset and at complete recovery and in HVs using three-dimensional sampling perfection and optimized contrast using different flip-angle evolution (3D-SPACE) sequences. The MRI parameters were as follows: TR = 3000 ms, TE = 560 ms, isotropic voxel size = 0.9 mm∩) operation.This paper divides the tasks into objection detection and semantic segmentation. Therefore, the conventional convolution neural networks (CNNs), designed for classification problems , are notYOLO v3 applied multi-scale predictions and a better backbone classifier to increase performance by increasing the number of bounding boxes and to enhance the ability to detect small objects. The details of YOLO v3 can be found in ,20. The The gold standard of spinal CSF from MRM images, separately segmented by two experts, was first manually segmented by a medical researcher and further verified by an experienced neuroradiologist. The segmentation performance of the cascade YOLO and U-net++ AI model was measured by the metric of intersection over union (IoU), also named Jaccard similarity, which measures the ratio of the intersection of the samples to their union. The IoUs of four combination modules, cascade of U-net, cascade U-net++, U-net++, and U-net, were calculated and compared.The AI model with the best performance was used to automatically quantify the spinal CSF volume of the other 13 SIH patients using their MRI at disease onset and after complete recovery. The spinal CSF leakage numbers for each patient at disease onset were recorded. Most of the patients received intravenous hydration for 3 days and target EBP near the spinal leakage site as the treatment method.t-test. The correlation of the spinal CSF leakage numbers and the measured CSF volume at disease onset was analyzed using Pearson correlation. Further, p-values less than 0.05 were indicative of statistical significance.Statistical analyses were performed in SPSS statistical analysis software, version 22.0 . The IoU between different AI models and the difference between spinal CSF before and after treatment were compared using the paired Since the training dataset focused on the spine, detection performance decays when the tested images are located above the craniocervical junction. In other images in the spine region, the performance of detection and the segmentation of spinal CSF was quite satisfactory. t-tests were conducted to determine whether the cascade models significantly outperformed the non-cascade models in terms of IoU. The test was also conducted to determine whether U-net++ significantly outperformed U-net in the non-cascade modes. Therefore, we tested the hypothesis:The results show that the performance from cascade of U-net++ and YOLO v3, from cascade of U-net and YOLO v3, from U-net++, and from U-net on average were 0.9374, 0.9373, 0.9102, and 0.9077, respectively. Single-tailed paired 0: IoU1 = IoU2Equation (1): H1: IoU1 > IoU2Equation (2): H1 is the IoU between U-net++ and the gold standard; IoU2 is the IoU between U-net and the gold standard. For the cascade models vs. non-cascade models, IoU1 is the IoU between U-net++ (or U-net) integrated with YOLO v3 and the gold standard; IoU2 is the IoU between Unit++ (or U-net) and the gold standard. t-test at a significance of α = 5%. Since 12,210 slices of MR images were enrolled in each type of model, the degree of freedom (DoF) was 12,209 in the paired t-test. A confidence interval with a lower limit greater than 0 implies that the null hypothesis H0 should be rejected. In non-cascade models, acceptance of the alternative hypothesis (H1) implies that U-net++ performs significantly better in segmentation than U-net. For the cascade models vs. non-cascade models, acceptance of the alternative hypothesis (H1) implies that the cascade models significantly outperform the corresponding non-cascade models (U-net++ or U-net). The experimental results showed that the proposed cascade models, the integration of object detection and semantic segmentation, provided more refined segmentation output than only semantic segmentation modules (non-cascade models).For the comparison of algorithms in non-cascade models, IoUp < 0.001) was found. The spinal CSF volume during disease onset was negatively correlated with spinal leakage numbers . The mean spinal CSF leakage number was 13.79 ± 7.69 at disease onset.We applied the YOLO v3 plus Unet++ to the MRM images at the disease onset and complete recovery stages in 13 SIH patients. Significantly decreased spinal CSF volume during disease onset compared to recovery stage  was the highest. The proposed cascade of YOLO v3 and U-net++ model took less than 45 s on average for 1130 slices of MR images  of each patient by using GUP Nvidia-GeForceRTX2080 Ti with 32 GB of RAM.U-net and the variants are widely used in medical image data, for example, segmentation of brain tumor and tissue, stroke lesion, cardiovascular structure, spinal cord, and various types of cancer in recent years . In our Therefore, we used YOLO v3 as our object detection module. After Redmon et al. first introduced the YOLO algorithm, it drew wide attention because of its speed and accuracy . Since tPhysiologically, abdominal compression and hyperventilation could change spinal CSF volume . Spinal After reviewing the literature, no articles using an AI model to segment spinal CSF were found. Considering spinal CSF quantification, Alperin et al.  enrolledSeveral limitations of this study need to be mentioned. First, we only included patients from a single institution, using the same imaging protocol. Our results could be undermined by the MRM protocol. We used 3D SPACE for MRM in our hospital. It is a heavy-T2WI image with high CSF–tissue contrast. However, due to the long TE effect, uneven signals might occur, leading to poor imaging quality. Using the cascade semantic segmentation for other 3D MRM protocols might lead to different performance levels. Our study might provide a basis for future research and software development for general situations. Second, due to the small size of the cohort study, it would not be feasible to analyze the individual differences in spinal CSF volume between patients. Applying our cascade semantic segmentation model to a large number of subjects, different MRM protocols, images from other institutions, and different MR machines are needed in future studies.The results show that the cascade AI model provides satisfactory performance in the fully automatic segmentation of spinal CSF from MRM images. The measured spinal CSF volume correlates well with spinal leakage levels and could reflect clinical status in SIH patients. Using this AI model to segment whole-spine CSF, improve diagnosis, evaluate treatment efficacy, and better understand the pathophysiology of diseases regarding CSF homeostasis, such as hydrocephalus, IIH, and SIH, is possible.


In this paper, an aliasing noise restraint technique and a system identification-based surface electromyography (sEMG)-force prediction model are proposed to realize a type of robust sEMG and muscle force prediction. For signal denoising, a novel non-negative matrix factorization screening empirical mode decomposition (NMFSEMD) and a fast orthogonal search (FOS)-based muscle force prediction model are developed. First, the NMFSEMD model is used to screen the empirical mode decomposition (EMD) results into the noisy intrinsic mode functions (IMF). Then, the noise matrix is computed using IMF translation and superposition, and the matrix is used as the input of NMF to obtain the denoised IMF. Furthermore, the reconstruction outcome of the NMFSEMD method can be used to estimate the denoised sEMG. Finally, a new sEMG muscle force prediction model, which considers a kind of candidate function in derivative form, is constructed, and a data-training-based linear weighted model is obtained. Extensive experimental results validate the suggested method’s correction: after the NMFSEMD denoising of raw sEMG signal, the signal-noise ratio (SNR) can be improved by about 15.0 dB, and the energy percentage (EP) can be greater than 90.0%. Comparing with the muscle force prediction models using the traditional pretreatment and LSSVM, and the NMFSEMD plus LSSVM-based method, the mean square error (MSE) of our approach can be reduced by at least 1.2%. When the astronauts work in the microgravity environment of space stations for an extended period, they experience bone loss and muscle atrophy –3, resulThe muscle force prediction model, including the physiological and the phenomenal models, establishes a nonlinear relationship between the sEMG signals and muscle force. A typical physiological model, the Hill model, is based on various physiological parameters during muscle contraction . Its simAnother phenomenal model used for muscle-force prediction is the system identification method. Popular methods are parallel cascade identification (PCI) and fast orthogonal search (FOS). Some authors used PCI to build a relationship between the upper arm sEMG signals and wrist induction force . PCI conSurface EMG signals have non-linear and non-stationary characteristics, and are usually processed by time-frequency analysis. However, traditional analysis is based on Fourier analysis, which cannot express local frequency change with time. Empirical mode decomposition (EMD) overcomes this limitation and is widely used in physiological signal analysis, fault diagnosis, and other fields. EMD has problems such as modal aliasing and end effect . ScholarIn this study, we propose a non-negative matrix factorization screening empirical mode (NMFSEMD) denoising approach for removing the aliasing noise in the sEMG signals by analyzing the noise distribution of sEMG at various time characteristic scales. Using the denoised sEMG signal as the input of muscle force prediction model, the reliability and accuracy of the model can be improved. Additionally, we apply the system identification model FOS to establish the prediction model. We preset several forms of candidate functions according to the time-domain characteristics of sEMG signals, then employ an iterative optimization technique to select several basis functions, and use their weighted sums as the muscle force prediction model.The major contributions of this investigation are as follows: a novel NMFSEMD method for denoising sEMG signals is proposed and a candidate function in the form of a derivative to the FOS model to predict the muscle force is considered. This noise reduction technique has three main advantages: (i) as an improved algorithm of EMD, NMFSEMD can better suppress mode mixture and improve the data reliability of sEMG in muscle force prediction; (ii) NMFSEMD can distinguish the noisy intrinsic mode functions (IMF) components and the real intrinsic mode functions components (IMFs) from the EMD decomposition results by using the designed IMFs screening process; (iii) NMFSEMD is a data fusion method essentially and can extract several sequences that can best represent the state of muscle activation when used in sEMG signals.The remainder of this paper is organized as follows. First, we will introduce the principle of proposed NMFSEMD. Second, we present the process of building a muscle force prediction model using FOS. Finally, experimental findings and discussions will be presented.In the muscle force prediction process, the sEMG signal preprocessing is first considered to improve the reliability of modeling data. Conventional preprocessing methods are initially used for sEMG signal, such as outlier detection. Then, the denoising and preprocessing of sEMG signals are conducted by integrating the EMD with NMF, i.e., the proposed NMFSEMD denoising method. Subsequently, the denoised sEMG signals are obtained and used as the input of the muscle force prediction model. Then, basic functions of FOS are computed using the denoised sEMG signals in the form of preseted candidate functions. Finally, the muscle force prediction model represented by the linear combination of several basic functions is obtained and can be calculated through FOS model training by comparing the measured and predictive muscle forces in the model training process. The flowchart of sEMG-based muscle force prediction is shown in While acquiring the sEMG signals, the power line interference, ambient noise, motion artifact interference –22, and The theory of EMD is based on the idea that any real signals can be decomposed into a set of simple IMFs, which are independent of each other . EMD decx(t), respectively.The upper and lower envelopes are obtained by cubic spline interpolation of the local maxima and local minima points of signal h(t) between x(t) and the average value m(t) of the upper and lower envelopes is calculated.The difference h(t) meets both the IMF conditions, it is the first IMF; otherwise, it is the new x(t). The preceding steps are repeated until the difference after k times satisfies the IMF conditions and is recorded c(t) = hk(t) as the first IMF.If c(t) from x(t) is removed to obtain the remaining component r(t), and r(t) is considered as the new x(t). Then the preceding steps are repeated to get the remaining (n– 1) IMFs.The existing IMF rN(t) is a monotone function, the algorithm can be stopped. The expression of signal x(t) after EMD decomposition is shown in Eq   is the IMF and rN(t) is the residual error.Some noises in the sEMG signals can be filtered out by discarding the corresponding IMFs after EMD decomposition . HoweverV, the basic principle of NMF can be described in [W and H, whose product approaches V, as shown in Eq  and upper = MAX         CR can be divided into three parts based on limited value, the position index set of each part, and    The set     IMFs set of the corresponding position indexes are as follows:inda = INDEX {CR ≤ lower}, indb = INDEX {lower < CR < upper}, and indc = INDEX {upper ≤ CR}        imfsa= IMF {inda}, imfsb= IMF {indb}, and imfsc= IMF {indc}        Algorithm *//* if set inda and indb are not empty:    if set indc is not empty, thenimfp is the superposition of all IMFs in set imfsc.        if set indb has more than 1 IMF component, thenimfnmf is the IMF with the smallest correlation in set imfsb,        imfadd is the superposition of other IMFs in the set imfsb except imfnmf.                    elseimfnmf is the IMF with the largest correlation in set imfsa, imfadd is the IMF in set imfsb.        if set inda and indc are not empty, set indb is empty:    if set indc has more than 2 IMF components, thenimfp is the IMF with the largest correlation in set imfsc,        imfnmf is the IMF with the smallest correlation in set imfsc,                                imfadd is the superposition of other IMFs in set imfsc except imfp and imfnmf.            elseimfp is the superposition of all IMFs in set imfsc,        imfnmf is the IMF with the largest correlation in set imfsa.            if set inda is not empty, set indb and indc are empty:        imfnmf is the IMF with the largest correlation in set imfsa,            imfadd is the superposition of other IMFs in set imfsa except imfnmf.            end    imfadd, imfnmf, and imfp are removed as noises. Next, NMF is used to denoise the imfnmf.Then, the IMFs that are not selected to construct V in NMF algorithm is greater than 1, and the selected imfnmf is a vector. Thus, the method of “signal time sequence translation” is used to construct the input matrix V in NMF. The details of this construction are as follows:3. Because the dimension of input matrix imfnmf to the left for p positions and splices the left overflow part to the right end of the sequence. This translation operation is repeated to obtain k different noise samples: s = {s1, s2, …, sk}.First, this method cyclically shifts the sequence imfp and imfadd as imfo: imfo(t) = imfp(t) + imfadd(t).Second, this method superimposes s and imfo (a row vector), respectively, to construct a noisy matrix V, as shown in Eq  of Vk × n into the weight matrix Wk × r and the activation coefficient matrix Hr × n is defined according to Eq , the activation intensity can be calculated using Eq .Intensr values, the corresponding number of muscle activation curves is obtained. In Section 3.4.2, we compare the denoising effect under different values of r and conclude that the denoising effect is best when r is equal to 2. Two muscle activation curves, h1 and h2, can be obtained, and the denoised imfnmf can be expressed as: imf’nmf = (h1 + h2)/2.After setting different imf’nmf and imfo: x’(t) = imf’nmf(t) + imfo(t), where x’(t) is the denoised signal of raw sEMG signal after applying the NMFSEMD denoising method, which is used in the subsequent muscle force prediction of FOS method.5. Finally, our method adds pm}, and their corresponding coefficients are expressed by am, as shown in Eq =∑Qm of each candidate function, and selecting the function add to model and remove from function set, which is corresponding to the largest Qm. We select a function from the remaining candidate functions and repeat the above steps until the termination conditions of FOS algorithm are met. The commonly used termination conditions include the following: the number of functions in the model reaches a preseted value; the model error is small enough, or the remaining candidate functions cannot considerably reduce the model error. When the search is stopped, the coefficients of each selected function can be calculated to complete the FOS algorithm modeling.All candidate functions are traversed by calculating the MSE reduction A series of tests and evaluation experiments were performed to assess the validity and effectiveness of the proposed models and methods. All the simulation programs were written in Python (Pycharm2020) on our PC  Core (TM), and i7-10700K CPU).In this study, we investigated the preprocessing and modeling of muscle force prediction, and used the experimental data from . The detcre and crth, the screening of IMF components based on threshold, the construction of noisy matrix using the idea of signal time sequence translation, the decomposition of noisy matrix considering the NMF and activation mode parameters, and the reconstruction and fusion of signal. Finally, the denoised sEMG signal can be obtained. Compared with the distribution of IMFs in time-domain and frequency-domain before and after signal denoising, it is verified that NMFSEMD algorithm can suppress the modal aliasing better than the traditional EMD.In this section, the proposed denoising algorithm NMFSEMD is experimentally verified. Based on the detailed principle of NMFSEMD designed in Section 2.1.3, the key steps of this algorithm are analyzed visually and quantitatively. The implementation process of NMFSEMD algorithm is shown in x(t) and obtain the signal distribution on different time scales. The decomposed signal was denoted as Q = {imf1, imf2, …, imfm}. The EMD method was used to decompose the sEMG signal x(t) has a wide frequency domain and a large low frequency amplitude. An increase of the rank of IMFs makes the frequency distribution gradually approach the low frequency. The frequencies of high-rank IMFs are distributed in the range of 0.0 to 50.0 Hz, and no overlap is recorded in the main frequency of sEMG signals. In addition to the discussion in Section 2.1.3, imfadd, imfnmf, and imfp should be found to construct the denoised sEMG signal for clarifying the various types of IMFs. Thus, the results obtained on calculating the correlations between raw sEMG signal x(t) and its IMFs using Eq  can be obtained by adding imfp, imfadd, and the denoised imfnmf. The SNR and EP are used to evaluate the denoising effect, as shown in Eqs  denotes the denoised signal. A high SNR value implies that the signal has a good noise reduction effect, whereas a high value of EP means the denoised signal can retain more characteristics of raw signal.Then, the noise mixed in Study in  has shown in Eqs  and 9)..imfnmf sBased on the comparison of Figs Ebi, Etr, and Ebr represent the denoised biceps brachii, triceps brachii, and brachioradialis signals, respectively. The expressions sigm and x, respectively.The denoised sEMG signals obtained from the raw sEMG signals using the NMFSEMD denoising method are used as the input of muscle force prediction models. In this study, the FOS method is used to establish the muscle force prediction model. In this model, the candidate functions can be constructed using different function forms, as shown in The denoised signals are used to construct candidate functions. However, the sEMG signal is weak, complex, and changeable, the waveform of candidate function varies considerably. Therefore, the predicted force outcome usually has a large oscillation. To better observe the prediction results, the direct predicted force is interpolated, and the average value of the upper and lower envelopes is taken to resist the fluctuation. Many experimental results verified the effect of applying the NMFSEMD method in the muscle force prediction model and the prediction effect of FOS prediction model. th and the empirical maximum correlation cre. The crth represents the boundary between the IMFs that should be discarded and the IMFs that should be denoised, and it has a small value which is usually less than 0.5. The cre represents the boundary between the IMFs, which should be denoised, and the IMFs, which can be directly used to construct the denoised sEMG signal, and it has a big value which is usually bigger than 0.5. To determine the values of both these parameters, we counted the correlation of 30 sets of IMFs, as shown in Figs According to Section 2.1.3, before screening the IMFs using NMFSEMD, the upper and lower bounds of the correlation should be determined by comparing the preseted correlation threshold crcre, which limited the upper bound to a big value and ensured that the IMF whose correlation was greater than this value had a high correlation with the raw signal and contained most of the useful information about the raw signal. In the 30-group experiments, cre can be written as cre = 0.6125±0.0744. crth, which can distinguish between the IMFs with small correlation (IMFs that need discarding) and the IMFs with medium correlation (IMFs that need denoising), is set in the range of 0.3 to 0.4.After the EMD decomposition of raw sEMG signal, a set of IMFs with position indexes at {1, 2, 3, …} can be obtained. The correlation distribution of each index in a set of IMFs is shown in r of matrix W and H should be preseted; where r also represents the number of muscle activation modes in a sEMG signal. Therefore, the performance of NMF method with different numbers of r can be discussed; and it also can be evaluated through the reconstruction rate (RSR) parameter, which is proposed in [RSR value is, the more reasonable the setting of the number of activation modes and the more reliable the activation information obtained from H would be.Vk × n is the input matrix of NMF, and Wk × r and Hr × n are the output matrices of NMF.In the abovementioned discussion, before using NMF to separate noise from sEMG signal, the rank posed in  and can W and H are obtained. RSR results of 6 groups of sEMG data with different numbers of r. Let us take Data 1 as an example. When r is 2, the RSR of V is 94.37%, which is large enough to represent muscle activity. When r is increased to 3, the RSR is 94.53%, which is an increase of < 0.5%. Therefore, continuing to increase the rank of matrix will not only bring more useful information but increase the noise-related information and subsequent amount of calculation, as shown in other groups of data in r) to 2 in this study is suitable for sEMG signal.When setting different numbers of activation modes, different matrices of In this study, an NMFSEMD algorithm is proposed to denoise the sEMG signals by analyzing the characteristics of non-stationarity and non-linearity of sEMG signals. First, the sEMG signal is decomposed into a set of IMFs, each of which contains different time scale characteristics. Then, the role of each IMF is distinguished in the denoised sEMG signal using the correlation between each IMF and the raw sEMG signal. Consequently, the screen operation of IMFs is performed to remove or reduce noise in each IMF. Finally, the denoised sEMG signal is obtained using the rule of reconstruction. By analyzing the results of the SNR and EP and the comparison of intuitive curves, it can be concluded that NMFSEMD can effectively filter out the noise of sEMG signals. For the muscle force prediction based on sEMG signals, the FOS algorithm in system identification is used to establish prediction model. The key point in establishing the model lies in setting of the form of candidate functions. The commonly used forms in sEMG applications are square, square-root, sigmoid activation functions, etc. Considering the input sEMG signal is the integral electromyogram value, a candidate function in the form of a derivative is added in this paper. In the experiment, when the input is the signal denoised using the NMFSEMD, the MSE value of FOS prediction model will be less than that of the LSSVM method. When the prediction model is FOS and the input data are the sEMG signals  and the sEMG signal denoised using the NMFSEMD, the MSE value of the latter will considerably be lower than that of the former. Comparing the predicted muscle force curve with the measured muscle strength curve, it can be concluded that the FOS prediction model uses the NMFSEMD-based denoised sEMG signal can achieve a better prediction result.S1 Dataset(ZIP)Click here for additional data file.


Eosinophilic nasosinusal polyposis is a chronic inflammatory infection with elevated infiltration of eosinophils, which presents high rate of recurrence after surgical treatment. The continuous inflammatory process that leads to the formation of polyps requires constant clinical treatment. Contributing to the maintenance of eosinophilia are cytokines IL5 (interleukin-5) and GM-CSF (granulocyte macrophages colony-stimulating factor), which show up in elevated concentrations. These oligoproteins diminish the rate of apoptosis and prolong the survival of eosinophils.By diminishing these cytokines, the action of Mitomycin C (MMC), an antineoplasic drug which inhibits the synthesis of DNA, was studied. In a recent study the power of this drug to cause apoptosis in eosinophils, in vitro, of nasal polyps was verified.A biopsy of the nasal polyps was undertaken in 15 patients carriers of eosinophilic nasosinusal polyposis 24 hours after applying 0.5 mg/ml of MMC during five minutes. RT-PCR (reverse transcription of polymerase chain reaction) for IL5 and GM-CSF was the method used to obtain the results.The comparison of the results of GM-CSF pre- and post-application of MMC, when the paired T-test was used, showed p=0.041 and for IL5 we found p<0.001.Topic use of MMC in patients with eosinophilic nasosinusal polyposis shows statistically significant reduction for GM-CSF and significant and important reduction for IL5. Naso-Sinus Polyposis (NSP) – eosinophylic NSP – is a chronic proliferative inflammatory disease that affects the nasal and nasal sinuses mucosa, and is characterized by a benign polypoid degeneration . It is uThey are Histologically characterized by eosinophilic granulocytes of which the pathogenic mechanism is not entirely known. They also bear morphologic alterations such as nasal membrane hyperplasia, irregularity of gland distribution, squamous metaplasia and edema 3, 4.FiguAlthough it has been related to IgE mediated hypersensitivity, or nasal allergy, studies have shown that allergy is only one possible cause or a contributing factorEosinophils express CD40 receptor for this GM-CSF and CD40L autocrine production and they are expressed by T CD4 cells that are present in eosinophilic NSPThe high IL5 concentration found on eosinophilic NSP suggests an autocrine effect for this cytokine in eosinophil activation, there still is a strong relation between IL5 and ECP (eosinophilic cationic protein)This cytokine increase is related to the genetic aspect of the NSP eosinophilic cellsMost of the treatment available is based on the use of systemic steroids for a short duration and topical steroid for a longer period, besides surgeryMMC is an antineoplastic agent produced by the Streptomyces caespitosus that has been used as antiproliferative agent in the 0.4mg/ml concentration. When used after surgery, its action on fibroblasts leads to a reduction in scar tissue formation. It has a time-dependent action e its application for 5 minutes has a measurable effect on cell morphology and proliferation for up to 36 days in vitro16. MMC mechanism of action is based on the selective induction of DNA synthesis, recombination and exchange of “sister” chromatin. Its association with other anti-tumoral drugs leads to an increase in its cytotoxic effect by increasing apoptosis inductionCrosara et al. (2004) showed that MMC is efficient in inducing the apoptosis of eosinophils present in the stroma of eosinophilic nasal polyps in vitro. Paired studies with nine cultivated samples were carried out. The polyps were treated with 0.4mg/ml for five minutes and were dyed with hematoxylin eosineIn topical use, this drug has been used in ophthalmology, otology and laryngology without systemic toxicity, thus being considered safe. It does not cause bleeding, necrosis or infection when used in external acriocistorhinostomy or permanent eyelash lesions in concentrations of up to 1mg/mlThis study aims at assessing MMC action, in vivo, in patients with eosinophilic NSP through IL5 and GM-CSF analysis, pre and 24hs post application, using RT-PCR (reverse transcription in polymerase chain reaction).This investigation was made up of eosinophilic NSP patients who agreed to participate in the study, after approval by the Research Ethics Committee. The variables were made up of GM-CSF and IL5 profiles. 15 patients were studied, 6 women and 9 men. Their ages varied from 30 to 57 years.As inclusion criteria, we used: presence of eosinophilic NSP and no aspirin intolerance. Eosinophilic polyps were considered when tissue eosinophylia as greater then 30% among inflammatory cells, determined by finding at least four eosinophils in each large magnification microscopic fieldThe patients selected underwent MMC application on the right nasal cavity polyps in the concentration of 0.5mg/ml in a cotton ball wet with 1 ml of the drug, where it remained for five minutes. The left nasal cavity was the control-group, considered as pre-use.PCR biopsies after 24h of MMC use were made in the right nasal cavity and, following that, in the left nasal cavity as well. Tissues collected had average size of 10mm, were then immediately taken to the University Immunology lab where they remained stored at 80 degrees below zero.The cytokines profile was checked on the polypoid tissue through reverse transcription in polymerase chain reaction (RT-PCR) for IL5 and GM-CSF.Primer sequence used in the PCRsGene Forward Reverse GM-CSF AGAAATGTTTGAC-CTCCAGGA TTGCACAGGAAGTTTCCG IL 5 CTGAGGAT-TCCTGTTCCTGT CAACTTTCTATTATCCACTCThe PCR products were analyzed by electrophoresis in silver dyed 8% polyacrylamide gel. The gels were photographed using the AlphaDigiDoc 1201 photo-documentation system, and the PCR resulting bands for each sample were analyzed by densitometry, using the AlphaEaseFC software version 3.3.0  . This soRegarding the values obtained for IL5, in Comparing the IL5 average pre and post, using the paired t test we saw a p<0.001, indicating important statistical significance.In relation to the GM-CSF values on No statistically significant correlation was observed, in other words, the IL5 and GM-CSF percentage variation reduction was not followed suit by the variation of any of the other variables.The participation of polymorph nuclear cells in the nasal mucosa epithelial interface and in other nose diseases is well understood. The increase in cytokine concentrations proves its inflammatory nature. Eosinophils are sources of interleukins that bear autocrine and modulator effect on the function of other cellsTherefore, they regulate proliferation and cell activation, broadening local immune response and polyp formation. Eosinophils express IL5 in all eosinophylic NSP and, approximately 30% of the eosinophils have genetic expression for GM-CSF, with evidences of autocrine effect for these citokinesCrossara et al. Showed that MMC is efficient in inducing eosinophil apoptosis when these are present in the stroma of eosinophylic nasal polyps. A paired study with nine cultivated samples was carried out and we observed that the MMC treated cultures presented 12-hour apoptosis rate significantly higher in relation to the control group (p<0.001)MMC proved to have inhibitory action on DNA synthesis, inhibiting the proliferation of many cell lines. Kim et al. showed that its anti human-cultivated-fibroblasts effect may be mediated not only by the antiproliferative action, but also by the degree of apoptosis inducedOur goal in this study was to show MMC action in reducing the concentration of major cytokines that act in maintaining the eosinophylic NSP inflammatory process. Such study has not yet been found in the medical literature.The paired t test showed that the post MMC GM-CSF values were significantly lower than the pre values (p=0.041). Comparing IL5 values pre and post MMC, we found one important reduction: the medication was efficient in reducing the concentration of these substances. Comparing the percentage with the other variables, we did not observe any variable that would present significant correlation.MMC may be an option in the pos-surgical treatment of eosinophylic NSP. Further studies are necessary to better use this drug against this disease.1.The IL5 post MMC use values were lower than pre MMC use, with important statistical significance;2.The GM-CSF post MMC use values were lower than pre MMC use, with statistical significance.As to the action of MMC over the main cytokines involved in NSP inflammatory process and in relation to the age and percentage of eosinophils, we concluded that:


The pressure difference between the eye and brain in upright postures may be affected by compartmentalization of the optic nerve subarachnoid space (ONSAS). Both pressure and deformation will depend on the microstructures of the ONSAS, and most likely also on ocular glymphatic clearance. Studying these factors could yield important knowledge regarding the translaminar pressure difference, which is suspected to play a role in normal-tension glaucoma.A compartment model coupling the ONSAS with the craniospinal CSF system was used to investigate the effects of microstructures on the pressure transfer through the ONSAS during a posture change from supine to upright body postures. ONSAS distensibility was based on MRI measurements. We also included ocular glymphatic flow to investigate how local pressure gradients alter this flow with changes in posture.A compartmentalization of the ONSAS occurred in the upright posture, with ONSAS porosity  affecting the ONSAS pressure (varying the supine/baseline porosity from 1.0 to 0.75 yielded pressures between − 5.3 and − 2 mmHg). Restricting the minimum computed porosity (occurring in upright postures) to 0.3 prevented compartmentalization, and the ONSAS pressure could equilibrate with subarachnoid space pressure (− 6.5 mmHg) in Microstructures affect pressure transmission along the ONSAS, potentially contributing to ONSAS compartmentalization in upright postures. Different pathways for ocular glymphatic flow were predicted for different postures. Severe ocular conditions such as glaucoma and papilledema have been related to abnormal flows and pressures in the cerebrospinal fluid (CSF) within the optic nerve subarachnoid space (ONSAS) –5. The CMost mathematical models and clinical fluid dynamic investigations of the ONSAS mainly reflect horizontal body positions and in special cases head-down tilt , 2, 9–14Furthermore, the findings of a possible glymphatic system for the eye, with CSF entering the ONSAS from the intraocular side and then passing through the optic nerve sheath , 22, proThus, the aim of the current study was to apply mathematical modelling to investigate how ONSAS microstructures affect ONSAS pressure and flow during a change from supine to an upright body posture, thereby further evaluating the previously suggested postural compartmentalization of the ONSAS. A secondary aim was to investigate how these flows and pressures would align with an ocular glymphatic flow pathway. This manuscript will describe the model implementation and showcase results of predicted flows and pressures for the ONSAS, including both temporal changes and steady state levels.craniospinal CSF space was first presented by Gehlen et al. [The postural model for the n et al.  buildingn et al.  and Magnn et al. . The ONSn et al.  and in tn et al. , 22. Thet > 0 and reads:The model consists of a system of three ordinary differential equations (ODEs) describing the CSF volume change cranially  . The out allowed , 32.For flows in between different CSF compartments, we used the following equations in the model:entclass1pt{minimaThe inflow across the lamina cribrosa was, in a similar way, set toThe resistances related to CSF absorption include  healthy . \documecine ONS  . From tth, or intra-canicular section, was assumed as rigid due to its connection to bone). The distensibility was assumed to be constant for each section (for the investigated pressure range) as the change in volume of the ONSAS is relatively small. The effects of this assumption were investigated by limiting the collapsibility of the ONSAS in a separate analysis (see description below).The distensibility was acquired from MRIs of healthy volunteers . Since eD in Eq.  correspoTo model changes in body posture, gravitational effects must be incorporated into the model, which affect both the CSF and the venous system. Within the CSF, hydrostatic effects are already included through Eqs. –7. On thThe collapse , 49, 50.collapse , as reflmentclasspt{minimaTo allow for compartmentalization of the ONSAS during the posture change, the ONS was allowed to change its size (radius) with changes in pressuren by Eq.  for eachFinally, the  posture . This deositions . In the When running the simulations, the posture change was achieved by linearly increasing the body tilt-angle  Fig. . The resFigure Imposing a lower limit for the calculated porosity introduced a substantial effect on the dynamics of the different models, as well as a lowering of the equilibrium see Fig. . From loThe flow rate through the ONSAS in the supine position was close to 0.002 mL/min (0.6% of LC) was very low for the calculated The ocular glymphatic flow across the lamina cribrosa  . Our modFinally, additional CSF compartments can subsequently be added to the current model, moving towards a more complete description of the CSF dynamics and glymphatic functions. As CSF dynamics do vary with body position, including the effects of posture in such models are likely essential. Additional outflow/absorption routes related to the glymphatic system could be included. However, this should not affect the dynamics investigated in this study as absorption in our model is based on the total outflow resistance We extrapolated the distensibility values from 13 degrees head-up tilt (maximum achievable in our scanner) to 90 degrees head-up tilt. Imaging in sitting MRIs could contribute important data here, however, in this study, this limitation was instead addressed by the porosity limitation analysis. We did not include any pulsations in this study, including general CSF pulsations as well as local arterial pulsations in the optic nerve and ONS, which may contribute to the mixing and distribution of CSF between the ONSAS and cranial SAS. The relative contribution from the spinal and cranial compartments to CSF compliance has been debated, where some estimations indicate a larger spinal contribution compared to the cranial contribution , 77. ChaThe current study presents a model for analyzing the ONSAS fluid dynamics and its interactions with the craniospinal CSF spaces with varying body posture. The porosity related to microstructures within the ONSAS was predicted to affect the ONSAS dynamics during the change in posture from supine to upright as well as the equilibrium ONSAS pressure in upright postures. The results thus suggest that measured ICP may not be an appropriate surrogate for the ONSAS pressure in upright postures. While the current study focused on the ONSAS and the analysis of ocular glymphatics, the model can be expanded to include other CSF compartments, offering a tool for gaining a more complete description of the CSF system and its posture dependency.


Cryptococcal meningitis who received a ventriculoperitoneal shunt. We analyzed the following parameters: white blood cell count, total protein concentration, CSF/blood glucose ratio, chloride ion concentration, and Cryptococcal count. All parameters between lumbar and ventricular CSF were remarkably different . White blood cell count, total protein level and Cryptococcal count were lower in ventricular CSF than in lumbar CSF, while CSF/blood glucose ratio and chloride ion concentration were higher. Compared to patients without ventriculomegaly, patients with ventriculomegaly had a significantly higher total protein concentration in ventricular CSF (p = 0.047). Compared to patients without surgical complications, patients with complications had a significantly lower CSF/blood glucose ratio in ventricular CSF (p = 0.032). The lumbar CSF parameters had no significant differences between these groups. The changes in lumbar CSF indices over time after shunt placement were also analyzed. After shunt placement, total protein concentration was transiently increased, white blood cell count, CSF/blood glucose ratio and chloride ion concentration were continued at the preoperative level until two months after shunting surgery. These findings suggest that the composition of ventricular CSF differs from that of lumbar CSF, and different CSF parameters have disparate rostro-caudal gradients in patients with Cryptococcal meningitis. Furthermore, ventricular and lumbar CSF parameters may have different clinical implications. Transient deterioration of lumbar CSF parameters after ventriculoperitoneal shunt placement may not be due to disease progression, but to change in CSF flow rate by CSF shunts.Lumbar cerebrospinal fluid (CSF) parameters are widely studied and have wide clinical applications, but ventricular CSF has rarely been studied since it is relatively difficult to obtain. To determine whether there are differences between ventricular and lumbar CSF parameters and whether the differences have clinical significance, we retrospectively reviewed 77 patients with  CSF samples are usually acquired by lumbar puncture4. In some cases, however, CSF is obtained during more invasive procedures such as external ventricular drainage (EVD) and ventriculoperitoneal shunt (VPS) placement7. Whether there is a difference between the composition of ventricular and lumbar CSF is unclear11. Of CSF components, the protein concentration gradient of ventricular to lumbar CSF is the most studied13. Some studies have indicated that the total protein concentration is higher in lumbar CSF than in ventricular CSF11. Most protein fractions have a higher concentration in lumbar CSF than in ventricular CSF11, while some protein fractions have the opposite concentration gradients11. Kamat et al. reported no difference of total protein level between ventricular and lumbar CSF in patients with tuberculous meningitis8. On the other hand, Figaji et al. reported contradictory results15. Although there are no normal reference ranges for various parameters of ventricular CSF, analysis of ventricular CSF may be used for evaluating certain diseases, such as meningeal carcinomatosis by detecting malignant cells and subarachnoid hemorrhage by detecting red blood cells. To date, there have been rare reports of ventricular CSF and CSF gradients in the study of human disease16.Cerebrospinal fluid (CSF) analysis is commonly used to identify and follow-up disease of the central nervous system (CNS)Cryptococcal meningitis (CM) is a subacute, but a life-threatening fungal infection of the CNS. Non-HIV infected patients with CM are more likely to have intractable, high intracranial pressure, which is the leading cause of death and poor outcomes17. Hydrocephalus was also common in CM. A VPS placement is effective in treating persistent increased CSF pressure and hydrocephalus. According to previous studies on hydrocephalus, VPS placement has a high complication rate23. Hence, VPS placement is usually delayed to treat CM patients, especially when the Cryptococcal infection is not controlled and ventricles are not enlarged. Whether CSF parameters are associated with hydrocephalus and post-VPS complications is not clear.The study aimed to determine whether there are differences between ventricular and lumbar CSF parameters in patients with CM and whether the differences have clinical significance.A total of 100 patients with CM were admitted to the Department of Neurosurgery of Lingnan Hospital, Branch of the Third Affiliated Hospital of Sun Yat-sen University, from January 2016 to August 2020. Only 77 patients with CM were retrospectively analyzed. The inclusion criterion was the definite diagnosis of CM. Exclusion criteria included the following: (1) Patients without VPS placement; (2) Patients who had received a shunt prior to admission to our hospital; (3) Patients who had VPS placement by other teams of neurosurgeons and patients without lumbar CSF test within 48 h before VPS  Positive India ink staining of CSF with centrifuged sediment for Cryptococcus; (2) Positive culture of Cryptococcus from CSF; (3) Compatible histopathology (5- to 10-μm encapsulated yeast observed in brain tissue); (4) Probable CM was considered in patients with clinical symptoms of meningitis and a positive test of Cryptococcal antigen, and/or positive CSF findings by metagenomic next-generation sequencing. All CM patients in this study had a definite diagnosis of CM. Tuberculosis was ruled out by T-SPOT, acid-fast bacilli (AFB) smear, and ELISA.A definite diagnosis of CM required at least one of the following criteriaAntifungal treatment before VPS placement was different based on the attending physician’s preference and the patient's tolerance of the drug. Therapeutic options are limited to amphotericin B, flucytosine, fluconazole, and voriconazole in our hospital. A relatively unified antifungal regimen of flucytosine plus fluconazole was used after VPS except for five patients Table . Cortico25. The non-dominant hemisphere side was the preferred side of placement. Accurate placement, which is very important to ensure all the drainage holes are in the ventricle, is essential for patients with CM without ventriculomegaly. Two types of adjustable shunt devices were used, the proGAV2.0 valve (Miethke) and the Strata II valve (Medtronic). Usually, the initial valve pressure was set to 200 mm H2O, or gear 2.5, respectively. The valve pressure was adjusted according to patient’s clinical response. The valve pressure would be adjusted down when a patient exhibited persistent neurological disorder, progressive ventricular enlargement, and a high opening pressure on lumbar puncture. The valve pressure would be adjusted up when a patient exhibits orthostatic headache and a low opening pressure on lumbar puncture. Prophylactic anti-epileptic drugs were usually administered peri-operatively. Shunt complications were recorded within six months after VPS. Three patients who had a shunt obstruction were successfully revised. Two patients were adjusted the depth of the tube insertion; one patient was revised by the replacement of a new shunt tube.The tip of the ventricular catheter was placed via a frontal approach into the foramen of Monro or the third ventricle by means of the anterior horn of the ipsilateral lateral ventricle, based on a prior definition of accurate ventricular catheter placementCryptococcal count. When visible blood contamination occurred, one leukocyte was subtracted per 1000–1500 red blood cells to estimate the true CSF WBC count26. Cryptococcal count was determined by counting the number of Cryptococcus per milliliter of CSF via India ink staining.We recorded the CSF testing results, which met the following requirements: Lumbar CSF samples were obtained by lumbar puncture within 48 h before VPS placement, and 1–3 days (stage 1), 5–7 days (stage 2), 14 ± 2 days (stage 3), 30 ± 5 days (stage 4), 60 ± 7 days (stage 5), 90 ± 7 days (stage 6), and 180 ± 14 days (stage 7) after VPS placement. Ventricular CSF was obtained during shunt insertion directly from the lateral ventricle . All numerical variables of CSF parameters were presented as median and range or interquartile range as they were non-normally distributed data. Categorical variables were expressed as counts and percentages. A Wilcoxon signed-rank test was used to compare lumbar and ventricular CSF parameters and analyze lumbar CSF change after shunting surgery. The Mann–Whitney U test were used to analyze CSF parameters in patients with and without ventriculomegaly, and patients with and without surgical complications. Chi-squared test or Fisher’s exact test was used for comparisons of binary categorical variables, as appropriate. Statistical significance was set at a value of The Clinical Research Ethical Committee of the Third Affiliated Hospital of Sun Yat-sen University approved the study (02-167-01) and waived the requirement of written informed consent. No experiments were conducted with human participants (or their tissue). All information was kept anonymous. The study complies with the Declaration of Helsinki.2O) assessed by lumbar puncture, but high intraventricular CSF pressures (≥ 200 mm H2O) detected during shunt insertion. All of these patients received a VPS placement due to neurological deterioration caused by intractable elevated intracranial pressure and/or progressive ventricular enlargement. All of these patients exhibited headaches. Other neurological symptoms included visual changes, hearing impairment, disturbance of consciousness, muscle weakness, seizures, cognitive impairment and urinary incontinence. The antifungal therapies differed greatly before VPS surgery due to doctors’ different experiences  with CM were included in this study, with ages ranging from 11 to 77 years. The clinical features of these patients are shown in Table Cryptococcal count were compared (Table p < 0.001). The WBC count, total protein level, and Cryptococcal count were significantly lower in ventricular CSF than in lumbar CSF, while CSF/blood glucose ratio and chloride ion concentration were significantly higher in ventricular CSF than in lumbar CSF. It's important to point out that the total protein concentration in ventricular CSF of CM patients was very low with a median value of 0.12 g/L, which was different from previous reports on tuberculous meningitis15. Since there were many factors, such as manipulation, patient's position, and the bore of the puncture needle, affecting the detection of CSF pressure, the comparison of lumbar and ventricular CSF pressures was not analyzed.To determine whether a rostro-caudal CSF gradient was present in patients with CM, lumbar and ventricular CSF parameters including WBC count, total protein concentration, CSF/blood glucose ratio, chloride ion concentration, and ed Table . The anap ≥ 0.05). Interestingly, compared to patients without ventriculomegaly, patients with ventriculomegaly had higher protein concentration in ventricular CSF (p = 0.047). Although the levels of lumbar CSF protein of the two groups had a similar trend, they were not statistically different (p = 0.239). Other CSF indices in both lumbar and ventricular CSF were not significantly different between the patients with and without ventriculomegaly .Ventricular enlargement was common in patients with CM. We analyzed lumbar and ventricular CSF parameters between patients with and without ventriculomegaly before VPS Table . The age27. To determine whether CSF indices were associated with post-VPS complications in CM, we analyzed both lumbar and ventricular CSF parameters between patients with and without post-operative complications (Table p > 0.05) (Table p > 0.05). Among ventricular CSF indices, the CSF/blood glucose ratio was statistically different between the two groups (p = 0.032). Ventricular CSF/blood glucose ratio was lower in patients with complications than in patients without complications  Table . All lumns Table .Table 4C16, we evaluated the changes of lumbar CSF parameters over time after VPS placement  by facilitative and energy-dependent transport, which only occurs in the brain29. The chloride ion is transported by sodium–potassium-chloride cotransporters, potassium-chloride cotransporters and chloride channels in the choroid plexus28. Blood-derived CSF proteins diffused through the blood-CSF barrier (BCSFB) is more widely distributed in the spinal cord than in the brain29. The pathway of entry of leukocytes into the CSF is still unclear. We speculate that the densities of WBCs and Cryptococcus are diluted by newly secreted CSF in the ventricle and accumulate in the subarachnoid space of the spinal cord as CSF travels from the ventricles to the spinal channel. Sometimes Cryptococcus aggregates into clumps, which leads to a large Cryptococcal count in the CSF. Our findings were consistent with previous literature except for one report from Kamat et al. mentioned in the background section12. They found no significant differences between ventricular and lumbar protein levels in tuberculous meningitis. It seemed that the ventricular protein level in Cryptococcal meningitis is obviously lower than that in tuberculous meningitis. This may be the reason why hydrocephalus occurs more often in tuberculous meningitis than in Cryptofcoccal meningitis. Even so, we believe that there are rostro-caudal gradients from ventricular to lumbar CSF, but they vary in different diseases.The results of this study demonstrated that the composition of ventricular CSF was different from lumbar CSF in patients with CM. Total protein level, WBC count, and 30. Although lumbar CSF protein level had a similar trend, it was not statistically different. It indicated that ventricular CSF protein concentration was more closely related to ventricular enlargement than lumbar CSF protein concentration. There are three hypotheses for the pathophysiological mechanism of hydrocephalus: (1) constantly elevated protein level defects reabsorption of CSF via obstruction of the arachnoid villi31; (2) higher protein level changes the osmotic gradient in favor of water transport into the ventricular system versus the level present at that time in blood32. (3) the abnormal CSF reflux34. Here, we propose the following hypothesis in CM: Cryptococcal infection induced disruption of BCSFB and BBB led to a rapid elevation of protein level in lumbar CSF and an increase of CSF secretion via choroid plexus in ventricles, which may explain why the rostro-caudal protein gradient is increased in patients with CM compared to previous study on patients with multiple sclerosis considered as an autoimmune disease11. The persistent high lumbar protein level would gradually increase the ventricular protein level through CSF reflux. The increased ventricular protein level may further exacerbate the CSF secretion, slow down the CSF flow and CSF reabsorption. Finally, ventriculomegaly occurred. In brief, we believe the pathophysiological changes precede the structural changes.Since CM patients are more likely to have intractable high intracranial pressure and hydrocephalus that were usually treated by VPS placement, we analyzed the relationships between CSF indices and ventriculomegaly before VPS and complications after VPS. Among the CSF parameters, only ventricular CSF protein levels were statistically higher in patients with ventriculomegaly than in patients without ventriculomegaly. Our study provided further evidence that a high CSF protein level could be a risk factor for ventriculomegaly35. According to our study, there was no correlation between protein concentration and shunt complications in CM. Surprisingly, patients with post-operative complications had a significantly lower ventricular CSF/blood glucose ratio than patients without complications. This may indicate that CM patients with a lower ventricular CSF/blood glucose ratio are more likely to have shunt complications. However, which complication is the most closely correlated with CSF/blood glucose ratio requires further study. Nevertheless, the lumbar CSF/blood glucose ratio was not statistically different between the two groups. It tends to support the conjecture that ventricular CSF indices were more closely related to post-VPS complications, since shunt tubes were inserted directly into the lateral ventricles.There was a common concern that a high CSF protein level increases the risk of shunt complications16, we evaluated the changes in lumbar CSF parameters over time after VPS placement. It seemed that WBC count, CSF/blood glucose ratio and chloride ion level had not improved until two months after VPS placement. Total protein levels were deteriorated during the first two weeks after shunt surgery. The elevated lumbar protein concentration did not return to pre-operative level until three months after shunt surgery. Based on the improvement of clinical symptoms and fast decline of CSF Cryptococcal count (Supplementary Fig. Since CSF diversion directly changes the CSF flow rate, which may affect the ventricular to lumbar protein gradientsOur study is limited by its retrospective approach and a single-center clinical data. Individual subgroups were partly small, especially patients with shunt complications. Although all participants were HIV-negative CM patients, they had different comorbid conditions, which may also lead to BBB/BCSFB dysfunctions. Other influencing factors, such as antifungal therapy and detection time, also existed in this study. The values of the CSF/blood glucose ratio were missing a lot due to a lack of contemporaneous blood glucose levels. However, to our knowledge, this is the most extensive study on human ventricular CSF parameters, and this is the first study on ventricular to lumbar CSF gradient in CM.The composition of ventricular CSF differs from that of lumbar CSF in HIV-negative patients with CM. Different CSF parameters have disparate ventricular to lumbar concentration gradients. Ventricular and lumbar CSF parameters may have different clinical implications. According to our study, ventricular but not lumbar CSF indices were closely related to ventriculomegaly and shunt complications. Transient deterioration of lumbar CSF parameters after VPS placement may not be due to disease progression but rather to a change of the CSF flow rate due to CSF shunts.Supplementary Figure 1.


Idiopathic intracranial hypertension can lead to dural defects and spontaneous leakage of cerebrospinal fluid (CSF) from the skull base. Skull base CSF leaks are rarely reported in pregnancy but pose unique challenges for obstetricians and anesthesiologists.A 31-year-old G4P1021 at 14 weeks developed debilitating headaches and CSF rhinorrhea. Brain imaging revealed a bony defect of the sphenoid sinus with a meningoencephalocele and a partially empty sella, consistent with CSF leakage from a skull base defect. The patient was neurologically stable without signs of meningitis; thus, management was focused on symptomatic alleviation. A planned cesarean section was performed at 38 weeks under spinal anesthesia. The patient had spontaneous marked improvement of her symptoms postpartum.Pregnancy may exacerbate skull base CSF leaks, requiring careful management with a multidisciplinary team. Neuraxial anesthesia can safely be performed in pregnant individuals with spontaneous skull base CSF leakage, but further studies are needed to determine the safest mode of delivery in these patients. Individuals with a spontaneous skull base cerebrospinal fluid (CSF) leak may experience worsening of symptoms during pregnancy.Neuraxial anesthesia can safely be performed in pregnant individuals with spontaneous skull base CSF leakage and is preferred over general anesthesia when possible.Mode of delivery in pregnant individuals with skull base CSF leakage should be individualized with involvement of a multidisciplinary team and patient shared decision-making.n = 3) were found prior to 2022 (published between 1979 and 2012) involving spontaneous CSF leak secondary to skull base defect in pregnancy were found via PubMed search and citation review [Cerebrospinal fluid (CSF) leaks from the skull base most often results from head and/or maxillofacial trauma, iatrogenically through surgery, congenitally through encephaloceles, or as anticipated from otolaryngologic and/or neurosurgical procedures, but can also occur spontaneously in some cases without any known precipitating event. These spontaneous CSF leaks most likely occur secondary to idiopathic intracranial hypertension (IIH), for which high body-mass index and female sex are correlated risk factors –3. The in review , 6, 7. TA 31-year-old G4P1021 at 14 weeks gestation was referred by her primary obstetrician to otolaryngology for new onset debilitating headaches and persistent right-sided clear fluid rhinorrhea for nearly 4 months, slightly pre-dating conception of her pregnancy. Her nasal fluid tested positive for beta-2 transferrin, consistent with CSF leak . She wasSpontaneous skull base CSF leak is a rare event   and evenTable Our patient’s development of headaches during pregnancy and subsequent postpartum improvement is related to her skull base defect and possible IIH. One explanation is that while CSF pressure is generally unaltered (7-15mmHg) during the course of a normal pregnancy, physiologic changes as well as weight gain in pregnancy could increase intracranial pressures, possibly exacerbating her baseline risk factors of female sex and obesity , 9. ThesThough not utilized in our patient’s case, acetazolamide which is commonly employed in the treatment of IIH has also been suggested as a potential treatment for spontaneous CSF skull base leaks in a small retrospective study by Tilak and colleagues in 2019. Acetazolamide was administered pre-operatively to 16 non-pregnant patients with spontaneous CSF rhinorrhea and found that 31.3% of the patients had resolution of their symptoms without the need for surgical repair, though it was most effective when combined with weight loss . AcetazoGiven the rarity of skull base CSF leakage reported in pregnancy, the safest mode of delivery and choice of anesthesia for a patient with skull base CSF leakage is not clear. The first and second stage of labor are known to increase CSF pressure, which could potentially worsen CSF leakage and lead to complications such as brain herniation. Similarly, general anesthesia can result in significant increases of intracranial pressure, especially during airway manipulation or Valsalva during intubation and extubation. However, creating dural defects during administration of spinal anesthesia or inadvertently during epidural anesthesia also has the possible risk of causing additional CSF leakage, though this risk is small, particularly with spinal anesthetic using a small gauge needle , 13. SevPregnant patients presenting with similar symptoms to our patient should be evaluated for IIH and/or skull base spontaneous CSF leak. Women with skull base CSF leaks may have worsening of their symptoms during pregnancy, but can be managed conservatively with intravenous fluids and/or analgesic medication, or if severe or intractable, with surgery. Though successful use of regional anesthesia and forceps-assisted vaginal delivery has been reported in patients with skull base CSF leaks, very little is definitively known about risks during pregnancy, and thus further studies are needed to determine the safest mode of delivery and anesthesia.


Potential biomarkers for neuropsychiatric disorders are cerebrospinal fluid (CSF) monoamines and their corresponding precursors and metabolites. During CSF sampling, CSF flows towards the lumbar sampling site from more cranial regions. To compare the results of studies in which different CSF volumes were acquired, it is important to know if ventricular-lumbar concentration gradients exist. This has only been addressed for a few biogenic amines, and almost exclusively in neurologically unwell patients due to the burden of a lumbar puncture (necessary to obtain CSF). The aim of our study was to determine if concentration gradients exist for routinely measured CSF constituents and biogenic amines in neurologically healthy patients. We applied a novel ultrasensitive liquid chromatography mass spectrometry (LC–MS/MS) method for the simultaneous quantification of multiple monoamines, precursors and metabolites in CSF and plasma.CSF and blood samples were collected from twenty neurologically healthy patients undergoing spinal anaesthesia. Ten mL of lumbar CSF was collected in five consecutive two mL fractions. We determined leucocyte and erythrocyte counts, glucose, albumin and protein concentrations and quantified monoamines, precursors and metabolites on each of the fractions using LC–MS/MS.In twenty patients , dopamine, DOPAC, 3-MT, HVA, noradrenaline, normetanephrine and 5-HIAA concentrations increased from the first to the last CSF fraction . CSF adrenaline concentrations were below the detection limit, whereas serotonin measurements were regarded as unreliable. Albumin and total protein levels decreased significantly across CSF fractions.A ventricular-lumbar CSF concentration gradient existed for most of the investigated analytes. This is a novel finding for dopamine, noradrenaline, 3-MT and normetanephrine. These results contribute to the understanding of the neurobiology and underline the importance of standardized procedures for CSF handling to allow comparisons between studies.The online version contains supplementary material available at 10.1186/s12987-023-00413-8. In healthy adults, approximately 500 mL of cerebrospinal fluid (CSF) is produced daily by the choroid plexuses, circulates to the subarachnoid space  and is aPotential biomarkers of interest are monoamines and their corresponding precursors and metabolites , 9. MajoDespite increasing evidence of disturbances in monoamine signaling in the pathophysiology of neuropsychiatric disorders , 9, resePrevious studies initially focused on blood-derived proteins and demonstrated a marked decrease in albumin and Immunoglobulin G (IgG) concentrations from the first to the last lumbar CSF fraction , 25. SubThe literature in this field is not only scarce, it also almost exclusively reports data from patients with (suspected) neurological disease , 28, 34 This study is a sub-study of the Anaesthetic Biobank of Cerebrospinal Fluid (ABC) of the University Medical Center Groningen (UMCG), The Netherlands. The purpose of the ABC is to collect and store CSF of relatively  healthy adult patients for future neuroscientific research. The ABC was approved by the Medical Ethical Committee of the UMCG (registration number 2016-174). Details of the main study design have been described in detail  when appropriate, discrete data by category frequencies (%). For every normally distributed laboratory measurement, a possible linear RCG was investigated by running a repeated measures ANOVA (laboratory measurement as dependent variable and fraction as independent within-subject factor). Analyses were checked for assumption violations. To test the within-subject effect, the Greenhouse–Geisser correction was used in case the assumption of sphericity was violated (according to Mauchly’s test). To assess the within-subject differences between the first and each of the subsequent fractions, post hoc pairwise comparisons were performed. P-values were adjusted for multiple testing using the Bonferroni correction. For non-normally distributed outcomes, a Friedman test with post hoc Wilcoxon signed-rank tests was used to investigate the presence of within-subject differences between fractions. Pearson/Spearman correlation coefficients were calculated to investigate the relationships between blood and CSF fractions. Qalb was calculated by dividing the concentration of albumin in CSF by the concentration of albumin in blood, times one thousand.2 (20 patients), relevant medical history or medication use (38 patients), excessive smoking, alcohol or drug use (9 patients). Additionally, five patients were excluded during the spinal procedure, because of failure to aspirate 10 mL CSF within 4 min. Patient characteristics of the 20 patients included in this study are shown in Table Between June 2019 and February 2020, 79 patients were enrolled for the ABC and screened for eligibility for this sub-study. Of them, 59 were considered ineligible for one or more of the following reasons: BMI > 30 kg/mMetanephrine and adrenaline concentrations were quantifiable in plasma, but were below the quantification limits in CSF (0.04 nmol/L and 0.03 nmol/L respectively).Concentrations of dopamine, DOPAC, 3-MT, HVA, noradrenaline, normetanephrine and 5-HIAA increased significantly across CSF fractions    measurable.Our results are consistent with the theoretical framework  and reflFor the dopaminergic system, we found similar results to those of Eklundt et al. who showed an higher level of dopamine and DOPAC in the second CSF fraction compared to the first fraction in fourteen male volunteers . As dopaRegarding the adrenergic system, our results showed a modest RCG for noradrenaline and normetanephrine. Eklundt et al. on the other hand found no gradient for noradrenaline (and did not study normetanephrine) . NoradreFor our secondary objective, an identical concentration gradient was found as demonstrated in earlier studies for albumin and Qalb , 25, 34.For the remaining routine analyses, no clear gradient effect was found. Presence of erythrocytes in CSF can indicate a haemorrhage, but is more commonly (~ 17%) the result of a ‘bloody tap’ . As we mAs has been mentioned previously, our subjects were relatively healthy patients. The extent of inter-individual variability in concentrations for all fractions was remarkable. In some cases (e.g. NMN) up to tenfold variability was seen. Despite this inter-individual variability, within-patient variability (i.e. differences across fractions) was only modest.This study has several strengths. Although metabolite concentrations are a reflection of monoamine turnover, the concentrations of the primary monoamines are preferred due to their functional status . Ours isSome study limitations deserve mention. First, only ten mL of lumbar CSF was collected, which is similar to the total volume of CSF below the conus medularis . This liThis study implies that concentrations of numerous CSF monoamine, precursors and metabolites, and routine analytes, will vary depending on the volume of CSF that is collected. If a gradient exists, standardized procedures for CSF collection and handling are essential to allow comparisons between studies and investigators should acquire CSF in discrete fractions and carefully report their CSF acquisition methodology . To ensuThis study showed novel evidence of ventricular-lumbar CSF concentration gradients for dopamine, noradrenaline, 3-MT and NMN, and confirmed prior findings of gradients for DOPAC, HVA, and 5-HIAA concentrations. For these analytes, concentrations increased from the first to the last CSF fraction. These results contribute to the understanding of the neurobiology and can help to identify novel biomarkers in neuroscience. In future studies analysing CSF biogenic amines concentrations, standardisation of CSF collection and handling is essential for comparisons between studies.Additional file 1: Fig. S1. Concentrations of analytes displayed per patient in five consecutive CSF fractions. Every line presents the median concentration of that analyte for one patient in the five consecutive fractions of CSF. 1: 0-2 mL, 2: 2-4 mL, 3: 4-6 mL, 4: 6-8 mL, 5: 8-10mL. The blue line shows the overall median concentration of that analyte and the grey area the corresponding IQR. (A) L-DOPA (levodopa), (B) 3-OMD (3-o-methyldopa), (C) DA (dopamine), (D) DOPAC , (E) 3-MT (3-methoxytyramine), (F) HVA (homovanillic acid), (G) NA , (H) NMN (normetanephrine), (I) MOPEG (3-Methoxy-4-hydroxyphenylglycol), (J) 5-HIAA (5-hydroxyindoleacetic acid). (K) albumin, (L) total protein, (M) glucose, (N) erythrocyte count and (O) leukocyte count.Additional file 2: Table S1. Correlation of concentrations of analytes between plasma and CSF fractions. Spearman (rs) or Pearson (rp) correlation coefficient and significance for the relationship between plasma and CSF fractions. Correlation is significant at 0.05 level (*), 0.01 level (**) or 0.001 level (***)(2-tailed). L-DOPA: levodopa; 3-OMD: 3-o-methyldopa; DA: dopamine; DOPAC: 3,4-Dihydroxyphenylacetic acid; 3-MT: 3-methoxytyramine; HVA: homovanillic acid; NA: noradrenalin; NMN: normetanephrine; MOPEG: 3-Methoxy-4-hydroxyphenylglycol; 5-HIAA: 5-hydroxyindoleacetic acid; Qalb: ratio of CSF to plasma albumin concentration.