Background: CSF protein concentrations vary greatly among individuals. Accounting for brain volume may lower the variance and increase the diagnostic value of CSF protein concentrations. Objective: To determine the relation between CSF protein concentrations and brain volume. Methods: Brain volumes (total intracranial, gray matter, white matter volumes) derived from brain MRI and CSF protein concentrations (total protein, albumin, albumin CSF/serum ratio) of 29 control patients and 497 patients with clinically isolated syndrome or multiple sclerosis were studied. Finding: We found significant positive correlations of CSF protein concentrations with intracranial, gray matter, and white matter volumes. None of the correlations remained significant after correction for age and sex. Conclusion: Accounting for brain volume derived from brain MRI is unlikely to improve the diagnostic value of protein concentrations in CSF. Volume 10 - 2019 | https://doi.org/10.3389/fneur.2019.00463 This article is part of the Research TopicAdvances of Neuroimaging and Data AnalysisView all 17 articles Background: CSF protein concentrations vary greatly among individuals Accounting for brain volume may lower the variance and increase the diagnostic value of CSF protein concentrations Objective: To determine the relation between CSF protein concentrations and brain volume Methods: Brain volumes (total intracranial white matter volumes) derived from brain MRI and CSF protein concentrations (total protein albumin CSF/serum ratio) of 29 control patients and 497 patients with clinically isolated syndrome or multiple sclerosis were studied Finding: We found significant positive correlations of CSF protein concentrations with intracranial None of the correlations remained significant after correction for age and sex Conclusion: Accounting for brain volume derived from brain MRI is unlikely to improve the diagnostic value of protein concentrations in CSF Cerebrospinal fluid (CSF) analysis is supportive of the diagnosis of many neurological diseases. CSF protein concentrations constitute a mainstay of CSF analysis. Despite age- and sex-dependent cut-offs (1–4) considerable interindividual variance may lower the diagnostic value of CSF protein concentrations We aimed to reduce variance of CSF protein concentrations and to increase their diagnostic value by considering brain volumes derived from magnetic resonance imaging (MRI) This idea may not seem practical at first glance but given latest developments with regard to modern hospital information systems and tools for automated MRI analysis linking of multiple paraclinical data seems to be in reach even in clinical routine since most CSF proteins are both released into CSF (mainly ultrafiltration of blood plasma in the choroid plexus) and retrieved from CSF (drainage into the venous system mostly through arachnoid granulations) in certain circumscribed brain structures differences in whole brain volumes may not perfectly parallel the net capacity of CSF protein filtration and drainage as only this would lead to independence between brain volumes and CSF protein concentrations we studied the relation of CSF protein concentrations (total protein and albumin CSF/serum ratio) and brain volumes (total intracranial volume and partial correlations in IBM SPSS Statistics for Windows (Version 25.0) age correlated with CSF protein concentrations (linear correlation; protein Although CP were significantly younger than CIS/MS patients [independent-samples t-test; 31.4 ± 9.1 vs none of the CSF protein concentrations significantly differed between the two groups [CP vs CIS/MS; independent-samples t-test; protein in mg/L we could replicate well-known associations: men had larger TIV than women [independent-samples t-test; 1578 ± 141 vs p < 0.001); GM volume negatively correlated with age (linear correlation Correlations of brain volumes with CSF protein concentrations derived from high-resolution MRI as available in clinical routine Our data are plausible as we could replicate well-known associations Men showed higher values of CSF protein concentrations than women we could replicate well-known associations of brain volumes with age and sex Age and sex are very important clinical parameters; they are available and considered in (almost) every patient in clinical routine and go along with differences in both CSF protein concentration and brain volumes we felt that an association of CSF protein concentration and brain volumes potentially meaningful in clinical routine should remain significant after correction for both age and sex after having failed to demonstrate a relationship of brain volumes and CSF protein concentrations beyond that explained by age and sex in as many as 526 subjects we conclude that accounting for individual brain volumes is unlikely to considerably decrease the variability of CSF protein concentrations and The study was approved by the local ethics committee of the medical faculty of the Technical University of Munich AW and MM contributed to the conception and design of the study and MM participated in the acquisition and analysis of data AW and MM contributed to drafting the text or preparing the tables AW was funded by the Kommission für Klinische Forschung (KKF) The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest Variation of total cerebrospinal fluid proteins and cells with sex and age PubMed Abstract | Google Scholar Principles of albumin and IgG analyses in neurological disorders Investigation of reference values of components of cerebrospinal fluid Influence of sex on cerebrospinal fluid density in adults PubMed Abstract | Google Scholar Protein profile of cerebrospinal fluid in multiple sclerosis with special reference to the function of the blood brain barrier The clinical use of cerebrospinal fluid studies in demyelinating neurological diseases PubMed Abstract | Google Scholar Fatigue in multiple sclerosis: associations with clinical A robust method to estimate the intracranial volume across MRI field strengths (1.5T and 3T) protein and IgG content of lumbar cerebrospinal fluid (CSF) of patients with inflammatory and hemorrhagic diseases or tumors of the central nervous system (CNS) Google Scholar Hemmer B and Mühlau M (2019) CSF Protein Concentration Shows No Correlation With Brain Volume Measures Received: 07 February 2019; Accepted: 16 April 2019; Published: 03 May 2019 Copyright © 2019 Wuschek, Grahl, Pongratz, Korn, Kirschke, Zimmer, Hemmer and Mühlau. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) distribution or reproduction in other forums is permitted provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited in accordance with accepted academic practice distribution or reproduction is permitted which does not comply with these terms *Correspondence: Mark Mühlau, bWFyay5tdWVobGF1QHR1bS5kZQ== Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher. 94% of researchers rate our articles as excellent or goodLearn more about the work of our research integrity team to safeguard the quality of each article we publish. This website is using a security service to protect itself from online attacks. The action you just performed triggered the security solution. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data. You can email the site owner to let them know you were blocked. Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page. Volume 11 - 2018 | https://doi.org/10.3389/fnmol.2018.00460 This article is part of the Research TopicThe Male and Female Brain: Molecular Mechanisms of Sex DifferencesView all 16 articles Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system with presumed autoimmune origin The development of lesions within the gray matter and white matter which are highly variable with respect to number morphology and spatial evolution and which only show a limited correlation with clinical disability Population-based studies indicate a distinct outcome depending on gender we studied gender-related differences in the evolution of white matter MS-lesions (MS-WML) in early MS by using geostatistical methods a female and a male MS patient group received disease modifying drugs and underwent standardized annual brain magnetic resonance imaging MS-WML were automatically extracted and the derived binary lesion masks were subject to geostatistical analysis yielding quantitative spatial-statistics metrics on MS-WML pattern morphology and total lesion volume (TLV) Through the MS-lesion pattern discrimination plot the following differences were disclosed: corresponding to gender and MS-WML pattern morphology at baseline M2) are discerned that follow a distinct MS-WML pattern evolution in space and time F1 and M1 start with medium-level MS-WML pattern smoothness and TLV F2 and M2 start with high-level MS-WML pattern smoothness and medium-level TLV F2 and M2 longitudinal development is characterized by strongly diminishing MS-WML pattern smoothness and TLV continued shrinking and break-up of MS-WML increased MS-WML pattern smoothness and TLV Data from neurological examination suggest a correlation of MS-WML pattern morphology metrics and EDSS Our results justify detailed studies on gender-related differences we established a methodology to study MS-WML pattern characteristics including lesion pattern morphology we tested our geostatistical methodology in a longitudinal group consisting of men and women in order to evaluate gender-related changes and differences in MS-WML evolution both groups were affected by marked shifts in disease type MRI acquisition intervals and clinical parameters of groups F and M MS-WML pattern morphology was quantified by means of geostatistical variography. Geostatistics is a collection of algorithms for the analysis, modeling, and simulation of multidimensional data in a variety of disciplines (Caers, 2010). Variography is the central explorative data analysis (EDA) tool of geostatistics for measuring spatial correlation (Gringarten and Deutsch, 1999) The empirical variogram is calculated as follows (Eq 1: z(x) value of variable z at some 3D location x here: voxel with z = binary variable (1 or 0); h lag vector of 3D separation between two relevant voxels (units: mm); n(h) number of voxel pairs [z(x) z(x+h)] at lag h; γ(h) empirical variogram value for lag h 2: c sill; a range; h lag vector of separation; γ(h) model variogram value for lag h Figure 1. (A) Axial projections of evolving MS-WML patterns of two patients, documented by MRI1-MRI2-MRI3-MRI4. Numbers identify MS-WML patterns. Compare with Table 2 and (B). (B) Abstraction of MS-WML pattern evolution in (A) to LDP framework. Numbers identify MS-WML patterns. Compare Table 2 stratified for groups F (red squares) and M (blue squares) Patient1 and Patient2 evolution paths proceed in opposite directions Patient1 vectors are generally pointing top right indicating both increasing TLV and MS-WML pattern smoothness The longest orange vector signals that prominent MS-WML pattern geometry changes occur between MRI2 and MRI3 the evolution path of Patient2 MS-WML pattern is mainly running bottom left with the longest green vector between MRI1 and MRI2 This is due to shrinkage of the big lesion and associated loss in TLV and loss in overall spatial correlation Short vectors between MRI2-MRI3-MRI4 indicate only minor pattern changes occurring The reversed vector direction between MRI3–MRI4 is consistent with two slightly increased lesions at posterior of pattern 241 the LDP straightforwardly communicates different geometries converse evolutions and dynamics of Patient1 and Patient2 MS-WML patterns MS-WML pattern evolution of two MS-patients: variogram model parameters and total lesion volume (TLV) derived from MRI1 and MRI4 Marginal A,C distributions of groups F (red) and M (blue) (A,B) LDPs with F and M subgroups derived by GMM clustering average maximum a posteriori probability of class membership (MAP) = 0.992; F2: red squares (B) Subgroups M1 and M2 (M1: light blue squares Tentatively, MS-WML patterns that are dominated by continuous, extended lesions should be more abundant in group F than in group M. To overcome the inherent limitations of visually interpreting MS-WML point-clouds in the LDP (Figure 2), we reviewed the marginal distributions with density plots. Figure 3 suggests that both F and M groups have multimodal A and C distributions Going into more detail, we used multidimensional Gaussian Mixture Modeling (GMM) on A, C data extracted at baseline (MRI1) to check groups F and M for possible clustering in the LDP space. Results are contrasted in Figures 4A,B at baseline GMM yields comparable clustering results for both F and M groups: a larger subgroup that stretches approximately along the diagonal of the LDP and a smaller subgroup that plots at comparatively high levels of A and C The larger subgroups are tagged F1 (pink) and M1 (light blue) and the smaller subgroups are F2 (red) and M2 (blue) Normality tests (Shapiro–Wilk, Lilliefors, Anderson-Darling, all with α = 0.05) on the respective marginal A and C distributions gave ambiguous results. Therefore, the evolution of A and C distributions in the above subgroups, from MRI1 (baseline) to MRI4 (end of study), is portrayed with box-whsiker plots (Chambers et al., 1983) M2 distributions were matched on a non-parametric basis with robust statistical tests [Kruskal–Wallis (KW) and Mann–Whitney (MW) tests (A,B) Box-whisker plots indicating longitudinal evolution of A distributions Figure 6. (A,B) Box-whisker plots indicating longitudinal evolution of C distributions, from MRI1 to MRI4. (A, left) Subgroups F1 (pink, n = 46) and F2 (red, n = 7). Figure 5B, right: subgroups M1 (light blue, n = 30) and M2 (blue, n = 6). Graphics parameters as in Figures 5A,B Figures 5A,B contrast the longitudinal evolution of the A distributions of subgroups F1 F1 and M1 show nearly constant longitudinal medians KW- and MW-tests do not indicate significant differences for the longitudinal A distributions in F1 and M1 both F2 and M2 start with high-level medians that decrease strongly at MRI2 followed by about constant values in F2 and a further decrease in M2 While F2 medians clearly remain higher than the respective F1 medians KW-tests yield significant longitudinal differences MW-tests show significant differences between subgroups F1 and F2 MW-tests also show significant differences between M1 and M2 at MRI1 and between F2 and M2 at MRI3 Figures 6A,B present the longitudinal development of C distributions in subgroups F1 F1 and M1 show only minor longitudinal variation in medians but F1 has a tendency of slightly increasing medians KW- and MW-tests do not indicate significant differences for longitudinal C distributions in F1 F2 and M2 show accented variations in medians F2 medians are longitudinally sinking but remain higher than respective F1 medians M2 medians are also sinking (exception: MRI4) but are clearly lower than the respective M1 medians MW-tests show significant differences between F1 (A,B) Longitudinal analysis of geostatistical parameters A and C in subgroups F2 and M2 Thick lines refer to the group-level change as estimated by the fixed effects – they indicate negative slopes for A and C in both groups F2 M2 without significant difference as well as a well-discernible offset (expected diff between M2 and F2 between MRI2 and MRI3: −0.35 (95% CI: [−0.63 −0.08]) for parameter A; −1.12 (95% CI: [−2.04 longitudinal analysis results are in accordance with EDA: in subgroups F2 and M2 females show significantly higher values of A and C than males (A,B) LDP-based MS-WML pattern evolution plot and M2 (blue) individual and subgroup evolution in the 3-year observation period As pointed out in Figure 1B, the evolution of MS-WML patterns can be conveniently visualized in the LDP by connecting longitudinal A,C data with vectors (Figure 8) Since the individual MS-WML patterns of F and M groups are in MNI geometry and time between MRI1 and MRI4 is 3 years the dynamics of pattern change can be visually checked M1 roughly start along the LDP diagonal and show mixed directions and magnitudes M1 start at medium A,C levels and have minor magnitudes indicating negligible changes: F1 points toward minimally increased A and C M1 points toward slightly increased A and decreased C and are mostly pointing to strongly decreased A Hence F2 and M2 total vectors start at high A and medium C have increased magnitudes and face bottom-left F2 starts and ends at clearly higher A,C than M2 Relations between subgroup A and EDSS at MRI1 and MRI4 Subgroup average EDSS deltas broadly correspond to subgroup A deltas (Table 3): from MRI1 to MRI4 subgroups F1 and M1 show no/minor increase in A and slightly increased average EDSS while subgroups F2 and M2 present clearly reduced A and reduced average EDSS The aim of this study was to evaluate gender-related MS-WML pattern morphology evolution in early MS MRI data acquired by standardized methodology and automatic MS-WML extraction geostatistical variography revealed differences in the spatiotemporal evolution of MS-WML patterns among women and men with early-MS The geostatistical work flow for quantifying MS-WML patterns is well-established and provides reliable measures on spatial correlation and TLV Variography parameters A,C are considered appropriate for reducing the potentially complex three-dimensional structures of MS-WML patterns to two dimensions for representing DIS in coherent form in the LDP We could use the LDP as an EDA tool for revealing the following differences in DIS and DIT: corresponding to gender and MS-WML pattern morphology at baseline M2 show clearly different evolution in space and time medium-level A,C values that remain practically unchanged till end of study – total vectors in the LDP indicate quasi-stationary behavior of F1 subgroups F2 and M2 show pronounced variations in the LDP: at baseline both subgroups start with high-level A and medium-level C and evolve toward markedly lower A,C values at end of study Both M2 and F2 total vectors indicate distinctly reduced pattern smoothness and TLV the F2 total vector starts and ends at significantly higher A,C levels The dominant physical equivalent of increasing parameters A and C is lesion growth or confluence while decreasing A and C point to shrinking lesions or to breakup of lesion aggregates. Given MS-WML occur at preferred locations (Filli et al., 2012) notably alongside the CSF system which is stretched in y and z directions MS-WML patterns are expected to show increased spatial correlation in these directions Separate examination of x,y,z components of A complies with this expectation when matching total vectors in the MS-WML pattern evolution plot in the 3-years observation period subgroups F1 and M1 show practically constant medium-level MS-WML pattern smoothness and TLV F2 and M2 that start with high-level MS-WML pattern smoothness and TLV are characterized by strongly diminishing MS-WML pattern smoothness and reducing TLV – i.e. MS-WML patterns with spatially highly correlated smooth lesions remain more abundant in F2 than in M2 the distinct reduction of A and C in F2 and M2 subgroups could relate to a stronger response to DMD of patients with smoother MS-WML data on subgroup parameter A respectively EDSS at baseline and at end of study suggest a positive correlation One problem in relating A and EDSS is the fact that A measures brain MS-WML pattern morphology only while EDSS combines brain and spine performance A limitation of the current approach to MS-WML pattern dynamics is that it relies on reproducible MRI input data matching longitudinal data involving different MRI equipment and varying MS-WML extraction methods we could not find significant gender differences in MS-WML patterns While the inherent strength of spatial summary statistics on MS-WML-patterns provided by variography is to communicate the “broad picture,” this is counterbalanced by loss of spatial granularity: variography is not sensitive to location MS-WML patterns need to be confined by ROIs these first explorative results on a sexually bimorphic evolution of MS-WML-patterns need to be verified with larger data sets in order to better quantify the uncertainties of estimates This study was carried out in accordance with the recommendations of Ethikkommission Land Salzburg The protocol was approved by the Ethikkommission Land Salzburg As this study involves only retrospective data and all subjects were anonymized before processing the study was exempt from a detailed ethics statement (see translation below) Translation: “Research Projects that do not include clinical exams as by the Drug Law Medical Products Law or new medical methods including non-interventional studies or applied medical research as by the Salzburg Clinics Law are not subject to evaluation by the Federal Country of Salzburg Ethics Committee” (Original in German) and JS contributed to the conception and design of the study RM and JS wrote the first draft of the manuscript PS performed longitudinal statistics and helped with general statistics SM performed longitudinal MS-WML extraction All authors contributed to manuscript revision This work was sponsored by the Paracelsus Medical University research funds (Project No Gender-related differences in MS: a study of conventional and nonconventional MRI measures Modeling Uncertainty in the Earth Sciences Google Scholar “Comparing data distributions,” in Graphical Methods for Data Analysis Google Scholar Google Scholar GSLIB Geostatistical Software Library and User’s Guide Google Scholar Development of gray matter atrophy in relapsing-remitting multiple sclerosis is not gender dependent: results of a 5-year follow-up study Sex-based differences in multiple sclerosis (Part I): biology of disease incidence Spatiotemporal distribution of white matter lesions in relapsing-remitting and secondary progressive multiple sclerosis Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque Methodology for variogram interpretation and modeling for improved reservoir characterization CrossRef Full Text | Google Scholar Sex and gender issues in multiple sclerosis Sex as a determinant of relapse incidence and progressive course of multiple sclerosis “Optimizing texture primitives 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and severity of white matter damage in multiple sclerosis: implications for cognitive decline Temporal trends of disability progression in multiple sclerosis: findings from British Columbia Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria brain damage and clinical course of multiple sclerosis Variogram maps from LiDAR data as fingerprints of surface morphology on scree slopes CrossRef Full Text | Google Scholar Schmidt P and Sellner J (2018) Geostatistical Analysis of White Matter Lesions in Multiple Sclerosis Identifies Gender Differences in Lesion Evolution Copyright © 2018 Marschallinger, Mühlau, Pongratz, Kirschke, Marschallinger, Schmidt and Sellner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) *Correspondence: Robert Marschallinger, cm9iZXJ0Lm1hcnNjaGFsbGluZ2VyQHNiZy5hYy5hdA== Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher 94% of researchers rate our articles as excellent or goodLearn more about the work of our research integrity team to safeguard the quality of each article we publish This website is using a security service to protect itself from online attacks The action you just performed triggered the security solution There are several actions that could trigger this block including submitting a certain word or phrase You can email the site owner to let them know you were blocked Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.